From 996c959472618cef0a0d462c9b42077f4fb278fc Mon Sep 17 00:00:00 2001 From: "mingjiang.li" Date: Tue, 25 Feb 2025 17:05:45 +0800 Subject: [PATCH 1/8] rename speech to be audio Signed-off-by: mingjiang.li --- README.md | 8 ++++---- .../speech_recognition/conformer/igie/README.md | 0 .../speech_recognition/conformer/igie/alter_onnx.py | 0 .../conformer/igie/build_engine.py | 0 .../speech_recognition/conformer/igie/ci/prepare.sh | 0 .../conformer/igie/compute_cer.py | 0 .../speech_recognition/conformer/igie/config.yaml | 0 .../conformer/igie/ctc_decoder/README.md | 0 .../ctc_decoder/swig/ctc_beam_search_decoder.cpp | 0 .../igie/ctc_decoder/swig/ctc_beam_search_decoder.h | 0 .../igie/ctc_decoder/swig/decoder_utils.cpp | 0 .../conformer/igie/ctc_decoder/swig/decoder_utils.h | 0 .../conformer/igie/ctc_decoder/swig/decoders.i | 0 .../igie/ctc_decoder/swig/decoders_wrap.cxx | 0 .../conformer/igie/ctc_decoder/swig/path_trie.cpp | 0 .../conformer/igie/ctc_decoder/swig/path_trie.h | 0 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rename from models/speech/speech_recognition/conformer/igie/wenet/examples/hkust/s0/wenet/squeezeformer/attention.py rename to models/audio/speech_recognition/conformer/igie/wenet/examples/hkust/s0/wenet/squeezeformer/attention.py diff --git a/models/speech/speech_recognition/conformer/igie/wenet/examples/hkust/s0/wenet/squeezeformer/conv2d.py b/models/audio/speech_recognition/conformer/igie/wenet/examples/hkust/s0/wenet/squeezeformer/conv2d.py similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/examples/hkust/s0/wenet/squeezeformer/conv2d.py rename to models/audio/speech_recognition/conformer/igie/wenet/examples/hkust/s0/wenet/squeezeformer/conv2d.py diff --git a/models/speech/speech_recognition/conformer/igie/wenet/examples/hkust/s0/wenet/squeezeformer/convolution.py b/models/audio/speech_recognition/conformer/igie/wenet/examples/hkust/s0/wenet/squeezeformer/convolution.py similarity index 100% rename from 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to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/bin/http_client_main.cc diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/bin/http_server_main.cc b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/bin/http_server_main.cc similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/bin/http_server_main.cc rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/bin/http_server_main.cc diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/bin/label_checker_main.cc b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/bin/label_checker_main.cc similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/bin/label_checker_main.cc 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a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/CMakeLists.txt b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/CMakeLists.txt similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/CMakeLists.txt rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/CMakeLists.txt diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/blocking_queue.h b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/blocking_queue.h similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/blocking_queue.h rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/blocking_queue.h diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/file.h b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/file.h similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/file.h rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/file.h diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/flags.h b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/flags.h similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/flags.h rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/flags.h diff --git 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a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/thread_pool.h b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/thread_pool.h similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/thread_pool.h rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/thread_pool.h diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/timer.h b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/timer.h similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/timer.h rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/utils/timer.h diff --git 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a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/wenet.cc b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/wenet.cc similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/wenet.cc rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/cpp/wenet.cc diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/MainActivity.java b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/MainActivity.java similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/MainActivity.java rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/MainActivity.java diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/Recognize.java b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/Recognize.java similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/Recognize.java rename to models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/Recognize.java diff --git a/models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/VoiceRectView.java b/models/audio/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/VoiceRectView.java similarity index 100% rename from models/speech/speech_recognition/conformer/igie/wenet/runtime/android/app/src/main/java/com/mobvoi/wenet/VoiceRectView.java rename to 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a/models/speech/speech_recognition/transformer_asr/ixrt/faster_layer_norm/transformer_helper.cuh b/models/audio/speech_recognition/transformer_asr/ixrt/faster_layer_norm/transformer_helper.cuh similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/faster_layer_norm/transformer_helper.cuh rename to models/audio/speech_recognition/transformer_asr/ixrt/faster_layer_norm/transformer_helper.cuh diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/faster_logsumexp/__init__.py b/models/audio/speech_recognition/transformer_asr/ixrt/faster_logsumexp/__init__.py similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/faster_logsumexp/__init__.py rename to models/audio/speech_recognition/transformer_asr/ixrt/faster_logsumexp/__init__.py diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/faster_logsumexp/build.sh b/models/audio/speech_recognition/transformer_asr/ixrt/faster_logsumexp/build.sh similarity 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a/models/speech/speech_recognition/transformer_asr/ixrt/faster_logsumexp/test.cpp b/models/audio/speech_recognition/transformer_asr/ixrt/faster_logsumexp/test.cpp similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/faster_logsumexp/test.cpp rename to models/audio/speech_recognition/transformer_asr/ixrt/faster_logsumexp/test.cpp diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/faster_logsumexp/test.py b/models/audio/speech_recognition/transformer_asr/ixrt/faster_logsumexp/test.py similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/faster_logsumexp/test.py rename to models/audio/speech_recognition/transformer_asr/ixrt/faster_logsumexp/test.py diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/faster_stack/__init__.py b/models/audio/speech_recognition/transformer_asr/ixrt/faster_stack/__init__.py similarity index 100% rename from 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b/models/audio/speech_recognition/transformer_asr/ixrt/faster_stack/setup.py similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/faster_stack/setup.py rename to models/audio/speech_recognition/transformer_asr/ixrt/faster_stack/setup.py diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/faster_stack/test.cpp b/models/audio/speech_recognition/transformer_asr/ixrt/faster_stack/test.cpp similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/faster_stack/test.cpp rename to models/audio/speech_recognition/transformer_asr/ixrt/faster_stack/test.cpp diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/faster_stack/test.py b/models/audio/speech_recognition/transformer_asr/ixrt/faster_stack/test.py similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/faster_stack/test.py rename to models/audio/speech_recognition/transformer_asr/ixrt/faster_stack/test.py diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/hparams/train_ASR_transformer.yaml b/models/audio/speech_recognition/transformer_asr/ixrt/hparams/train_ASR_transformer.yaml similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/hparams/train_ASR_transformer.yaml rename to models/audio/speech_recognition/transformer_asr/ixrt/hparams/train_ASR_transformer.yaml diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/inference.py b/models/audio/speech_recognition/transformer_asr/ixrt/inference.py similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/inference.py rename to models/audio/speech_recognition/transformer_asr/ixrt/inference.py diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/load_ixrt_plugin.py b/models/audio/speech_recognition/transformer_asr/ixrt/load_ixrt_plugin.py similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/load_ixrt_plugin.py rename to models/audio/speech_recognition/transformer_asr/ixrt/load_ixrt_plugin.py diff --git a/models/speech/speech_recognition/transformer_asr/ixrt/requirements.txt b/models/audio/speech_recognition/transformer_asr/ixrt/requirements.txt similarity index 100% rename from models/speech/speech_recognition/transformer_asr/ixrt/requirements.txt rename to models/audio/speech_recognition/transformer_asr/ixrt/requirements.txt -- Gitee From 512333548660ec4b880d70116d305c9b71233385 Mon Sep 17 00:00:00 2001 From: "mingjiang.li" Date: Tue, 25 Feb 2025 17:09:34 +0800 Subject: [PATCH 2/8] format audio models and move llm/multimodal to top Signed-off-by: mingjiang.li --- README.md | 52 +++++++++---------- docs/README_TEMPLATE.md | 3 ++ .../conformer/igie/README.md | 11 ++-- .../transformer_asr/ixrt/README.md | 6 ++- 4 files changed, 40 insertions(+), 32 deletions(-) diff --git a/README.md b/README.md index 11bd52cf..9847b61b 100644 --- a/README.md +++ b/README.md @@ -10,6 +10,32 @@ IxRT（Iluvatar CoreX RunTime）是天数智芯自研的高性能推理引擎， DeepSparkInference将按季度进行版本更新，后续会逐步丰富模型类别并拓展大模型推理。 +## LLM (Large Language Model) + +| Model | vLLM | TRT-LLM | TGI | +|-------------------------------|-----------------------------------------------------------------------------------|------------------------------------------------------------------|-------------------------------------------------------------------------------------| +| Baichuan2-7B | [✅](models/nlp/large_language_model/baichuan2-7b/vllm/README.md) | | | +| ChatGLM-3-6B | [✅](models/nlp/large_language_model/chatglm3-6b/vllm/README.md) | | | +| ChatGLM-3-6B-32K | [✅](models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md) | | | +| DeepSeek-R1-Distill-Llama-8B | [✅](models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm/README.md) | | | +| DeepSeek-R1-Distill-Llama-70B | [✅](models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm/README.md) | | | +| DeepSeek-R1-Distill-Qwen-1.5B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm/README.md) | | | +| DeepSeek-R1-Distill-Qwen-7B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm/README.md) | | | +| DeepSeek-R1-Distill-Qwen-14B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm/README.md) | | | +| DeepSeek-R1-Distill-Qwen-32B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm/README.md) | | | +| Llama2-7B | [✅](models/nlp/large_language_model/llama2-7b/vllm/README.md) | [✅](models/nlp/large_language_model/llama2-7b/trtllm/README.md) | | +| Llama2-13B | | [✅](models/nlp/large_language_model/llama2-13b/trtllm/README.md) | | +| Llama2-70B | | [✅](models/nlp/large_language_model/llama2-70b/trtllm/README.md) | | +| Llama3-70B | [✅](models/nlp/large_language_model/llama3-70b/vllm/README.md) | | | +| Qwen-7B | [✅](models/nlp/large_language_model/qwen-7b/vllm/README.md) | | | +| Qwen1.5-7B | [✅](models/nlp/large_language_model/qwen1.5-7b/vllm/README.md) | | [✅](models/nlp/large_language_model/qwen1.5-7b/text-generation-inference/README.md) | +| Qwen1.5-14B | [✅](models/nlp/large_language_model/qwen1.5-14b/vllm/README.md) | | | +| Qwen1.5-32B Chat | [✅](models/nlp/large_language_model/qwen1.5-32b/vllm/README.md) | | | +| Qwen1.5-72B | [✅](models/nlp/large_language_model/qwen1.5-72b/vllm/README.md) | | | +| Qwen2-7B Instruct | [✅](models/nlp/large_language_model/qwen2-7b/vllm/README.md) | | | +| Qwen2-72B Instruct | [✅](models/nlp/large_language_model/qwen2-72b/vllm/README.md) | | | +| StableLM2-1.6B | [✅](models/nlp/large_language_model/stablelm/vllm/README.md) | | | + ## Computer Vision ### Classification @@ -181,32 +207,6 @@ DeepSparkInference将按季度进行版本更新，后续会逐步丰富模型 | | INT8 | [✅](models/cv/trace/deepsort/igie/README.md#int8) | | | RepNet-Vehicle-ReID | FP16 | [✅](models/cv/trace/repnet/igie/README.md#fp16) | | -## LLM (Large Language Model) - -| Model | vLLM | TRT-LLM | TGI | -|-------------------------------|-----------------------------------------------------------------------------------|------------------------------------------------------------------|-------------------------------------------------------------------------------------| -| Baichuan2-7B | [✅](models/nlp/large_language_model/baichuan2-7b/vllm/README.md) | | | -| ChatGLM-3-6B | [✅](models/nlp/large_language_model/chatglm3-6b/vllm/README.md) | | | -| ChatGLM-3-6B-32K | [✅](models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md) | | | -| DeepSeek-R1-Distill-Llama-8B | [✅](models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Llama-70B | [✅](models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Qwen-1.5B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Qwen-7B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Qwen-14B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Qwen-32B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm/README.md) | | | -| Llama2-7B | [✅](models/nlp/large_language_model/llama2-7b/vllm/README.md) | [✅](models/nlp/large_language_model/llama2-7b/trtllm/README.md) | | -| Llama2-13B | | [✅](models/nlp/large_language_model/llama2-13b/trtllm/README.md) | | -| Llama2-70B | | [✅](models/nlp/large_language_model/llama2-70b/trtllm/README.md) | | -| Llama3-70B | [✅](models/nlp/large_language_model/llama3-70b/vllm/README.md) | | | -| Qwen-7B | [✅](models/nlp/large_language_model/qwen-7b/vllm/README.md) | | | -| Qwen1.5-7B | [✅](models/nlp/large_language_model/qwen1.5-7b/vllm/README.md) | | [✅](models/nlp/large_language_model/qwen1.5-7b/text-generation-inference/README.md) | -| Qwen1.5-14B | [✅](models/nlp/large_language_model/qwen1.5-14b/vllm/README.md) | | | -| Qwen1.5-32B Chat | [✅](models/nlp/large_language_model/qwen1.5-32b/vllm/README.md) | | | -| Qwen1.5-72B | [✅](models/nlp/large_language_model/qwen1.5-72b/vllm/README.md) | | | -| Qwen2-7B Instruct | [✅](models/nlp/large_language_model/qwen2-7b/vllm/README.md) | | | -| Qwen2-72B Instruct | [✅](models/nlp/large_language_model/qwen2-72b/vllm/README.md) | | | -| StableLM2-1.6B | [✅](models/nlp/large_language_model/stablelm/vllm/README.md) | | | - ## Multimodal | Model | vLLM | IxFormer | diff --git a/docs/README_TEMPLATE.md b/docs/README_TEMPLATE.md index 14661a10..345a38ed 100644 --- a/docs/README_TEMPLATE.md +++ b/docs/README_TEMPLATE.md @@ -1,6 +1,7 @@ # MODEL_NAME ## Description + A brief introduction about this model. ## Setup @@ -20,6 +21,7 @@ bash test_fp16.sh ``` ### INT8 + ```bash bash test_int8.sh ``` @@ -31,4 +33,5 @@ Model | BatchSize | Precision | FPS | ACC MODEL_NAME | | | | ## Referenece (remove if not necessary) + - [refer-page-name](https://refer-links) diff --git a/models/audio/speech_recognition/conformer/igie/README.md b/models/audio/speech_recognition/conformer/igie/README.md index 28cb7f7b..47bafe82 100644 --- a/models/audio/speech_recognition/conformer/igie/README.md +++ b/models/audio/speech_recognition/conformer/igie/README.md @@ -2,7 +2,10 @@ ## Description -Conformer is a speech recognition model proposed by Google in 2020. It combines the advantages of CNN and Transformer. CNN efficiently extracts local features, while Transformer is more effective in capturing long sequence dependencies. Conformer applies convolution to the Encoder layer of Transformer, enhancing the performance of Transformer in the ASR (Automatic Speech Recognition) domain. +Conformer is a speech recognition model proposed by Google in 2020. It combines the advantages of CNN and Transformer. +CNN efficiently extracts local features, while Transformer is more effective in capturing long sequence dependencies. +Conformer applies convolution to the Encoder layer of Transformer, enhancing the performance of Transformer in the ASR +(Automatic Speech Recognition) domain. ## Setup @@ -62,6 +65,6 @@ bash scripts/infer_conformer_fp16_performance.sh ## Results -Model |BatchSize |Precision |FPS |ACC | ------------|-----------|----------|----------|----------| -Conformer | 32 | FP16 | 1940.759 | 95.29 | +| Model | BatchSize | Precision | FPS | ACC | +|-----------|-----------|-----------|----------|-------| +| Conformer | 32 | FP16 | 1940.759 | 95.29 | diff --git a/models/audio/speech_recognition/transformer_asr/ixrt/README.md b/models/audio/speech_recognition/transformer_asr/ixrt/README.md index 9d809a7d..601362a2 100644 --- a/models/audio/speech_recognition/transformer_asr/ixrt/README.md +++ b/models/audio/speech_recognition/transformer_asr/ixrt/README.md @@ -2,7 +2,9 @@ ## Description -Beam search allows us to exert control over the output of text generation. This is useful because we sometimes know exactly what we want inside the output. For example, in a Neural Machine Translation task, we might know which words must be included in the final translation with a dictionary lookup. +Beam search allows us to exert control over the output of text generation. This is useful because we sometimes know +exactly what we want inside the output. For example, in a Neural Machine Translation task, we might know which words +must be included in the final translation with a dictionary lookup. ## Setup @@ -79,5 +81,5 @@ python3 inference.py hparams/train_ASR_transformer.yaml --data_folder=/home/data ## Results | Model | BatchSize | Precision | QPS | CER | -| --------------- | --------- | --------- | ----- | ---- | +|-----------------|-----------|-----------|-------|------| | Transformer ASR | 32 | FP16 | 15.64 | 5.95 | -- Gitee From 92c04cb874c1f296a4575a0fea3ad5cdee8b090c Mon Sep 17 00:00:00 2001 From: "mingjiang.li" Date: Tue, 25 Feb 2025 17:46:47 +0800 Subject: [PATCH 3/8] format llm models markdown --- .../chameleon_7b/vllm/README.md | 2 +- .../fuyu_8b/vllm/README.md | 2 +- .../intern_vl/vllm/README.md | 2 +- .../llava_next_video_7b/vllm/README.md | 2 +- .../minicpm_v_2/vllm/README.md | 2 +- .../baichuan2-7b/vllm/README.md | 21 ++++++++++++------- .../chatglm3-6b-32k/vllm/README.md | 11 +++++++--- .../chatglm3-6b/vllm/README.md | 9 +++++--- .../vllm/README.md | 8 +++---- .../vllm/README.md | 8 +++---- .../vllm/README.md | 8 +++---- .../vllm/README.md | 2 +- .../vllm/README.md | 2 +- .../vllm/README.md | 8 +++---- .../llama2-13b/trtllm/README.md | 9 ++++---- .../llama2-70b/trtllm/README.md | 11 +++++++--- .../llama2-7b/trtllm/README.md | 17 +++++++++------ .../llama2-7b/vllm/README.md | 17 ++++++++++----- .../llama3-70b/vllm/README.md | 13 ++++++++---- .../qwen-7b/vllm/README.md | 18 +++++++++++++--- .../qwen1.5-14b/vllm/README.md | 14 ++++++++----- .../qwen1.5-32b/vllm/README.md | 7 +++++-- .../qwen1.5-72b/vllm/README.md | 14 ++++++++----- .../text-generation-inference/README.md | 18 ++++++++++++---- .../qwen1.5-7b/vllm/README.md | 14 ++++++++----- .../qwen2-72b/vllm/README.md | 14 +++++++++---- .../qwen2-7b/vllm/README.md | 13 ++++++++---- .../stablelm/vllm/README.md | 10 ++++++--- 28 files changed, 182 insertions(+), 94 deletions(-) diff --git a/models/multimodal/vision_language_understanding/chameleon_7b/vllm/README.md b/models/multimodal/vision_language_understanding/chameleon_7b/vllm/README.md index 568dbd70..de779d7f 100755 --- a/models/multimodal/vision_language_understanding/chameleon_7b/vllm/README.md +++ b/models/multimodal/vision_language_understanding/chameleon_7b/vllm/README.md @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-dev ### Download --Model: +- Model: ```bash # Download model from the website and make sure the model's path is "data/chameleon-7b" diff --git a/models/multimodal/vision_language_understanding/fuyu_8b/vllm/README.md b/models/multimodal/vision_language_understanding/fuyu_8b/vllm/README.md index 96d5acc3..3a3bb30e 100755 --- a/models/multimodal/vision_language_understanding/fuyu_8b/vllm/README.md +++ b/models/multimodal/vision_language_understanding/fuyu_8b/vllm/README.md @@ -22,7 +22,7 @@ apt install -y libgl1-mesa-dev ### Download --Model: +- Model: ```bash # Download model from the website and make sure the model's path is "data/fuyu-8b" diff --git a/models/multimodal/vision_language_understanding/intern_vl/vllm/README.md b/models/multimodal/vision_language_understanding/intern_vl/vllm/README.md index cafe64f9..33db9fa5 100644 --- a/models/multimodal/vision_language_understanding/intern_vl/vllm/README.md +++ b/models/multimodal/vision_language_understanding/intern_vl/vllm/README.md @@ -25,7 +25,7 @@ pip3 install ixformer ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/vision-language-understanding/Intern_VL/vllm diff --git a/models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/README.md b/models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/README.md index bf4b2683..2dff6d1f 100755 --- a/models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/README.md +++ b/models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/README.md @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-dev ### Download --Model: +- Model: ```bash # Download model from the website and make sure the model's path is "data/LLaVA-NeXT-Video-7B-hf" diff --git a/models/multimodal/vision_language_understanding/minicpm_v_2/vllm/README.md b/models/multimodal/vision_language_understanding/minicpm_v_2/vllm/README.md index 149f01f1..e71d0043 100644 --- a/models/multimodal/vision_language_understanding/minicpm_v_2/vllm/README.md +++ b/models/multimodal/vision_language_understanding/minicpm_v_2/vllm/README.md @@ -23,7 +23,7 @@ pip3 install --user --upgrade pillow -i https://pypi.tuna.tsinghua.edu.cn/simple ### Download --Model: +- Model: Note: Due to the official weights missing some necessary files for vllm execution, you can download the additional files from here: to ensure that the file directory matches the structure shown here: . ```bash diff --git a/models/nlp/large_language_model/baichuan2-7b/vllm/README.md b/models/nlp/large_language_model/baichuan2-7b/vllm/README.md index dab6b79d..06894d8a 100755 --- a/models/nlp/large_language_model/baichuan2-7b/vllm/README.md +++ b/models/nlp/large_language_model/baichuan2-7b/vllm/README.md @@ -1,14 +1,18 @@ -# Baichuan-2-7B (vLLM) +# Baichuan2-7B (vLLM) ## Description -Baichuan 2 is a new generation open-source large language model launched by Baichuan Intelligence. It is trained on high-quality data with 26 trillion tokens, which sounds like a substantial dataset. Baichuan 2 achieves state-of-the-art performance on various authoritative Chinese, multilingual, and domain-specific benchmarks of similar size, indicating its excellent capabilities in language understanding and generation.This release includes Base and Chat versions of 7B. +Baichuan 2 is a new generation open-source large language model launched by Baichuan Intelligence. It is trained on +high-quality data with 26 trillion tokens, which sounds like a substantial dataset. Baichuan 2 achieves state-of-the-art +performance on various authoritative Chinese, multilingual, and domain-specific benchmarks of similar size, indicating +its excellent capabilities in language understanding and generation.This release includes Base and Chat versions of 7B. ## Setup ### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL @@ -22,7 +26,8 @@ pip3 install transformers ### Download -Pretrained model: [https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/tree/main](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/tree/main) +Pretrained model: +[https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/tree/main](https://huggingface.co/baichuan-inc/Baichuan2-7B-Base/tree/main) ```bash mkdir /data/baichuan/ @@ -53,7 +58,7 @@ python3 offline_inference.py --model /data/baichuan/Baichuan2-7B-Base/int8/ --ch ## Results -| Model | Precision | tokens | QPS | -| ------------- | --------- | ------ | ------ | -| Baichuan-2-7B | FP16 | 768 | 109.27 | -| Baichuan-2-7B | w8a16 | 740 | 59.82 | +| Model | Precision | tokens | QPS | +|--------------|-----------|--------|--------| +| Baichuan2-7B | FP16 | 768 | 109.27 | +| Baichuan2-7B | w8a16 | 740 | 59.82 | diff --git a/models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md b/models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md index 57f75404..5062e74e 100644 --- a/models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md +++ b/models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md @@ -2,13 +2,18 @@ ## Description -ChatGLM3-6B-32K further enhances the understanding of long text capabilities based on ChatGLM3-6B, enabling better handling of contexts up to 32K in length. Specifically, we have updated the positional encoding and designed more targeted long text training methods, using a 32K context length during the training phase. In practical use, if your context length is mostly within 8K, we recommend using ChatGLM3-6B; if you need to handle context lengths exceeding 8K, we recommend using ChatGLM3-6B-32K. +ChatGLM3-6B-32K further enhances the understanding of long text capabilities based on ChatGLM3-6B, enabling better +handling of contexts up to 32K in length. Specifically, we have updated the positional encoding and designed more +targeted long text training methods, using a 32K context length during the training phase. In practical use, if your +context length is mostly within 8K, we recommend using ChatGLM3-6B; if you need to handle context lengths exceeding 8K, +we recommend using ChatGLM3-6B-32K. ## Setup ### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL @@ -53,5 +58,5 @@ python3 server_inference.py --host 127.0.0.1 --port 12345 --model_path /data/cha ## Results | Model | Precision | tokens | QPS | -| --------------- | --------- | ------ | ------ | +|-----------------|-----------|--------|--------| | ChatGLM3-6B-32K | FP16 | 745 | 110.85 | diff --git a/models/nlp/large_language_model/chatglm3-6b/vllm/README.md b/models/nlp/large_language_model/chatglm3-6b/vllm/README.md index fb782cee..1ffe0632 100644 --- a/models/nlp/large_language_model/chatglm3-6b/vllm/README.md +++ b/models/nlp/large_language_model/chatglm3-6b/vllm/README.md @@ -2,13 +2,16 @@ ## Description -ChatGLM3-6B is trained on large-scale natural language text data, enabling it to understand and generate text. It can be applied to various natural language processing tasks such as dialogue generation, text summarization, and language translation. +ChatGLM3-6B is trained on large-scale natural language text data, enabling it to understand and generate text. It can be +applied to various natural language processing tasks such as dialogue generation, text summarization, and language +translation. ## Setup ### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL @@ -79,4 +82,4 @@ python3 -m vllm.entrypoints.openai.api_server --model /data/chatglm/chatglm3-6b ```bash python3 benchmark_serving.py --host 127.0.0.1 --num-prompts 16 --model /data/chatglm/chatglm3-6b --dataset-name sharegpt \ --dataset-path /data/dataset/ShareGPT_V3_unfiltered_cleaned_split.json --sharegpt-output-len 130 --trust-remote-code -``` \ No newline at end of file +``` diff --git a/models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm/README.md b/models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm/README.md index af8b5abb..5b475134 100644 --- a/models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm/README.md +++ b/models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm/README.md @@ -2,9 +2,9 @@ ## Description -DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by -DeepSeek-R1. We slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, -8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community. +DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1. We +slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints +based on Qwen2.5 and Llama3 series to the community. ## Setup @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd deepSeek-r1-distill-llama-70b/vllm diff --git a/models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm/README.md b/models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm/README.md index b5b9c6d0..45f29110 100644 --- a/models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm/README.md +++ b/models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm/README.md @@ -2,9 +2,9 @@ ## Description -DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by -DeepSeek-R1. We slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, -8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community. +DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1. We +slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints +based on Qwen2.5 and Llama3 series to the community. ## Setup @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd deepSeek-r1-distill-llama-8b/vllm diff --git a/models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm/README.md b/models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm/README.md index 88eb5163..e10d0045 100644 --- a/models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm/README.md +++ b/models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm/README.md @@ -2,9 +2,9 @@ ## Description -DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by -DeepSeek-R1. We slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, -8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community. +DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1. We +slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints +based on Qwen2.5 and Llama3 series to the community. ## Setup @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd deepseek-r1-distill-qwen-1.5b/vllm diff --git a/models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm/README.md b/models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm/README.md index f24c6904..27cade52 100644 --- a/models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm/README.md +++ b/models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm/README.md @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd deepseek-r1-distill-qwen-14b/vllm diff --git a/models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm/README.md b/models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm/README.md index 4a2b85bc..bc3fab89 100644 --- a/models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm/README.md +++ b/models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm/README.md @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd deepseek-r1-distill-qwen-32b/vllm diff --git a/models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm/README.md b/models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm/README.md index fba0db66..8cab93a6 100644 --- a/models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm/README.md +++ b/models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm/README.md @@ -2,9 +2,9 @@ ## Description -DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by -DeepSeek-R1. We slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, -8B, 14B, 32B, and 70B checkpoints based on Qwen2.5 and Llama3 series to the community. +DeepSeek-R1-Distill models are fine-tuned based on open-source models, using samples generated by DeepSeek-R1. We +slightly change their configs and tokenizers. We open-source distilled 1.5B, 7B, 8B, 14B, 32B, and 70B checkpoints +based on Qwen2.5 and Llama3 series to the community. ## Setup @@ -20,7 +20,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd deepseek-r1-distill-qwen-7b/vllm diff --git a/models/nlp/large_language_model/llama2-13b/trtllm/README.md b/models/nlp/large_language_model/llama2-13b/trtllm/README.md index 3c36a98f..8605e11d 100755 --- a/models/nlp/large_language_model/llama2-13b/trtllm/README.md +++ b/models/nlp/large_language_model/llama2-13b/trtllm/README.md @@ -2,11 +2,14 @@ ## Description -The Llama2 model is part of the Llama project which aims to unlock the power of large language models. The latest version of the Llama model is now accessible to individuals, creators, researchers, and businesses of all sizes. It includes model weights and starting code for pre-trained and fine-tuned Llama language models with parameters ranging from 7B to 70B. +The Llama2 model is part of the Llama project which aims to unlock the power of large language models. The latest +version of the Llama model is now accessible to individuals, creators, researchers, and businesses of all sizes. It +includes model weights and starting code for pre-trained and fine-tuned Llama language models with parameters ranging +from 7B to 70B. ## Setup -### Instal +### Install ```bash # Install libGL @@ -15,9 +18,7 @@ yum install -y mesa-libGL ## Ubuntu apt install -y libgl1-mesa-dev - bash scripts/set_environment.sh . - ``` ### Download diff --git a/models/nlp/large_language_model/llama2-70b/trtllm/README.md b/models/nlp/large_language_model/llama2-70b/trtllm/README.md index 428dc7c5..7a9d6a8d 100644 --- a/models/nlp/large_language_model/llama2-70b/trtllm/README.md +++ b/models/nlp/large_language_model/llama2-70b/trtllm/README.md @@ -2,7 +2,12 @@ ## Description -we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs. +we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale +from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. +Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for +helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our +approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work +and contribute to the responsible development of LLMs. ## Setup @@ -20,9 +25,9 @@ bash scripts/set_environment.sh . ### Download --Model: +- Model: --Dataset: +- Dataset: ```bash # Download model from the website and make sure the model's path is "data/llama2-70b-chat" diff --git a/models/nlp/large_language_model/llama2-7b/trtllm/README.md b/models/nlp/large_language_model/llama2-7b/trtllm/README.md index bcf06d48..ca384327 100644 --- a/models/nlp/large_language_model/llama2-7b/trtllm/README.md +++ b/models/nlp/large_language_model/llama2-7b/trtllm/README.md @@ -2,13 +2,19 @@ ## Description -we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs. +we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale +from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. +Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for +helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our +approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work +and contribute to the responsible development of LLMs. ## Setup -### Instal +### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL @@ -17,15 +23,14 @@ yum install -y mesa-libGL ## Ubuntu apt install -y libgl1-mesa-dev - bash scripts/set_environment.sh . ``` ### Download --Model: +- Model: --Dataset: +- Dataset: ```bash # Download model from the website and make sure the model's path is "data/llama2-7b-chat" diff --git a/models/nlp/large_language_model/llama2-7b/vllm/README.md b/models/nlp/large_language_model/llama2-7b/vllm/README.md index 5fa70127..ad676926 100755 --- a/models/nlp/large_language_model/llama2-7b/vllm/README.md +++ b/models/nlp/large_language_model/llama2-7b/vllm/README.md @@ -1,14 +1,20 @@ -# LlaMa2 7B (vLLM) +# Llama2 7B (vLLM) ## Description -we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work and contribute to the responsible development of LLMs. +we develop and release Llama 2, a collection of pretrained and fine-tuned large language models (LLMs) ranging in scale +from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama 2-Chat, are optimized for dialogue use cases. +Our models outperform open-source chat models on most benchmarks we tested, and based on our human evaluations for +helpfulness and safety, may be a suitable substitute for closed-source models. We provide a detailed description of our +approach to fine-tuning and safety improvements of Llama 2-Chat in order to enable the community to build on our work +and contribute to the responsible development of LLMs. ## Setup -### Instal +### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL @@ -22,9 +28,10 @@ pip3 install vllm pip3 install triton pip3 install ixformer ``` + ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/llama2-7b/vllm diff --git a/models/nlp/large_language_model/llama3-70b/vllm/README.md b/models/nlp/large_language_model/llama3-70b/vllm/README.md index 8374dd94..8d2e6b38 100644 --- a/models/nlp/large_language_model/llama3-70b/vllm/README.md +++ b/models/nlp/large_language_model/llama3-70b/vllm/README.md @@ -2,9 +2,13 @@ ## Description -This model is the Meta Llama 3 large language model series (LLMs) released by Meta, which is a series of pre-trained and instruction-tuned generative text models, available in 8B and 70B models. The model is 70B in size and is designed for large-scale AI applications. +This model is the Meta Llama 3 large language model series (LLMs) released by Meta, which is a series of pre-trained and +instruction-tuned generative text models, available in 8B and 70B models. The model is 70B in size and is designed for +large-scale AI applications. -The Llama 3 command-tuned model is optimized for conversational use cases and outperforms many available open source chat models on common industry benchmarks. In addition, when developing these models, the research team paid great attention to optimizing beneficialness and safety. +The Llama 3 command-tuned model is optimized for conversational use cases and outperforms many available open source +chat models on common industry benchmarks. In addition, when developing these models, the research team paid great +attention to optimizing beneficialness and safety. Llama 3 is a major improvement over Llama 2 and other publicly available models: @@ -18,7 +22,8 @@ Llama 3 is a major improvement over Llama 2 and other publicly available models: ### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL @@ -30,7 +35,7 @@ apt install -y libgl1-mesa-dev ### Download --Model: +- Model: ```bash # Download model from the website and make sure the model's path is "data/Meta-Llama-3-70B-Instruct" diff --git a/models/nlp/large_language_model/qwen-7b/vllm/README.md b/models/nlp/large_language_model/qwen-7b/vllm/README.md index 50c7cb09..7b2433b6 100644 --- a/models/nlp/large_language_model/qwen-7b/vllm/README.md +++ b/models/nlp/large_language_model/qwen-7b/vllm/README.md @@ -2,13 +2,25 @@ ## Description -Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind the proprietary models. +Large language models (LLMs) have revolutionized the field of artificial intelligence, enabling natural language +processing tasks that were previously thought to be exclusive to humans. In this work, we introduce Qwen, the first +installment of our large language model series. Qwen is a comprehensive language model series that encompasses distinct +models with varying parameter counts. It includes Qwen, the base pretrained language models, and Qwen-Chat, the chat +models finetuned with human alignment techniques. The base language models consistently demonstrate superior performance +across a multitude of downstream tasks, and the chat models, particularly those trained using Reinforcement Learning +from Human Feedback (RLHF), are highly competitive. The chat models possess advanced tool-use and planning capabilities +for creating agent applications, showcasing impressive performance even when compared to bigger models on complex tasks +like utilizing a code interpreter. Furthermore, we have developed coding-specialized models, Code-Qwen and +Code-Qwen-Chat, as well as mathematics-focused models, Math-Qwen-Chat, which are built upon base language models. These +models demonstrate significantly improved performance in comparison with open-source models, and slightly fall behind +the proprietary models. ## Setup ### Install -In order to run the model smoothly, you need to get the sdk from [resource center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. +In order to run the model smoothly, you need to get the sdk from [resource +center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX official website. ```bash # Install libGL @@ -25,7 +37,7 @@ pip3 install ixformer ### Download --Model: -Model: +- Model: - Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen-7b/vllm diff --git a/models/nlp/large_language_model/qwen1.5-14b/vllm/README.md b/models/nlp/large_language_model/qwen1.5-14b/vllm/README.md index d1792860..07741c3f 100644 --- a/models/nlp/large_language_model/qwen1.5-14b/vllm/README.md +++ b/models/nlp/large_language_model/qwen1.5-14b/vllm/README.md @@ -2,7 +2,11 @@ ## Description -Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention. +Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release +the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, +attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we +have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did +not include GQA (except for 32B) and the mixture of SWA and full attention. ## Setup @@ -18,7 +22,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen1.5-14b/vllm @@ -34,6 +38,6 @@ python3 offline_inference.py --model ./data/qwen1.5/Qwen1.5-14B --max-tokens 256 ## Results -| Model | QPS | -| ---------- | ----- | -| Qwen1.5-14B| 57.15 | +| Model | QPS | +|-------------|-------| +| Qwen1.5-14B | 57.15 | diff --git a/models/nlp/large_language_model/qwen1.5-32b/vllm/README.md b/models/nlp/large_language_model/qwen1.5-32b/vllm/README.md index 4e92d517..97fdeca8 100755 --- a/models/nlp/large_language_model/qwen1.5-32b/vllm/README.md +++ b/models/nlp/large_language_model/qwen1.5-32b/vllm/README.md @@ -2,7 +2,10 @@ ## Description -Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. +Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release +the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, +attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we +have an improved tokenizer adaptive to multiple natural languages and codes. ## Setup @@ -23,7 +26,7 @@ pip3 install ixformer ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen1.5-32b/vllm diff --git a/models/nlp/large_language_model/qwen1.5-72b/vllm/README.md b/models/nlp/large_language_model/qwen1.5-72b/vllm/README.md index c9785998..340eb83d 100644 --- a/models/nlp/large_language_model/qwen1.5-72b/vllm/README.md +++ b/models/nlp/large_language_model/qwen1.5-72b/vllm/README.md @@ -2,7 +2,11 @@ ## Description -Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention. +Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release +the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, +attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we +have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did +not include GQA (except for 32B) and the mixture of SWA and full attention. ## Setup @@ -18,7 +22,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen1.5-72b/vllm @@ -35,6 +39,6 @@ python3 offline_inference.py --model ./data/qwen1.5/Qwen1.5-72B --max-tokens 256 ## Results -| Model | QPS | -| ---------- | ----- | -| Qwen1.5-72B| 41.24 | +| Model | QPS | +|-------------|-------| +| Qwen1.5-72B | 41.24 | diff --git a/models/nlp/large_language_model/qwen1.5-7b/text-generation-inference/README.md b/models/nlp/large_language_model/qwen1.5-7b/text-generation-inference/README.md index 41d34f20..5df36b1b 100644 --- a/models/nlp/large_language_model/qwen1.5-7b/text-generation-inference/README.md +++ b/models/nlp/large_language_model/qwen1.5-7b/text-generation-inference/README.md @@ -2,7 +2,11 @@ ## Description -Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention. +Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release +the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, +attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we +have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did +not include GQA (except for 32B) and the mixture of SWA and full attention. ## Setup @@ -18,7 +22,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen1.5-7b/text-generation-inference @@ -33,7 +37,13 @@ ln -s /path/to/Qwen1.5-7B ./data/qwen1.5 ```bash # Use one docker container to start webserver export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python -CUDA_VISIBLE_DEVICES=0 USE_FLASH_ATTENTION=true text-generation-launcher --model-id ./data/qwen1.5/Qwen1.5-7B --sharded false --dtype float16 --disable-custom-kernels --port 8001 --max-input-length 2048 --max-batch-prefill-tokens 2048 --max-total-tokens 4096 --max-batch-total-tokens 4096 +CUDA_VISIBLE_DEVICES=0 USE_FLASH_ATTENTION=true text-generation-launcher --model-id ./data/qwen1.5/Qwen1.5-7B \ + --sharded false --dtype float16 \ + --disable-custom-kernels \ + --port 8001 --max-input-length 2048 \ + --max-batch-prefill-tokens 2048 \ + --max-total-tokens 4096 \ + --max-batch-total-tokens 4096 ``` ### Offline test @@ -47,5 +57,5 @@ python3 offline_inference.py --model2path ./data/qwen1.5/Qwen1.5-7B ## Results | Model | QPS | -| ---------- | ----- | +|------------|-------| | Qwen1.5-7B | 39.11 | diff --git a/models/nlp/large_language_model/qwen1.5-7b/vllm/README.md b/models/nlp/large_language_model/qwen1.5-7b/vllm/README.md index 44f0c6b5..d991b2d4 100644 --- a/models/nlp/large_language_model/qwen1.5-7b/vllm/README.md +++ b/models/nlp/large_language_model/qwen1.5-7b/vllm/README.md @@ -2,7 +2,11 @@ ## Description -Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention. +Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release +the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, +attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we +have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did +not include GQA (except for 32B) and the mixture of SWA and full attention. ## Setup @@ -18,7 +22,7 @@ apt install -y libgl1-mesa-glx ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen1.5-7b/vllm @@ -34,6 +38,6 @@ python3 offline_inference.py --model ./data/qwen1.5/Qwen1.5-7B --max-tokens 256 ## Results -| Model | QPS | -| ---------- | ----- | -| Qwen1.5-7B | 109.56| +| Model | QPS | +|------------|--------| +| Qwen1.5-7B | 109.56 | diff --git a/models/nlp/large_language_model/qwen2-72b/vllm/README.md b/models/nlp/large_language_model/qwen2-72b/vllm/README.md index b8598790..08859625 100755 --- a/models/nlp/large_language_model/qwen2-72b/vllm/README.md +++ b/models/nlp/large_language_model/qwen2-72b/vllm/README.md @@ -2,11 +2,17 @@ ## Description -Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 72B Qwen2 model. +Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and +instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This +repo contains the instruction-tuned 72B Qwen2 model. -Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. +Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has +generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series +of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, +reasoning, etc. -Qwen2-72B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. Please refer to this section for detailed instructions on how to deploy Qwen2 for handling long texts. +Qwen2-72B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. +Please refer to this section for detailed instructions on how to deploy Qwen2 for handling long texts. ## Setup @@ -27,7 +33,7 @@ pip3 install ixformer ### Download --Model: +- Model: ```bash cd ${DeepSparkInference}/models/nlp/large_language_model/qwen2-72b/vllm diff --git a/models/nlp/large_language_model/qwen2-7b/vllm/README.md b/models/nlp/large_language_model/qwen2-7b/vllm/README.md index dce3c564..fb1556b0 100755 --- a/models/nlp/large_language_model/qwen2-7b/vllm/README.md +++ b/models/nlp/large_language_model/qwen2-7b/vllm/README.md @@ -2,9 +2,14 @@ ## Description -Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This repo contains the instruction-tuned 7B Qwen2 model. +Qwen2 is the new series of Qwen large language models. For Qwen2, we release a number of base language models and +instruction-tuned language models ranging from 0.5 to 72 billion parameters, including a Mixture-of-Experts model. This +repo contains the instruction-tuned 7B Qwen2 model. -Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, reasoning, etc. +Compared with the state-of-the-art opensource language models, including the previous released Qwen1.5, Qwen2 has +generally surpassed most opensource models and demonstrated competitiveness against proprietary models across a series +of benchmarks targeting for language understanding, language generation, multilingual capability, coding, mathematics, +reasoning, etc. Qwen2-7B-Instruct supports a context length of up to 131,072 tokens, enabling the processing of extensive inputs. @@ -27,10 +32,10 @@ pip3 install ixformer ### Download --Model: https://modelscope.cn/models/Qwen/Qwen2-7B-Instruct +- Model: https://modelscope.cn/models/Qwen/Qwen2-7B-Instruct ```bash -cd ${DeepSparkInference}/models/nlp/large_language_model/qwen2-7b/vllm +cd models/nlp/large_language_model/qwen2-7b/vllm mkdir -p data/qwen2 ln -s /path/to/Qwen2-7B-Instruct ./data/qwen2 ``` diff --git a/models/nlp/large_language_model/stablelm/vllm/README.md b/models/nlp/large_language_model/stablelm/vllm/README.md index 18fd9a8f..bea23654 100644 --- a/models/nlp/large_language_model/stablelm/vllm/README.md +++ b/models/nlp/large_language_model/stablelm/vllm/README.md @@ -2,7 +2,11 @@ ## Description -Stable LM 2 1.6B is a decoder-only language model with 1.6 billion parameters. It has been pre-trained on a diverse multilingual and code dataset, comprising 2 trillion tokens, for two epochs. This model is designed for various natural language processing tasks, including text generation and dialogue systems. Due to its extensive training on such a large and diverse dataset, Stable LM 2 1.6B can effectively capture the nuances of language, including grammar, semantics, and contextual relationships, which enhances the quality and accuracy of the generated text. +Stable LM 2 1.6B is a decoder-only language model with 1.6 billion parameters. It has been pre-trained on a diverse +multilingual and code dataset, comprising 2 trillion tokens, for two epochs. This model is designed for various natural +language processing tasks, including text generation and dialogue systems. Due to its extensive training on such a large +and diverse dataset, Stable LM 2 1.6B can effectively capture the nuances of language, including grammar, semantics, and +contextual relationships, which enhances the quality and accuracy of the generated text. ## Setup @@ -19,7 +23,7 @@ pip3 install transformers ### Download --Model: +- Model: ```bash # Download model from the website and make sure the model's path is "data/stablelm/stablelm-2-1_6b" @@ -36,5 +40,5 @@ python3 offline_inference.py --model ./data/stablelm/stablelm-2-1_6b --max-token ## Results | Model | QPS | -| -------- | ----- | +|----------|-------| | StableLM | 254.3 | -- Gitee From 4d9171ef23082889dbf452790051beb0a252b82f Mon Sep 17 00:00:00 2001 From: "mingjiang.li" Date: Tue, 25 Feb 2025 17:49:33 +0800 Subject: [PATCH 4/8] install libgl1-mesa-glx is enough Signed-off-by: mingjiang.li --- models/cv/classification/alexnet/ixrt/README.md | 2 +- models/cv/classification/convnext_base/ixrt/README.md | 2 +- models/cv/classification/convnext_s/igie/README.md | 2 +- models/cv/classification/convnext_small/ixrt/README.md | 2 +- models/cv/classification/cspdarknet53/igie/README.md | 2 +- models/cv/classification/cspdarknet53/ixrt/README.md | 2 +- models/cv/classification/cspresnet50/igie/README.md | 2 +- models/cv/classification/cspresnet50/ixrt/README.md | 2 +- models/cv/classification/deit_tiny/igie/README.md | 2 +- models/cv/classification/deit_tiny/ixrt/README.md | 2 +- models/cv/classification/densenet121/ixrt/README.md | 2 +- models/cv/classification/densenet161/ixrt/README.md | 2 +- models/cv/classification/densenet169/ixrt/README.md | 2 +- models/cv/classification/densenet201/ixrt/README.md | 2 +- models/cv/classification/efficientnet_b0/ixrt/README.md | 2 +- models/cv/classification/efficientnet_b1/ixrt/README.md | 2 +- models/cv/classification/efficientnet_b2/ixrt/README.md | 2 +- models/cv/classification/efficientnet_b3/ixrt/README.md | 2 +- models/cv/classification/efficientnet_v2/ixrt/README.md | 2 +- models/cv/classification/efficientnetv2_rw_t/ixrt/README.md | 2 +- models/cv/classification/googlenet/ixrt/README.md | 2 +- models/cv/classification/hrnet_w18/igie/README.md | 2 +- models/cv/classification/hrnet_w18/ixrt/README.md | 2 +- models/cv/classification/inception_v3/ixrt/README.md | 2 +- models/cv/classification/inceptionresnetv2/ixrt/README.md | 2 +- models/cv/classification/mlp_mixer_base/igie/README.md | 2 +- models/cv/classification/mobilenet_v3/ixrt/README.md | 2 +- models/cv/classification/mvitv2_base/igie/README.md | 2 +- models/cv/classification/repvgg/igie/README.md | 2 +- models/cv/classification/repvgg/ixrt/README.md | 2 +- models/cv/classification/res2net50/igie/README.md | 2 +- models/cv/classification/res2net50/ixrt/README.md | 2 +- models/cv/classification/resnest50/igie/README.md | 2 +- models/cv/classification/resnet101/ixrt/README.md | 2 +- models/cv/classification/resnet18/ixrt/README.md | 2 +- models/cv/classification/resnet34/ixrt/README.md | 2 +- models/cv/classification/resnet50/ixrt/README.md | 2 +- models/cv/classification/resnetv1d50/ixrt/README.md | 2 +- models/cv/classification/seresnet50/igie/README.md | 2 +- models/cv/classification/shufflenet_v1/ixrt/README.md | 2 +- models/cv/classification/squeezenet_v1_0/ixrt/README.md | 2 +- models/cv/classification/squeezenet_v1_1/ixrt/README.md | 2 +- models/cv/classification/svt_base/igie/README.md | 2 +- models/cv/classification/vgg16/ixrt/README.md | 2 +- models/cv/detection/atss/igie/README.md | 2 +- models/cv/detection/centernet/igie/README.md | 2 +- models/cv/detection/centernet/ixrt/README.md | 2 +- models/cv/detection/detr/ixrt/README.md | 2 +- models/cv/detection/fcos/igie/README.md | 2 +- models/cv/detection/fcos/ixrt/README.md | 2 +- models/cv/detection/foveabox/igie/README.md | 2 +- models/cv/detection/foveabox/ixrt/README.md | 2 +- models/cv/detection/fsaf/igie/README.md | 2 +- models/cv/detection/fsaf/ixrt/README.md | 2 +- models/cv/detection/hrnet/igie/README.md | 2 +- models/cv/detection/hrnet/ixrt/README.md | 2 +- models/cv/detection/paa/igie/README.md | 2 +- models/cv/detection/retinaface/igie/README.md | 2 +- models/cv/detection/retinaface/ixrt/README.md | 2 +- models/cv/detection/retinanet/igie/README.md | 2 +- models/cv/detection/rtmdet/igie/README.md | 2 +- models/cv/detection/sabl/igie/README.md | 2 +- models/cv/detection/yolov3/igie/README.md | 2 +- models/cv/detection/yolov3/ixrt/README.md | 2 +- models/cv/detection/yolov4/igie/README.md | 2 +- models/cv/detection/yolov4/ixrt/README.md | 2 +- models/cv/detection/yolov5/igie/README.md | 2 +- models/cv/detection/yolov5/ixrt/README.md | 2 +- models/cv/detection/yolov5s/ixrt/README.md | 2 +- models/cv/detection/yolov7/igie/README.md | 2 +- models/cv/detection/yolov7/ixrt/README.md | 2 +- models/cv/detection/yolov8/igie/README.md | 2 +- models/cv/detection/yolox/igie/README.md | 2 +- models/cv/detection/yolox/ixrt/README.md | 2 +- models/cv/pose_estimation/hrnetpose/igie/README.md | 2 +- models/cv/pose_estimation/lightweight_openpose/ixrt/README.md | 2 +- models/cv/pose_estimation/rtmpose/igie/README.md | 2 +- models/cv/pose_estimation/rtmpose/ixrt/README.md | 2 +- .../vision_language_understanding/chameleon_7b/vllm/README.md | 2 +- .../vision_language_understanding/clip/ixformer/README.md | 2 +- .../vision_language_understanding/fuyu_8b/vllm/README.md | 2 +- .../vision_language_understanding/intern_vl/vllm/README.md | 2 +- .../vision_language_understanding/llava/vllm/README.md | 2 +- .../llava_next_video_7b/vllm/README.md | 2 +- .../vision_language_understanding/minicpm_v_2/vllm/README.md | 2 +- models/nlp/large_language_model/baichuan2-7b/vllm/README.md | 2 +- models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md | 2 +- models/nlp/large_language_model/chatglm3-6b/vllm/README.md | 2 +- models/nlp/large_language_model/llama2-13b/trtllm/README.md | 2 +- models/nlp/large_language_model/llama2-70b/trtllm/README.md | 2 +- models/nlp/large_language_model/llama2-7b/trtllm/README.md | 2 +- models/nlp/large_language_model/llama2-7b/vllm/README.md | 2 +- models/nlp/large_language_model/llama3-70b/vllm/README.md | 2 +- 93 files changed, 93 insertions(+), 93 deletions(-) diff --git a/models/cv/classification/alexnet/ixrt/README.md b/models/cv/classification/alexnet/ixrt/README.md index 65fa53ef..54981bb5 100644 --- a/models/cv/classification/alexnet/ixrt/README.md +++ b/models/cv/classification/alexnet/ixrt/README.md @@ -13,7 +13,7 @@ AlexNet is a classic convolutional neural network architecture. It consists of c ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/convnext_base/ixrt/README.md b/models/cv/classification/convnext_base/ixrt/README.md index 62bfb23c..0e4da2e6 100644 --- a/models/cv/classification/convnext_base/ixrt/README.md +++ b/models/cv/classification/convnext_base/ixrt/README.md @@ -13,7 +13,7 @@ The ConvNeXt Base model represents a significant stride in the evolution of conv ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install tqdm pip3 install onnx diff --git a/models/cv/classification/convnext_s/igie/README.md b/models/cv/classification/convnext_s/igie/README.md index 55072b2a..9d133a66 100644 --- a/models/cv/classification/convnext_s/igie/README.md +++ b/models/cv/classification/convnext_s/igie/README.md @@ -13,7 +13,7 @@ ConvNeXt-S is a small-sized model in the ConvNeXt family, designed to balance pe ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/convnext_small/ixrt/README.md b/models/cv/classification/convnext_small/ixrt/README.md index 2e9f8521..70d1e0a8 100644 --- a/models/cv/classification/convnext_small/ixrt/README.md +++ b/models/cv/classification/convnext_small/ixrt/README.md @@ -13,7 +13,7 @@ The ConvNeXt Small model represents a significant stride in the evolution of con ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/cspdarknet53/igie/README.md b/models/cv/classification/cspdarknet53/igie/README.md index 889b61b4..ca15f15a 100644 --- a/models/cv/classification/cspdarknet53/igie/README.md +++ b/models/cv/classification/cspdarknet53/igie/README.md @@ -13,7 +13,7 @@ CSPDarkNet53 is an enhanced convolutional neural network architecture that reduc ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/cspdarknet53/ixrt/README.md b/models/cv/classification/cspdarknet53/ixrt/README.md index b8fc83ae..40addd41 100644 --- a/models/cv/classification/cspdarknet53/ixrt/README.md +++ b/models/cv/classification/cspdarknet53/ixrt/README.md @@ -13,7 +13,7 @@ CSPDarkNet53 is an enhanced convolutional neural network architecture that reduc ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/cspresnet50/igie/README.md b/models/cv/classification/cspresnet50/igie/README.md index 8fc072a9..46341ba6 100644 --- a/models/cv/classification/cspresnet50/igie/README.md +++ b/models/cv/classification/cspresnet50/igie/README.md @@ -13,7 +13,7 @@ CSPResNet50 combines the strengths of ResNet50 and CSPNet (Cross-Stage Partial N ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/cspresnet50/ixrt/README.md b/models/cv/classification/cspresnet50/ixrt/README.md index a95c9a4c..fa054551 100644 --- a/models/cv/classification/cspresnet50/ixrt/README.md +++ b/models/cv/classification/cspresnet50/ixrt/README.md @@ -14,7 +14,7 @@ CSPResNet50 is the one of best models. ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/deit_tiny/igie/README.md b/models/cv/classification/deit_tiny/igie/README.md index a39dbfd9..89cb1aa4 100644 --- a/models/cv/classification/deit_tiny/igie/README.md +++ b/models/cv/classification/deit_tiny/igie/README.md @@ -13,7 +13,7 @@ DeiT Tiny is a lightweight vision transformer designed for data-efficient learni ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/deit_tiny/ixrt/README.md b/models/cv/classification/deit_tiny/ixrt/README.md index 15fb025e..42710f0f 100644 --- a/models/cv/classification/deit_tiny/ixrt/README.md +++ b/models/cv/classification/deit_tiny/ixrt/README.md @@ -13,7 +13,7 @@ DeiT Tiny is a lightweight vision transformer designed for data-efficient learni ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install tqdm pip3 install onnx diff --git a/models/cv/classification/densenet121/ixrt/README.md b/models/cv/classification/densenet121/ixrt/README.md index f506c085..7e2afd49 100644 --- a/models/cv/classification/densenet121/ixrt/README.md +++ b/models/cv/classification/densenet121/ixrt/README.md @@ -13,7 +13,7 @@ Dense Convolutional Network (DenseNet), connects each layer to every other layer ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/densenet161/ixrt/README.md b/models/cv/classification/densenet161/ixrt/README.md index 5e225c5e..58659f82 100644 --- a/models/cv/classification/densenet161/ixrt/README.md +++ b/models/cv/classification/densenet161/ixrt/README.md @@ -13,7 +13,7 @@ DenseNet161 is a convolutional neural network architecture that belongs to the f ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/densenet169/ixrt/README.md b/models/cv/classification/densenet169/ixrt/README.md index 480e9df6..0e3aee4c 100644 --- a/models/cv/classification/densenet169/ixrt/README.md +++ b/models/cv/classification/densenet169/ixrt/README.md @@ -13,7 +13,7 @@ Dense Convolutional Network (DenseNet), connects each layer to every other layer ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/densenet201/ixrt/README.md b/models/cv/classification/densenet201/ixrt/README.md index 94755355..e7e2b9ae 100644 --- a/models/cv/classification/densenet201/ixrt/README.md +++ b/models/cv/classification/densenet201/ixrt/README.md @@ -13,7 +13,7 @@ DenseNet201 is a deep convolutional neural network that stands out for its uniqu ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install tqdm pip3 install onnx diff --git a/models/cv/classification/efficientnet_b0/ixrt/README.md b/models/cv/classification/efficientnet_b0/ixrt/README.md index 84065777..e7c96a28 100644 --- a/models/cv/classification/efficientnet_b0/ixrt/README.md +++ b/models/cv/classification/efficientnet_b0/ixrt/README.md @@ -13,7 +13,7 @@ EfficientNet B0 is a convolutional neural network architecture that belongs to t ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/efficientnet_b1/ixrt/README.md b/models/cv/classification/efficientnet_b1/ixrt/README.md index 282b6440..a2076bbb 100644 --- a/models/cv/classification/efficientnet_b1/ixrt/README.md +++ b/models/cv/classification/efficientnet_b1/ixrt/README.md @@ -13,7 +13,7 @@ EfficientNet B1 is one of the variants in the EfficientNet family of neural netw ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/efficientnet_b2/ixrt/README.md b/models/cv/classification/efficientnet_b2/ixrt/README.md index cc225491..059c80ba 100644 --- a/models/cv/classification/efficientnet_b2/ixrt/README.md +++ b/models/cv/classification/efficientnet_b2/ixrt/README.md @@ -13,7 +13,7 @@ EfficientNet B2 is a member of the EfficientNet family, a series of convolutiona ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/efficientnet_b3/ixrt/README.md b/models/cv/classification/efficientnet_b3/ixrt/README.md index 9963f34f..1860a5e2 100644 --- a/models/cv/classification/efficientnet_b3/ixrt/README.md +++ b/models/cv/classification/efficientnet_b3/ixrt/README.md @@ -13,7 +13,7 @@ EfficientNet B3 is a member of the EfficientNet family, a series of convolutiona ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install tqdm pip3 install onnx diff --git a/models/cv/classification/efficientnet_v2/ixrt/README.md b/models/cv/classification/efficientnet_v2/ixrt/README.md index ee919749..c83e812f 100755 --- a/models/cv/classification/efficientnet_v2/ixrt/README.md +++ b/models/cv/classification/efficientnet_v2/ixrt/README.md @@ -13,7 +13,7 @@ EfficientNetV2 is an improved version of the EfficientNet architecture proposed ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/efficientnetv2_rw_t/ixrt/README.md b/models/cv/classification/efficientnetv2_rw_t/ixrt/README.md index bc6836d0..1e5d56d0 100644 --- a/models/cv/classification/efficientnetv2_rw_t/ixrt/README.md +++ b/models/cv/classification/efficientnetv2_rw_t/ixrt/README.md @@ -13,7 +13,7 @@ EfficientNetV2_rw_t is an enhanced version of the EfficientNet family of convolu ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install tqdm pip3 install timm diff --git a/models/cv/classification/googlenet/ixrt/README.md b/models/cv/classification/googlenet/ixrt/README.md index fa96b475..31170f0d 100644 --- a/models/cv/classification/googlenet/ixrt/README.md +++ b/models/cv/classification/googlenet/ixrt/README.md @@ -13,7 +13,7 @@ GoogLeNet is a type of convolutional neural network based on the Inception archi ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/hrnet_w18/igie/README.md b/models/cv/classification/hrnet_w18/igie/README.md index bb1aaf23..5284b949 100644 --- a/models/cv/classification/hrnet_w18/igie/README.md +++ b/models/cv/classification/hrnet_w18/igie/README.md @@ -13,7 +13,7 @@ HRNet, short for High-Resolution Network, presents a paradigm shift in handling ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/hrnet_w18/ixrt/README.md b/models/cv/classification/hrnet_w18/ixrt/README.md index 2d38c4d4..93691d2b 100644 --- a/models/cv/classification/hrnet_w18/ixrt/README.md +++ b/models/cv/classification/hrnet_w18/ixrt/README.md @@ -13,7 +13,7 @@ HRNet-W18 is a powerful image classification model developed by Jingdong AI Rese ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/inception_v3/ixrt/README.md b/models/cv/classification/inception_v3/ixrt/README.md index 0a841b9f..e0d938d3 100755 --- a/models/cv/classification/inception_v3/ixrt/README.md +++ b/models/cv/classification/inception_v3/ixrt/README.md @@ -13,7 +13,7 @@ Inception v3 is a convolutional neural network architecture designed for image r ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/inceptionresnetv2/ixrt/README.md b/models/cv/classification/inceptionresnetv2/ixrt/README.md index e55a13a2..22da2963 100755 --- a/models/cv/classification/inceptionresnetv2/ixrt/README.md +++ b/models/cv/classification/inceptionresnetv2/ixrt/README.md @@ -13,7 +13,7 @@ Inception-ResNetV2 is a deep learning model proposed by Google in 2016, which co ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/mlp_mixer_base/igie/README.md b/models/cv/classification/mlp_mixer_base/igie/README.md index b2e9d47f..d82c728b 100644 --- a/models/cv/classification/mlp_mixer_base/igie/README.md +++ b/models/cv/classification/mlp_mixer_base/igie/README.md @@ -13,7 +13,7 @@ MLP-Mixer Base is a foundational model in the MLP-Mixer family, designed to use ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/mobilenet_v3/ixrt/README.md b/models/cv/classification/mobilenet_v3/ixrt/README.md index 1ff5e158..7a80ec49 100644 --- a/models/cv/classification/mobilenet_v3/ixrt/README.md +++ b/models/cv/classification/mobilenet_v3/ixrt/README.md @@ -13,7 +13,7 @@ MobileNetV3 is a convolutional neural network that is tuned to mobile phone CPUs ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/mvitv2_base/igie/README.md b/models/cv/classification/mvitv2_base/igie/README.md index b161bc10..c6a112ae 100644 --- a/models/cv/classification/mvitv2_base/igie/README.md +++ b/models/cv/classification/mvitv2_base/igie/README.md @@ -13,7 +13,7 @@ MViTv2_base is an efficient multi-scale vision Transformer model designed specif ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/repvgg/igie/README.md b/models/cv/classification/repvgg/igie/README.md index f71b37ee..0f02b88d 100644 --- a/models/cv/classification/repvgg/igie/README.md +++ b/models/cv/classification/repvgg/igie/README.md @@ -13,7 +13,7 @@ RepVGG is an innovative convolutional neural network architecture that combines ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/repvgg/ixrt/README.md b/models/cv/classification/repvgg/ixrt/README.md index e93c626a..a1b2a363 100644 --- a/models/cv/classification/repvgg/ixrt/README.md +++ b/models/cv/classification/repvgg/ixrt/README.md @@ -14,7 +14,7 @@ It was developed by researchers at the University of Oxford and introduced in th ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/res2net50/igie/README.md b/models/cv/classification/res2net50/igie/README.md index b02548c5..eb698705 100644 --- a/models/cv/classification/res2net50/igie/README.md +++ b/models/cv/classification/res2net50/igie/README.md @@ -13,7 +13,7 @@ Res2Net50 is a convolutional neural network architecture that introduces the con ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/res2net50/ixrt/README.md b/models/cv/classification/res2net50/ixrt/README.md index 1ec7a251..6700543f 100644 --- a/models/cv/classification/res2net50/ixrt/README.md +++ b/models/cv/classification/res2net50/ixrt/README.md @@ -13,7 +13,7 @@ A novel building block for CNNs, namely Res2Net, by constructing hierarchical re ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/resnest50/igie/README.md b/models/cv/classification/resnest50/igie/README.md index 3bb04255..21137c42 100644 --- a/models/cv/classification/resnest50/igie/README.md +++ b/models/cv/classification/resnest50/igie/README.md @@ -13,7 +13,7 @@ ResNeSt50 is a deep convolutional neural network model based on the ResNeSt arch ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/resnet101/ixrt/README.md b/models/cv/classification/resnet101/ixrt/README.md index d85e3120..3869ea55 100644 --- a/models/cv/classification/resnet101/ixrt/README.md +++ b/models/cv/classification/resnet101/ixrt/README.md @@ -13,7 +13,7 @@ ResNet-101 is a variant of the ResNet (Residual Network) architecture, and it be ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r reuirements.txt ``` diff --git a/models/cv/classification/resnet18/ixrt/README.md b/models/cv/classification/resnet18/ixrt/README.md index 71857e65..84ce36f6 100644 --- a/models/cv/classification/resnet18/ixrt/README.md +++ b/models/cv/classification/resnet18/ixrt/README.md @@ -13,7 +13,7 @@ ResNet-18 is a variant of the ResNet (Residual Network) architecture, which was ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/resnet34/ixrt/README.md b/models/cv/classification/resnet34/ixrt/README.md index 8855611e..48df97d3 100644 --- a/models/cv/classification/resnet34/ixrt/README.md +++ b/models/cv/classification/resnet34/ixrt/README.md @@ -13,7 +13,7 @@ Residual Networks, or ResNets, learn residual functions with reference to the la ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/resnet50/ixrt/README.md b/models/cv/classification/resnet50/ixrt/README.md index 91700b76..0d5fc9fa 100644 --- a/models/cv/classification/resnet50/ixrt/README.md +++ b/models/cv/classification/resnet50/ixrt/README.md @@ -13,7 +13,7 @@ Residual Networks, or ResNets, learn residual functions with reference to the la ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/resnetv1d50/ixrt/README.md b/models/cv/classification/resnetv1d50/ixrt/README.md index 3b49e01e..7445f458 100644 --- a/models/cv/classification/resnetv1d50/ixrt/README.md +++ b/models/cv/classification/resnetv1d50/ixrt/README.md @@ -13,7 +13,7 @@ Residual Networks, or ResNets, learn residual functions with reference to the la ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirments.txt ``` diff --git a/models/cv/classification/seresnet50/igie/README.md b/models/cv/classification/seresnet50/igie/README.md index 876f13dd..ddd891ec 100644 --- a/models/cv/classification/seresnet50/igie/README.md +++ b/models/cv/classification/seresnet50/igie/README.md @@ -13,7 +13,7 @@ SEResNet50 is an enhanced version of the ResNet50 network integrated with Squeez ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/shufflenet_v1/ixrt/README.md b/models/cv/classification/shufflenet_v1/ixrt/README.md index 94c88432..ae50050c 100644 --- a/models/cv/classification/shufflenet_v1/ixrt/README.md +++ b/models/cv/classification/shufflenet_v1/ixrt/README.md @@ -14,7 +14,7 @@ It uses techniques such as deep separable convolution and channel shuffle to red ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/squeezenet_v1_0/ixrt/README.md b/models/cv/classification/squeezenet_v1_0/ixrt/README.md index 6af82041..faea4b18 100644 --- a/models/cv/classification/squeezenet_v1_0/ixrt/README.md +++ b/models/cv/classification/squeezenet_v1_0/ixrt/README.md @@ -15,7 +15,7 @@ It was developed by researchers at DeepScale and released in 2016. ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/squeezenet_v1_1/ixrt/README.md b/models/cv/classification/squeezenet_v1_1/ixrt/README.md index eb6126c3..ada0742a 100644 --- a/models/cv/classification/squeezenet_v1_1/ixrt/README.md +++ b/models/cv/classification/squeezenet_v1_1/ixrt/README.md @@ -15,7 +15,7 @@ It was developed by researchers at DeepScale and released in 2016. ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/svt_base/igie/README.md b/models/cv/classification/svt_base/igie/README.md index 98dd7791..26d020d0 100644 --- a/models/cv/classification/svt_base/igie/README.md +++ b/models/cv/classification/svt_base/igie/README.md @@ -13,7 +13,7 @@ SVT Base is a mid-sized variant of the Sparse Vision Transformer (SVT) series, d ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/classification/vgg16/ixrt/README.md b/models/cv/classification/vgg16/ixrt/README.md index 789fcc8b..7681a924 100644 --- a/models/cv/classification/vgg16/ixrt/README.md +++ b/models/cv/classification/vgg16/ixrt/README.md @@ -14,7 +14,7 @@ It finished second in the 2014 ImageNet Massive Visual Identity Challenge (ILSVR ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/atss/igie/README.md b/models/cv/detection/atss/igie/README.md index 3d887918..23f0ae94 100644 --- a/models/cv/detection/atss/igie/README.md +++ b/models/cv/detection/atss/igie/README.md @@ -13,7 +13,7 @@ ATSS is an advanced adaptive training sample selection method that effectively e ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/centernet/igie/README.md b/models/cv/detection/centernet/igie/README.md index d3987840..64156b2e 100644 --- a/models/cv/detection/centernet/igie/README.md +++ b/models/cv/detection/centernet/igie/README.md @@ -13,7 +13,7 @@ CenterNet is an efficient object detection model that simplifies the traditional ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/centernet/ixrt/README.md b/models/cv/detection/centernet/ixrt/README.md index 8978b2b4..776b54fb 100644 --- a/models/cv/detection/centernet/ixrt/README.md +++ b/models/cv/detection/centernet/ixrt/README.md @@ -13,7 +13,7 @@ CenterNet is an efficient object detection model that simplifies the traditional ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt # Contact the Iluvatar administrator to get the mmcv install package. diff --git a/models/cv/detection/detr/ixrt/README.md b/models/cv/detection/detr/ixrt/README.md index 1704b35c..cde0acda 100755 --- a/models/cv/detection/detr/ixrt/README.md +++ b/models/cv/detection/detr/ixrt/README.md @@ -13,7 +13,7 @@ DETR (DEtection TRansformer) is a novel approach that views object detection as ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/fcos/igie/README.md b/models/cv/detection/fcos/igie/README.md index c042feac..2bcdbee2 100644 --- a/models/cv/detection/fcos/igie/README.md +++ b/models/cv/detection/fcos/igie/README.md @@ -13,7 +13,7 @@ FCOS is an innovative one-stage object detection framework that abandons traditi ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/fcos/ixrt/README.md b/models/cv/detection/fcos/ixrt/README.md index bc3f0461..56b18c69 100755 --- a/models/cv/detection/fcos/ixrt/README.md +++ b/models/cv/detection/fcos/ixrt/README.md @@ -14,7 +14,7 @@ For more details, please refer to our [report on Arxiv](https://arxiv.org/abs/19 ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/foveabox/igie/README.md b/models/cv/detection/foveabox/igie/README.md index aded8a5c..cbed39fd 100644 --- a/models/cv/detection/foveabox/igie/README.md +++ b/models/cv/detection/foveabox/igie/README.md @@ -13,7 +13,7 @@ FoveaBox is an advanced anchor-free object detection framework that enhances acc ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/foveabox/ixrt/README.md b/models/cv/detection/foveabox/ixrt/README.md index d86a2c39..2682a253 100644 --- a/models/cv/detection/foveabox/ixrt/README.md +++ b/models/cv/detection/foveabox/ixrt/README.md @@ -13,7 +13,7 @@ FoveaBox is an advanced anchor-free object detection framework that enhances acc ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install tqdm pip3 install onnx diff --git a/models/cv/detection/fsaf/igie/README.md b/models/cv/detection/fsaf/igie/README.md index 6df84b12..d6fdbcfd 100644 --- a/models/cv/detection/fsaf/igie/README.md +++ b/models/cv/detection/fsaf/igie/README.md @@ -13,7 +13,7 @@ The FSAF (Feature Selective Anchor-Free) module is an innovative component for s ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/fsaf/ixrt/README.md b/models/cv/detection/fsaf/ixrt/README.md index 5d75a2e6..03935e62 100644 --- a/models/cv/detection/fsaf/ixrt/README.md +++ b/models/cv/detection/fsaf/ixrt/README.md @@ -13,7 +13,7 @@ The FSAF (Feature Selective Anchor-Free) module is an innovative component for s ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/hrnet/igie/README.md b/models/cv/detection/hrnet/igie/README.md index e0966ccd..49fc4dcd 100644 --- a/models/cv/detection/hrnet/igie/README.md +++ b/models/cv/detection/hrnet/igie/README.md @@ -13,7 +13,7 @@ HRNet is an advanced deep learning architecture for human pose estimation, chara ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/hrnet/ixrt/README.md b/models/cv/detection/hrnet/ixrt/README.md index cade087c..284faafd 100644 --- a/models/cv/detection/hrnet/ixrt/README.md +++ b/models/cv/detection/hrnet/ixrt/README.md @@ -13,7 +13,7 @@ HRNet is an advanced deep learning architecture for human pose estimation, chara ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/paa/igie/README.md b/models/cv/detection/paa/igie/README.md index ddf47326..5cbc5505 100644 --- a/models/cv/detection/paa/igie/README.md +++ b/models/cv/detection/paa/igie/README.md @@ -13,7 +13,7 @@ PAA (Probabilistic Anchor Assignment) is an algorithm for object detection that ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/retinaface/igie/README.md b/models/cv/detection/retinaface/igie/README.md index 82d127ec..3610a8bf 100755 --- a/models/cv/detection/retinaface/igie/README.md +++ b/models/cv/detection/retinaface/igie/README.md @@ -13,7 +13,7 @@ RetinaFace is an efficient single-stage face detection model that employs a mult ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/retinaface/ixrt/README.md b/models/cv/detection/retinaface/ixrt/README.md index 8fff5439..2513eb34 100644 --- a/models/cv/detection/retinaface/ixrt/README.md +++ b/models/cv/detection/retinaface/ixrt/README.md @@ -13,7 +13,7 @@ RetinaFace is an efficient single-stage face detection model that employs a mult ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt diff --git a/models/cv/detection/retinanet/igie/README.md b/models/cv/detection/retinanet/igie/README.md index 648ad5d0..18067d85 100644 --- a/models/cv/detection/retinanet/igie/README.md +++ b/models/cv/detection/retinanet/igie/README.md @@ -13,7 +13,7 @@ RetinaNet, an innovative object detector, challenges the conventional trade-off ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/rtmdet/igie/README.md b/models/cv/detection/rtmdet/igie/README.md index 3b2526d1..8ed35963 100644 --- a/models/cv/detection/rtmdet/igie/README.md +++ b/models/cv/detection/rtmdet/igie/README.md @@ -13,7 +13,7 @@ RTMDet, presented by the Shanghai AI Laboratory, is a novel framework for real-t ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/sabl/igie/README.md b/models/cv/detection/sabl/igie/README.md index 28d0242e..25732e82 100644 --- a/models/cv/detection/sabl/igie/README.md +++ b/models/cv/detection/sabl/igie/README.md @@ -13,7 +13,7 @@ SABL (Side-Aware Boundary Localization) is an innovative approach in object dete ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov3/igie/README.md b/models/cv/detection/yolov3/igie/README.md index e0c696d6..7e1d769a 100644 --- a/models/cv/detection/yolov3/igie/README.md +++ b/models/cv/detection/yolov3/igie/README.md @@ -13,7 +13,7 @@ YOLOv3 is a influential object detection algorithm.The key innovation of YOLOv3 ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov3/ixrt/README.md b/models/cv/detection/yolov3/ixrt/README.md index fc531ab3..d1f37fec 100644 --- a/models/cv/detection/yolov3/ixrt/README.md +++ b/models/cv/detection/yolov3/ixrt/README.md @@ -13,7 +13,7 @@ YOLOv3 is a influential object detection algorithm.The key innovation of YOLOv3 ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov4/igie/README.md b/models/cv/detection/yolov4/igie/README.md index a88ea5b3..f2a0320d 100644 --- a/models/cv/detection/yolov4/igie/README.md +++ b/models/cv/detection/yolov4/igie/README.md @@ -13,7 +13,7 @@ YOLOv4 employs a two-step process, involving regression for bounding box positio ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov4/ixrt/README.md b/models/cv/detection/yolov4/ixrt/README.md index ae94ae43..8b53a752 100644 --- a/models/cv/detection/yolov4/ixrt/README.md +++ b/models/cv/detection/yolov4/ixrt/README.md @@ -13,7 +13,7 @@ YOLOv4 employs a two-step process, involving regression for bounding box positio ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov5/igie/README.md b/models/cv/detection/yolov5/igie/README.md index f8d7d5a7..f71018d4 100644 --- a/models/cv/detection/yolov5/igie/README.md +++ b/models/cv/detection/yolov5/igie/README.md @@ -13,7 +13,7 @@ The YOLOv5 architecture is designed for efficient and accurate object detection ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov5/ixrt/README.md b/models/cv/detection/yolov5/ixrt/README.md index ea1fc180..fd4e7e0b 100644 --- a/models/cv/detection/yolov5/ixrt/README.md +++ b/models/cv/detection/yolov5/ixrt/README.md @@ -13,7 +13,7 @@ The YOLOv5 architecture is designed for efficient and accurate object detection ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov5s/ixrt/README.md b/models/cv/detection/yolov5s/ixrt/README.md index 3847db5a..cf048372 100755 --- a/models/cv/detection/yolov5s/ixrt/README.md +++ b/models/cv/detection/yolov5s/ixrt/README.md @@ -13,7 +13,7 @@ The YOLOv5 architecture is designed for efficient and accurate object detection ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov7/igie/README.md b/models/cv/detection/yolov7/igie/README.md index 3d6cd5fa..d23f22d6 100644 --- a/models/cv/detection/yolov7/igie/README.md +++ b/models/cv/detection/yolov7/igie/README.md @@ -13,7 +13,7 @@ YOLOv7 is a state-of-the-art real-time object detector that surpasses all known ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov7/ixrt/README.md b/models/cv/detection/yolov7/ixrt/README.md index 7b6e91c3..2174c199 100644 --- a/models/cv/detection/yolov7/ixrt/README.md +++ b/models/cv/detection/yolov7/ixrt/README.md @@ -13,7 +13,7 @@ YOLOv7 is an object detection model based on the YOLO (You Only Look Once) serie ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolov8/igie/README.md b/models/cv/detection/yolov8/igie/README.md index 36fab034..b3ee9d5e 100644 --- a/models/cv/detection/yolov8/igie/README.md +++ b/models/cv/detection/yolov8/igie/README.md @@ -13,7 +13,7 @@ Yolov8 combines speed and accuracy in real-time object detection tasks. With a f ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/detection/yolox/igie/README.md b/models/cv/detection/yolox/igie/README.md index a1de5863..32c3d638 100644 --- a/models/cv/detection/yolox/igie/README.md +++ b/models/cv/detection/yolox/igie/README.md @@ -13,7 +13,7 @@ YOLOX is an anchor-free version of YOLO, with a simpler design but better perfor ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt diff --git a/models/cv/detection/yolox/ixrt/README.md b/models/cv/detection/yolox/ixrt/README.md index 475a5002..0a19d5bf 100644 --- a/models/cv/detection/yolox/ixrt/README.md +++ b/models/cv/detection/yolox/ixrt/README.md @@ -14,7 +14,7 @@ For more details, please refer to our [report on Arxiv](https://arxiv.org/abs/21 ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/pose_estimation/hrnetpose/igie/README.md b/models/cv/pose_estimation/hrnetpose/igie/README.md index c4f0758f..09771216 100644 --- a/models/cv/pose_estimation/hrnetpose/igie/README.md +++ b/models/cv/pose_estimation/hrnetpose/igie/README.md @@ -13,7 +13,7 @@ HRNetPose (High-Resolution Network for Pose Estimation) is a high-performance hu ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/pose_estimation/lightweight_openpose/ixrt/README.md b/models/cv/pose_estimation/lightweight_openpose/ixrt/README.md index ca18417a..4fa8923c 100644 --- a/models/cv/pose_estimation/lightweight_openpose/ixrt/README.md +++ b/models/cv/pose_estimation/lightweight_openpose/ixrt/README.md @@ -13,7 +13,7 @@ This work heavily optimizes the OpenPose approach to reach real-time inference o ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/pose_estimation/rtmpose/igie/README.md b/models/cv/pose_estimation/rtmpose/igie/README.md index dd565700..9497dcfd 100644 --- a/models/cv/pose_estimation/rtmpose/igie/README.md +++ b/models/cv/pose_estimation/rtmpose/igie/README.md @@ -13,7 +13,7 @@ RTMPose, a state-of-the-art framework developed by Shanghai AI Laboratory, excel ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/cv/pose_estimation/rtmpose/ixrt/README.md b/models/cv/pose_estimation/rtmpose/ixrt/README.md index ea69ea32..51a832a1 100644 --- a/models/cv/pose_estimation/rtmpose/ixrt/README.md +++ b/models/cv/pose_estimation/rtmpose/ixrt/README.md @@ -13,7 +13,7 @@ RTMPose, a state-of-the-art framework developed by Shanghai AI Laboratory, excel ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -r requirements.txt ``` diff --git a/models/multimodal/vision_language_understanding/chameleon_7b/vllm/README.md b/models/multimodal/vision_language_understanding/chameleon_7b/vllm/README.md index de779d7f..879367dd 100755 --- a/models/multimodal/vision_language_understanding/chameleon_7b/vllm/README.md +++ b/models/multimodal/vision_language_understanding/chameleon_7b/vllm/README.md @@ -15,7 +15,7 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx ``` ### Download diff --git a/models/multimodal/vision_language_understanding/clip/ixformer/README.md b/models/multimodal/vision_language_understanding/clip/ixformer/README.md index b180a045..e8b90f57 100644 --- a/models/multimodal/vision_language_understanding/clip/ixformer/README.md +++ b/models/multimodal/vision_language_understanding/clip/ixformer/README.md @@ -15,7 +15,7 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install -U transformers==4.27.1 ``` diff --git a/models/multimodal/vision_language_understanding/fuyu_8b/vllm/README.md b/models/multimodal/vision_language_understanding/fuyu_8b/vllm/README.md index 3a3bb30e..7f2526b9 100755 --- a/models/multimodal/vision_language_understanding/fuyu_8b/vllm/README.md +++ b/models/multimodal/vision_language_understanding/fuyu_8b/vllm/README.md @@ -17,7 +17,7 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx ``` ### Download diff --git a/models/multimodal/vision_language_understanding/intern_vl/vllm/README.md b/models/multimodal/vision_language_understanding/intern_vl/vllm/README.md index 33db9fa5..c8a52779 100644 --- a/models/multimodal/vision_language_understanding/intern_vl/vllm/README.md +++ b/models/multimodal/vision_language_understanding/intern_vl/vllm/README.md @@ -15,7 +15,7 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx # Contact the iluvatar manager to get adapted install packages of vllm, triton, and ixformer pip3 install vllm diff --git a/models/multimodal/vision_language_understanding/llava/vllm/README.md b/models/multimodal/vision_language_understanding/llava/vllm/README.md index bbc251d2..2ceeaed8 100644 --- a/models/multimodal/vision_language_understanding/llava/vllm/README.md +++ b/models/multimodal/vision_language_understanding/llava/vllm/README.md @@ -15,7 +15,7 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install transformers ``` diff --git a/models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/README.md b/models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/README.md index 2dff6d1f..4fb09f4f 100755 --- a/models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/README.md +++ b/models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/README.md @@ -15,7 +15,7 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx ``` ### Download diff --git a/models/multimodal/vision_language_understanding/minicpm_v_2/vllm/README.md b/models/multimodal/vision_language_understanding/minicpm_v_2/vllm/README.md index e71d0043..d2b2dd86 100644 --- a/models/multimodal/vision_language_understanding/minicpm_v_2/vllm/README.md +++ b/models/multimodal/vision_language_understanding/minicpm_v_2/vllm/README.md @@ -15,7 +15,7 @@ In order to run the model smoothly, you need to get the sdk from [resource cente ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install timm==0.9.10 pip3 install transformers pip3 install --user --upgrade pillow -i https://pypi.tuna.tsinghua.edu.cn/simple diff --git a/models/nlp/large_language_model/baichuan2-7b/vllm/README.md b/models/nlp/large_language_model/baichuan2-7b/vllm/README.md index 06894d8a..9a8cb1ec 100755 --- a/models/nlp/large_language_model/baichuan2-7b/vllm/README.md +++ b/models/nlp/large_language_model/baichuan2-7b/vllm/README.md @@ -19,7 +19,7 @@ center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX offic ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install transformers ``` diff --git a/models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md b/models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md index 5062e74e..c74b04fe 100644 --- a/models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md +++ b/models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md @@ -20,7 +20,7 @@ center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX offic ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install transformers ``` diff --git a/models/nlp/large_language_model/chatglm3-6b/vllm/README.md b/models/nlp/large_language_model/chatglm3-6b/vllm/README.md index 1ffe0632..5dc17403 100644 --- a/models/nlp/large_language_model/chatglm3-6b/vllm/README.md +++ b/models/nlp/large_language_model/chatglm3-6b/vllm/README.md @@ -18,7 +18,7 @@ center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX offic ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx pip3 install vllm pip3 install transformers diff --git a/models/nlp/large_language_model/llama2-13b/trtllm/README.md b/models/nlp/large_language_model/llama2-13b/trtllm/README.md index 8605e11d..d525ad9f 100755 --- a/models/nlp/large_language_model/llama2-13b/trtllm/README.md +++ b/models/nlp/large_language_model/llama2-13b/trtllm/README.md @@ -16,7 +16,7 @@ from 7B to 70B. ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx bash scripts/set_environment.sh . ``` diff --git a/models/nlp/large_language_model/llama2-70b/trtllm/README.md b/models/nlp/large_language_model/llama2-70b/trtllm/README.md index 7a9d6a8d..628ee896 100644 --- a/models/nlp/large_language_model/llama2-70b/trtllm/README.md +++ b/models/nlp/large_language_model/llama2-70b/trtllm/README.md @@ -18,7 +18,7 @@ and contribute to the responsible development of LLMs. ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx bash scripts/set_environment.sh . ``` diff --git a/models/nlp/large_language_model/llama2-7b/trtllm/README.md b/models/nlp/large_language_model/llama2-7b/trtllm/README.md index ca384327..3be201f1 100644 --- a/models/nlp/large_language_model/llama2-7b/trtllm/README.md +++ b/models/nlp/large_language_model/llama2-7b/trtllm/README.md @@ -21,7 +21,7 @@ center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX offic ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx bash scripts/set_environment.sh . ``` diff --git a/models/nlp/large_language_model/llama2-7b/vllm/README.md b/models/nlp/large_language_model/llama2-7b/vllm/README.md index ad676926..44e6db71 100755 --- a/models/nlp/large_language_model/llama2-7b/vllm/README.md +++ b/models/nlp/large_language_model/llama2-7b/vllm/README.md @@ -21,7 +21,7 @@ center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX offic ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx # Contact the iluvatar manager to get adapted install packages of vllm, triton, and ixformer pip3 install vllm diff --git a/models/nlp/large_language_model/llama3-70b/vllm/README.md b/models/nlp/large_language_model/llama3-70b/vllm/README.md index 8d2e6b38..a6c4f488 100644 --- a/models/nlp/large_language_model/llama3-70b/vllm/README.md +++ b/models/nlp/large_language_model/llama3-70b/vllm/README.md @@ -30,7 +30,7 @@ center](https://support.iluvatar.com/#/ProductLine?id=2) of Iluvatar CoreX offic ## CentOS yum install -y mesa-libGL ## Ubuntu -apt install -y libgl1-mesa-dev +apt install -y libgl1-mesa-glx ``` ### Download -- Gitee From 4fe2d91446c559e84ef583198aa7cca51a8701a3 Mon Sep 17 00:00:00 2001 From: "mingjiang.li" Date: Tue, 25 Feb 2025 17:52:24 +0800 Subject: [PATCH 5/8] use model path as link other than README Signed-off-by: mingjiang.li --- README.md | 46 +++++++++++++++++++++++----------------------- 1 file changed, 23 insertions(+), 23 deletions(-) diff --git a/README.md b/README.md index 9847b61b..a71902bd 100644 --- a/README.md +++ b/README.md @@ -12,29 +12,29 @@ DeepSparkInference将按季度进行版本更新，后续会逐步丰富模型 ## LLM (Large Language Model) -| Model | vLLM | TRT-LLM | TGI | -|-------------------------------|-----------------------------------------------------------------------------------|------------------------------------------------------------------|-------------------------------------------------------------------------------------| -| Baichuan2-7B | [✅](models/nlp/large_language_model/baichuan2-7b/vllm/README.md) | | | -| ChatGLM-3-6B | [✅](models/nlp/large_language_model/chatglm3-6b/vllm/README.md) | | | -| ChatGLM-3-6B-32K | [✅](models/nlp/large_language_model/chatglm3-6b-32k/vllm/README.md) | | | -| DeepSeek-R1-Distill-Llama-8B | [✅](models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Llama-70B | [✅](models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Qwen-1.5B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Qwen-7B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Qwen-14B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm/README.md) | | | -| DeepSeek-R1-Distill-Qwen-32B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm/README.md) | | | -| Llama2-7B | [✅](models/nlp/large_language_model/llama2-7b/vllm/README.md) | [✅](models/nlp/large_language_model/llama2-7b/trtllm/README.md) | | -| Llama2-13B | | [✅](models/nlp/large_language_model/llama2-13b/trtllm/README.md) | | -| Llama2-70B | | [✅](models/nlp/large_language_model/llama2-70b/trtllm/README.md) | | -| Llama3-70B | [✅](models/nlp/large_language_model/llama3-70b/vllm/README.md) | | | -| Qwen-7B | [✅](models/nlp/large_language_model/qwen-7b/vllm/README.md) | | | -| Qwen1.5-7B | [✅](models/nlp/large_language_model/qwen1.5-7b/vllm/README.md) | | [✅](models/nlp/large_language_model/qwen1.5-7b/text-generation-inference/README.md) | -| Qwen1.5-14B | [✅](models/nlp/large_language_model/qwen1.5-14b/vllm/README.md) | | | -| Qwen1.5-32B Chat | [✅](models/nlp/large_language_model/qwen1.5-32b/vllm/README.md) | | | -| Qwen1.5-72B | [✅](models/nlp/large_language_model/qwen1.5-72b/vllm/README.md) | | | -| Qwen2-7B Instruct | [✅](models/nlp/large_language_model/qwen2-7b/vllm/README.md) | | | -| Qwen2-72B Instruct | [✅](models/nlp/large_language_model/qwen2-72b/vllm/README.md) | | | -| StableLM2-1.6B | [✅](models/nlp/large_language_model/stablelm/vllm/README.md) | | | +| Model | vLLM | TRT-LLM | TGI | +|-------------------------------|-------------------------------------------------------------------------|--------------------------------------------------------|---------------------------------------------------------------------------| +| Baichuan2-7B | [✅](models/nlp/large_language_model/baichuan2-7b/vllm) | | | +| ChatGLM-3-6B | [✅](models/nlp/large_language_model/chatglm3-6b/vllm) | | | +| ChatGLM-3-6B-32K | [✅](models/nlp/large_language_model/chatglm3-6b-32k/vllm) | | | +| DeepSeek-R1-Distill-Llama-8B | [✅](models/nlp/large_language_model/deepseek-r1-distill-llama-8b/vllm) | | | +| DeepSeek-R1-Distill-Llama-70B | [✅](models/nlp/large_language_model/deepseek-r1-distill-llama-70b/vllm) | | | +| DeepSeek-R1-Distill-Qwen-1.5B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-1.5b/vllm) | | | +| DeepSeek-R1-Distill-Qwen-7B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-7b/vllm) | | | +| DeepSeek-R1-Distill-Qwen-14B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-14b/vllm) | | | +| DeepSeek-R1-Distill-Qwen-32B | [✅](models/nlp/large_language_model/deepseek-r1-distill-qwen-32b/vllm) | | | +| Llama2-7B | [✅](models/nlp/large_language_model/llama2-7b/vllm) | [✅](models/nlp/large_language_model/llama2-7b/trtllm) | | +| Llama2-13B | | [✅](models/nlp/large_language_model/llama2-13b/trtllm) | | +| Llama2-70B | | [✅](models/nlp/large_language_model/llama2-70b/trtllm) | | +| Llama3-70B | [✅](models/nlp/large_language_model/llama3-70b/vllm) | | | +| Qwen-7B | [✅](models/nlp/large_language_model/qwen-7b/vllm) | | | +| Qwen1.5-7B | [✅](models/nlp/large_language_model/qwen1.5-7b/vllm) | | [✅](models/nlp/large_language_model/qwen1.5-7b/text-generation-inference) | +| Qwen1.5-14B | [✅](models/nlp/large_language_model/qwen1.5-14b/vllm) | | | +| Qwen1.5-32B Chat | [✅](models/nlp/large_language_model/qwen1.5-32b/vllm) | | | +| Qwen1.5-72B | [✅](models/nlp/large_language_model/qwen1.5-72b/vllm) | | | +| Qwen2-7B Instruct | [✅](models/nlp/large_language_model/qwen2-7b/vllm) | | | +| Qwen2-72B Instruct | [✅](models/nlp/large_language_model/qwen2-72b/vllm) | | | +| StableLM2-1.6B | [✅](models/nlp/large_language_model/stablelm/vllm) | | | ## Computer Vision -- Gitee From 3e5fb98790924c53c94c8896256e4ff88c4edccb Mon Sep 17 00:00:00 2001 From: "mingjiang.li" Date: Tue, 25 Feb 2025 17:56:01 +0800 Subject: [PATCH 6/8] format model README.md markdown Signed-off-by: mingjiang.li --- .../cv/classification/alexnet/igie/README.md | 14 ++++++++----- .../cv/classification/alexnet/ixrt/README.md | 11 +++++----- .../bert_large_squad/ixrt/README.md | 15 +++++++------ .../nlp/language_model/deberta/ixrt/README.md | 17 +++++++++++---- .../nlp/language_model/roberta/ixrt/README.md | 19 +++++++++++++---- .../language_model/roformer/ixrt/README.md | 21 +++++++++++++++---- .../language_model/videobert/ixrt/README.md | 6 ++++-- 7 files changed, 71 insertions(+), 32 deletions(-) diff --git a/models/cv/classification/alexnet/igie/README.md b/models/cv/classification/alexnet/igie/README.md index 4e8418bd..0720e0ff 100644 --- a/models/cv/classification/alexnet/igie/README.md +++ b/models/cv/classification/alexnet/igie/README.md @@ -2,7 +2,11 @@ ## Description -AlexNet, developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, is a groundbreaking convolutional neural network (CNN) architecture that achieved remarkable success in the 2012 ImageNet Large Scale Visual Recognition Challenge (ILSVRC). This neural network comprises eight layers, incorporating five convolutional layers and three fully connected layers. The architecture employs the Rectified Linear Unit (ReLU) activation function to introduce non-linearity, allowing the model to learn complex features from input images. +AlexNet, developed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton, is a groundbreaking convolutional neural +network (CNN) architecture that achieved remarkable success in the 2012 ImageNet Large Scale Visual Recognition +Challenge (ILSVRC). This neural network comprises eight layers, incorporating five convolutional layers and three fully +connected layers. The architecture employs the Rectified Linear Unit (ReLU) activation function to introduce +non-linearity, allowing the model to learn complex features from input images. ## Setup @@ -50,7 +54,7 @@ bash scripts/infer_alexnet_int8_performance.sh ## Results -Model |BatchSize |Precision |FPS |Top-1(%) |Top-5(%) ---------|-----------|----------|----------|---------|-------- -AlexNet | 32 | FP16 | 20456.16 | 56.53 | 79.05 -AlexNet | 32 | INT8 | 22465.46 | 55.96 | 78.83 +| Model | BatchSize | Precision | FPS | Top-1(%) | Top-5(%) | +|---------|-----------|-----------|----------|----------|----------| +| AlexNet | 32 | FP16 | 20456.16 | 56.53 | 79.05 | +| AlexNet | 32 | INT8 | 22465.46 | 55.96 | 78.83 | diff --git a/models/cv/classification/alexnet/ixrt/README.md b/models/cv/classification/alexnet/ixrt/README.md index 54981bb5..a111e406 100644 --- a/models/cv/classification/alexnet/ixrt/README.md +++ b/models/cv/classification/alexnet/ixrt/README.md @@ -2,7 +2,8 @@ ## Description -AlexNet is a classic convolutional neural network architecture. It consists of convolutions, max pooling and dense layers as the basic building blocks. +AlexNet is a classic convolutional neural network architecture. It consists of convolutions, max pooling and dense +layers as the basic building blocks. ## Setup @@ -61,7 +62,7 @@ bash scripts/infer_alexnet_int8_performance.sh ## Results -Model |BatchSize |Precision |FPS |Top-1(%) |Top-5(%) ---------|-----------|----------|----------|----------|-------- -AlexNet | 32 | FP16 | 17644.90 | 56.54 | 79.08 -AlexNet | 32 | INT8 | 18276.83 | 55.37 | 79.04 +| Model | BatchSize | Precision | FPS | Top-1(%) | Top-5(%) | +|---------|-----------|-----------|----------|----------|----------| +| AlexNet | 32 | FP16 | 17644.90 | 56.54 | 79.08 | +| AlexNet | 32 | INT8 | 18276.83 | 55.37 | 79.04 | diff --git a/models/nlp/language_model/bert_large_squad/ixrt/README.md b/models/nlp/language_model/bert_large_squad/ixrt/README.md index b824a8ee..a8bcf5b7 100644 --- a/models/nlp/language_model/bert_large_squad/ixrt/README.md +++ b/models/nlp/language_model/bert_large_squad/ixrt/README.md @@ -6,6 +6,9 @@ BERT is designed to pre-train deep bidirectional representations from unlabeled ## Setup +Get `bert-large-uncased.zip` from [Google +Drive](https://drive.google.com/file/d/1eD8QBkbK6YN-_YXODp3tmpp3cZKlrPTA/view?usp=drive_link) + ### NV requirement(tensorrt_version >= 8.6) ```bash @@ -59,14 +62,10 @@ pip install onnx pycuda bash script/build_engine.sh --bs 32 --int8 bash script/inference_squad.sh --bs 32 --int8 ``` - -## Results - | Model | BatchSize | Precision | Latency QPS | exact_match | f1 | -| ---------------- | --------- | --------- | ------------------- | ----------- | ----- | +|------------------|-----------|-----------|---------------------|-------------|-------| +| BERT-Large-SQuAD | 32 | FP16 | 470.26 sentences/s | 82.36 | 89.68 | +| BERT-Large-SQuAD | 32 | INT8 | 1490.47 sentences/s | 80.92 | 88.20 | +|------------------|-----------|-----------|---------------------|-------------|-------| | BERT-Large-SQuAD | 32 | FP16 | 470.26 sentences/s | 82.36 | 89.68 | | BERT-Large-SQuAD | 32 | INT8 | 1490.47 sentences/s | 80.92 | 88.20 | - -## Referenece - -- [bert-large-uncased.zip](https://drive.google.com/file/d/1eD8QBkbK6YN-_YXODp3tmpp3cZKlrPTA/view?usp=drive_link) diff --git a/models/nlp/language_model/deberta/ixrt/README.md b/models/nlp/language_model/deberta/ixrt/README.md index fd1757cd..3026d51d 100644 --- a/models/nlp/language_model/deberta/ixrt/README.md +++ b/models/nlp/language_model/deberta/ixrt/README.md @@ -2,7 +2,12 @@ ## Description -DeBERTa (Decoding-enhanced BERT with disentangled attention) is an enhanced version of the BERT (Bidirectional Encoder Representations from Transformers) model. It improves text representation learning by introducing disentangled attention mechanisms and decoding enhancement techniques.DeBERTa introduces disentangled attention mechanisms that decompose the self-attention matrix into different parts, focusing on different semantic information. This helps the model better capture relationships between texts.By incorporating decoding enhancement techniques, DeBERTa adjusts the decoder during fine-tuning to better suit specific downstream tasks, thereby improving the model’s performance on those tasks. +DeBERTa (Decoding-enhanced BERT with disentangled attention) is an enhanced version of the BERT (Bidirectional Encoder +Representations from Transformers) model. It improves text representation learning by introducing disentangled attention +mechanisms and decoding enhancement techniques.DeBERTa introduces disentangled attention mechanisms that decompose the +self-attention matrix into different parts, focusing on different semantic information. This helps the model better +capture relationships between texts.By incorporating decoding enhancement techniques, DeBERTa adjusts the decoder during +fine-tuning to better suit specific downstream tasks, thereby improving the model’s performance on those tasks. ## Setup @@ -57,9 +62,13 @@ bash scripts/infer_deberta_fp16_performance.sh ### Accuracy -If you want to evaluate the accuracy of this model, please visit the website: < >, which integrates inference and training of many models under this framework, supporting the ILUVATAR backend +If you want to evaluate the accuracy of this model, please visit the website: < + >, which integrates inference and training of many +models under this framework, supporting the ILUVATAR backend -For detailed steps regarding this model, please refer to this document: < > Note: You need to modify the relevant paths in the code to your own correct paths. +For detailed steps regarding this model, please refer to this document: < + +> Note: You need to modify the relevant paths in the code to your own correct paths. ```bash # link and install requirements @@ -90,5 +99,5 @@ python3 core/perf_engine.py --hardware_type ILUVATAR --task deberta-torch-fp32 ## Results | Model | BatchSize | Precision | QPS | Exact Match | F1 Score | -| ------- | --------- | --------- | ----- | ----------- | -------- | +|---------|-----------|-----------|-------|-------------|----------| | DeBERTa | 1 | FP16 | 18.58 | 73.76 | 81.24 | diff --git a/models/nlp/language_model/roberta/ixrt/README.md b/models/nlp/language_model/roberta/ixrt/README.md index f37d5f3c..4e45bfa8 100644 --- a/models/nlp/language_model/roberta/ixrt/README.md +++ b/models/nlp/language_model/roberta/ixrt/README.md @@ -2,7 +2,14 @@ ## Description -Language model pretraining has led to significant performance gains but careful comparison between different approaches is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of previously overlooked design choices, and raise questions about the source of recently reported improvements. We release our models and code. +Language model pretraining has led to significant performance gains but careful comparison between different approaches +is challenging. Training is computationally expensive, often done on private datasets of different sizes, and, as we +will show, hyperparameter choices have significant impact on the final results. We present a replication study of BERT +pretraining (Devlin et al., 2019) that carefully measures the impact of many key hyperparameters and training data size. +We find that BERT was significantly undertrained, and can match or exceed the performance of every model published after +it. Our best model achieves state-of-the-art results on GLUE, RACE and SQuAD. These results highlight the importance of +previously overlooked design choices, and raise questions about the source of recently reported improvements. We release +our models and code. ## Setup @@ -59,9 +66,13 @@ bash scripts/infer_roberta_fp16_performance.sh ### Accuracy -If you want to evaluate the accuracy of this model, please visit the website: , which integrates inference and training of many models under this framework, supporting the ILUVATAR backend +If you want to evaluate the accuracy of this model, please visit the website: +, which integrates inference and training of many +models under this framework, supporting the ILUVATAR backend -For detailed steps regarding this model, please refer to this document: Note: You need to modify the relevant paths in the code to your own correct paths. +For detailed steps regarding this model, please refer to this document: + +Note: You need to modify the relevant paths in the code to your own correct paths. ```bash # Link and install requirements @@ -95,5 +106,5 @@ python3 core/perf_engine.py --hardware_type ILUVATAR --task roberta-torch-fp32 ## Results | Model | BatchSize | Precision | FPS | F1 | Exact Match | -| ------- | --------- | --------- | ------ | -------- | ----------- | +|---------|-----------|-----------|--------|----------|-------------| | RoBERTa | 1 | FP16 | 355.48 | 83.14387 | 76.50175 | diff --git a/models/nlp/language_model/roformer/ixrt/README.md b/models/nlp/language_model/roformer/ixrt/README.md index 59750879..6d125955 100644 --- a/models/nlp/language_model/roformer/ixrt/README.md +++ b/models/nlp/language_model/roformer/ixrt/README.md @@ -2,7 +2,16 @@ ## Description -Position encoding recently has shown effective in the transformer architecture. It enables valuable supervision for dependency modeling between elements at different positions of the sequence. In this paper, we first investigate various methods to integrate positional information into the learning process of transformer-based language models. Then, we propose a novel method named Rotary Position Embedding(RoPE) to effectively leverage the positional information. Specifically, the proposed RoPE encodes the absolute position with a rotation matrix and meanwhile incorporates the explicit relative position dependency in self-attention formulation. Notably, RoPE enables valuable properties, including the flexibility of sequence length, decaying inter-token dependency with increasing relative distances, and the capability of equipping the linear self-attention with relative position encoding. Finally, we evaluate the enhanced transformer with rotary position embedding, also called RoFormer, on various long text classification benchmark datasets. +Position encoding recently has shown effective in the transformer architecture. It enables valuable supervision for +dependency modeling between elements at different positions of the sequence. In this paper, we first investigate various +methods to integrate positional information into the learning process of transformer-based language models. Then, we +propose a novel method named Rotary Position Embedding(RoPE) to effectively leverage the positional information. +Specifically, the proposed RoPE encodes the absolute position with a rotation matrix and meanwhile incorporates the +explicit relative position dependency in self-attention formulation. Notably, RoPE enables valuable properties, +including the flexibility of sequence length, decaying inter-token dependency with increasing relative distances, and +the capability of equipping the linear self-attention with relative position encoding. Finally, we evaluate the enhanced +transformer with rotary position embedding, also called RoFormer, on various long text classification benchmark +datasets. ## Setup @@ -65,9 +74,13 @@ bash scripts/infer_roformer_fp16_performance.sh ### Accuracy -If you want to evaluate the accuracy of this model, please visit the website: , which integrates inference and training of many models under this framework, supporting the ILUVATAR backend. +If you want to evaluate the accuracy of this model, please visit the website: +, which integrates inference and training of many +models under this framework, supporting the ILUVATAR backend. -For detailed steps regarding this model, please refer to this document: Note: You need to modify the relevant paths in the code to your own correct paths. +For detailed steps regarding this model, please refer to this document: + +Note: You need to modify the relevant paths in the code to your own correct paths. ```bash # link ByteMLPerf and install requirements @@ -100,5 +113,5 @@ python3 core/perf_engine.py --hardware_type ILUVATAR --task roformer-tf-fp32 ## Results | Model | BatchSize | Precision | FPS | ACC | -| -------- | --------- | --------- | ------- | ------- | +|----------|-----------|-----------|---------|---------| | RoFormer | 2 | FP16 | 195.186 | 0.33789 | diff --git a/models/nlp/language_model/videobert/ixrt/README.md b/models/nlp/language_model/videobert/ixrt/README.md index 7bfb8c92..cb368384 100644 --- a/models/nlp/language_model/videobert/ixrt/README.md +++ b/models/nlp/language_model/videobert/ixrt/README.md @@ -2,7 +2,9 @@ ## Description -VideoBERT is a model designed for video understanding tasks, extending the capabilities of BERT (Bidirectional Encoder Representations from Transformers) to video data. It enhances video representation learning by integrating both visual and textual information into a unified framework. +VideoBERT is a model designed for video understanding tasks, extending the capabilities of BERT (Bidirectional Encoder +Representations from Transformers) to video data. It enhances video representation learning by integrating both visual +and textual information into a unified framework. ## Setup @@ -73,5 +75,5 @@ python3 core/perf_engine.py --hardware_type ILUVATAR --task videobert-onnx-fp32 ## Results | Model | BatchSize | Precision | QPS | Top-1 ACC | -| --------- | --------- | --------- | ----- | --------- | +|-----------|-----------|-----------|-------|-----------| | VideoBERT | 4 | FP16 | 37.68 | 61.67 | -- Gitee From 0f0402c8564d6bcd092f7eddea207f2fe9b04ce1 Mon Sep 17 00:00:00 2001 From: "mingjiang.li" Date: Thu, 27 Feb 2025 14:25:50 +0800 Subject: [PATCH 7/8] reorg multimodal models path (sync with dshub) Signed-off-by: mingjiang.li --- README.md | 18 +++++++++--------- .../stable-diffusion/README.md | 0 .../stable-diffusion/ci/prepare.sh | 0 .../stable-diffusion/demo.py | 0 .../stable-diffusion/requirements.txt | 0 .../chameleon_7b/vllm/README.md | 0 .../vllm/offline_inference_vision_language.py | 0 .../chameleon_7b/vllm/utils.py | 0 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-|---------------------|-----------------------------------------------------------------------------------------|------------------------------------------------------------------------------| -| Chameleon-7B | [✅](models/multimodal/vision_language_understanding/chameleon_7b/vllm/README.md) | | -| CLIP | | [✅](models/multimodal/vision_language_understanding/clip/ixformer/README.md) | -| Fuyu-8B | [✅](models/multimodal/vision_language_understanding/fuyu_8b/vllm/README.md) | | -| InternVL2-4B | [✅](models/multimodal/vision_language_understanding/intern_vl/vllm/README.md) | | -| LLaVA | [✅](models/multimodal/vision_language_understanding/llava/vllm/README.md) | | -| LLaVA-Next-Video-7B | [✅](models/multimodal/vision_language_understanding/llava_next_video_7b/vllm/README.md) | | -| MiniCPM V2 | [✅](models/multimodal/vision_language_understanding/minicpm_v_2/vllm/README.md) | | +| Model | vLLM | IxFormer | +|---------------------|---------------------------------------------------------------------------------|----------------------------------------------------------------------| +| Chameleon-7B | [✅](models/multimodal/vision_language_model/chameleon_7b/vllm/README.md) | | +| CLIP | | [✅](models/multimodal/vision_language_model/clip/ixformer/README.md) | +| Fuyu-8B | [✅](models/multimodal/vision_language_model/fuyu_8b/vllm/README.md) | | +| InternVL2-4B | [✅](models/multimodal/vision_language_model/intern_vl/vllm/README.md) | | +| LLaVA | [✅](models/multimodal/vision_language_model/llava/vllm/README.md) | | +| LLaVA-Next-Video-7B | [✅](models/multimodal/vision_language_model/llava_next_video_7b/vllm/README.md) | | +| MiniCPM V2 | [✅](models/multimodal/vision_language_model/minicpm_v_2/vllm/README.md) | | ## NLP diff --git a/models/multimodal/diffusion/stable-diffusion/README.md b/models/multimodal/diffusion_model/stable-diffusion/README.md similarity index 100% rename from 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a/models/multimodal/vision_language_understanding/minicpm_v_2/vllm/minicpmv-2.0-offline.py b/models/multimodal/vision_language_model/minicpm_v_2/vllm/minicpmv-2.0-offline.py similarity index 100% rename from models/multimodal/vision_language_understanding/minicpm_v_2/vllm/minicpmv-2.0-offline.py rename to models/multimodal/vision_language_model/minicpm_v_2/vllm/minicpmv-2.0-offline.py -- Gitee From 7b8ebd012220a90534af3e963062d7db9005ff9f Mon Sep 17 00:00:00 2001 From: "mingjiang.li" Date: Fri, 28 Feb 2025 16:59:46 +0800 Subject: [PATCH 8/8] clean and improve readme Signed-off-by: mingjiang.li --- README.md | 354 +++++++++++++++++++++++++++--------------------------- 1 file changed, 178 insertions(+), 176 deletions(-) diff --git a/README.md b/README.md index 303bc4ed..1df474ce 100644 --- a/README.md +++ b/README.md @@ -40,210 +40,212 @@ DeepSparkInference将按季度进行版本更新，后续会逐步丰富模型 ### Classification -| Model | Prec. | IGIE | IxRT | -|------------------------|-------|-----------------------------------------------------------------------|-----------------------------------------------------------------------| -| AlexNet | FP16 | [✅](models/cv/classification/alexnet/igie/README.md#fp16) | [✅](models/cv/classification/alexnet/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/alexnet/igie/README.md#int8) | [✅](models/cv/classification/alexnet/ixrt/README.md#int8) | -| CLIP | FP16 | [✅](models/cv/classification/clip/igie/README.md#fp16) | | -| Conformer-B | FP16 | [✅](models/cv/classification/conformer_base/igie/README.md#fp16) | | -| ConvNeXt-Base | FP16 | [✅](models/cv/classification/convnext_base/igie/README.md#fp16) | [✅](models/cv/classification/convnext_base/ixrt/README.md#fp16) | -| ConvNext-S | FP16 | [✅](models/cv/classification/convnext_s/igie/README.md#fp16) | | -| ConvNeXt-Small | FP16 | [✅](models/cv/classification/convnext_small/igie/README.md#fp16) | [✅](models/cv/classification/convnext_small/ixrt/README.md#fp16) | -| CSPDarkNet53 | FP16 | [✅](models/cv/classification/cspdarknet53/igie/README.md#fp16) | [✅](models/cv/classification/cspdarknet53/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/cspdarknet53/ixrt/README.md#int8) | -| CSPResNet50 | FP16 | [✅](models/cv/classification/cspresnet50/igie/README.md#fp16) | [✅](models/cv/classification/cspresnet50/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/cspresnet50/ixrt/README.md#int8) | -| DeiT-tiny | FP16 | [✅](models/cv/classification/deit_tiny/igie/README.md#fp16) | [✅](models/cv/classification/deit_tiny/ixrt/README.md#fp16) | -| DenseNet121 | FP16 | [✅](models/cv/classification/densenet121/igie/README.md#fp16) | [✅](models/cv/classification/densenet121/ixrt/README.md#fp16) | -| DenseNet161 | FP16 | [✅](models/cv/classification/densenet161/igie/README.md#fp16) | [✅](models/cv/classification/densenet161/ixrt/README.md#fp16) | -| DenseNet169 | FP16 | [✅](models/cv/classification/densenet169/igie/README.md#fp16) | [✅](models/cv/classification/densenet169/ixrt/README.md#fp16) | -| DenseNet201 | FP16 | [✅](models/cv/classification/densenet201/igie/README.md#fp16) | [✅](models/cv/classification/densenet201/ixrt/README.md#fp16) | -| EfficientNet-B0 | FP16 | [✅](models/cv/classification/efficientnet_b0/igie/README.md#fp16) | [✅](models/cv/classification/efficientnet_b0/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/efficientnet_b0/ixrt/README.md#int8) | -| EfficientNet-B1 | FP16 | [✅](models/cv/classification/efficientnet_b1/igie/README.md#fp16) | [✅](models/cv/classification/efficientnet_b1/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/efficientnet_b1/ixrt/README.md#int8) | -| EfficientNet-B2 | FP16 | [✅](models/cv/classification/efficientnet_b2/igie/README.md#fp16) | [✅](models/cv/classification/efficientnet_b2/ixrt/README.md#fp16) | -| EfficientNet-B3 | FP16 | [✅](models/cv/classification/efficientnet_b3/igie/README.md#fp16) | [✅](models/cv/classification/efficientnet_b3/ixrt/README.md#fp16) | -| EfficientNet-B4 | FP16 | [✅](models/cv/classification/efficientnet_b4/igie/README.md#fp16) | | -| EfficientNetV2 | FP16 | [✅](models/cv/classification/efficientnet_v2/igie/README.md#fp16) | [✅](models/cv/classification/efficientnet_v2/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/efficientnet_v2/ixrt/README.md#int8) | -| EfficientNetv2_rw_t | FP16 | [✅](models/cv/classification/efficientnetv2_rw_t/igie/README.md#fp16) | [✅](models/cv/classification/efficientnetv2_rw_t/ixrt/README.md#fp16) | -| EfficientNetv2_s | FP16 | [✅](models/cv/classification/efficientnet_v2_s/igie/README.md#fp16) | [✅](models/cv/classification/efficientnet_v2_s/ixrt/README.md#fp16) | -| GoogLeNet | FP16 | [✅](models/cv/classification/googlenet/igie/README.md#fp16) | [✅](models/cv/classification/googlenet/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/googlenet/igie/README.md#int8) | [✅](models/cv/classification/googlenet/ixrt/README.md#int8) | -| HRNet-W18 | FP16 | [✅](models/cv/classification/hrnet_w18/igie/README.md#fp16) | [✅](models/cv/classification/hrnet_w18/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/hrnet_w18/ixrt/README.md#int8) | -| InceptionV3 | FP16 | [✅](models/cv/classification/inception_v3/igie/README.md#fp16) | [✅](models/cv/classification/inception_v3/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/inception_v3/igie/README.md#int8) | [✅](models/cv/classification/inception_v3/ixrt/README.md#int8) | -| Inception_ResNet_V2 | FP16 | | [✅](models/cv/classification/inceptionresnetv2/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/inceptionresnetv2/ixrt/README.md#int8) | -| Mixer_B | FP16 | [✅](models/cv/classification/mlp_mixer_base/igie/README.md#fp16) | | -| MNASNet0_5 | FP16 | [✅](models/cv/classification/mnasnet0_5/igie/README.md#fp16) | | -| MNASNet0_75 | FP16 | [✅](models/cv/classification/mnasnet0_75/igie/README.md#fp16) | | -| MobileNetV2 | FP16 | [✅](models/cv/classification/mobilenet_v2/igie/README.md#fp16) | [✅](models/cv/classification/mobilenet_v2/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/mobilenet_v2/igie/README.md#int8) | [✅](models/cv/classification/mobilenet_v2/ixrt/README.md#int8) | -| MobileNetV3_Large | FP16 | [✅](models/cv/classification/mobilenet_v3_large/igie/README.md#fp16) | | -| MobileNetV3_Small | FP16 | [✅](models/cv/classification/mobilenet_v3/igie/README.md#fp16) | [✅](models/cv/classification/mobilenet_v3/ixrt/README.md#fp16) | -| MViTv2_base | FP16 | [✅](models/cv/classification/mvitv2_base/igie/README.md#fp16) | | -| RegNet_x_16gf | FP16 | [✅](models/cv/classification/regnet_x_16gf/igie/README.md#fp16) | | -| RegNet_x_1_6gf | FP16 | [✅](models/cv/classification/regnet_x_1_6gf/igie/README.md#fp16) | | -| RegNet_y_1_6gf | FP16 | [✅](models/cv/classification/regnet_y_1_6gf/igie/README.md#fp16) | | -| RepVGG | FP16 | [✅](models/cv/classification/repvgg/igie/README.md#fp16) | [✅](models/cv/classification/repvgg/ixrt/README.md#fp16) | -| Res2Net50 | FP16 | [✅](models/cv/classification/res2net50/igie/README.md#fp16) | [✅](models/cv/classification/res2net50/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/res2net50/ixrt/README.md#int8) | -| ResNeSt50 | FP16 | [✅](models/cv/classification/resnest50/igie/README.md#fp16) | | -| ResNet101 | FP16 | [✅](models/cv/classification/resnet101/igie/README.md#fp16) | [✅](models/cv/classification/resnet101/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/resnet101/igie/README.md#int8) | [✅](models/cv/classification/resnet101/ixrt/README.md#int8) | -| ResNet152 | FP16 | [✅](models/cv/classification/resnet152/igie/README.md#fp16) | | -| | INT8 | [✅](models/cv/classification/resnet152/igie/README.md#int8) | | -| ResNet18 | FP16 | [✅](models/cv/classification/resnet18/igie/README.md#fp16) | [✅](models/cv/classification/resnet18/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/resnet18/igie/README.md#int8) | [✅](models/cv/classification/resnet18/ixrt/README.md#int8) | -| ResNet34 | FP16 | | [✅](models/cv/classification/resnet34/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/resnet34/ixrt/README.md#int8) | -| ResNet50 | FP16 | [✅](models/cv/classification/resnet50/igie/README.md#fp16) | [✅](models/cv/classification/resnet50/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/resnet50/igie/README.md#int8) | | -| ResNetV1D50 | FP16 | [✅](models/cv/classification/resnetv1d50/igie/README.md#fp16) | [✅](models/cv/classification/resnetv1d50/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/resnetv1d50/ixrt/README.md#int8) | -| ResNeXt50_32x4d | FP16 | [✅](models/cv/classification/resnext50_32x4d/igie/README.md#fp16) | [✅](models/cv/classification/resnext50_32x4d/ixrt/README.md#fp16) | -| ResNeXt101_64x4d | FP16 | [✅](models/cv/classification/resnext101_64x4d/igie/README.md#fp16) | | -| ResNeXt101_32x8d | FP16 | [✅](models/cv/classification/resnext101_32x8d/igie/README.md#fp16) | | -| SEResNet50 | FP16 | [✅](models/cv/classification/se_resnet50/igie/README.md#fp16) | | -| ShuffleNetV1 | FP16 | | [✅](models/cv/classification/shufflenet_v1/ixrt/README.md#fp16) | -| ShuffleNetV2_x0_5 | FP16 | [✅](models/cv/classification/shufflenetv2_x0_5/igie/README.md#fp16) | | -| ShuffleNetV2_x1_0 | FP16 | [✅](models/cv/classification/shufflenetv2_x1_0/igie/README.md#fp16) | | -| ShuffleNetV2_x1_5 | FP16 | [✅](models/cv/classification/shufflenetv2_x1_5/igie/README.md#fp16) | | -| ShuffleNetV2_x2_0 | FP16 | [✅](models/cv/classification/shufflenetv2_x2_0/igie/README.md#fp16) | | -| SqueezeNet 1.0 | FP16 | [✅](models/cv/classification/squeezenet_v1_0/igie/README.md#fp16) | [✅](models/cv/classification/squeezenet_v1_0/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/squeezenet_v1_0/ixrt/README.md#int8) | -| SqueezeNet 1.1 | FP16 | | [✅](models/cv/classification/squeezenet_v1_1/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/classification/squeezenet_v1_1/ixrt/README.md#int8) | -| SVT Base | FP16 | [✅](models/cv/classification/svt_base/igie/README.md#fp16) | | -| Swin Transformer | FP16 | [✅](models/cv/classification/swin_transformer/igie/README.md#fp16) | | -| Swin Transformer Large | FP16 | | [✅](models/cv/classification/swin_transformer_large/ixrt/README.md) | -| VGG11 | FP16 | [✅](models/cv/classification/vgg11/igie/README.md#fp16) | | -| VGG16 | FP16 | [✅](models/cv/classification/vgg16/igie/README.md#fp16) | [✅](models/cv/classification/vgg16/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/vgg16/igie/README.md#int8) | | -| Wide ResNet50 | FP16 | [✅](models/cv/classification/wide_resnet50/igie/README.md#fp16) | [✅](models/cv/classification/wide_resnet50/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/classification/wide_resnet50/igie/README.md#int8) | [✅](models/cv/classification/wide_resnet50/ixrt/README.md#int8) | -| Wide ResNet101 | FP16 | [✅](models/cv/classification/wide_resnet101/igie/README.md#fp16) | | - -### Detection - -| Model | Prec. | IGIE | IxRT | -|------------|-------|---------------------------------------------------------|---------------------------------------------------------| -| ATSS | FP16 | [✅](models/cv/detection/atss/igie/README.md#fp16) | | -| CenterNet | FP16 | [✅](models/cv/detection/centernet/igie/README.md#fp16) | [✅](models/cv/detection/centernet/ixrt/README.md#fp16) | -| DETR | FP16 | | [✅](models/cv/detection/detr/ixrt/README.md#fp16) | -| FCOS | FP16 | [✅](models/cv/detection/fcos/igie/README.md#fp16) | [✅](models/cv/detection/fcos/ixrt/README.md#fp16) | -| FoveaBox | FP16 | [✅](models/cv/detection/foveabox/igie/README.md#fp16) | [✅](models/cv/detection/foveabox/ixrt/README.md#fp16) | -| FSAF | FP16 | [✅](models/cv/detection/fsaf/igie/README.md#fp16) | [✅](models/cv/detection/fsaf/ixrt/README.md#fp16) | -| HRNet | FP16 | [✅](models/cv/detection/hrnet/igie/README.md#fp16) | [✅](models/cv/detection/hrnet/ixrt/README.md#fp16) | -| PAA | FP16 | [✅](models/cv/detection/paa/igie/README.md#fp16) | | -| RetinaFace | FP16 | [✅](models/cv/detection/retinaface/igie/README.md#fp16) | [✅](models/cv/detection/retinaface/ixrt/README.md#fp16) | -| RetinaNet | FP16 | [✅](models/cv/detection/retinanet/igie/README.md#fp16) | | -| RTMDet | FP16 | [✅](models/cv/detection/rtmdet/igie/README.md#fp16) | | -| SABL | FP16 | [✅](models/cv/detection/sabl/igie/README.md#fp16) | | -| YOLOv3 | FP16 | [✅](models/cv/detection/yolov3/igie/README.md#fp16) | [✅](models/cv/detection/yolov3/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/detection/yolov3/igie/README.md#int8) | [✅](models/cv/detection/yolov3/ixrt/README.md#int8) | -| YOLOv4 | FP16 | [✅](models/cv/detection/yolov4/igie/README.md#fp16) | [✅](models/cv/detection/yolov4/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/detection/yolov4/igie/README.md#int816) | [✅](models/cv/detection/yolov4/ixrt/README.md#int816) | -| YOLOv5 | FP16 | [✅](models/cv/detection/yolov5/igie/README.md#fp16) | [✅](models/cv/detection/yolov5/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/detection/yolov5/igie/README.md#int8) | [✅](models/cv/detection/yolov5/ixrt/README.md#int8) | -| YOLOv5s | FP16 | | [✅](models/cv/detection/yolov5s/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/detection/yolov5s/ixrt/README.md#int8) | -| YOLOv6 | FP16 | [✅](models/cv/detection/yolov6/igie/README.md#fp16) | [✅](models/cv/detection/yolov6/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/detection/yolov6/ixrt/README.md#int8) | -| YOLOv7 | FP16 | [✅](models/cv/detection/yolov7/igie/README.md#fp16) | [✅](models/cv/detection/yolov7/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/detection/yolov7/igie/README.md#int8) | [✅](models/cv/detection/yolov7/ixrt/README.md#int8) | -| YOLOv8 | FP16 | [✅](models/cv/detection/yolov8/igie/README.md#fp16) | [✅](models/cv/detection/yolov8/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/detection/yolov8/igie/README.md#int8) | [✅](models/cv/detection/yolov8/ixrt/README.md#int8) | -| YOLOv9 | FP16 | [✅](models/cv/detection/yolov9/igie/README.md#fp16) | | -| YOLOv10 | FP16 | [✅](models/cv/detection/yolov10/igie/README.md#fp16) | | -| YOLOv11 | FP16 | [✅](models/cv/detection/yolov11/igie/README.md#fp16) | | -| YOLOX | FP16 | [✅](models/cv/detection/yolox/igie/README.md#fp16) | [✅](models/cv/detection/yolox/ixrt/README.md#fp16) | -| | INT8 | [✅](models/cv/detection/yolox/igie/README.md#int8) | [✅](models/cv/detection/yolox/ixrt/README.md#int8) | +| Model | Prec. | IGIE | IxRT | +|------------------------|-------|--------------------------------------------------------|-----------------------------------------------------------| +| AlexNet | FP16 | [✅](models/cv/classification/alexnet/igie) | [✅](models/cv/classification/alexnet/ixrt) | +| | INT8 | [✅](models/cv/classification/alexnet/igie) | [✅](models/cv/classification/alexnet/ixrt) | +| CLIP | FP16 | [✅](models/cv/classification/clip/igie) | | +| Conformer-B | FP16 | [✅](models/cv/classification/conformer_base/igie) | | +| ConvNeXt-Base | FP16 | [✅](models/cv/classification/convnext_base/igie) | [✅](models/cv/classification/convnext_base/ixrt) | +| ConvNext-S | FP16 | [✅](models/cv/classification/convnext_s/igie) | | +| ConvNeXt-Small | FP16 | [✅](models/cv/classification/convnext_small/igie) | [✅](models/cv/classification/convnext_small/ixrt) | +| CSPDarkNet53 | FP16 | [✅](models/cv/classification/cspdarknet53/igie) | [✅](models/cv/classification/cspdarknet53/ixrt) | +| | INT8 | | [✅](models/cv/classification/cspdarknet53/ixrt) | +| CSPResNet50 | FP16 | [✅](models/cv/classification/cspresnet50/igie) | [✅](models/cv/classification/cspresnet50/ixrt) | +| | INT8 | | [✅](models/cv/classification/cspresnet50/ixrt) | +| DeiT-tiny | FP16 | [✅](models/cv/classification/deit_tiny/igie) | [✅](models/cv/classification/deit_tiny/ixrt) | +| DenseNet121 | FP16 | [✅](models/cv/classification/densenet121/igie) | [✅](models/cv/classification/densenet121/ixrt) | +| DenseNet161 | FP16 | [✅](models/cv/classification/densenet161/igie) | [✅](models/cv/classification/densenet161/ixrt) | +| DenseNet169 | FP16 | [✅](models/cv/classification/densenet169/igie) | [✅](models/cv/classification/densenet169/ixrt) | +| DenseNet201 | FP16 | [✅](models/cv/classification/densenet201/igie) | [✅](models/cv/classification/densenet201/ixrt) | +| EfficientNet-B0 | FP16 | [✅](models/cv/classification/efficientnet_b0/igie) | [✅](models/cv/classification/efficientnet_b0/ixrt) | +| | INT8 | | [✅](models/cv/classification/efficientnet_b0/ixrt) | +| EfficientNet-B1 | FP16 | [✅](models/cv/classification/efficientnet_b1/igie) | [✅](models/cv/classification/efficientnet_b1/ixrt) | +| | INT8 | | [✅](models/cv/classification/efficientnet_b1/ixrt) | +| EfficientNet-B2 | FP16 | [✅](models/cv/classification/efficientnet_b2/igie) | [✅](models/cv/classification/efficientnet_b2/ixrt) | +| EfficientNet-B3 | FP16 | [✅](models/cv/classification/efficientnet_b3/igie) | [✅](models/cv/classification/efficientnet_b3/ixrt) | +| EfficientNet-B4 | FP16 | [✅](models/cv/classification/efficientnet_b4/igie) | | +| EfficientNetV2 | FP16 | [✅](models/cv/classification/efficientnet_v2/igie) | [✅](models/cv/classification/efficientnet_v2/ixrt) | +| | INT8 | | [✅](models/cv/classification/efficientnet_v2/ixrt) | +| EfficientNetv2_rw_t | FP16 | [✅](models/cv/classification/efficientnetv2_rw_t/igie) | [✅](models/cv/classification/efficientnetv2_rw_t/ixrt) | +| EfficientNetv2_s | FP16 | [✅](models/cv/classification/efficientnet_v2_s/igie) | [✅](models/cv/classification/efficientnet_v2_s/ixrt) | +| GoogLeNet | FP16 | [✅](models/cv/classification/googlenet/igie) | [✅](models/cv/classification/googlenet/ixrt) | +| | INT8 | [✅](models/cv/classification/googlenet/igie) | [✅](models/cv/classification/googlenet/ixrt) | +| HRNet-W18 | FP16 | [✅](models/cv/classification/hrnet_w18/igie) | [✅](models/cv/classification/hrnet_w18/ixrt) | +| | INT8 | | [✅](models/cv/classification/hrnet_w18/ixrt) | +| InceptionV3 | FP16 | [✅](models/cv/classification/inception_v3/igie) | [✅](models/cv/classification/inception_v3/ixrt) | +| | INT8 | [✅](models/cv/classification/inception_v3/igie) | [✅](models/cv/classification/inception_v3/ixrt) | +| Inception_ResNet_V2 | FP16 | | [✅](models/cv/classification/inceptionresnetv2/ixrt) | +| | INT8 | | [✅](models/cv/classification/inceptionresnetv2/ixrt) | +| Mixer_B | FP16 | [✅](models/cv/classification/mlp_mixer_base/igie) | | +| MNASNet0_5 | FP16 | [✅](models/cv/classification/mnasnet0_5/igie) | | +| MNASNet0_75 | FP16 | [✅](models/cv/classification/mnasnet0_75/igie) | | +| MobileNetV2 | FP16 | [✅](models/cv/classification/mobilenet_v2/igie) | [✅](models/cv/classification/mobilenet_v2/ixrt) | +| | INT8 | [✅](models/cv/classification/mobilenet_v2/igie) | [✅](models/cv/classification/mobilenet_v2/ixrt) | +| MobileNetV3_Large | FP16 | [✅](models/cv/classification/mobilenet_v3_large/igie) | | +| MobileNetV3_Small | FP16 | [✅](models/cv/classification/mobilenet_v3/igie) | [✅](models/cv/classification/mobilenet_v3/ixrt) | +| MViTv2_base | FP16 | [✅](models/cv/classification/mvitv2_base/igie) | | +| RegNet_x_16gf | FP16 | [✅](models/cv/classification/regnet_x_16gf/igie) | | +| RegNet_x_1_6gf | FP16 | [✅](models/cv/classification/regnet_x_1_6gf/igie) | | +| RegNet_y_1_6gf | FP16 | [✅](models/cv/classification/regnet_y_1_6gf/igie) | | +| RepVGG | FP16 | [✅](models/cv/classification/repvgg/igie) | [✅](models/cv/classification/repvgg/ixrt) | +| Res2Net50 | FP16 | [✅](models/cv/classification/res2net50/igie) | [✅](models/cv/classification/res2net50/ixrt) | +| | INT8 | | [✅](models/cv/classification/res2net50/ixrt) | +| ResNeSt50 | FP16 | [✅](models/cv/classification/resnest50/igie) | | +| ResNet101 | FP16 | [✅](models/cv/classification/resnet101/igie) | [✅](models/cv/classification/resnet101/ixrt) | +| | INT8 | [✅](models/cv/classification/resnet101/igie) | [✅](models/cv/classification/resnet101/ixrt) | +| ResNet152 | FP16 | [✅](models/cv/classification/resnet152/igie) | | +| | INT8 | [✅](models/cv/classification/resnet152/igie) | | +| ResNet18 | FP16 | [✅](models/cv/classification/resnet18/igie) | [✅](models/cv/classification/resnet18/ixrt) | +| | INT8 | [✅](models/cv/classification/resnet18/igie) | [✅](models/cv/classification/resnet18/ixrt) | +| ResNet34 | FP16 | | [✅](models/cv/classification/resnet34/ixrt) | +| | INT8 | | [✅](models/cv/classification/resnet34/ixrt) | +| ResNet50 | FP16 | [✅](models/cv/classification/resnet50/igie) | [✅](models/cv/classification/resnet50/ixrt) | +| | INT8 | [✅](models/cv/classification/resnet50/igie) | | +| ResNetV1D50 | FP16 | [✅](models/cv/classification/resnetv1d50/igie) | [✅](models/cv/classification/resnetv1d50/ixrt) | +| | INT8 | | [✅](models/cv/classification/resnetv1d50/ixrt) | +| ResNeXt50_32x4d | FP16 | [✅](models/cv/classification/resnext50_32x4d/igie) | [✅](models/cv/classification/resnext50_32x4d/ixrt) | +| ResNeXt101_64x4d | FP16 | [✅](models/cv/classification/resnext101_64x4d/igie) | | +| ResNeXt101_32x8d | FP16 | [✅](models/cv/classification/resnext101_32x8d/igie) | | +| SEResNet50 | FP16 | [✅](models/cv/classification/se_resnet50/igie) | | +| ShuffleNetV1 | FP16 | | [✅](models/cv/classification/shufflenet_v1/ixrt) | +| ShuffleNetV2_x0_5 | FP16 | [✅](models/cv/classification/shufflenetv2_x0_5/igie) | | +| ShuffleNetV2_x1_0 | FP16 | [✅](models/cv/classification/shufflenetv2_x1_0/igie) | | +| ShuffleNetV2_x1_5 | FP16 | [✅](models/cv/classification/shufflenetv2_x1_5/igie) | | +| ShuffleNetV2_x2_0 | FP16 | [✅](models/cv/classification/shufflenetv2_x2_0/igie) | | +| SqueezeNet 1.0 | FP16 | [✅](models/cv/classification/squeezenet_v1_0/igie) | [✅](models/cv/classification/squeezenet_v1_0/ixrt) | +| | INT8 | | [✅](models/cv/classification/squeezenet_v1_0/ixrt) | +| SqueezeNet 1.1 | FP16 | | [✅](models/cv/classification/squeezenet_v1_1/ixrt) | +| | INT8 | | [✅](models/cv/classification/squeezenet_v1_1/ixrt) | +| SVT Base | FP16 | [✅](models/cv/classification/svt_base/igie) | | +| Swin Transformer | FP16 | [✅](models/cv/classification/swin_transformer/igie) | | +| Swin Transformer Large | FP16 | | [✅](models/cv/classification/swin_transformer_large/ixrt) | +| VGG11 | FP16 | [✅](models/cv/classification/vgg11/igie) | | +| VGG16 | FP16 | [✅](models/cv/classification/vgg16/igie) | [✅](models/cv/classification/vgg16/ixrt) | +| | INT8 | [✅](models/cv/classification/vgg16/igie) | | +| Wide ResNet50 | FP16 | [✅](models/cv/classification/wide_resnet50/igie) | [✅](models/cv/classification/wide_resnet50/ixrt) | +| | INT8 | [✅](models/cv/classification/wide_resnet50/igie) | [✅](models/cv/classification/wide_resnet50/ixrt) | +| Wide ResNet101 | FP16 | [✅](models/cv/classification/wide_resnet101/igie) | | + +### Object Detection + +| Model | Prec. | IGIE | IxRT | +|------------|-------|------------------------------------------|------------------------------------------| +| ATSS | FP16 | [✅](models/cv/detection/atss/igie) | | +| CenterNet | FP16 | [✅](models/cv/detection/centernet/igie) | [✅](models/cv/detection/centernet/ixrt) | +| DETR | FP16 | | [✅](models/cv/detection/detr/ixrt) | +| FCOS | FP16 | [✅](models/cv/detection/fcos/igie) | [✅](models/cv/detection/fcos/ixrt) | +| FoveaBox | FP16 | [✅](models/cv/detection/foveabox/igie) | [✅](models/cv/detection/foveabox/ixrt) | +| FSAF | FP16 | [✅](models/cv/detection/fsaf/igie) | [✅](models/cv/detection/fsaf/ixrt) | +| HRNet | FP16 | [✅](models/cv/detection/hrnet/igie) | [✅](models/cv/detection/hrnet/ixrt) | +| PAA | FP16 | [✅](models/cv/detection/paa/igie) | | +| RetinaFace | FP16 | [✅](models/cv/detection/retinaface/igie) | [✅](models/cv/detection/retinaface/ixrt) | +| RetinaNet | FP16 | [✅](models/cv/detection/retinanet/igie) | | +| RTMDet | FP16 | [✅](models/cv/detection/rtmdet/igie) | | +| SABL | FP16 | [✅](models/cv/detection/sabl/igie) | | +| YOLOv3 | FP16 | [✅](models/cv/detection/yolov3/igie) | [✅](models/cv/detection/yolov3/ixrt) | +| | INT8 | [✅](models/cv/detection/yolov3/igie) | [✅](models/cv/detection/yolov3/ixrt) | +| YOLOv4 | FP16 | [✅](models/cv/detection/yolov4/igie) | [✅](models/cv/detection/yolov4/ixrt) | +| | INT8 | [✅](models/cv/detection/yolov4/igie16) | [✅](models/cv/detection/yolov4/ixrt16) | +| YOLOv5 | FP16 | [✅](models/cv/detection/yolov5/igie) | [✅](models/cv/detection/yolov5/ixrt) | +| | INT8 | [✅](models/cv/detection/yolov5/igie) | [✅](models/cv/detection/yolov5/ixrt) | +| YOLOv5s | FP16 | | [✅](models/cv/detection/yolov5s/ixrt) | +| | INT8 | | [✅](models/cv/detection/yolov5s/ixrt) | +| YOLOv6 | FP16 | [✅](models/cv/detection/yolov6/igie) | [✅](models/cv/detection/yolov6/ixrt) | +| | INT8 | | [✅](models/cv/detection/yolov6/ixrt) | +| YOLOv7 | FP16 | [✅](models/cv/detection/yolov7/igie) | [✅](models/cv/detection/yolov7/ixrt) | +| | INT8 | [✅](models/cv/detection/yolov7/igie) | [✅](models/cv/detection/yolov7/ixrt) | +| YOLOv8 | FP16 | [✅](models/cv/detection/yolov8/igie) | [✅](models/cv/detection/yolov8/ixrt) | +| | INT8 | [✅](models/cv/detection/yolov8/igie) | [✅](models/cv/detection/yolov8/ixrt) | +| YOLOv9 | FP16 | [✅](models/cv/detection/yolov9/igie) | | +| YOLOv10 | FP16 | [✅](models/cv/detection/yolov10/igie) | | +| YOLOv11 | FP16 | [✅](models/cv/detection/yolov11/igie) | | +| YOLOX | FP16 | [✅](models/cv/detection/yolox/igie) | [✅](models/cv/detection/yolox/ixrt) | +| | INT8 | [✅](models/cv/detection/yolox/igie) | [✅](models/cv/detection/yolox/ixrt) | ### Face Recognition -| Model | Prec. | IGIE | IxRT | -|---------|-------|------|-------------------------------------------------| -| FaceNet | FP16 | | [✅](models/cv/face/facenet/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/cv/face/facenet/ixrt/README.md#int8) | +| Model | Prec. | IGIE | IxRT | +|---------|-------|------|----------------------------------| +| FaceNet | FP16 | | [✅](models/cv/face/facenet/ixrt) | +| | INT8 | | [✅](models/cv/face/facenet/ixrt) | ### OCR -| Model | Prec. | IGIE | -|---------------|-----------|------------------------------------------------------| -| Kie_layoutXLM | FP16 | [✅](models/cv/ocr/kie_layoutxlm/igie/README.md#fp16) | -| SVTR | FP16 | [✅](models/cv/ocr/svtr/igie/README.md#fp16) | +| Model | Prec. | IGIE | +|---------------|-------|---------------------------------------| +| Kie_layoutXLM | FP16 | [✅](models/cv/ocr/kie_layoutxlm/igie) | +| SVTR | FP16 | [✅](models/cv/ocr/svtr/igie) | ### Pose Estimation -| Model | Prec. | IGIE | IxRT | -|----------------------|-------|--------------------------------------------------------------|-------------------------------------------------------------------------| -| HRNetPose | FP16 | [✅](models/cv/pose_estimation/hrnetpose/igie/README.md#fp16) | | -| Lightweight OpenPose | FP16 | | [✅](models/cv/pose_estimation/lightweight_openpose/ixrt/README.md#fp16) | -| RTMPose | FP16 | [✅](models/cv/pose_estimation/rtmpose/igie/README.md#fp16) | [✅](models/cv/pose_estimation/rtmpose/ixrt/README.md#fp16) | - -### Recommendation Systems - -| Model | Prec. | IGIE | IxRT | -|-------------|-------|------|------------------------------------------------------------------------| -| Wide & Deep | FP16 | | [✅](models/recommendation/ctr-prediction/widedeep/ixrt/README.md#fp16) | +| Model | Prec. | IGIE | IxRT | +|----------------------|-------|-----------------------------------------------|----------------------------------------------------------| +| HRNetPose | FP16 | [✅](models/cv/pose_estimation/hrnetpose/igie) | | +| Lightweight OpenPose | FP16 | | [✅](models/cv/pose_estimation/lightweight_openpose/ixrt) | +| RTMPose | FP16 | [✅](models/cv/pose_estimation/rtmpose/igie) | [✅](models/cv/pose_estimation/rtmpose/ixrt) | ### Segmentation -| Model | Prec. | IGIE | IxRT | -|------------|-------|------|-----------------------------------------------------------| -| Mask R-CNN | FP16 | | [✅](models/cv/segmentation/mask_rcnn/ixrt/README.md#fp16) | -| SOLOv1 | FP16 | | [✅](models/cv/segmentation/solov1/ixrt/README.md#fp16) | +| Model | Prec. | IGIE | IxRT | +|------------|-------|------|--------------------------------------------| +| Mask R-CNN | FP16 | | [✅](models/cv/segmentation/mask_rcnn/ixrt) | +| SOLOv1 | FP16 | | [✅](models/cv/segmentation/solov1/ixrt) | ### Trace -| Model | Prec. | IGIE | IxRT | -|---------------------|-------|---------------------------------------------------|------| -| FastReID | FP16 | [✅](models/cv/trace/fastreid/igie/README.md#fp16) | | -| DeepSort | FP16 | [✅](models/cv/trace/deepsort/igie/README.md#fp16) | | -| | INT8 | [✅](models/cv/trace/deepsort/igie/README.md#int8) | | -| RepNet-Vehicle-ReID | FP16 | [✅](models/cv/trace/repnet/igie/README.md#fp16) | | +| Model | Prec. | IGIE | IxRT | +|---------------------|-------|------------------------------------|------| +| FastReID | FP16 | [✅](models/cv/trace/fastreid/igie) | | +| DeepSort | FP16 | [✅](models/cv/trace/deepsort/igie) | | +| | INT8 | [✅](models/cv/trace/deepsort/igie) | | +| RepNet-Vehicle-ReID | FP16 | [✅](models/cv/trace/repnet/igie) | | ## Multimodal -| Model | vLLM | IxFormer | -|---------------------|---------------------------------------------------------------------------------|----------------------------------------------------------------------| -| Chameleon-7B | [✅](models/multimodal/vision_language_model/chameleon_7b/vllm/README.md) | | -| CLIP | | [✅](models/multimodal/vision_language_model/clip/ixformer/README.md) | -| Fuyu-8B | [✅](models/multimodal/vision_language_model/fuyu_8b/vllm/README.md) | | -| InternVL2-4B | [✅](models/multimodal/vision_language_model/intern_vl/vllm/README.md) | | -| LLaVA | [✅](models/multimodal/vision_language_model/llava/vllm/README.md) | | -| LLaVA-Next-Video-7B | [✅](models/multimodal/vision_language_model/llava_next_video_7b/vllm/README.md) | | -| MiniCPM V2 | [✅](models/multimodal/vision_language_model/minicpm_v_2/vllm/README.md) | | +| Model | vLLM | IxFormer | +|---------------------|-----------------------------------------------------------------------|------------------------------------------------------------| +| Chameleon-7B | [✅](models/multimodal/vision_language_model/chameleon_7b/vllm) | | +| CLIP | | [✅](models/multimodal/vision_language_model/clip/ixformer) | +| Fuyu-8B | [✅](models/multimodal/vision_language_model/fuyu_8b/vllm) | | +| InternVL2-4B | [✅](models/multimodal/vision_language_model/intern_vl/vllm) | | +| LLaVA | [✅](models/multimodal/vision_language_model/llava/vllm) | | +| LLaVA-Next-Video-7B | [✅](models/multimodal/vision_language_model/llava_next_video_7b/vllm) | | +| MiniCPM V2 | [✅](models/multimodal/vision_language_model/minicpm_v_2/vllm) | | ## NLP ### Language Modelling -| Model | Prec. | IGIE | IxRT | -|------------------|-------|---------------------------------------------------------------------|---------------------------------------------------------------------| -| ALBERT | FP16 | | [✅](models/nlp/language_model/albert/ixrt/README.md) | -| BERT Base NER | INT8 | [✅](models/nlp/language_model/bert_base_ner/igie/README.md#int8) | | -| BERT Base SQuAD | FP16 | [✅](models/nlp/language_model/bert_base_squad/igie/README.md#fp16) | [✅](models/nlp/language_model/bert_base_squad/ixrt/README.md#fp16) | -| | INT8 | | [✅](models/nlp/language_model/bert_base_squad/ixrt/README.md#int8) | -| BERT Large SQuAD | FP16 | [✅](models/nlp/language_model/bert_large_squad/igie/README.md#fp16) | [✅](models/nlp/language_model/bert_large_squad/ixrt/README.md#fp16) | -| | INT8 | [✅](models/nlp/language_model/bert_large_squad/igie/README.md#int8) | [✅](models/nlp/language_model/bert_large_squad/ixrt/README.md#int8) | -| DeBERTa | FP16 | | [✅](models/nlp/language_model/deberta/ixrt/README.md) | -| RoBERTa | FP16 | | [✅](models/nlp/language_model/roberta/ixrt/README.md) | -| RoFormer | FP16 | | [✅](models/nlp/language_model/roformer/ixrt/README.md) | -| VideoBERT | FP16 | | [✅](models/nlp/language_model/videobert/ixrt/README.md) | +| Model | Prec. | IGIE | IxRT | +|------------------|-------|------------------------------------------------------|------------------------------------------------------| +| ALBERT | FP16 | | [✅](models/nlp/language_model/albert/ixrt) | +| BERT Base NER | INT8 | [✅](models/nlp/language_model/bert_base_ner/igie) | | +| BERT Base SQuAD | FP16 | [✅](models/nlp/language_model/bert_base_squad/igie) | [✅](models/nlp/language_model/bert_base_squad/ixrt) | +| | INT8 | | [✅](models/nlp/language_model/bert_base_squad/ixrt) | +| BERT Large SQuAD | FP16 | [✅](models/nlp/language_model/bert_large_squad/igie) | [✅](models/nlp/language_model/bert_large_squad/ixrt) | +| | INT8 | [✅](models/nlp/language_model/bert_large_squad/igie) | [✅](models/nlp/language_model/bert_large_squad/ixrt) | +| DeBERTa | FP16 | | [✅](models/nlp/language_model/deberta/ixrt) | +| RoBERTa | FP16 | | [✅](models/nlp/language_model/roberta/ixrt) | +| RoFormer | FP16 | | [✅](models/nlp/language_model/roformer/ixrt) | +| VideoBERT | FP16 | | [✅](models/nlp/language_model/videobert/ixrt) | ## Speech ### Speech Recognition -| Model | Prec. | IGIE | IxRT | -|-----------------|-------|--------------------------------------------------------------------|---------------------------------------------------------------------| -| Conformer | FP16 | [✅](models/audio/speech_recognition/conformer/igie/README.md#fp16) | [✅](models/audio/speech_recognition/conformer/ixrt/README.md#fp16) | -| Transformer ASR | FP16 | | [✅](models/audio/speech_recognition/transformer_asr/ixrt/README.md) | +| Model | Prec. | IGIE | IxRT | +|-----------------|-------|-----------------------------------------------------|-----------------------------------------------------------| +| Conformer | FP16 | [✅](models/audio/speech_recognition/conformer/igie) | [✅](models/audio/speech_recognition/conformer/ixrt) | +| Transformer ASR | FP16 | | [✅](models/audio/speech_recognition/transformer_asr/ixrt) | + +## Others + +### Recommendation Systems + +| Model | Prec. | IGIE | IxRT | +|-------------|-------|------|---------------------------------------------------------| +| Wide & Deep | FP16 | | [✅](models/recommendation/ctr-prediction/widedeep/ixrt) | --- -- Gitee