# MiniCPM4-0.5B-QAT-Int4-GPTQ-format
**Repository Path**: hf-models/MiniCPM4-0.5B-QAT-Int4-GPTQ-format
## Basic Information
- **Project Name**: MiniCPM4-0.5B-QAT-Int4-GPTQ-format
- **Description**: Mirror of https://huggingface.co/openbmb/MiniCPM4-0.5B-QAT-Int4-GPTQ-format
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2025-06-10
- **Last Updated**: 2025-06-15
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
---
license: apache-2.0
language:
- zh
- en
pipeline_tag: text-generation
library_name: transformers
---
GitHub Repo |
Technical Report
π Join us on Discord and WeChat
## What's New
- [2025.06.06] **MiniCPM4** series are released! This model achieves ultimate efficiency improvements while maintaining optimal performance at the same scale! It can achieve over 5x generation acceleration on typical end-side chips! You can find technical report [here](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf).π₯π₯π₯
## MiniCPM4 Series
MiniCPM4 series are highly efficient large language models (LLMs) designed explicitly for end-side devices, which achieves this efficiency through systematic innovation in four key dimensions: model architecture, training data, training algorithms, and inference systems.
- [MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B): The flagship of MiniCPM4, with 8B parameters, trained on 8T tokens.
- [MiniCPM4-0.5B](https://huggingface.co/openbmb/MiniCPM4-0.5B): The small version of MiniCPM4, with 0.5B parameters, trained on 1T tokens.
- [MiniCPM4-8B-Eagle-FRSpec](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec): Eagle head for FRSpec, accelerating speculative inference for MiniCPM4-8B.
- [MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-FRSpec-QAT-cpmcu): Eagle head trained with QAT for FRSpec, efficiently integrate speculation and quantization to achieve ultra acceleration for MiniCPM4-8B.
- [MiniCPM4-8B-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-Eagle-vLLM): Eagle head in vLLM format, accelerating speculative inference for MiniCPM4-8B.
- [MiniCPM4-8B-marlin-Eagle-vLLM](https://huggingface.co/openbmb/MiniCPM4-8B-marlin-Eagle-vLLM): Quantized Eagle head for vLLM format, accelerating speculative inference for MiniCPM4-8B.
- [BitCPM4-0.5B](https://huggingface.co/openbmb/BitCPM4-0.5B): Extreme ternary quantization applied to MiniCPM4-0.5B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
- [BitCPM4-1B](https://huggingface.co/openbmb/BitCPM4-1B): Extreme ternary quantization applied to MiniCPM3-1B compresses model parameters into ternary values, achieving a 90% reduction in bit width.
- [MiniCPM4-Survey](https://huggingface.co/openbmb/MiniCPM4-Survey): Based on MiniCPM4-8B, accepts users' quiries as input and autonomously generate trustworthy, long-form survey papers.
- [MiniCPM4-MCP](https://huggingface.co/openbmb/MiniCPM4-MCP): Based on MiniCPM4-8B, accepts users' queries and available MCP tools as input and autonomously calls relevant MCP tools to satisfy users' requirements.
- [MiniCPM4-0.5B-QAT-Int4-unquantized](https://huggingface.co/openbmb/MiniCPM4-0.5B-QAT-Int4-unquantized): Int4 version of MiniCPM4-0.5B, trained by QAT and stored in fake quantization style.
- [MiniCPM4-0.5B-QAT-Int4-GPTQ-format](https://huggingface.co/openbmb/MiniCPM4-0.5B-QAT-Int4-GPTQ-format): Int4 version of MiniCPM4-0.5B, trained by QAT and stored in GPTQ format. (**<-- you are here**)
- [MiniCPM4-0.5B-QAT-Int4-GGUF](https://huggingface.co/openbmb/MiniCPM4-0.5B-QAT-Int4-GGUF): Int4 version of MiniCPM4-0.5B in GGUF.
## Introduction
MiniCPM 4 is an extremely efficient edge-side large model that has undergone efficient optimization across four dimensions: model architecture, learning algorithms, training data, and inference systems, achieving ultimate efficiency improvements.
- ποΈ **Efficient Model Architecture:**
- InfLLM v2 -- Trainable Sparse Attention Mechanism: Adopts a trainable sparse attention mechanism architecture where each token only needs to compute relevance with less than 5% of tokens in 128K long text processing, significantly reducing computational overhead for long texts
- π§ **Efficient Learning Algorithms:**
- Model Wind Tunnel 2.0 -- Efficient Predictable Scaling: Introduces scaling prediction methods for performance of downstream tasks, enabling more precise model training configuration search
- BitCPM -- Ultimate Ternary Quantization: Compresses model parameter bit-width to 3 values, achieving 90% extreme model bit-width reduction
- Efficient Training Engineering Optimization: Adopts FP8 low-precision computing technology combined with Multi-token Prediction training strategy
- π **High-Quality Training Data:**
- UltraClean -- High-quality Pre-training Data Filtering and Generation: Builds iterative data cleaning strategies based on efficient data verification, open-sourcing high-quality Chinese and English pre-training dataset [UltraFinweb](https://huggingface.co/datasets/openbmb/Ultra-FineWeb)
- UltraChat v2 -- High-quality Supervised Fine-tuning Data Generation: Constructs large-scale high-quality supervised fine-tuning datasets covering multiple dimensions including knowledge-intensive data, reasoning-intensive data, instruction-following data, long text understanding data, and tool calling data
- β‘ **Efficient Inference System:**
- CPM.cu -- Lightweight and Efficient CUDA Inference Framework: Integrates sparse attention, model quantization, and speculative sampling to achieve efficient prefilling and decoding
- ArkInfer -- Cross-platform Deployment System: Supports efficient deployment across multiple backend environments, providing flexible cross-platform adaptation capabilities
## Usage
### Inference with Transformers
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
path = "openbmb/MiniCPM4-0.5B-QAT-Int4-GPTQ-format"
device = "cuda"
tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
messages = [
{"role": "user", "content": "ζ¨θ5δΈͺεδΊ¬ηζ―ηΉγ"},
]
model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(device)
model_outputs = model.generate(
model_inputs,
max_new_tokens=1024,
top_p=0.7,
temperature=0.7
)
output_token_ids = [
model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs))
]
responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
print(responses)
```
### Inference with [vLLM](https://github.com/vllm-project/vllm)
You can inference MiniCPM4-0.5B-QAT-Int4-GPTQ-format with vLLM:
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
model_name = "openbmb/MiniCPM4-0.5B-QAT-Int4-GPTQ-format"
prompt = [{"role": "user", "content": "ζ¨θ5δΈͺεδΊ¬ηζ―ηΉγ"}]
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
input_text = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
llm = LLM(
model=model_name,
quantization="gptq_marlin",
trust_remote_code=True,
max_num_batched_tokens=32768,
dtype="bfloat16",
gpu_memory_utilization=0.8,
)
sampling_params = SamplingParams(top_p=0.7, temperature=0.7, max_tokens=1024, repetition_penalty=1.02)
outputs = llm.generate(prompts=input_text, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
```
## Evaluation Results
| Model | Qwen3 | Llama3.2 | Gemma3 | MiniCPM4 | MiniCPM4 | MiniCPM4 |
|----------------|-------|----------|--------|----------|----------|----------|
| #Paramete | 0.6B | 1B | 1B | 0.5B | 0.5B | 0.5B |
| #Precision | BF16 | BF16 | BF16 | BF16 |Int4(Fake)|Int4(GPTQ)|
| MMLU | 42.95 | 46.89 | 41.64 | 55.55 | 55.46 | 53.93 |
| CMMLU | 42.05 | 23.73 | 25.09 | 65.22 | 63.91 | 63.73 |
| CEval | 45.53 | 36.74 | 31.83 | 66.11 | 64.85 | 65.22 |
| BBH | 28.32 | 25.42 | 33.21 | 49.87 | 48.81 | 49.09 |
| GSM8K | 61.71 | 39.76 | 61.26 | 52.08 | 45.41 | 45.49 |
| MBPP | 47.86 | 47.47 | 59.92 | 59.14 | 55.64 | 55.25 |
| AVERAGE | 44.73 | 36.66 | 42.15 | 58.00 | 55.68 | 55.45 |
## Statement
- As a language model, MiniCPM generates content by learning from a vast amount of text.
- However, it does not possess the ability to comprehend or express personal opinions or value judgments.
- Any content generated by MiniCPM does not represent the viewpoints or positions of the model developers.
- Therefore, when using content generated by MiniCPM, users should take full responsibility for evaluating and verifying it on their own.
## LICENSE
- This repository and MiniCPM models are released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
## Citation
- Please cite our [paper](https://github.com/OpenBMB/MiniCPM/tree/main/report/MiniCPM_4_Technical_Report.pdf) if you find our work valuable.
```bibtex
@article{minicpm4,
title={{MiniCPM4}: Ultra-Efficient LLMs on End Devices},
author={MiniCPM Team},
year={2025}
}
``` 