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2024-11-12 09:40:44 +08:00
---
license: apache-2.0
language:
- zh
- en
pipeline_tag: text-generation
---
<div align="center">
<img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img>
</div>
Initial commit
2024-11-12 09:37:15 +08:00
first commit
2024-11-12 09:40:44 +08:00
<p align="center">
<a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">MiniCPM Repo</a> |
<a href="https://arxiv.org/abs/2404.06395" target="_blank">MiniCPM Paper</a> |
<a href="https://github.com/OpenBMB/MiniCPM-V/" target="_blank">MiniCPM-V Repo</a> |
Join us in <a href="https://discord.gg/3cGQn9b3YM" target="_blank">Discord</a> and <a href="https://github.com/OpenBMB/MiniCPM/blob/main/assets/wechat.jpg" target="_blank">WeChat</a>
</p>
## Introduction
MiniCPM3-4B is the 3rd generation of MiniCPM series. The overall performance of MiniCPM3-4B surpasses Phi-3.5-mini-Instruct and GPT-3.5-Turbo-0125, being comparable with many recent 7B~9B models.
Compared to MiniCPM1.0/MiniCPM2.0, MiniCPM3-4B has a more powerful and versatile skill set to enable more general usage. MiniCPM3-4B supports function call, along with code interpreter. Please refer to [Advanced Features](https://github.com/OpenBMB/MiniCPM/tree/main?tab=readme-ov-file#%E8%BF%9B%E9%98%B6%E5%8A%9F%E8%83%BD) for usage guidelines.
MiniCPM3-4B has a 32k context window. Equipped with LLMxMapReduce, MiniCPM3-4B can handle infinite context theoretically, without requiring huge amount of memory.
## Usage
### Inference with Transformers
```python
from modelscope import AutoModelForCausalLM, AutoTokenizer
import torch
path = "OpenBMB/MiniCPM3-4B"
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": "推荐5个北京的景点。"},
]
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)
For now, you need to install our forked version of vLLM.
```bash
pip install git+https://github.com/OpenBMB/vllm.git@minicpm3
```
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
model_name = "openbmb/MiniCPM3-4B"
prompt = [{"role": "user", "content": "推荐5个北京的景点。"}]
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,
trust_remote_code=True,
tensor_parallel_size=1
)
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
<table>
<tr>
<td>Benchmark</td>
<td>Qwen2-7B-Instruct</td>
<td>GLM-4-9B-Chat</td>
<td>Gemma2-9B-it</td>
<td>Llama3.1-8B-Instruct</td>
<td>GPT-3.5-Turbo-0125</td>
<td>Phi-3.5-mini-Instruct(3.8B)</td>
<td>MiniCPM3-4B </td>
</tr>
<tr>
<td colspan="15" align="left"><strong>English</strong></td>
</tr>
<tr>
<td>MMLU</td>
<td>70.5</td>
<td>72.4</td>
<td>72.6</td>
<td>69.4</td>
<td>69.2</td>
<td>68.4</td>
<td>67.2 </td>
</tr>
<tr>
<td>BBH</td>
<td>64.9</td>
<td>76.3</td>
<td>65.2</td>
<td>67.8</td>
<td>70.3</td>
<td>68.6</td>
<td>70.2 </td>
</tr>
<tr>
<td>MT-Bench</td>
<td>8.41</td>
<td>8.35</td>
<td>7.88</td>
<td>8.28</td>
<td>8.17</td>
<td>8.60</td>
<td>8.41 </td>
</tr>
<tr>
<td>IFEVAL (Prompt Strict-Acc.)</td>
<td>51.0</td>
<td>64.5</td>
<td>71.9</td>
<td>71.5</td>
<td>58.8</td>
<td>49.4</td>
<td>68.4 </td>
</tr>
<tr>
<td colspan="15" align="left"><strong>Chinese</strong></td>
</tr>
<tr>
<td>CMMLU</td>
<td>80.9</td>
<td>71.5</td>
<td>59.5</td>
<td>55.8</td>
<td>54.5</td>
<td>46.9</td>
<td>73.3 </td>
</tr>
<tr>
<td>CEVAL</td>
<td>77.2</td>
<td>75.6</td>
<td>56.7</td>
<td>55.2</td>
<td>52.8</td>
<td>46.1</td>
<td>73.6 </td>
</tr>
<tr>
<td>AlignBench v1.1</td>
<td>7.10</td>
<td>6.61</td>
<td>7.10</td>
<td>5.68</td>
<td>5.82</td>
<td>5.73</td>
<td>6.74 </td>
</tr>
<tr>
<td>FollowBench-zh (SSR)</td>
<td>63.0</td>
<td>56.4</td>
<td>57.0</td>
<td>50.6</td>
<td>64.6</td>
<td>58.1</td>
<td>66.8 </td>
</tr>
<tr>
<td colspan="15" align="left"><strong>Math</strong></td>
</tr>
<tr>
<td>MATH</td>
<td>49.6</td>
<td>50.6</td>
<td>46.0</td>
<td>51.9</td>
<td>41.8</td>
<td>46.4</td>
<td>46.6 </td>
</tr>
<tr>
<td>GSM8K</td>
<td>82.3</td>
<td>79.6</td>
<td>79.7</td>
<td>84.5</td>
<td>76.4</td>
<td>82.7</td>
<td>81.1 </td>
</tr>
<tr>
<td>MathBench</td>
<td>63.4</td>
<td>59.4</td>
<td>45.8</td>
<td>54.3</td>
<td>48.9</td>
<td>54.9</td>
<td>65.6 </td>
</tr>
<tr>
<td colspan="15" align="left"><strong>Code</strong></td>
</tr>
<tr>
<td>HumanEval+</td>
<td>70.1</td>
<td>67.1</td>
<td>61.6</td>
<td>62.8</td>
<td>66.5</td>
<td>68.9</td>
<td>68.3 </td>
</tr>
<tr>
<td>MBPP+</td>
<td>57.1</td>
<td>62.2</td>
<td>64.3</td>
<td>55.3</td>
<td>71.4</td>
<td>55.8</td>
<td>63.2 </td>
</tr>
<tr>
<td>LiveCodeBench v3</td>
<td>22.2</td>
<td>20.2</td>
<td>19.2</td>
<td>20.4</td>
<td>24.0</td>
<td>19.6</td>
<td>22.6 </td>
</tr>
<tr>
<td colspan="15" align="left"><strong>Function Call</strong></td>
</tr>
<tr>
<td>BFCL v2</td>
<td>71.6</td>
<td>70.1</td>
<td>19.2</td>
<td>73.3</td>
<td>75.4</td>
<td>48.4</td>
<td>76.0 </td>
</tr>
<tr>
<td colspan="15" align="left"><strong>Overall</strong></td>
</tr>
<tr>
<td>Average</td>
<td>65.3</td>
<td>65.0</td>
<td>57.9</td>
<td>60.8</td>
<td>61.0</td>
<td>57.2</td>
<td><strong>66.3</strong></td>
</tr>
</table>
## Statement
* As a language model, MiniCPM3-4B 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 MiniCPM3-4B does not represent the viewpoints or positions of the model developers.
* Therefore, when using content generated by MiniCPM3-4B, users should take full responsibility for evaluating and verifying it on their own.
## LICENSE
* This repository is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License.
* The usage of MiniCPM3-4B model weights must strictly follow [MiniCPM Model License.md](https://github.com/OpenBMB/MiniCPM/blob/main/MiniCPM%20Model%20License.md).
* The models and weights of MiniCPM3-4B are completely free for academic research. after filling out a ["questionnaire"](https://modelbest.feishu.cn/share/base/form/shrcnpV5ZT9EJ6xYjh3Kx0J6v8g) for registration, are also available for free commercial use.
## Citation
```
@article{hu2024minicpm,
title={MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies},
author={Hu, Shengding and Tu, Yuge and Han, Xu and He, Chaoqun and Cui, Ganqu and Long, Xiang and Zheng, Zhi and Fang, Yewei and Huang, Yuxiang and Zhao, Weilin and others},
journal={arXiv preprint arXiv:2404.06395},
year={2024}
}
```