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openbmb/MiniCPM3-4B-GPTQ-Int4

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face
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Model Card

<div align="center"> <img src="https://github.com/OpenBMB/MiniCPM/blob/main/assets/minicpm_logo.png?raw=true" width="500em" ></img> </div>

<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 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 transformers import AutoModelForCausalLM, AutoTokenizer
import torch

path = "openbmb/MiniCPM3-4B-GPTQ-Int4"
device = "cuda"

tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.float16, 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

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-GPTQ-Int4"
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,
    quantization='gptq'
)
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 License.
  • —The usage of MiniCPM3-4B model weights must strictly follow MiniCPM Model License.md.
  • —The models and weights of MiniCPM3-4B are completely free for academic research. after filling out a "questionnaire" 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}
}