| --- |
| license: apache-2.0 |
| language: |
| - zh |
| pipeline_tag: text-generation |
| library_name: transformers |
| tags: |
| - medical |
| --- |
| <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](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 transformers 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)) |
| ] |
| 这种 |
| 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} |
| } |
| ``` |
