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--- |
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license: apache-2.0 |
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language: |
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- zh |
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- en |
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--- |
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<div align="center"> |
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<img src="https://github.com/OpenBMB/MiniCPM/tree/main/assets/minicpm_logo.png" width="500em" ></img> |
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</div> |
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<p align="center"> |
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<a href="https://github.com/OpenBMB/MiniCPM/" target="_blank">MiniCPM Repo</a> | |
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<a href="https://arxiv.org/abs/2404.06395" target="_blank">MiniCPM Paper</a> | |
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<a href="https://github.com/OpenBMB/MiniCPM-V/" target="_blank">MiniCPM-V Repo</a> | |
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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> |
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</p> |
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## Introduction |
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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. |
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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/zh-zheng/minicpm?tab=readme-ov-file#%E8%BF%9B%E9%98%B6%E5%8A%9F%E8%83%BD) for usage guidelines. |
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MiniCPM3-4B has a 32k context window. Equipped with LLMxMapReduce, MiniCPM3-4B can handle infinite context theoretically, without requiring huge amount of memory. |
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## Usage |
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### Inference with Transformers |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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path = "openbmb/MiniCPM3-4B" |
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device = "cuda" |
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tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True) |
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model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True) |
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messages = [ |
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{"role": "user", "content": "推荐5个北京的景点。"}, |
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] |
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model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device) |
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model_outputs = model.generate( |
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model_inputs, |
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max_new_tokens=1024, |
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top_p=0.7, |
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temperature=0.7, |
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repetition_penalty=1.02 |
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) |
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output_token_ids = [ |
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model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs)) |
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] |
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responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0] |
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print(responses) |
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``` |
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### Inference with [vLLM](https://github.com/vllm-project/vllm) |
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```python |
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from transformers import AutoTokenizer |
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from vllm import LLM, SamplingParams |
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model_name = "openbmb/MiniCPM3-4B" |
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prompt = [{"role": "user", "content": "推荐5个北京的景点。"}] |
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tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True) |
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input_text = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True) |
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llm = LLM( |
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model=model_name, |
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trust_remote_code=True, |
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tensor_parallel_size=1 |
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) |
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sampling_params = SamplingParams(top_p=0.7, temperature=0.7, max_tokens=1024, repetition_penalty=1.02) |
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outputs = llm.generate(prompts=input_text, sampling_params=sampling_params) |
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print(outputs[0].outputs[0].text) |
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``` |
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## Evaluation Results |
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<table> |
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<tr> |
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<td>Benchmark</td> |
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<td>Qwen2-7B-Instruct</td> |
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<td>GLM-4-9B-Chat</td> |
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<td>Gemma2-9B-it</td> |
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<td>Llama3.1-8B-Instruct</td> |
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<td>GPT-3.5-Turbo-0125</td> |
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<td>Phi-3.5-mini-Instruct(3.8B)</td> |
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<td>MiniCPM3-4B </td> |
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</tr> |
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<tr> |
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<td colspan="15" align="left"><strong>English</strong></td> |
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</tr> |
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<tr> |
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<td>MMLU</td> |
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<td>70.5</td> |
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<td>72.4</td> |
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<td>72.6</td> |
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<td>69.4</td> |
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<td>69.2</td> |
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<td>68.4</td> |
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<td>67.2 </td> |
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</tr> |
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<tr> |
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<td>BBH</td> |
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<td>64.9</td> |
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<td>76.3</td> |
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<td>65.2</td> |
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<td>67.8</td> |
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<td>70.3</td> |
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<td>68.6</td> |
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<td>70.2 </td> |
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</tr> |
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<tr> |
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<td>MT-Bench</td> |
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<td>8.41</td> |
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<td>8.35</td> |
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<td>7.88</td> |
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<td>8.28</td> |
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<td>8.17</td> |
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<td>8.60</td> |
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<td>8.41 </td> |
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</tr> |
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<tr> |
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<td>IFEVAL (Prompt Strict-Acc.)</td> |
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<td>51.0</td> |
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<td>64.5</td> |
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<td>71.9</td> |
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<td>71.5</td> |
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<td>58.8</td> |
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<td>49.4</td> |
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<td>68.4 </td> |
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</tr> |
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<tr> |
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<td colspan="15" align="left"><strong>Chinese</strong></td> |
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</tr> |
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<tr> |
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<td>CMMLU</td> |
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<td>80.9</td> |
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<td>71.5</td> |
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<td>59.5</td> |
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<td>55.8</td> |
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<td>54.5</td> |
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<td>46.9</td> |
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<td>73.3 </td> |
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</tr> |
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<tr> |
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<td>CEVAL</td> |
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<td>77.2</td> |
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<td>75.6</td> |
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<td>56.7</td> |
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<td>55.2</td> |
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<td>52.8</td> |
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<td>46.1</td> |
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<td>73.6 </td> |
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</tr> |
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<tr> |
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<td>AlignBench v1.1</td> |
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<td>7.10</td> |
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<td>6.61</td> |
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<td>7.10</td> |
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<td>5.68</td> |
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<td>5.82</td> |
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<td>5.73</td> |
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<td>6.74 </td> |
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</tr> |
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<tr> |
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<td>FollowBench-zh (SSR)</td> |
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<td>63.0</td> |
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<td>56.4</td> |
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<td>57.0</td> |
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<td>50.6</td> |
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<td>64.6</td> |
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<td>58.1</td> |
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<td>66.8 </td> |
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</tr> |
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<tr> |
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<td colspan="15" align="left"><strong>Math</strong></td> |
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</tr> |
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<tr> |
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<td>MATH</td> |
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<td>49.6</td> |
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<td>50.6</td> |
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<td>46.0</td> |
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<td>51.9</td> |
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<td>41.8</td> |
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<td>46.4</td> |
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<td>46.6 </td> |
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</tr> |
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<tr> |
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<td>GSM8K</td> |
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<td>82.3</td> |
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<td>79.6</td> |
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<td>79.7</td> |
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<td>84.5</td> |
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<td>76.4</td> |
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<td>82.7</td> |
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<td>81.1 </td> |
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</tr> |
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<tr> |
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<td>MathBench</td> |
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<td>63.4</td> |
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<td>59.4</td> |
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<td>45.8</td> |
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<td>54.3</td> |
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<td>48.9</td> |
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<td>54.9</td> |
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<td>65.6 </td> |
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</tr> |
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<tr> |
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<td colspan="15" align="left"><strong>Code</strong></td> |
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</tr> |
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<tr> |
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<td>HumanEval+</td> |
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<td>70.1</td> |
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<td>67.1</td> |
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<td>61.6</td> |
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<td>62.8</td> |
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<td>66.5</td> |
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<td>68.9</td> |
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<td>68.3 </td> |
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</tr> |
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<tr> |
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<td>MBPP+</td> |
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<td>57.1</td> |
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<td>62.2</td> |
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<td>64.3</td> |
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<td>55.3</td> |
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<td>71.4</td> |
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<td>55.8</td> |
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<td>63.2 </td> |
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</tr> |
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<tr> |
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<td>LiveCodeBench</td> |
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<td>22.2</td> |
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<td>20.2</td> |
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<td>19.2</td> |
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<td>20.4</td> |
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<td>24.0</td> |
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<td>19.6</td> |
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<td>22.6 </td> |
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</tr> |
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<tr> |
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<td colspan="15" align="left"><strong>Function Call</strong></td> |
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</tr> |
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<tr> |
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<td>BFCL</td> |
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<td>71.6</td> |
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<td>70.1</td> |
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<td>19.2</td> |
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<td>73.3</td> |
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<td>75.4</td> |
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<td>48.4</td> |
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<td>76.0 </td> |
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</tr> |
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<tr> |
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<td colspan="15" align="left"><strong>Overall</strong></td> |
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</tr> |
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<tr> |
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<td>Average</td> |
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<td>65.3</td> |
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<td>65.0</td> |
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<td>57.9</td> |
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<td>60.8</td> |
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<td>61.0</td> |
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<td>57.2</td> |
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<td><strong>66.3</strong></td> |
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</tr> |
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</table> |
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## Statement |
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* As a language model, MiniCPM3-4B generates content by learning from a vast amount of text. |
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* However, it does not possess the ability to comprehend or express personal opinions or value judgments. |
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* Any content generated by MiniCPM3-4B does not represent the viewpoints or positions of the model developers. |
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* Therefore, when using content generated by MiniCPM3-4B, users should take full responsibility for evaluating and verifying it on their own. |
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## LICENSE |
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* This repository is released under the [Apache-2.0](https://github.com/OpenBMB/MiniCPM/blob/main/LICENSE) License. |
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* 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). |
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* 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. |
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## Citation |
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``` |
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@article{hu2024minicpm, |
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title={MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies}, |
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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}, |
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journal={arXiv preprint arXiv:2404.06395}, |
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year={2024} |
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} |
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``` |
