--- license: apache-2.0 ---

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## 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/zh-zheng/minicpm?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-GPTQ-int4" 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").to(device) model_outputs = model.generate( model_inputs, max_new_tokens=1024, top_p=0.7, temperature=0.7, repetition_penalty=1.02 ) 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) ```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
Benchmark Qwen2-7B-Instruct GLM-4-9B-Chat Gemma2-9B-it Llama3.1-8B-Instruct GPT-3.5-Turbo-0125 Phi-3.5-mini-Instruct(3.8B) MiniCPM3-4B
English
MMLU 70.5 72.4 72.6 69.4 69.2 68.4 67.2
BBH 64.9 76.3 65.2 67.8 70.3 68.6 70.2
MT-Bench 8.41 8.35 7.88 8.28 8.17 8.60 8.41
IFEVAL (Prompt Strict-Acc.) 51.0 64.5 71.9 71.5 58.8 49.4 68.4
Chinese
CMMLU 80.9 71.5 59.5 55.8 54.5 46.9 73.3
CEVAL 77.2 75.6 56.7 55.2 52.8 46.1 73.6
AlignBench v1.1 7.10 6.61 7.10 5.68 5.82 5.73 6.74
FollowBench-zh (SSR) 63.0 56.4 57.0 50.6 64.6 58.1 66.8
Math
MATH 49.6 50.6 46.0 51.9 41.8 46.4 46.6
GSM8K 82.3 79.6 79.7 84.5 76.4 82.7 81.1
MathBench 63.4 59.4 45.8 54.3 48.9 54.9 65.6
Code
HumanEval+ 70.1 67.1 61.6 62.8 66.5 68.9 68.3
MBPP+ 57.1 62.2 64.3 55.3 71.4 55.8 63.2
LiveCodeBench 22.2 20.2 19.2 20.4 24.0 19.6 22.6
Function Call
BFCL 71.6 70.1 19.2 73.3 75.4 48.4 76.0
Overall
Average 65.3 65.0 57.9 60.8 61.0 57.2 66.3
## 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} } ```