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1from transformers import AutoModelForCausalLM, AutoTokenizer
2import torch
3
4path = "openbmb/MiniCPM3-4B"
5device = "cuda"
6
7tokenizer = AutoTokenizer.from_pretrained(path, trust_remote_code=True)
8model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map=device, trust_remote_code=True)
9
10messages = [
11 {"role": "user", "content": "推荐5个北京的景点。"},
12]
13model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(device)
14
15model_outputs = model.generate(
16 model_inputs,
17 max_new_tokens=1024,
18 top_p=0.7,
19 temperature=0.7
20)
21
22output_token_ids = [
23 model_outputs[i][len(model_inputs[i]):] for i in range(len(model_inputs))
24]
25
26responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
27print(responses)pip install git+https://github.com/OpenBMB/vllm.git@minicpm31from transformers import AutoTokenizer
2from vllm import LLM, SamplingParams
3
4model_name = "openbmb/MiniCPM3-4B"
5prompt = [{"role": "user", "content": "推荐5个北京的景点。"}]
6
7tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
8input_text = tokenizer.apply_chat_template(prompt, tokenize=False, add_generation_prompt=True)
9
10llm = LLM(
11 model=model_name,
12 trust_remote_code=True,
13 tensor_parallel_size=1
14)
15sampling_params = SamplingParams(top_p=0.7, temperature=0.7, max_tokens=1024, repetition_penalty=1.02)
16
17outputs = llm.generate(prompts=input_text, sampling_params=sampling_params)
18
19print(outputs[0].outputs[0].text)| 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 v3 | 22.2 | 20.2 | 19.2 | 20.4 | 24.0 | 19.6 | 22.6 |
| Function Call | |||||||
| BFCL v2 | 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 |
@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}
}