MiniCPM

MiniCPM3-4B: An edge-side LLM that surpasses GPT-3.5-Turbo.

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MiniCPM is a series of open-source large models on the client side jointly developed by Face Intelligence and Tsinghua University Natural Language Processing Laboratory. The main language model MiniCPM-2B has only 2.4 billion (2.4B) non-word embedding parameters, with a total of 2.7B parameters. - After SFT, MiniCPM-2B performs similarly to Mistral-7B on public comprehensive evaluation sets (better in Chinese, mathematics, and code capabilities), and outperforms models such as Llama2-13B, MPT-30B, and Falcon-40B overall. - After DPO, MiniCPM-2B also surpasses many representative open-source large models such as Llama2-70B-Chat, Vicuna-33B, Mistral-7B-Instruct-v0.1, and Zephyr-7B-alpha on the current evaluation set MTBench, which is closest to the user experience. - Based on MiniCPM-2B, a multi-modal large model MiniCPM-V 2.0 on the client side is constructed, which achieves the best performance of models below 7B in multiple test benchmarks, and surpasses larger parameter scale models such as Qwen-VL-Chat 9.6B, CogVLM-Chat 17.4B, and Yi-VL 34B on the OpenCompass leaderboard. MiniCPM-V 2.0 also demonstrates leading OCR capabilities, approaching Gemini Pro in scene text recognition capabilities. - After Int4 quantization, MiniCPM can be deployed and inferred on mobile phones, with a streaming output speed slightly higher than human speech speed. MiniCPM-V also directly runs through the deployment of multi-modal large models on mobile phones. - A single 1080/2080 can efficiently fine-tune parameters, and a single 3090/4090 can fully fine-tune parameters. A single machine can continuously train MiniCPM, and the secondary development cost is relatively low.

README:

中文 | English

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更新日志🔥

[2024.09.28] LLMxMapReduce 开源，支持MiniCPM3-4B，理论上支持无限长文本输入！
[2024.09.18] SGLang 已经支持 MiniCPM3-4B (推荐使用)！由于 SGLang v0.3 对 MiniCPM3 中使用的 MLA 结构进行了推理优化，吞吐量相比于 vLLM 提高 70%！[用法]
[2024.09.16] llama.cpp 已经官方支持 MiniCPM3-4B！[GGUF模型|用法]
[2024.09.05] 发布 MiniCPM3-4B！该模型的表现超越 Phi-3.5-mini-instruct 和 GPT-3.5-Turbo-0125，并且能够比肩 Llama3.1-8B-Instruct、Qwen2-7B-Instruct、GLM-4-9B-Chat 等多个 7B-9B 参数量的模型。
[2024.07.09] MiniCPM-2B 已经支持使用 SGLang 推理！
[2024.07.05] 发布 MiniCPM-S-1B！该模型在保持下游任务性能无损的前提下，FFN 层实现了 87.89% 的平均稀疏度，将 FFN FLOPs 降低了 84%。
[2024.04.11] 发布 MiniCPM-2B-128k、MiniCPM-MoE-8x2B 和 MiniCPM-1B！点击这里查看技术博客。
[2024.03.16] MiniCPM-2B 的 30 余个中间检查点开放了！HuggingFace链接
[2024.02.01] 发布 MiniCPM-2B！该模型在公开评测集上与 Mistral-7B 表现相近（中文、数学、代码能力更优），整体性能超越 Llama2-13B、MPT-30B、Falcon-40B 等模型。

模型下载

HuggingFace	ModelScope
MiniCPM3-4B	MiniCPM3-4B
MiniCPM-2B-sft	MiniCPM-2B-sft
MiniCPM-2B-dpo	MiniCPM-2B-dpo
MiniCPM-2B-128k	MiniCPM-2B-128k
MiniCPM-MoE-8x2B	MiniCPM-MoE-8x2B
MiniCPM-1B	MiniCPM-1B
MiniCPM-S-1B	MiniCPM-S-1B

注: 更多模型版本见这里。

MiniCPM 3.0

MiniCPM 3.0 是一个 4B 参数量的语言模型，相比 MiniCPM1.0/2.0，功能更加全面，综合能力大幅提升，多数评测集上的效果比肩甚至超越众多 7B-9B 模型。

支持工具调用🛠️（Function Calling）和代码解释器💻（Code Interpreter）：Berkeley Function Calling Leaderboard (BFCL) 上取得 9B 规模以下 SOTA，超越 GLM-4-9B-Chat、Qwen2-7B-Instruct。
超强的推理能力🧮：数学能力方面，MathBench 上的效果超越 GPT-3.5-Turbo 以及多个 7B-9B 模型。在非常具有挑战性的 LiveCodeBench 上，效果超越 Llama3.1-8B-Instruct。
出色的中英文指令遵循能力🤖：英文指令遵循 IFEval、中文指令遵循 FollowBench-zh 效果超越 GLM-4-9B-Chat、Qwen2-7B-Instruct。
长文本能力：原生支持 32k 上下文长度，32k 长度内大海捞针全绿。提出 LLMxMapReduce ，理论可处理的上下文长度达到 +∞，在综合性长文本评测基准 InfiniteBench 平均得分超越GPT-4、KimiChat等标杆模型。
RAG能力：我们发布了 MiniCPM RAG 套件。基于 MiniCPM 系列模型的 MiniCPM-Embedding、MiniCPM-Reranker 在中文、中英跨语言检索测试中取得 SOTA 表现；针对 RAG 场景的 MiniCPM3-RAG-LoRA 在开放域问答等多项任务上超越 Llama3-8B、Baichuan2-13B 等模型。

评测结果

综合评测

评测集	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
英文能力
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
中文能力
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
数学能力
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
代码能力
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
工具调用能力
BFCL v2	71.6	70.1	19.2	73.3	75.4	48.4	76.0
综合能力
平均分	65.3	65.0	57.9	60.8	61.0	57.2	66.3

工具调用能力

我们在 Berkeley Function Calling Leaderboard (BFCL) 上测试了模型的工具调用能力，MiniCPM3-4B 在该榜单上的表现超越了多个 7B-9B 参数量的模型，优于 GPT-3.5-Turbo-0125。

模型	总体准确率	AST Summary	Exec Summary	Irrelevance Detection	Relevance Detection
MiniCPM3-4B	76.03%	68.55%	85.54%	53.71%	90.24%
Llama3.1-8B-Instruct	73.28%	64.61%	86.48%	43.12%	85.37%
Qwen2-7B-Instruct	71.61%	65.71%	79.57%	44.70%	90.24%
GLM-4-9B-Chat	70.08%	60.69%	80.02%	55.02%	82.93%
Phi-3.5-mini-instruct	48.44%	38.89%	54.04%	46.78%	65.85%
Gemma2-9B-it	19.18%	5.41%	18.50%	88.88%	7.32%

长文本能力

在 32k 的上下文长度进行大海捞针测试，结果如下图：

同时我们提出LLMxMapReduce，利用分治的策略，理论上可以处理无限长度的文本。我们在InfiniteBench上测试了模型的长文本处理能力，在LLMxMapReduce框架的加持下，MiniCPM3-4B在这个榜单的平均得分能够超越 GPT-4、KimiChat 等标杆模型。

	Context length	Qwen2-70b	Kimi-Chat(2024.06)	GPT-4 (From InfiniteBench)	MiniCPM 3.0 x MR	Qwen2-70b x MR	Llama3-70bx MR
Math.Find	87.9k	59.71%	18.57%	60.00%	83.43%	54.29%	91.43%
Retrieve.KV	89.9k	29.00%	69.20%	89.00%	93.80%	98.80%	98.89%
En.Dia	103.6K	23.00%	23.00%	7.50%	12.50%	46.50%	17.50%
Code.Debug	114.7k	45.43%	38.32%	54.31%	25.63%	54.82%	62.94%
Retrieve.Number	122.4k	100.00%	97.45%	100.00%	99.32%	100.00%	99.79%
Retrieve.PassKey	122.4k	100.00%	99.32%	100.00%	98.81%	100.00%	100.00%
En.Sum	171.5K	31.85%	29.94%	14.73%	25.89%	32.39%	30.63%
En.MC	184.4k	81.66%	79.91%	68.12%	66.38%	83.84%	82.10%
En.QA	192.6k	21.97%	18.80%	22.44%	28.39%	23.13%	34.70%
Zh.QA	2068.6k	21.40%	19.84%	25.96%	23.66%	19.10%	N/A
avg w/o Zh.QA	/	51.92%	52.96%	55.33%	59.29%	64.98%	68.64%
avg	/	48.86%	49.65%	52.39%	55.55%	60.39%	N/A

模型推理

Huggingface

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
torch.manual_seed(0)

path = 'openbmb/MiniCPM3-4B'
tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True)

responds, history = model.chat(tokenizer, "请写一篇关于人工智能的文章，详细介绍人工智能的未来发展和隐患。", temperature=0.7, top_p=0.7)
print(responds)

SGLang（推荐）

安装

参考 SGLang 官方仓库，通过源码安装最新版本。

启动推理服务

python -m sglang.launch_server --model openbmb/MiniCPM3-4B --trust-remote-code --port 30000 --chat-template chatml

使用示例

from sglang import function, system, user, assistant, gen, set_default_backend, RuntimeEndpoint

@function
def multi_turn_question(s, question_1, question_2):
    s += user(question_1)
    s += assistant(gen("answer_1", max_tokens=1024))
    s += user(question_2)
    s += assistant(gen("answer_2", max_tokens=1024))

set_default_backend(RuntimeEndpoint("http://localhost:30000"))

state = multi_turn_question.run(
    question_1="介绍一下人工智能",
    question_2="写一篇关于它的文章",
)

for m in state.messages():
    print(m["role"], ":", m["content"])

vLLM

安装 vllm
```
pip install "vllm>=0.6.2"
```

推理

from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

model_name = "openbmb/MiniCPM3-4B"
prompt = [{"role": "user", "content": "请写一篇关于人工智能的文章，详细介绍人工智能的未来发展和隐患。"}]

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)

outputs = llm.generate(prompts=input_text, sampling_params=sampling_params)

print(outputs[0].outputs[0].text)

llama.cpp

我们提供了 MiniCPM3 的 GGUF 版本，可以直接使用 llama.cpp 推理。

安装 llama.cpp

  git clone https://github.com/ggerganov/llama.cpp
  cd llama.cpp
  make

推理

./llama-cli -c 1024 -m minicpm3-4b-fp16.gguf -n 1024 --top-p 0.7 --temp 0.7 --prompt "<|im_start|>user\n请写一篇关于人工智能的文章，详细介绍人工智能的未来发展和隐患。<|im_end|>\n<|im_start|>assistant\n"

模型微调

LLaMA-Factory

目前模型微调支持 LLaMA-Factory，使用方法参考 LLaMA-Factory 微调。

进阶功能

对于以下进阶功能，我们的样例代码中使用 vLLM 进行推理。

工具调用

我们提供了使用 MiniCPM3 调用工具的示例代码：

cd demo/minicpm3/function_call
python function_call.py

如果你想启动一个能够调用工具的推理服务，使用以下代码：

cd demo/minicpm3/function_call
pip install -r requirements.txt
python openai_api_server.py \
    --model openbmb/MiniCPM3-4B \
    --served-model-name MiniCPM3-4B \
    --chat-template chatml.jinja \
    --dtype auto \
    --api-key token-abc123 \
    --tensor-parallel-size 1 \
    --trust-remote-code

下面是一个调用搜索工具回答问题的演示：

代码解释器

我们提供了一个 MiniCPM3 使用代码解释器的示例代码：

cd demo/minicpm3/code_interpreter
pip install -r requirements.txt
python code_interpreter.py openbmb/MiniCPM3-4B

下面是一个使用代码解释器生成二维码的演示：

MiniCPM 2.0

查看 MiniCPM 2.0 的详细信息

MiniCPM 2.0 系列模型对 MiniCPM 进行了多个维度的升级，包括以下模型版本：

MiniCPM-2B-128k：将 MiniCPM-2B 的上下文长度从 4k 扩展至 128k，在 InfiniteBench 测试集上优于 ChatGLM3-6B-128k、Yi-6B-200k 等更大参数量的模型。
MiniCPM-MoE-8x2B：基于 MiniCPM-2B 进行 MoE 扩展，综合表现相比于 MiniCPM-2B 平均提高 4.5 个百分点。
MiniCPM-1B：相比于 MiniCPM-2B 成本下降 60%，综合表现仍然优于 LLaMA2-13B。
MiniCPM-S-1B：在保持下游任务性能无损的前提下，FFN 层实现了 87.89% 的平均稀疏度，将 FFN FLOPs 降低了 84%。结合 PowerInfer 推理框架，解码速度提升约 2.8 倍。

评测结果

MiniCPM-2B-128k 模型评测

Model	avg	avg w/o code&math	passkey	number_string	kv_retrieval	longbook_choice_eng	longbook_qa_chn	longbook_qa_eng	longbook_sum_eng	longdialogue_qa_eng	math_find	code_debug	code_run
LWM-Text-128k	24.45	33.62	100	97.8	0.6	28.82	15.93	14.31	9.99	1.5	3.43	20.05	1
Yarn-Mistral-7b-128k	19.84	27.36	92.71		0	27.95	15.49	9.55	9.06	7.5	17.14	0.76	1.25
Mistral-7B-Instruct-v0.2(ABF 1000w)	27.75	36.9	100	78.98	3.6	37.12	11.74	17.37	21.12	9.5	29.43	17.51	0
Yi-6B-200k	22.15	32.54	100	94.92	0	36.68	15.07	9.2	0.92	3.5	4.29	0.51	0.75
chatglm3-6b-128k	25.58	36.57	89.93	99.66	5.2	46.29	10.7	8.38	25.91	6.5	8	5.33	1
MiniCPM-2.4B-128k	27.32	37.68	98.31	99.83	9	29.69	23.06	16.33	15.73	9.5	4.29	22.08	0

MiniCPM-MoE-8x2B 模型评测

Model	BBH	MMLU	CEval	CMMLU	HumanEval	MBPP†	GSM8K	MATH
Llama2-34B*	44.1	62.6	-	-	22.6	33.0	42.2	6.24
Mistral-7B-Instruct-v0.2	39.81	60.51	42.55	41.92	36.59	39.63	40.49	4.95
Gemma-7B*	55.1	64.3	-	-	32.3	44.4	46.4	24.3
Qwen1.5-7B*	40.2	61	74.1	73.1	36	37.4	62.5	20.3
Deepseek-MoE(16B)*	-	45.0	40.6	42.5	26.8	39.2	18.8	4.3
MiniCPM-2.4B	36.87	53.46	51.13	51.07	50.00	35.93	53.83	10.24
MiniCPM-MoE-8x2B	39.22	58.90	58.11	58.80	55.49	41.68	61.56	10.52

注：* 表示结果取自技术报告。† 表示评测集为MBPP全集。

MiniCPM-S-1B 评测结果

代码生成：在 HumanEval（0-shot）和 MBPP（3-shot）上的平均 pass@1 得分。
常识推理：在 PIQA、SIQA、HellaSwag、WinoGrande 和 COPA 上的平均 0-shot 准确率。
阅读理解：在 BoolQ、LAMBADA 和 TyDi QA 上的平均 0-shot 准确率。

其他测试集：我们报告在GSM8K（8-shot）、MMLU（5-shot）、BBH（3-shot）和 AGI-Eval（0-shot）上的平均准确率。

Setting	Average Sparsity	Average Performance	Code Generation	Commonsense Reasoning	Reading Comprehension	GSM8K	MMLU	BBH	AGI Eval
LLaMA2-7B	-	37.96	16.37	69.59	61.87	12.96	44.45	32.96	27.53
ReluLLaMA-7B	66.98	37.62	15.85	69.64	70.54	5.84	38.64	35.07	27.73
ProSparse-7B*	88.11	38.31	19.47	66.29	63.33	12.74	45.21	33.59	27.55
ProSparse-7B	89.32	38.46	19.42	66.27	63.50	12.13	45.48	34.99	27.46
LLaMA2-13B	-	44.06	20.19	72.58	71.55	22.21	54.69	37.89	29.33
ReluLLaMA-13B	71.56	42.74	20.19	70.44	73.29	18.50	50.58	37.97	28.22
ProSparse-13B*	87.97	45.07	29.03	69.75	67.54	25.40	54.78	40.20	28.76
ProSparse-13B	88.80	44.90	28.42	69.76	66.91	26.31	54.35	39.90	28.67
MiniCPM-1B	-	44.44	36.85	63.67	60.90	35.48	50.44	35.03	28.71
MiniCPM-S-1B*	86.25	44.72	41.38	64.55	60.69	34.72	49.36	34.04	28.27
MiniCPM-S-1B	87.89	44.72	42.04	64.37	60.73	34.57	49.51	34.08	27.77

注：

ReluLLaMA-7B 和 ReluLLaMA-13B 的下载链接分别是 7B and 13B。"ProSparse-7B*"、"ProSparse-13B*" 和 "MiniCPM-S-1B*" 代表没有激活阈值偏移的 ProSparse 版本。
对于 PIQA、SIQA、HellaSwag、WinoGrande、COPA、BoolQ、LAMBADA、TyDi QA 和 AGI-Eval，我们根据各个选项的 PPL 来进行答案选择。对于 GSM8K、MMLU 和 BBH，我们直接生成答案。

模型推理

HuggingFace、vLLM推理

参考 MiniCPM 1.0 中的模型推理部分。

Powerinfer 推理

针对 MiniCPM-S-1B 模型，我们可以使用 Powerinfer 进行推理加速，使用方法如下：

保证cmake版本3.17以上，如果已经安装过，则跳过此步骤

  # 下载安装包
  sudo wget https://cmake.org/files/v3.23/cmake-3.23.0.tar.gz
  # 解压安装包
  sudo tar -zxvf cmake-3.23.0.tar.gz
  # 配置安装环境
  sudo ./configure
  sudo make -j8
  # 编译安装
  sudo make install
  # 查看安装后版本
  cmake --version
  # 返回版本号则安装成功
  #cmake version 3.23.0

安装powerinfer：

  git clone https://github.com/SJTU-IPADS/PowerInfer
  cd PowerInfer
  pip install -r requirements.txt # install Python helpers' dependencies

cpu版本powerinfer编译,如果你的机器只有cpu，或者只想使用cpu进行推理，则运行以下命令：

  cmake -S . -B build
  cmake --build build --config Release

gpu版本powerinfer编译,如果你的机器有gpu，则可以运行以下命令：

  cmake -S . -B build -DLLAMA_CUBLAS=ON
  cmake --build build --config Release

获取稀疏模型

git clone https://huggingface.co/openbmb/MiniCPM-S-1B-sft-gguf/tree/main
#or
git clone https://modelscope.cn/models/OpenBMB/MiniCPM-S-1B-sft-gguf

模型推理：

cd PowerInfer
# 以下是命令模版，output_token_count为最大输出tokens，thread_num 为线程数，prompt为输入prompt字符
#./build/bin/main -m /PATH/TO/MODEL -n $output_token_count -t $thread_num -p $prompt
# 以下是示例
./build/bin/main -m /root/ld/ld_model_pretrain/1b-s-minicpm/MiniCPM-S-1B-sft.gguf -n 2048 -t 8 -p '<用户>hello,tell me a story please.<AI>'

MiniCPM 1.0

查看 MiniCPM 1.0 的详细信息

MiniCPM-2B 语言模型有 24亿（2.4B）的非词嵌入参数量, 总计 2.7B 参数量。

经过 SFT 后，MiniCPM-2B 在公开评测集上与 Mistral-7B 表现相近（中文、数学、代码能力更优），整体性能超越 Llama2-13B、MPT-30B、Falcon-40B 等模型。
经过 DPO 后，MiniCPM-2B 在 MTBench 上也超越了 Llama2-70B-Chat、Vicuna-33B、Mistral-7B-Instruct-v0.1、Zephyr-7B-alpha 等众多代表性开源大模型。

注意：为了保证在学术研究用途上模型的通用性，我们未对 MiniCPM-2B 进行任何身份认同训练。同时由于我们用 ShareGPT 开源语料作为部分训练数据，模型可能会输出类似 GPT 系列模型的身份认同信息。

评测结果

评测设置

由于大模型评测难以统一，且大量评测也没有公开的prompt和测试代码，对于具体评测方式，我们只能尽量做到适合各类模型。
整体而言，我们测试时采用统一的prompt输入，并按照各模型对应的模板进行输入调整。
评测脚本及prompt已开源在我们的Github仓库中，也欢迎更多开发者来不断改进我们的评测方式。
- 文本评测部分，采用了我们的开源大模型能力评测框架UltraEval。以下为开源模型复现流程：
  - 安装UltraEval
```
git clone https://github.com/OpenBMB/UltraEval.git
cd UltraEval
pip install -e .
```
  - 下载相关数据并解压处理
```
wget -O RawData.zip "https://cloud.tsinghua.edu.cn/f/71b5232264ae4833a4d0/?dl=1"
unzip RawData.zip
python data_process.py
```
  - 执行评测脚本(提供了模板，可自定义)
```
bash run_eval.sh
```

部署模式

因为MiniCPM采用Mup的结构，与现有模型在具体计算上有细微差别，我们是基于vllm=0.2.2版本进行了我们模型的实现。
对于非MiniCPM模型，我们采用了vllm=0.2.7的最新版本进行推理。

评测度量

对于QA任务（选择题任务），我们选用两种方式进行测试：
- PPL：将选项作为题目生成的延续，并根据各个选项的PPL来进行答案选择；
- 第二种是直接生成答案选项。
对于不同模型，这两种方式得到的结果差异较大。MiniCPM两种模式上的结果较为接近，而Mistral-7B-v0.1等模型在PPL上表现较好，直接生成上效果较差。
在具体评测时，我们以两种评测方式得分的最高者为最终结果，以此保证对比的公平性(以下表格中*号表示采用PPL)。

文本模型评测

越级比较:

模型	平均分	英文均分	中文均分	C-Eval	CMMLU	MMLU	HumanEval	MBPP	GSM8K	MATH	BBH	ARC-E	ARC-C	HellaSwag
Llama2-7B	35.40	36.21	31.765	32.42	31.11	44.32	12.2	27.17	13.57	1.8	33.23	75.25	42.75	75.62*
Qwen-7B	49.46	47.19	59.655	58.96	60.35	57.65	17.07	42.15	41.24	5.34	37.75	83.42	64.76	75.32*
Deepseek-7B	39.96	39.15	43.64	42.82	44.45	47.82	20.12	41.45	15.85	1.53	33.38	74.58*	42.15*	75.45*
Mistral-7B	48.97	49.96	44.54	46.12	42.96	62.69	27.44	45.2	33.13	5.0	41.06	83.92	70.73	80.43*
Llama2-13B	41.48	42.44	37.19	37.32	37.06	54.71	17.07	32.55	21.15	2.25	37.92	78.87*	58.19	79.23*
MPT-30B	38.17	39.82	30.72	29.34	32.09	46.56	21.95	35.36	10.31	1.56	38.22	78.66*	46.08*	79.72*
Falcon-40B	43.62	44.21	40.93	40.29	41.57	53.53	24.39	36.53	22.44	1.92	36.24	81.94*	57.68	83.26*
MiniCPM-2B	52.33	52.6	51.1	51.13	51.07	53.46	50.00	47.31	53.83	10.24	36.87	85.44	68.00	68.25

同级比较：

模型	平均分	英文均分	中文均分	C-Eval	CMMLU	MMLU	HumanEval	MBPP	GSM8K	MATH	BBH	ARC-E	ARC-C	HellaSwag
TinyLlama-1.1B	25.36	25.55	24.525	25.02	24.03	24.3	6.71	19.91	2.27	0.74	28.78	60.77*	28.15*	58.33*
Qwen-1.8B	34.72	31.87	47.57	49.81	45.32	43.37	7.93	17.80	19.26	2.42	29.07	63.97*	43.69	59.28*
Gemini Nano-3B	-	-	-	-	-	-	-	27.2(report)	22.8(report)	-	42.4(report)	-	-	-
StableLM-Zephyr-3B	43.46	46.31	30.62	30.34	30.89	45.9	35.37	31.85	52.54	12.49	37.68	73.78	55.38	71.87*
Phi-2-2B	48.84	54.41	23.78	23.37	24.18	52.66	47.56	55.04	57.16	3.5	43.39	86.11	71.25	73.07*
MiniCPM-2B	52.33	52.6	51.10	51.13	51.07	53.46	50.00	47.31	53.83	10.24	36.87	85.44	68.00	68.25

Chat模型比较：

模型	平均分	英文均分	中文均分	C-Eval	CMMLU	MMLU	HumanEval	MBPP	GSM8K	MATH	BBH	ARC-E	ARC-C	HellaSwag
ChatGLM2-6B	37.98	35.17	50.63	52.05	49.21	45.77	10.37	9.38	22.74	5.96	32.6	74.45	56.82	58.48*
Mistral-7B-Instruct-v0.1	44.36	45.89	37.51	38.06	36.96	53.56	29.27	39.34	28.73	3.48	39.52	81.61	63.99	73.47*
Mistral-7B-Instruct-v0.2	50.91	52.83	42.235	42.55	41.92	60.51	36.59	48.95	40.49	4.95	39.81	86.28	73.38	84.55*
Qwen-7B-Chat	44.93	42.05	57.9	58.57	57.23	56.03	15.85	40.52	42.23	8.3	37.34	64.44*	39.25*	74.52*
Yi-6B-Chat	50.46	45.89	70.995	70.88	71.11	62.95	14.02	28.34	36.54	3.88	37.43	84.89	70.39	74.6*
Baichuan2-7B-Chat	44.68	42.74	53.39	53.28	53.5	53	21.34	32.32	25.25	6.32	37.46	79.63	60.15	69.23*
Deepseek-7B-chat	49.34	49.56	48.335	46.95	49.72	51.67	40.85	48.48	48.52	4.26	35.7	76.85	63.05	76.68*
Llama2-7B-Chat	38.16	39.17	33.59	34.54	32.64	47.64	14.02	27.4	21.15	2.08	35.54	74.28	54.78	75.65*
MiniCPM-2B	52.33	52.6	51.10	51.13	51.07	53.46	50.00	47.31	53.83	10.24	36.87	85.44	68.00	68.25

DPO后模型比较：

模型	MT-bench
GPT-4-turbo	9.32
GPT-3.5-turbo	8.39
Mistral-8*7b-Instruct-v0.1	8.30
Claude-2.1	8.18
Zephyr-7B-beta	7.34
MiniCPM-2B	7.25
Vicuna-33B	7.12
Zephyr-7B-alpha	6.88
LLaMA-2-70B-chat	6.86
Mistral-7B-Instruct-v0.1	6.84
MPT-34B-instruct	6.39

快速上手

在线体验

Colab

基于Gradio的网页版Demo

使用如下命令启动基于Gradio的网页版demo：

# generation powered by vllm
python demo/minicpm/vllm_based_demo.py --model_path <vllmcpm_repo_path>
# generation powered by huggingface
python demo/minicpm/hf_based_demo.py --model_path <hf_repo_path>

HuggingFace 推理

MiniCPM-2B

安装transformers>=4.36.0以及accelerate后，运行以下代码：

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
torch.manual_seed(0)

path = 'openbmb/MiniCPM-2B-dpo-bf16'
tokenizer = AutoTokenizer.from_pretrained(path)
model = AutoModelForCausalLM.from_pretrained(path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True)

responds, history = model.chat(tokenizer, "山东省最高的山是哪座山, 它比黄山高还是矮？差距多少？", temperature=0.5, top_p=0.8, repetition_penalty=1.02)
print(responds)

MiniCPM-2B （Llama Format）

我们将MiniCPM的模型权重转化成了Llama代码可以直接调用的格式，以便大家尝试:

import torch
from transformers import LlamaTokenizerFast, LlamaForCausalLM
model_path = "openbmb/MiniCPM-2B-dpo-bf16-llama-format"
tokenizer = LlamaTokenizerFast.from_pretrained(model_path)
model = LlamaForCausalLM.from_pretrained(model_path, torch_dtype=torch.bfloat16, device_map='cuda', trust_remote_code=True)

prompt="Now you act like a terminal situated within a beginner's C++ practice repository folder, please provide the output for the command: `ls -l`"
input_ids = tokenizer.encode("<用户>{}<AI>".format(prompt), return_tensors='pt', add_special_tokens=True).cuda()
responds = model.generate(input_ids, temperature=0.3, top_p=0.8, repetition_penalty=1.02, max_length=1024)
responds = tokenizer.decode(responds[0], skip_special_tokens=True)
print(responds)

vLLM 推理

安装 vLLM。

pip install "vllm>=0.4.1"

具体推理代码见这里。

SGLang 推理

安装 SGLang。

首先需要启动一个服务:

python -m sglang.launch_server --model-path openbmb/MiniCPM-2B-dpo-fp16 --trust-remote-code --port 30000

下面是一个推理代码的样例:

from sglang import function, gen, set_default_backend, RuntimeEndpoint

@function
def text_qa(s, question):
    s += "<用户>" + question + "<AI>"
    s += gen("answer", max_tokens=1024, temperature=0.7, top_p=0.7)

set_default_backend(RuntimeEndpoint("http://localhost:30000"))

state = text_qa.run(
    question="What is the capital of China?",
)

print(state["answer"])

llama.cpp、Ollama、fastllm、mlx_lm推理

MiniCPM支持llama.cpp 、ollama、fastllm、mlx_lm推理。感谢@runfuture对llama.cpp和ollama的适配。

请参考 MiniCPM 知识库中的边端部署教程。

模型量化

请参考 MiniCPM 知识库中的量化指南。

模型微调

一张 1080/2080 可实现高效参数微调：代码
mlx 微调：教程
xtuner: MiniCPM高效率微调的不二选择
LLaMA-Factory：MiniCPM微调一键式解决方案

开源协议

模型协议

本仓库中代码依照 Apache-2.0 协议开源
MiniCPM 模型权重的使用则需要遵循 MiniCPM 模型商用许可协议。
MiniCPM 模型权重对学术研究完全开放，在填写问卷进行登记后亦允许免费商业使用。

声明

作为一个语言模型，MiniCPM 通过学习大量的文本来生成内容，但它无法理解、表达个人观点或价值判断，它所输出的任何内容都不代表模型开发者的观点和立场。
因此用户在使用 MiniCPM 生成的内容时，应自行负责对其进行评估和验证。
如果由于使用 MiniCPM 开源模型而导致的任何问题，包括但不限于数据安全问题、公共舆论风险，或模型被误导、滥用、传播或不当利用所带来的任何风险和问题，我们将不承担任何责任。

开发机构

本项目由以下机构共同开发：

工作引用

如果觉得MiniCPM有助于您的工作，请引用我们的论文

@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}
}

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ScaleLLM is a cutting-edge inference system engineered for large language models (LLMs), meticulously designed to meet the demands of production environments. It extends its support to a wide range of popular open-source models, including Llama3, Gemma, Bloom, GPT-NeoX, and more. ScaleLLM is currently undergoing active development. We are fully committed to consistently enhancing its efficiency while also incorporating additional features. Feel free to explore our **_Roadmap_** for more details. ## Key Features * High Efficiency: Excels in high-performance LLM inference, leveraging state-of-the-art techniques and technologies like Flash Attention, Paged Attention, Continuous batching, and more. * Tensor Parallelism: Utilizes tensor parallelism for efficient model execution. * OpenAI-compatible API: An efficient golang rest api server that compatible with OpenAI. * Huggingface models: Seamless integration with most popular HF models, supporting safetensors. * Customizable: Offers flexibility for customization to meet your specific needs, and provides an easy way to add new models. * Production Ready: Engineered with production environments in mind, ScaleLLM is equipped with robust system monitoring and management features to ensure a seamless deployment experience.

github

: 364

MiniCPM

github

: 7.0k

neural-speed

Neural Speed is an innovative library designed to support the efficient inference of large language models (LLMs) on Intel platforms through the state-of-the-art (SOTA) low-bit quantization powered by Intel Neural Compressor. The work is inspired by llama.cpp and further optimized for Intel platforms with our innovations in NeurIPS' 2023

github

: 327

MiniCPM

README:

更新日志🔥

目录

模型下载

MiniCPM 3.0

评测结果

综合评测

工具调用能力

长文本能力

模型推理

Huggingface

SGLang（推荐）

vLLM

llama.cpp

模型微调

LLaMA-Factory

进阶功能

工具调用

代码解释器

MiniCPM 2.0

评测结果

MiniCPM-2B-128k 模型评测

MiniCPM-MoE-8x2B 模型评测

MiniCPM-S-1B 评测结果

模型推理

HuggingFace、vLLM推理

Powerinfer 推理

MiniCPM 1.0

评测结果

评测设置

部署模式

评测度量

文本模型评测

快速上手

在线体验

基于Gradio的网页版Demo

HuggingFace 推理

MiniCPM-2B

MiniCPM-2B （Llama Format）

vLLM 推理

SGLang 推理

llama.cpp、Ollama、fastllm、mlx_lm推理

模型量化

模型微调

开源协议

模型协议

声明

开发机构

工作引用

For Tasks:

For Jobs:

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