Instructions to use QuantFactory/MiniCPM3-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/MiniCPM3-4B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/MiniCPM3-4B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/MiniCPM3-4B-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/MiniCPM3-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/MiniCPM3-4B-GGUF",
	filename="MiniCPM3-4B.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/MiniCPM3-4B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/MiniCPM3-4B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/MiniCPM3-4B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/MiniCPM3-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M

SGLang

How to use QuantFactory/MiniCPM3-4B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/MiniCPM3-4B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/MiniCPM3-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/MiniCPM3-4B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/MiniCPM3-4B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/MiniCPM3-4B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/MiniCPM3-4B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/MiniCPM3-4B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/MiniCPM3-4B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/MiniCPM3-4B-GGUF to start chatting

Pi new

How to use QuantFactory/MiniCPM3-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/MiniCPM3-4B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/MiniCPM3-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/MiniCPM3-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/MiniCPM3-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MiniCPM3-4B-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/MiniCPM3-4B-GGUF

This is quantized version of openbmb/MiniCPM3-4B created using llama.cpp

Original Model Card

MiniCPM Repo | MiniCPM Paper | MiniCPM-V Repo | Join us in Discord and WeChat

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 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

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))
]

responses = tokenizer.batch_decode(output_token_ids, skip_special_tokens=True)[0]
print(responses)

Inference with vLLM

For now, you need to install our forked version of vLLM.

pip install git+https://github.com/OpenBMB/vllm.git@minicpm3

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

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

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 License.
The usage of MiniCPM3-4B model weights must strictly follow MiniCPM Model License.md.
The models and weights of MiniCPM3-4B are completely free for academic research. after filling out a "questionnaire" 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}
}

Downloads last month: 218

GGUF

Model size

4B params

Architecture

minicpm3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Paper for QuantFactory/MiniCPM3-4B-GGUF

MiniCPM: Unveiling the Potential of Small Language Models with Scalable Training Strategies

Paper • 2404.06395 • Published Apr 9, 2024 • 25