Instructions to use QuantFactory/Unichat-llama3-Chinese-8B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use QuantFactory/Unichat-llama3-Chinese-8B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Unichat-llama3-Chinese-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Unichat-llama3-Chinese-8B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Unichat-llama3-Chinese-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Unichat-llama3-Chinese-8B-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/Unichat-llama3-Chinese-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Unichat-llama3-Chinese-8B-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/Unichat-llama3-Chinese-8B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Unichat-llama3-Chinese-8B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Unichat-llama3-Chinese-8B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Unichat-llama3-Chinese-8B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Unichat-llama3-Chinese-8B-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/Unichat-llama3-Chinese-8B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Unichat-llama3-Chinese-8B-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Unichat-llama3-Chinese-8B-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Unichat-llama3-Chinese-8B-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Unichat-llama3-Chinese-8B-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/Unichat-llama3-Chinese-8B-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/Unichat-llama3-Chinese-8B-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/Unichat-llama3-Chinese-8B-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Unichat-llama3-Chinese-8B-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Unichat-llama3-Chinese-8B-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Unichat-llama3-Chinese-8B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Unichat-llama3-Chinese-8B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Unichat-llama3-Chinese-8B-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Unichat-llama3-Chinese-8B- GGUF

This is quantized version of UnicomLLM/Unichat-llama3-Chinese-8B

Model Description (Translated)

China Unicom AI Innovation Center released the industry's first llama3 Chinese instruction fine-tuning model (full parameter fine-tuning), uploaded at 22:00 on April 19, 2024
This model is based on Meta Llama 3 , adds Chinese data for training, and achieves high-quality Chinese question and answer using the llama3 model.
The model context maintains the native length of 8K, and a version that supports 64K length will be released later.
Base model Meta-Llama-3-8B

📊 Data

High-quality instruction data, covering multiple fields and industries, providing sufficient data support for model training
Fine-tuning instruction data undergoes strict manual screening to ensure high-quality instruction data is used for model fine-tuning.

For more details on models, datasets and training please refer to:

Github：Unichat-llama3-Chinese

Downloads last month: 613

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Unichat-llama3-Chinese-8B-GGUF

Base model

UnicomLLM/Unichat-llama3-Chinese-8B

Quantized

(3)

this model