Instructions to use shaowenchen/baichuan2-7b-chat-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shaowenchen/baichuan2-7b-chat-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="shaowenchen/baichuan2-7b-chat-gguf",
	filename="baichuan2-7b-chat.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use shaowenchen/baichuan2-7b-chat-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shaowenchen/baichuan2-7b-chat-gguf:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf shaowenchen/baichuan2-7b-chat-gguf:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf shaowenchen/baichuan2-7b-chat-gguf:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf shaowenchen/baichuan2-7b-chat-gguf:Q4_K_S

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 shaowenchen/baichuan2-7b-chat-gguf:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf shaowenchen/baichuan2-7b-chat-gguf:Q4_K_S

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 shaowenchen/baichuan2-7b-chat-gguf:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf shaowenchen/baichuan2-7b-chat-gguf:Q4_K_S

Use Docker

docker model run hf.co/shaowenchen/baichuan2-7b-chat-gguf:Q4_K_S

LM Studio
Jan

vLLM

How to use shaowenchen/baichuan2-7b-chat-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shaowenchen/baichuan2-7b-chat-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shaowenchen/baichuan2-7b-chat-gguf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/shaowenchen/baichuan2-7b-chat-gguf:Q4_K_S

Ollama
How to use shaowenchen/baichuan2-7b-chat-gguf with Ollama:
```
ollama run hf.co/shaowenchen/baichuan2-7b-chat-gguf:Q4_K_S
```

Unsloth Studio new

How to use shaowenchen/baichuan2-7b-chat-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 shaowenchen/baichuan2-7b-chat-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 shaowenchen/baichuan2-7b-chat-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for shaowenchen/baichuan2-7b-chat-gguf to start chatting

Docker Model Runner
How to use shaowenchen/baichuan2-7b-chat-gguf with Docker Model Runner:
```
docker model run hf.co/shaowenchen/baichuan2-7b-chat-gguf:Q4_K_S
```

Lemonade

How to use shaowenchen/baichuan2-7b-chat-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull shaowenchen/baichuan2-7b-chat-gguf:Q4_K_S

Run and chat with the model

lemonade run user.baichuan2-7b-chat-gguf-Q4_K_S

List all available models

lemonade list

Provided files

Name	Quant method	Size
baichuan2-7b-chat.Q2_K.gguf	Q2_K	3.0 GB
baichuan2-7b-chat.Q3_K.gguf	Q3_K	3.5 GB
baichuan2-7b-chat.Q3_K_L.gguf	Q3_K_L	3.8 GB
baichuan2-7b-chat.Q3_K_S.gguf	Q3_K_S	3.2 GB
baichuan2-7b-chat.Q4_0.gguf	Q4_0	4.1 GB
baichuan2-7b-chat.Q4_1.gguf	Q4_1	4.5 GB
baichuan2-7b-chat.Q4_K.gguf	Q4_K	4.3 GB
baichuan2-7b-chat.Q4_K_S.gguf	Q4_K_S	4.1 GB
baichuan2-7b-chat.Q5_0.gguf	Q5_0	4.9 GB
baichuan2-7b-chat.Q5_1.gguf	Q5_1	5.3 GB
baichuan2-7b-chat.Q5_K.gguf	Q5_K	5.0 GB
baichuan2-7b-chat.Q5_K_S.gguf	Q5_K_S	4.9 GB
baichuan2-7b-chat.Q6_K.gguf	Q6_K	5.7 GB
baichuan2-7b-chat.Q8_0.gguf	Q8_0	7.4 GB
baichuan2-7b-chat.gguf	full	14 GB

Usage:

docker run --rm -it -p 8000:8000 -v /path/to/models:/models -e MODEL=/models/gguf-model-name.gguf hubimage/llama-cpp-python:latest

and you can view http://localhost:8000/docs to see the swagger UI.

Provided images

Name	Quant method	Size
`shaowenchen/baichuan2-7b-chat-gguf:Q2_K`	Q2_K	7.59 GB
`shaowenchen/baichuan2-7b-chat-gguf:Q3_K`	Q3_K	8.61 GB
`shaowenchen/baichuan2-7b-chat-gguf:Q3_K_L`	Q3_K_L	9.23 GB
`shaowenchen/baichuan2-7b-chat-gguf:Q3_K_S`	Q3_K_S	7.93 GB
`shaowenchen/baichuan2-7b-chat-gguf:Q4_0`	Q4_0	9.6 GB

Usage:

docker run --rm -p 8000:8000 shaowenchen/baichuan2-7b-chat-gguf:Q2_K

and you can view http://localhost:8000/docs to see the swagger UI.

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

View +1 variant

shaowenchen
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baichuan2-7b-chat-gguf

Provided files

Provided images

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