Instructions to use h31337/Qwen-7B-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 h31337/Qwen-7B-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 h31337/Qwen-7B-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf h31337/Qwen-7B-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf h31337/Qwen-7B-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf h31337/Qwen-7B-GGUF:Q2_K

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 h31337/Qwen-7B-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf h31337/Qwen-7B-GGUF:Q2_K

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 h31337/Qwen-7B-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf h31337/Qwen-7B-GGUF:Q2_K

Use Docker

docker model run hf.co/h31337/Qwen-7B-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use h31337/Qwen-7B-GGUF with Ollama:
```
ollama run hf.co/h31337/Qwen-7B-GGUF:Q2_K
```

Unsloth Studio

How to use h31337/Qwen-7B-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 h31337/Qwen-7B-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 h31337/Qwen-7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for h31337/Qwen-7B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use h31337/Qwen-7B-GGUF with Docker Model Runner:
```
docker model run hf.co/h31337/Qwen-7B-GGUF:Q2_K
```

Lemonade

How to use h31337/Qwen-7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull h31337/Qwen-7B-GGUF:Q2_K

Run and chat with the model

lemonade run user.Qwen-7B-GGUF-Q2_K

List all available models

lemonade list

Qwen-7B-GGUF

이 저장소는 Qwen/Qwen-7B 모델을 GGUF로 변환하고 양자화한 결과물입니다.

생성 정보

원본 모델: Qwen/Qwen-7B
원본 revision: main
샤드 병합 사용: False
기본 GGUF 타입: f16
양자화 타입: Q2_K, Q3_K, Q4_K, Q5_K, Q6_K, Q8_0

파일 목록

Qwen-7B-f16.gguf
Qwen-7B-Q2_K.gguf
Qwen-7B-Q3_K.gguf
Qwen-7B-Q4_K.gguf
Qwen-7B-Q5_K.gguf
Qwen-7B-Q6_K.gguf
Qwen-7B-Q8_0.gguf
quantizer-manifest.json

사용 예시

./llama-cli -m Qwen-7B-f16.gguf -p "안녕하세요"

참고

변환/양자화는 llama.cpp 도구 체인을 사용했습니다.
생성 과정 메타데이터는 quantizer-manifest.json 파일에 포함되어 있습니다.

Downloads last month: 62

GGUF

Model size

8B params

Architecture

qwen

Hardware compatibility

2-bit

6-bit

8-bit

16-bit

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Model tree for h31337/Qwen-7B-GGUF

Base model

Qwen/Qwen-7B

Quantized

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