Instructions to use QuantFactory/CodeQwen1.5-7B-Chat-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/CodeQwen1.5-7B-Chat-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/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/CodeQwen1.5-7B-Chat-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/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/CodeQwen1.5-7B-Chat-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/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/CodeQwen1.5-7B-Chat-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/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/CodeQwen1.5-7B-Chat-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/CodeQwen1.5-7B-Chat-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/CodeQwen1.5-7B-Chat-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M

Ollama
How to use QuantFactory/CodeQwen1.5-7B-Chat-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/CodeQwen1.5-7B-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 QuantFactory/CodeQwen1.5-7B-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 QuantFactory/CodeQwen1.5-7B-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 QuantFactory/CodeQwen1.5-7B-Chat-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/CodeQwen1.5-7B-Chat-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/CodeQwen1.5-7B-Chat-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/CodeQwen1.5-7B-Chat-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.CodeQwen1.5-7B-Chat-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

CodeQwen1.5-7B-Chat-GGUF

This is quantized version of CodeQwen1.5-7B-Chat created using llama.cpp

Model Description

CodeQwen1.5 is the Code-Specific version of Qwen1.5. It is a transformer-based decoder-only language model pretrained on a large amount of data of codes. CodeQwen1.5 is based on Qwen1.5, a language model series including decoder language models of different model sizes. It is trained on 3 trillion tokens of data of codes, and it includes group query attention (GQA) for efficient inference.

Strong code generation capabilities and competitve performance across a series of benchmarks;
Supporting long context understanding and generation with the context length of 64K tokens;
Supporting 92 coding languages
Excellent performance in text-to-SQL, bug fix, etc.

For more details, please refer to Qwen blog post and GitHub repo.

Requirements

The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install transformers>=4.37.0, or you might encounter the following error:

KeyError: 'qwen2'.

Tips

If you encounter code switching or other bad cases, we advise you to use our provided hyper-parameters in generation_config.json.

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

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Model tree for QuantFactory/CodeQwen1.5-7B-Chat-GGUF

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