- Libraries
- llama-cpp-python
How to use reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF",
filename="codeqwen1.5-7b-chat-q3_k_s.gguf",
)
llm.create_chat_completion(
messages = [
{
"role": "user",
"content": "What is the capital of France?"
}
]
) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF with llama.cpp:
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF:Q3_K_S
# Run inference directly in the terminal:
llama-cli -hf reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF:Q3_K_S
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF:Q3_K_S
# Run inference directly in the terminal:
llama-cli -hf reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF:Q3_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 reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF:Q3_K_S
# Run inference directly in the terminal:
./llama-cli -hf reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF:Q3_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 reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF:Q3_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF:Q3_K_S
Use Docker
docker model run hf.co/reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF:Q3_K_S
- LM Studio
- Jan
- vLLM
How to use reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-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": "reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF",
"messages": [
{
"role": "user",
"content": "What is the capital of France?"
}
]
}'Use Docker
docker model run hf.co/reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF:Q3_K_S
- Ollama
How to use reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF with Ollama:
ollama run hf.co/reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF:Q3_K_S
- Unsloth Studio
How to use reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-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 reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-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 reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF to start chatting
- Docker Model Runner
How to use reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF with Docker Model Runner:
docker model run hf.co/reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF:Q3_K_S
- Lemonade
How to use reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/
lemonade pull reach-vb/CodeQwen1.5-7B-Chat-Q3_K_S-GGUF:Q3_K_S
Run and chat with the model
lemonade run user.CodeQwen1.5-7B-Chat-Q3_K_S-GGUF-Q3_K_S
List all available models
lemonade list
