Instructions to use reecdev/Qwen3.5-2B-LQ8-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use reecdev/Qwen3.5-2B-LQ8-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="reecdev/Qwen3.5-2B-LQ8-GGUF", filename="model-LQ8.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 reecdev/Qwen3.5-2B-LQ8-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf reecdev/Qwen3.5-2B-LQ8-GGUF # Run inference directly in the terminal: llama-cli -hf reecdev/Qwen3.5-2B-LQ8-GGUF
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf reecdev/Qwen3.5-2B-LQ8-GGUF # Run inference directly in the terminal: llama-cli -hf reecdev/Qwen3.5-2B-LQ8-GGUF
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 reecdev/Qwen3.5-2B-LQ8-GGUF # Run inference directly in the terminal: ./llama-cli -hf reecdev/Qwen3.5-2B-LQ8-GGUF
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 reecdev/Qwen3.5-2B-LQ8-GGUF # Run inference directly in the terminal: ./build/bin/llama-cli -hf reecdev/Qwen3.5-2B-LQ8-GGUF
Use Docker
docker model run hf.co/reecdev/Qwen3.5-2B-LQ8-GGUF
- LM Studio
- Jan
- vLLM
How to use reecdev/Qwen3.5-2B-LQ8-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "reecdev/Qwen3.5-2B-LQ8-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": "reecdev/Qwen3.5-2B-LQ8-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/reecdev/Qwen3.5-2B-LQ8-GGUF
- Ollama
How to use reecdev/Qwen3.5-2B-LQ8-GGUF with Ollama:
ollama run hf.co/reecdev/Qwen3.5-2B-LQ8-GGUF
- Unsloth Studio
How to use reecdev/Qwen3.5-2B-LQ8-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 reecdev/Qwen3.5-2B-LQ8-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 reecdev/Qwen3.5-2B-LQ8-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for reecdev/Qwen3.5-2B-LQ8-GGUF to start chatting
- Pi
How to use reecdev/Qwen3.5-2B-LQ8-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf reecdev/Qwen3.5-2B-LQ8-GGUF
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "reecdev/Qwen3.5-2B-LQ8-GGUF" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use reecdev/Qwen3.5-2B-LQ8-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf reecdev/Qwen3.5-2B-LQ8-GGUF
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default reecdev/Qwen3.5-2B-LQ8-GGUF
Run Hermes
hermes
- Atomic Chat new
- Docker Model Runner
How to use reecdev/Qwen3.5-2B-LQ8-GGUF with Docker Model Runner:
docker model run hf.co/reecdev/Qwen3.5-2B-LQ8-GGUF
- Lemonade
How to use reecdev/Qwen3.5-2B-LQ8-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull reecdev/Qwen3.5-2B-LQ8-GGUF
Run and chat with the model
lemonade run user.Qwen3.5-2B-LQ8-GGUF-{{QUANT_TAG}}List all available models
lemonade list
This repository contains model weights for the unofficial LQ8 quantizations of Qwen3.5 2B.
LQ8 is an experimental quantization technique that is still in early beta, designed to provide fp16-level quality with the same or lower memory footprint as Q8_0.
LQ8 is currently compatible with llama.cpp and Ollama out of the box. Please create a discussion if you find a bug.
| File Name | Quant Type | Bit Depth | Size | Download Link |
|---|---|---|---|---|
model-LQ8.gguf |
LQ8 | ~8 bpw | 1.82 GB | ๐ฅ Download LQ8 |
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We're not able to determine the quantization variants.