Instructions to use grapevine-AI/QwQ-32B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use grapevine-AI/QwQ-32B-GGUF with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="grapevine-AI/QwQ-32B-GGUF", filename="QwQ-32B-BF16-00001-of-00002.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- llama.cpp
How to use grapevine-AI/QwQ-32B-GGUF with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf grapevine-AI/QwQ-32B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf grapevine-AI/QwQ-32B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf grapevine-AI/QwQ-32B-GGUF:Q4_K_M # Run inference directly in the terminal: llama-cli -hf grapevine-AI/QwQ-32B-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 grapevine-AI/QwQ-32B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf grapevine-AI/QwQ-32B-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 grapevine-AI/QwQ-32B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf grapevine-AI/QwQ-32B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/grapevine-AI/QwQ-32B-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use grapevine-AI/QwQ-32B-GGUF with Ollama:
ollama run hf.co/grapevine-AI/QwQ-32B-GGUF:Q4_K_M
- Unsloth Studio new
How to use grapevine-AI/QwQ-32B-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 grapevine-AI/QwQ-32B-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 grapevine-AI/QwQ-32B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for grapevine-AI/QwQ-32B-GGUF to start chatting
- Pi new
How to use grapevine-AI/QwQ-32B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf grapevine-AI/QwQ-32B-GGUF:Q4_K_M
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": "grapevine-AI/QwQ-32B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use grapevine-AI/QwQ-32B-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 grapevine-AI/QwQ-32B-GGUF:Q4_K_M
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 grapevine-AI/QwQ-32B-GGUF:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use grapevine-AI/QwQ-32B-GGUF with Docker Model Runner:
docker model run hf.co/grapevine-AI/QwQ-32B-GGUF:Q4_K_M
- Lemonade
How to use grapevine-AI/QwQ-32B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull grapevine-AI/QwQ-32B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.QwQ-32B-GGUF-Q4_K_M
List all available models
lemonade list
What is this?
Alibaba CloudのReasoningモデル、QwQ-32Bを日本語imatrixで量子化したものです。
imatrix dataset
日本語能力を重視し、日本語が多量に含まれるTFMC/imatrix-dataset-for-japanese-llmデータセットを使用しました。
Chat template
<|im_start|>system
ここにSystem Promptを書きます。<|im_end|>
<|im_start|>user
ここにMessageを書きます。<|im_end|>
<|im_start|>assistant
Quants
各クオンツとそのベンチマークスコア(Gemini 2.0 Flash採点によるElyza_tasks 100)をまとめておきます。
| クオンツ | スコア | コメント |
|---|---|---|
| Q8_0 | 4.28 | |
| Q6_K | 4.38 | |
| Q5_K_M | 4.48 | |
| Q4_K_M | 4.43 | |
| IQ4_XS | 4.4 |
Environment
Windows版llama.cpp-b5074および同時リリースのconvert-hf-to-gguf.pyを使用して量子化作業を実施しました。
License
Apache 2.0
Developer
Alibaba Cloud
- Downloads last month
- 12
Hardware compatibility
Log In to add your hardware
4-bit
5-bit
6-bit
8-bit
16-bit
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support