Instructions to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound", filename="Qwen3.5-397B-A17B-Q2_K_S-00001-of-00004.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S # Run inference directly in the terminal: llama-cli -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S # Run inference directly in the terminal: llama-cli -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_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 Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S # Run inference directly in the terminal: ./llama-cli -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_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 Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S # Run inference directly in the terminal: ./build/bin/llama-cli -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
Use Docker
docker model run hf.co/Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
- LM Studio
- Jan
- Ollama
How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with Ollama:
ollama run hf.co/Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
- Unsloth Studio
How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound 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 Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound 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 Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound to start chatting
- Pi
How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
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": "Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
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 Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
Run Hermes
hermes
- Docker Model Runner
How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with Docker Model Runner:
docker model run hf.co/Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
- Lemonade
How to use Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Intel/Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound:Q2_K_S
Run and chat with the model
lemonade run user.Qwen3.5-397B-A17B-gguf-q2ks-mixed-AutoRound-Q2_K_S
List all available models
lemonade list
Model Details
This model is a mixed gguf q2ks format of Qwen/Qwen3.5-397B-A17B generated by intel/auto-round algorithm. Embedding layer and lm-head layer are fallback to 8 bits and non expert layers are fallback to 4 bits. Please refer to Section Generate the model for more details.
Please follow the license of the original model.
How To Use
Llama.cpp inference.
> wget https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg
> ./llama-cli -m Qwen3.5-397B-A17B-w2g16/Qwen3.5-397B-A17B-Q2_K_S-00001-of-00004.gguf -mm Qwen3.5-397B-A17B-w2g16/mmproj-model.gguf
> /image demo.jpeg
Loaded media from 'demo.jpeg'
> Describe this image.
[Start thinking]
This image shows a young woman and a large, light-colored dog sitting together on a sandy beach, likely at sunset or sunrise given the warm, golden lighting. The woman, positioned on the right, is smiling and looking down at the dog, who is sitting up on its hindquarters and gently touching her hand with its paw. Both appear relaxed and affectionate. The woman wears a plaid shirt and dark pants, while the dog has a harness on. The background features a calm ocean with gentle waves and a bright, hazy sky, creating a serene and peaceful atmosphere. The lighting casts a soft glow over the scene, highlighting the bond between the woman and her dog.
[End thinking]
This image shows a young woman and a large, light-colored dog sitting together on a sandy beach, likely at sunset or sunrise given the warm, golden lighting. The woman, positioned on the right, is smiling and looking down at the dog, who is sitting up on its hindquarters and gently touching her hand with its paw. Both appear relaxed and affectionate. The woman wears a plaid shirt and dark pants, while the dog has a harness on. The background features a calm ocean with gentle waves and a bright, hazy sky, creating a serene and peaceful atmosphere. The lighting casts a soft glow over the scene, highlighting the bond between the woman and her dog.
Generate the model
Here is the sample command to reproduce the model
auto_round --model Qwen/Qwen3.5-397B-A17B --iters 0 --output_dir tmp_autoround --scheme gguf:q2_k_mixed
Ethical Considerations and Limitations
The model can produce factually incorrect output, and should not be relied on to produce factually accurate information. Because of the limitations of the pretrained model and the finetuning datasets, it is possible that this model could generate lewd, biased or otherwise offensive outputs.
Therefore, before deploying any applications of the model, developers should perform safety testing.
Caveats and Recommendations
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Here are a couple of useful links to learn more about Intel's AI software:
- Intel Neural Compressor link
Disclaimer
The license on this model does not constitute legal advice. We are not responsible for the actions of third parties who use this model. Please consult an attorney before using this model for commercial purposes.
Cite
@article{cheng2023optimize, title={Optimize weight rounding via signed gradient descent for the quantization of llms}, author={Cheng, Wenhua and Zhang, Weiwei and Shen, Haihao and Cai, Yiyang and He, Xin and Lv, Kaokao and Liu, Yi}, journal={arXiv preprint arXiv:2309.05516}, year={2023} }
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