Instructions to use havenoammo/Qwen3.6-27B-MTP-UD-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use havenoammo/Qwen3.6-27B-MTP-UD-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="havenoammo/Qwen3.6-27B-MTP-UD-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("havenoammo/Qwen3.6-27B-MTP-UD-GGUF", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use havenoammo/Qwen3.6-27B-MTP-UD-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "havenoammo/Qwen3.6-27B-MTP-UD-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "havenoammo/Qwen3.6-27B-MTP-UD-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/havenoammo/Qwen3.6-27B-MTP-UD-GGUF
- SGLang
How to use havenoammo/Qwen3.6-27B-MTP-UD-GGUF with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "havenoammo/Qwen3.6-27B-MTP-UD-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "havenoammo/Qwen3.6-27B-MTP-UD-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "havenoammo/Qwen3.6-27B-MTP-UD-GGUF" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "havenoammo/Qwen3.6-27B-MTP-UD-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio
How to use havenoammo/Qwen3.6-27B-MTP-UD-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 havenoammo/Qwen3.6-27B-MTP-UD-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 havenoammo/Qwen3.6-27B-MTP-UD-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for havenoammo/Qwen3.6-27B-MTP-UD-GGUF to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="havenoammo/Qwen3.6-27B-MTP-UD-GGUF", max_seq_length=2048, ) - Docker Model Runner
How to use havenoammo/Qwen3.6-27B-MTP-UD-GGUF with Docker Model Runner:
docker model run hf.co/havenoammo/Qwen3.6-27B-MTP-UD-GGUF
- Xet hash:
- 2ee7dcdaee82b52855ee4124ed7f47675950d4ec2ddd80cb236f0fbc98e82958
- Size of remote file:
- 457 MB
- SHA256:
- 50184d2bf0f3f9de04058120cf2a35244999b27e072eb34e0045f1d2cba4e488
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