Instructions to use codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF
- SGLang
How to use codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-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 "codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-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": "codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-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 "codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-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": "codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio
How to use codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-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 codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-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 codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF", max_seq_length=2048, ) - Docker Model Runner
How to use codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF
codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF
This model was converted to GGUF format from MuXodious/Qwen3.5-4B-MiniFantasy-MTP using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
brew install llama.cpp
Invoke the llama.cpp server or the CLI.
CLI:
llama-cli --hf-repo codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF --hf-file qwen3.5-4b-minifantasy-mtp-q4_k_m.gguf -p "The meaning to life and the universe is"
Server:
llama-server --hf-repo codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF --hf-file qwen3.5-4b-minifantasy-mtp-q4_k_m.gguf -c 2048
Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
git clone https://github.com/ggerganov/llama.cpp
Step 2: Move into the llama.cpp folder and build it with LLAMA_CURL=1 flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
cd llama.cpp && LLAMA_CURL=1 make
Step 3: Run inference through the main binary.
./llama-cli --hf-repo codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF --hf-file qwen3.5-4b-minifantasy-mtp-q4_k_m.gguf -p "The meaning to life and the universe is"
or
./llama-server --hf-repo codemichaeld/Qwen3.5-4B-MiniFantasy-MTP-Q4_K_M-GGUF --hf-file qwen3.5-4b-minifantasy-mtp-q4_k_m.gguf -c 2048
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