Instructions to use unsloth/Qwen3.5-4B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use unsloth/Qwen3.5-4B-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="unsloth/Qwen3.5-4B-Base")# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("unsloth/Qwen3.5-4B-Base") model = AutoModelForImageTextToText.from_pretrained("unsloth/Qwen3.5-4B-Base") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use unsloth/Qwen3.5-4B-Base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "unsloth/Qwen3.5-4B-Base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "unsloth/Qwen3.5-4B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/unsloth/Qwen3.5-4B-Base
- SGLang
How to use unsloth/Qwen3.5-4B-Base 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 "unsloth/Qwen3.5-4B-Base" \ --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": "unsloth/Qwen3.5-4B-Base", "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 "unsloth/Qwen3.5-4B-Base" \ --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": "unsloth/Qwen3.5-4B-Base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio new
How to use unsloth/Qwen3.5-4B-Base 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 unsloth/Qwen3.5-4B-Base 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 unsloth/Qwen3.5-4B-Base to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unsloth/Qwen3.5-4B-Base to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="unsloth/Qwen3.5-4B-Base", max_seq_length=2048, ) - Docker Model Runner
How to use unsloth/Qwen3.5-4B-Base with Docker Model Runner:
docker model run hf.co/unsloth/Qwen3.5-4B-Base
- Xet hash:
- 458bcbf483ed805b4297af928f717e64bd00c633a07be5fae5717cacbd48e2ef
- Size of remote file:
- 20 MB
- SHA256:
- 87a7830d63fcf43bf241c3c5242e96e62dd3fdc29224ca26fed8ea333db72de4
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