Instructions to use haritzpuerto/unsloth-Qwen3-4B-IF-RT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use haritzpuerto/unsloth-Qwen3-4B-IF-RT with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("/storage/ukp/shared/shared_model_weights/models--unsloth--Qwen3-4B-unsloth-bnb-4bit") model = PeftModel.from_pretrained(base_model, "haritzpuerto/unsloth-Qwen3-4B-IF-RT") - Transformers
How to use haritzpuerto/unsloth-Qwen3-4B-IF-RT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="haritzpuerto/unsloth-Qwen3-4B-IF-RT")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("haritzpuerto/unsloth-Qwen3-4B-IF-RT", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use haritzpuerto/unsloth-Qwen3-4B-IF-RT with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "haritzpuerto/unsloth-Qwen3-4B-IF-RT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "haritzpuerto/unsloth-Qwen3-4B-IF-RT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/haritzpuerto/unsloth-Qwen3-4B-IF-RT
- SGLang
How to use haritzpuerto/unsloth-Qwen3-4B-IF-RT 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 "haritzpuerto/unsloth-Qwen3-4B-IF-RT" \ --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": "haritzpuerto/unsloth-Qwen3-4B-IF-RT", "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 "haritzpuerto/unsloth-Qwen3-4B-IF-RT" \ --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": "haritzpuerto/unsloth-Qwen3-4B-IF-RT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Studio
How to use haritzpuerto/unsloth-Qwen3-4B-IF-RT 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 haritzpuerto/unsloth-Qwen3-4B-IF-RT 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 haritzpuerto/unsloth-Qwen3-4B-IF-RT to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for haritzpuerto/unsloth-Qwen3-4B-IF-RT to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="haritzpuerto/unsloth-Qwen3-4B-IF-RT", max_seq_length=2048, ) - Docker Model Runner
How to use haritzpuerto/unsloth-Qwen3-4B-IF-RT with Docker Model Runner:
docker model run hf.co/haritzpuerto/unsloth-Qwen3-4B-IF-RT
Ctrl+K