Text Generation
Transformers
Safetensors
qwen3
Generated from Trainer
trl
unsloth
sft
text-generation-inference
Instructions to use alwaysgood/qwen3-it with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use alwaysgood/qwen3-it with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="alwaysgood/qwen3-it")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("alwaysgood/qwen3-it") model = AutoModelForCausalLM.from_pretrained("alwaysgood/qwen3-it", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use alwaysgood/qwen3-it with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "alwaysgood/qwen3-it" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "alwaysgood/qwen3-it", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/alwaysgood/qwen3-it
- SGLang
How to use alwaysgood/qwen3-it 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 "alwaysgood/qwen3-it" \ --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": "alwaysgood/qwen3-it", "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 "alwaysgood/qwen3-it" \ --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": "alwaysgood/qwen3-it", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Unsloth Desktop
- Docker Model Runner
How to use alwaysgood/qwen3-it with Docker Model Runner:
docker model run hf.co/alwaysgood/qwen3-it
Download tokenizer.json from alwaysgood/qwen3-it: direct link, hf CLI and curl.
- Browser
- Download file 11.4 MB
-
https://huggingface.co/alwaysgood/qwen3-it/resolve/main/tokenizer.json
- Command line
-
hf download hf://alwaysgood/qwen3-it/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/alwaysgood/qwen3-it/resolve/main/tokenizer.json
11.4 MB
- Xet hash:
- a3e80a0c7329b91f5fce702283fd80864a9012820ce5d04b832359d0955ba1f0
- Size of remote file:
- 11.4 MB
- SHA256:
- 45c4ffda6666cf6d75d0b1f961f25964e2a52a62e78aaecb2f458e9ba9824112
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.