Text Generation
Transformers
PyTorch
qwen3_5
image-text-to-text
bf16
clef
full-precision
vllm
xinference
Instructions to use rty813/clef-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rty813/clef-bf16 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="rty813/clef-bf16")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("rty813/clef-bf16") model = AutoModelForMultimodalLM.from_pretrained("rty813/clef-bf16", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use rty813/clef-bf16 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "rty813/clef-bf16" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "rty813/clef-bf16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/rty813/clef-bf16
- SGLang
How to use rty813/clef-bf16 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 "rty813/clef-bf16" \ --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": "rty813/clef-bf16", "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 "rty813/clef-bf16" \ --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": "rty813/clef-bf16", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use rty813/clef-bf16 with Docker Model Runner:
docker model run hf.co/rty813/clef-bf16
Download pytorch_model.bin from rty813/clef-bf16: direct link, hf CLI and curl.
- Browser
- Download file 10.2 kB
-
https://huggingface.co/rty813/clef-bf16/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://rty813/clef-bf16/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/rty813/clef-bf16/resolve/main/pytorch_model.bin
10.2 kB
- Xet hash:
- ee78613bffe91ac70cb848588ad396758354277e1c2edcc734a7a4c183fe8fd7
- Size of remote file:
- 10.2 kB
- SHA256:
- fbe2470610f3c66d3c5cd301c56ac3c70b7fdacf6cec83433e3c773f4d920aa2
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