Instructions to use VALOR0316/sn92-ra-9b-a with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use VALOR0316/sn92-ra-9b-a with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="VALOR0316/sn92-ra-9b-a")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("VALOR0316/sn92-ra-9b-a") model = AutoModelForCausalLM.from_pretrained("VALOR0316/sn92-ra-9b-a", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use VALOR0316/sn92-ra-9b-a with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "VALOR0316/sn92-ra-9b-a" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "VALOR0316/sn92-ra-9b-a", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/VALOR0316/sn92-ra-9b-a
- SGLang
How to use VALOR0316/sn92-ra-9b-a 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 "VALOR0316/sn92-ra-9b-a" \ --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": "VALOR0316/sn92-ra-9b-a", "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 "VALOR0316/sn92-ra-9b-a" \ --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": "VALOR0316/sn92-ra-9b-a", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use VALOR0316/sn92-ra-9b-a with Docker Model Runner:
docker model run hf.co/VALOR0316/sn92-ra-9b-a
Download tokenizer.json from VALOR0316/sn92-ra-9b-a: direct link, hf CLI and curl.
- Browser
- Download file 20 MB
-
https://huggingface.co/VALOR0316/sn92-ra-9b-a/resolve/main/tokenizer.json
- Command line
-
hf download hf://VALOR0316/sn92-ra-9b-a/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/VALOR0316/sn92-ra-9b-a/resolve/main/tokenizer.json
20 MB
- Xet hash:
- 777bcaa63794fa47b8f53680be9d6d176f1fcbd7ba03cdc6c3bae2b3d76b323f
- Size of remote file:
- 20 MB
- SHA256:
- 06b9509352d2af50381ab2247e083b80d32d5c0aba91c272ca9ff729b6a0e523
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