Instructions to use RANITBAG/output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use RANITBAG/output with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="RANITBAG/output")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("RANITBAG/output") model = AutoModelForCausalLM.from_pretrained("RANITBAG/output", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use RANITBAG/output with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "RANITBAG/output" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "RANITBAG/output", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/RANITBAG/output
- SGLang
How to use RANITBAG/output 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 "RANITBAG/output" \ --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": "RANITBAG/output", "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 "RANITBAG/output" \ --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": "RANITBAG/output", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use RANITBAG/output with Docker Model Runner:
docker model run hf.co/RANITBAG/output
Download pytorch_model.bin from RANITBAG/output: direct link, hf CLI and curl.
- Browser
- Download file 498 MB
-
https://huggingface.co/RANITBAG/output/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://RANITBAG/output/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/RANITBAG/output/resolve/main/pytorch_model.bin
498 MB
- Xet hash:
- 1862b142e583f2dd2d6113b137243c89d77bbc946b31d7dc6d4eabc3827ec166
- Size of remote file:
- 498 MB
- SHA256:
- caf666e40631915319733812bdef9e0d9c19200dc95a81a18ea487589b4a042a
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