Instructions to use herMaster/pythia70M-finetuned-on-lamini-docs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use herMaster/pythia70M-finetuned-on-lamini-docs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="herMaster/pythia70M-finetuned-on-lamini-docs")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("herMaster/pythia70M-finetuned-on-lamini-docs") model = AutoModelForCausalLM.from_pretrained("herMaster/pythia70M-finetuned-on-lamini-docs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use herMaster/pythia70M-finetuned-on-lamini-docs with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "herMaster/pythia70M-finetuned-on-lamini-docs" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "herMaster/pythia70M-finetuned-on-lamini-docs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/herMaster/pythia70M-finetuned-on-lamini-docs
- SGLang
How to use herMaster/pythia70M-finetuned-on-lamini-docs 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 "herMaster/pythia70M-finetuned-on-lamini-docs" \ --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": "herMaster/pythia70M-finetuned-on-lamini-docs", "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 "herMaster/pythia70M-finetuned-on-lamini-docs" \ --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": "herMaster/pythia70M-finetuned-on-lamini-docs", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use herMaster/pythia70M-finetuned-on-lamini-docs with Docker Model Runner:
docker model run hf.co/herMaster/pythia70M-finetuned-on-lamini-docs
Download training_args.bin from herMaster/pythia70M-finetuned-on-lamini-docs: direct link, hf CLI and curl.
- Browser
- Download file 4.09 kB
-
https://huggingface.co/herMaster/pythia70M-finetuned-on-lamini-docs/resolve/main/training_args.bin
- Command line
-
hf download hf://herMaster/pythia70M-finetuned-on-lamini-docs/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/herMaster/pythia70M-finetuned-on-lamini-docs/resolve/main/training_args.bin
4.09 kB
- Xet hash:
- 2e2f730ff8c4d7e4d8226d9cd1ab54092aea51ff0f287d807577e304f4432258
- Size of remote file:
- 4.09 kB
- SHA256:
- 4f24676dcb5c555f8707b69aecc549e0dcc88b5ee2a3f0d50b6cb6daf5fcdcd2
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