Instructions to use MolecularReality/tinyshakespeare-13m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MolecularReality/tinyshakespeare-13m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MolecularReality/tinyshakespeare-13m")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("MolecularReality/tinyshakespeare-13m") model = AutoModelForCausalLM.from_pretrained("MolecularReality/tinyshakespeare-13m", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MolecularReality/tinyshakespeare-13m with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MolecularReality/tinyshakespeare-13m" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MolecularReality/tinyshakespeare-13m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MolecularReality/tinyshakespeare-13m
- SGLang
How to use MolecularReality/tinyshakespeare-13m 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 "MolecularReality/tinyshakespeare-13m" \ --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": "MolecularReality/tinyshakespeare-13m", "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 "MolecularReality/tinyshakespeare-13m" \ --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": "MolecularReality/tinyshakespeare-13m", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MolecularReality/tinyshakespeare-13m with Docker Model Runner:
docker model run hf.co/MolecularReality/tinyshakespeare-13m
Download training_args.bin from MolecularReality/tinyshakespeare-13m: direct link, hf CLI and curl.
- Browser
- Download file 5.5 kB
-
https://huggingface.co/MolecularReality/tinyshakespeare-13m/resolve/main/training_args.bin
- Command line
-
hf download hf://MolecularReality/tinyshakespeare-13m/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/MolecularReality/tinyshakespeare-13m/resolve/main/training_args.bin
5.5 kB
- Xet hash:
- b07f8dfa243c03e612ad6d71d13f4ecd5d681e2116434efe8cf8fbd33f4f67da
- Size of remote file:
- 5.5 kB
- SHA256:
- 4c805d0ede8c0c3fd435e52cbe9680246e5152c031e34803a66e1718d5452b89
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