Instructions to use allenai/open-instruct-self-instruct-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use allenai/open-instruct-self-instruct-13b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="allenai/open-instruct-self-instruct-13b")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("allenai/open-instruct-self-instruct-13b") model = AutoModelForCausalLM.from_pretrained("allenai/open-instruct-self-instruct-13b", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use allenai/open-instruct-self-instruct-13b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "allenai/open-instruct-self-instruct-13b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "allenai/open-instruct-self-instruct-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/allenai/open-instruct-self-instruct-13b
- SGLang
How to use allenai/open-instruct-self-instruct-13b 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 "allenai/open-instruct-self-instruct-13b" \ --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": "allenai/open-instruct-self-instruct-13b", "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 "allenai/open-instruct-self-instruct-13b" \ --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": "allenai/open-instruct-self-instruct-13b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use allenai/open-instruct-self-instruct-13b with Docker Model Runner:
docker model run hf.co/allenai/open-instruct-self-instruct-13b
Download pytorch_model-00005-of-00006.bin from allenai/open-instruct-self-instruct-13b: direct link, hf CLI and curl.
- Browser
- Download file 9.87 GB
-
https://huggingface.co/allenai/open-instruct-self-instruct-13b/resolve/main/pytorch_model-00005-of-00006.bin
- Command line
-
hf download hf://allenai/open-instruct-self-instruct-13b/pytorch_model-00005-of-00006.bin
-
curl -L -o pytorch_model-00005-of-00006.bin https://huggingface.co/allenai/open-instruct-self-instruct-13b/resolve/main/pytorch_model-00005-of-00006.bin
9.87 GB
- Xet hash:
- bcbedaea7fb4679b5d553e6d34bf8bd67c63a9ef678c0147f061b8fcde5a3cdb
- Size of remote file:
- 9.87 GB
- SHA256:
- ab1d87eea2bb54f3c51977bcabba246bc206162786785e871c2e5df5ff7a7071
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