Instructions to use Lajonbot/pythia-160m-33k-steps-self-instruct-polish with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lajonbot/pythia-160m-33k-steps-self-instruct-polish with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Lajonbot/pythia-160m-33k-steps-self-instruct-polish")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Lajonbot/pythia-160m-33k-steps-self-instruct-polish") model = AutoModelForCausalLM.from_pretrained("Lajonbot/pythia-160m-33k-steps-self-instruct-polish") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Lajonbot/pythia-160m-33k-steps-self-instruct-polish with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Lajonbot/pythia-160m-33k-steps-self-instruct-polish" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Lajonbot/pythia-160m-33k-steps-self-instruct-polish", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Lajonbot/pythia-160m-33k-steps-self-instruct-polish
- SGLang
How to use Lajonbot/pythia-160m-33k-steps-self-instruct-polish 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 "Lajonbot/pythia-160m-33k-steps-self-instruct-polish" \ --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": "Lajonbot/pythia-160m-33k-steps-self-instruct-polish", "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 "Lajonbot/pythia-160m-33k-steps-self-instruct-polish" \ --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": "Lajonbot/pythia-160m-33k-steps-self-instruct-polish", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Lajonbot/pythia-160m-33k-steps-self-instruct-polish with Docker Model Runner:
docker model run hf.co/Lajonbot/pythia-160m-33k-steps-self-instruct-polish
- Xet hash:
- 12fdabeba6d4a60881ed6faefd69a6d24454bb268b63ce5db874378e3a7bf9a0
- Size of remote file:
- 700 MB
- SHA256:
- 1585f84ac5754d723e72eac24b48cf9de3739e9d3108144f1a15a45057e8723e
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