Instructions to use Tverous/sft-trl with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Tverous/sft-trl with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Tverous/sft-trl")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Tverous/sft-trl") model = AutoModelForCausalLM.from_pretrained("Tverous/sft-trl") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Tverous/sft-trl with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Tverous/sft-trl" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Tverous/sft-trl", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Tverous/sft-trl
- SGLang
How to use Tverous/sft-trl 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 "Tverous/sft-trl" \ --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": "Tverous/sft-trl", "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 "Tverous/sft-trl" \ --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": "Tverous/sft-trl", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Tverous/sft-trl with Docker Model Runner:
docker model run hf.co/Tverous/sft-trl
Librarian Bot: Update Hugging Face dataset ID
This pull request updates the ID of the dataset used to train the model to the new Hub identifier facebook/anli (which has been migrated moved from anli). We have been working to migrate datasets to their own repositories on the Hub, and this is part of that effort.
Updating the dataset ID in the model card will ensure that the model card is correctly linked to the dataset repository on the Hub. This will also make it easier for people to find your model via the training data used to create it.
This PR comes courtesy of Librarian Bot. If you have any feedback, queries, or need assistance, please don't hesitate to reach out to @davanstrien.