Instructions to use luffycodes/tutorbot-spock-bio-llama-diff with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use luffycodes/tutorbot-spock-bio-llama-diff with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="luffycodes/tutorbot-spock-bio-llama-diff")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("luffycodes/tutorbot-spock-bio-llama-diff") model = AutoModelForCausalLM.from_pretrained("luffycodes/tutorbot-spock-bio-llama-diff") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use luffycodes/tutorbot-spock-bio-llama-diff with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "luffycodes/tutorbot-spock-bio-llama-diff" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "luffycodes/tutorbot-spock-bio-llama-diff", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/luffycodes/tutorbot-spock-bio-llama-diff
- SGLang
How to use luffycodes/tutorbot-spock-bio-llama-diff 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 "luffycodes/tutorbot-spock-bio-llama-diff" \ --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": "luffycodes/tutorbot-spock-bio-llama-diff", "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 "luffycodes/tutorbot-spock-bio-llama-diff" \ --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": "luffycodes/tutorbot-spock-bio-llama-diff", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use luffycodes/tutorbot-spock-bio-llama-diff with Docker Model Runner:
docker model run hf.co/luffycodes/tutorbot-spock-bio-llama-diff
NOTE: This "diff model" cannot be used directly.
Users have to apply it on top of the original LLaMA weights to get actual Spock weights.
Please find the instructions here: https://github.com/luffycodes/Tutorbot-Spock-Bio.
Spock Model Card
Github details
Please checkout the repo: https://github.com/luffycodes/Tutorbot-Spock-Bio.
Model details
Model type: Spock is an open-source educational tutoring chatbot trained by fine-tuning LLaMA and Vicuna model on synthetic student-tutorbot conversations generated using a specialized prompt.
Model date: Spock was trained between April 2023 and May 2023.
Organizations developing the model: The Spock team with members from Rice University and OpenStax.
Training dataset
700 conversations generated using a specialized prompt from GPT-4. Dataset link: https://huggingface.co/datasets/luffycodes/Tutorbot-Spock-Bio-Dataset
Paper or resources for more information: https://arxiv.org/abs/2305.13272
Code or resources for more information: https://github.com/luffycodes/Tutorbot-Spock-Bio
License: Apache License 2.0
Where to send questions or comments about the model: Shashank Sonkar (ss164@rice.edu)
If you use this work, please cite: CLASS Meet SPOCK: An Education Tutoring Chatbot based on Learning Science Principles https://arxiv.org/abs/2305.13272
@misc{sonkar2023class,
title={CLASS Meet SPOCK: An Education Tutoring Chatbot based on Learning Science Principles},
author={Shashank Sonkar and Lucy Liu and Debshila Basu Mallick and Richard G. Baraniuk},
year={2023},
eprint={2305.13272},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
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