Instructions to use Spico/Humback-M0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Spico/Humback-M0 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Spico/Humback-M0")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Spico/Humback-M0") model = AutoModelForCausalLM.from_pretrained("Spico/Humback-M0") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Spico/Humback-M0 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Spico/Humback-M0" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Spico/Humback-M0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Spico/Humback-M0
- SGLang
How to use Spico/Humback-M0 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 "Spico/Humback-M0" \ --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": "Spico/Humback-M0", "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 "Spico/Humback-M0" \ --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": "Spico/Humback-M0", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Spico/Humback-M0 with Docker Model Runner:
docker model run hf.co/Spico/Humback-M0
| license: apache-2.0 | |
| datasets: | |
| - OpenAssistant/oasst1 | |
| language: | |
| - en | |
| ## ๐ Humback | |
| The proposed Humback is a novel framework that can augment the instruction data for supervised fine-tuning with high quality. | |
| This is a SFT (supervised fine-tuning) model $M_{0}$ for [Humback](https://arxiv.org/pdf/2308.06259.pdf) reproduction. | |
| This model is trained on the seed data. | |
| The seed data is a sampled dataset from [oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1). | |
| You may find more details and usage examples in [Spico197/Humback](https://github.com/Spico197/Humback) . | |
| ## ๐ Reference | |
| ```bibtex | |
| @misc{li2023selfalignment, | |
| title={Self-Alignment with Instruction Backtranslation}, | |
| author={Xian Li and Ping Yu and Chunting Zhou and Timo Schick and Luke Zettlemoyer and Omer Levy and Jason Weston and Mike Lewis}, | |
| year={2023}, | |
| eprint={2308.06259}, | |
| archivePrefix={arXiv}, | |
| primaryClass={cs.CL} | |
| } | |
| ``` |