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", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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
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license: apache-2.0
datasets:
- OpenAssistant/oasst1
language:
- en
---
## 🐋 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) .
## 📜 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}
}
``` |