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