Instructions to use Data-Selection/BSL-1.7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Data-Selection/BSL-1.7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Data-Selection/BSL-1.7B")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Data-Selection/BSL-1.7B") model = AutoModelForCausalLM.from_pretrained("Data-Selection/BSL-1.7B") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Data-Selection/BSL-1.7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Data-Selection/BSL-1.7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Data-Selection/BSL-1.7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Data-Selection/BSL-1.7B
- SGLang
How to use Data-Selection/BSL-1.7B 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 "Data-Selection/BSL-1.7B" \ --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": "Data-Selection/BSL-1.7B", "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 "Data-Selection/BSL-1.7B" \ --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": "Data-Selection/BSL-1.7B", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Data-Selection/BSL-1.7B with Docker Model Runner:
docker model run hf.co/Data-Selection/BSL-1.7B
metadata
datasets:
- togethercomputer/RedPajama-Data-1T
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
## BSL-1.7B
[paper](https://arxiv.org/abs/2410.07064) | [code](https://github.com/microsoft/LMOps/tree/main/data_selection)
**BSL-1.7B** is a 1.7B model with [Mistral](https://arxiv.org/abs/2310.06825) achitecture pre-trained from scratch on the CC split of [Redpajama](https://github.com/togethercomputer/RedPajama-Data).
**It is used as the baseline for [PDS-1.7B](https://huggingface.co/Data-Selection/PDS-1.7B).**
### Evaluation
PDS-selected data improves the performance of language models pre-trained from scratch and saves pre-training comptation. The improvement scales up to large model sizes.
<p align='left'>
<img src="https://cdn-uploads.huggingface.co/production/uploads/624ac662102fcdff87be51b9/6undIr37d10qD73TDiPDK.png" width="600">
</p>
### Citation
```bibtex
@article{gu2024data,
title={Data Selection via Optimal Control for Language Models},
author={Gu, Yuxian and Dong, Li and Wang, Hongning and Hao, Yaru and Dong, Qingxiu and Wei, Furu and Huang, Minlie},
journal={arXiv preprint arXiv:2410.07064},
year={2024}
}