Instructions to use Data-Selection/PDS-470M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Data-Selection/PDS-470M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Data-Selection/PDS-470M")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Data-Selection/PDS-470M") model = AutoModelForCausalLM.from_pretrained("Data-Selection/PDS-470M") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Data-Selection/PDS-470M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Data-Selection/PDS-470M" # 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/PDS-470M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Data-Selection/PDS-470M
- SGLang
How to use Data-Selection/PDS-470M 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/PDS-470M" \ --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/PDS-470M", "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/PDS-470M" \ --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/PDS-470M", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Data-Selection/PDS-470M with Docker Model Runner:
docker model run hf.co/Data-Selection/PDS-470M
Clarify Model Description and Add Project Page Link
Browse filesThis PR clarifies the model description to emphasize that PDS-470M is pretrained from scratch using the PDS framework. It also adds a link to the project page.
README.md
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---
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license: apache-2.0
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datasets:
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- togethercomputer/RedPajama-Data-1T
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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---
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## PDS-470M
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[paper](https://arxiv.org/abs/2410.07064) | [code](https://github.com/microsoft/LMOps/tree/main/data_selection)
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**PDS-470M** is a 470M
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The PDS framework is based on the [Pontryagin's maximum principle](https://en.wikipedia.org/wiki/Pontryagin%27s_maximum_principle#:~:text=Pontryagin's%20maximum%20principle%20is%20used,the%20state%20or%20input%20controls.) for optimal pre-training data selection,
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Please refer to our [paper](https://arxiv.org/abs/2410.07064) for more details.
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journal={arXiv preprint arXiv:2410.07064},
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year={2024}
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}
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```
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---
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datasets:
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- togethercomputer/RedPajama-Data-1T
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language:
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- en
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library_name: transformers
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license: apache-2.0
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pipeline_tag: text-generation
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---
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## PDS-470M
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[paper](https://arxiv.org/abs/2410.07064) | [code](https://github.com/microsoft/LMOps/tree/main/data_selection) | [project page](https://github.com/microsoft/LMOps/tree/main/data_selection)
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**PDS-470M** is a 470M parameter Mistral architecture model **pretrained from scratch** using the PDS framework on data selected from the CC split of [Redpajama](https://github.com/togethercomputer/RedPajama-Data).
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The PDS framework is based on the [Pontryagin's maximum principle](https://en.wikipedia.org/wiki/Pontryagin%27s_maximum_principle#:~:text=Pontryagin's%20maximum%20principle%20is%20used,the%20state%20or%20input%20controls.) for optimal pre-training data selection, offering strong theoretical support and scalability for training large language models.
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Please refer to our [paper](https://arxiv.org/abs/2410.07064) for more details.
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journal={arXiv preprint arXiv:2410.07064},
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year={2024}
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}
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```
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