GitHub - at-aaims/forge (original) (raw)

FORGE: Pre-training Open Foundation Models for Science

Contributions

• Best practices for the end-to-end pre-training LLMs for science on HPC
• Open releases of a set of foundation models (and domain datasets) on scientific corpus
• Propose scientific related down-stream benchmarks for evaluating LLMs for science
• Provide heuristics for large-batch training and communication requirment
• Evaluate current practices and share our observations

FORGE models

Data sources

• CORE: https://core.ac.uk/documentation/dataset (core_2020-12-20)
• MAG: https://www.microsoft.com/en-us/research/project/open-academic-graph/ (v2-1)
• Aminer: https://www.microsoft.com/en-us/research/project/open-academic-graph/ (v2-1)
• Arixv: https://huggingface.co/datasets/arxiv_dataset
• Scopus: 6M abstracts for the DOIs extracted via Scopus API

Example usages

• Forge models can be used using standard Hugging Face API

from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast model = GPTNeoXForCausalLM.from_pretrained("path_to_forge_model") tokenizer = GPTNeoXTokenizerFast.from_pretrained("path_to_forge_model") prompt = "high entropy alloy applications include" input_ids = tokenizer(prompt, return_tensors="pt").input_ids gen_tokens = model.generate(input_ids, do_sample=True, temperature=0.7, max_length=100) gen_text = tokenizer.batch_decode(gen_tokens)[0] print(gen_text)

high entropy alloy applications include high strength steels, alloys, composites, as well some metal alloys. In recent years, there has been much interest the use of such materials for manufacturing parts, components, machinery. For example, automotive sector an increasing number applications. most widely used is steels.

Pre-processing

• Steps on preprocessing CORE, MAG and Aminer
• Steps on domain partitioning

Training

• Software envrionment, configurations, and steps on pre-training

Scientific downstream tasks

• Domain subject and material phase classifications
• Energy regression

Raw performance data and plots

• The raw performance data including computation performance, loss, downstream evaluations, etc are available
• The jupyter notebook to plot is also provided

Reference

@INPROCEEDINGS{10.1145/3581784.3613215,
  author={Junqi Yin and Sajal Dash and Feiyi Wang and Mallikarjun Shankar},
  title={FORGE: Pre-training Open Foundation Models for Science}, 
  booktitle={SC23: International Conference for High Performance Computing, Networking, Storage and Analysis}, 
  year={2023},
  doi={10.1145/3581784.3613215}}