| --- |
| base_model: google/flan-t5-base |
| library_name: peft |
| pipeline_tag: text2text-generation |
| tags: |
| - equation-reasoning |
| - p3 |
| - repository-library |
| - research-library |
| - t3_pdf |
| --- |
| |
| # Equation Reasoning |
|
|
| Models paper equations and mathematical reasoning spans. |
|
|
| ## Model Details |
|
|
| - Artifact type: LoRA adapter |
| - Base model: `google/flan-t5-base` |
| - Backbone type: `encoder_decoder` |
| - Model ID: `P3` |
| - Tier: `T3_pdf` |
| - Role in stack: full-paper or structured-PDF component |
|
|
| This model is part of the Repository Library stack, a research system for indexing, retrieving, aligning, and reasoning over scientific papers, structured paper content, repositories, and cross-domain links between them. |
|
|
| ## Model Sources |
|
|
| - Hugging Face repo: `https://huggingface.co/PeytonT/equation-reasoning` |
| - Hugging Face collection: `https://huggingface.co/collections/PeytonT/research-library-6a49c589ef4d763f7539b50d` |
| - GitHub repository: `https://github.com/peytontolbert/research_library` |
| - Experiment config: `https://github.com/peytontolbert/research_library/blob/main/models/experiments/p3_equation_reasoning.json` |
| - Models directory: `https://github.com/peytontolbert/research_library/tree/main/models` |
|
|
| ## Intended Use |
|
|
| - Primary use: Models paper equations and mathematical reasoning spans. |
| - Downstream use: retrieval, ranking, planning, paper understanding, or cross-domain reasoning inside the broader Repository Library system, depending on the model family. |
| - Out of scope: production safety claims, benchmark claims beyond the tracked experiment config, or deployment without task-specific validation. |
|
|
| ## Training Data |
|
|
| The training inputs for this package were assembled from the following Repository Library data sources: |
|
|
| - Source `arxiv_pdfs_structured`: structured PDF shards containing text, equations, figures, and tables. |
|
|
| ## Training Procedure |
|
|
| - Sources: `arxiv_pdfs_structured` |
| - Input fields: `equation, context` |
| - Target fields: `reasoning_steps` |
| - Train/val/test split: `[0.9, 0.1, 0.0]` |
| - Max samples: `4000` |
| - Batch size: `2` |
| - Precision: `bf16` |
| - Objective: `cross_entropy` |
| - Learning rate: `5e-05` |
| - Max source tokens: `512` |
| - Max target tokens: `256` |
| - Fine-tune strategy: `peft_lora` |
| - Max steps: `1000` |
|
|
| ## Compute |
|
|
| - Hardware: 4x RTX_3090 (24 GB) |
| - Distributed strategy: `ddp` |
| - Estimated GPU hours in config: `0` |
| |
| ## Evaluation |
| |
| - Declared metrics: `perplexity` |
| - Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack. |
| |
| ## Usage |
| |
| ```python |
| from transformers import AutoModelForSeq2SeqLM, AutoTokenizer |
| from peft import PeftModel |
| |
| repo_id = "PeytonT/equation-reasoning" |
| base_id = "google/flan-t5-base" |
| |
| tokenizer = AutoTokenizer.from_pretrained(repo_id) |
| base = AutoModelForSeq2SeqLM.from_pretrained(base_id) |
| model = PeftModel.from_pretrained(base, repo_id) |
| ``` |
| |
| ## Limitations |
| |
| - These cards are generated from tracked experiment metadata and packaged artifacts, not from a separate benchmark report or external audit. |
| - Several training sources are pipeline outputs from the Repository Library codebase rather than standalone public datasets. |
| - These models are components of a larger research system and should be validated in their target workflow before deployment. |
| |
| ## Project Context |
| |
| - GitHub repository: `https://github.com/peytontolbert/research_library` |
| - Model collection: `https://huggingface.co/collections/PeytonT/research-library-6a49c589ef4d763f7539b50d` |
| - Publisher: `PeytonT` |
|
|
