---
base_model: google/flan-t5-base
library_name: peft
pipeline_tag: text2text-generation
tags:
- c1
- paper-to-code
- papers
- repository-library
- research-library
- scientific-papers
- t5_cross
---

# Paper To Code

Generates code-oriented outputs conditioned on paper content.

## Model Details

- Artifact type: LoRA adapter
- Base model: `google/flan-t5-base`
- Backbone type: `encoder_decoder`
- Model ID: `C1`
- Tier: `T5_cross`
- Role in stack: specialized Repository Library 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/paper-to-code`
- 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/c1_paper_to_code.json`
- Models directory: `https://github.com/peytontolbert/research_library/tree/main/models`

## Intended Use

- Primary use: Generates code-oriented outputs conditioned on paper content.
- 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.
- Source `github_repos`: repository graph and code chunk data exported from the Repository Library repo pipeline.

## Training Procedure

- Sources: `arxiv_pdfs_structured, github_repos`
- Input fields: `method_text`
- Target fields: `code_snippet`
- 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, pass_at_1, pass_at_5`
- 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/paper-to-code"
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`