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/abstract-repo-planning
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/a4_abstract_repo_planning.json
Models directory: https://github.com/peytontolbert/research_library/tree/main/models

Intended Use

Primary use: Predicts repository-oriented implementation plans from abstract-level paper descriptions.
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_metadata: arXiv metadata records spanning titles, abstracts, authors, and category labels.
Source github_repos: repository graph and code chunk data exported from the Repository Library repo pipeline.

Training Procedure

Sources: arxiv_metadata, github_repos
Input fields: abstract, repo_graph
Target fields: modules
Train/val/test split: [0.9, 0.1, 0.0]
Max samples: 4000
Batch size: 8
Precision: bf16
Objective: cross_entropy
Learning rate: 5e-05
Max source tokens: 512
Max target tokens: 128
Fine-tune strategy: full_finetune
Max steps: 1000

Compute

Hardware: 4x RTX_3090 (24 GB)
Distributed strategy: ddp
Estimated GPU hours in config: 0

Evaluation

Declared metrics: accuracy, macro_f1
Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack.

Usage

python

from transformers import AutoModelForSequenceClassification, AutoTokenizer

repo_id = "PeytonT/abstract-repo-planning"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
model = AutoModelForSequenceClassification.from_pretrained(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