Abstract Repo Planning
Predicts repository-oriented implementation plans from abstract-level paper descriptions.
Model Details
- Artifact type: full fine-tuned model
- Base model:
allenai/scibert_scivocab_uncased - Backbone type:
encoder - Model ID:
A4 - Tier:
T2_abstract - Role in stack: abstract-layer component for fast paper interpretation
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
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