README.md

---
base_model: meta-llama/Llama-3.2-1B
library_name: peft
pipeline_tag: feature-extraction
tags:
- a1
- abstract-code-relevance
- repository-library
- research-library
- t2_abstract
---

# Abstract Code Relevance

Scores whether a paper abstract is relevant to a repository or code task.

## Model Details

- Artifact type: LoRA adapter
- Base model: `meta-llama/Llama-3.2-1B`
- Backbone type: `decoder`
- Model ID: `A1`
- 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-code-relevance`
- 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/a1_abstract_code_relevance.json`
- Models directory: `https://github.com/peytontolbert/research_library/tree/main/models`

## Intended Use

- Primary use: Scores whether a paper abstract is relevant to a repository or code task.
- 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`
- Target fields: `repo_relevance`
- Train/val/test split: `[0.8, 0.1, 0.1]`
- Max samples: `0`
- Batch size: `4`
- Precision: `bf16`
- Objective: `contrastive`
- Learning rate: `0.0001`
- Max source tokens: `2048`
- Max target tokens: `512`
- 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: `recall_at_10, ndcg_at_10`
- Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack.

## Usage

```python
from transformers import AutoModel, AutoTokenizer
from peft import PeftModel

repo_id = "PeytonT/abstract-code-relevance"
base_id = "meta-llama/Llama-3.2-1B"

tokenizer = AutoTokenizer.from_pretrained(repo_id)
base = AutoModel.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`