metadata
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
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