Enhance model card with repository context and sources

README.md

@@ -18,44 +18,60 @@ Scores abstract and code pairs using the paired-training variant of the abstract
 
 - Artifact type: LoRA adapter
 - Base model: `meta-llama/Llama-3.2-1B`
+- Backbone type: `decoder`
 - Model ID: `A1`
 - Tier: `T2_abstract`
-- Local mirror: `/arxiv/models/repository_library/abstract-code-relevance-pairs`
-- Source checkpoint: `models/checkpoints/a1`
+- Role in stack: abstract-layer component for fast paper interpretation
 
-This repository is part of the `repository_library` model stack and is mirrored from `/data/repository_library/models/checkpoints` for publication under the `PeytonT` namespace.
+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-pairs`
+- 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 abstract and code pairs using the paired-training variant of the abstract-code-relevance model.
-- Secondary use: retrieval, ranking, planning, or scientific paper tooling inside the broader Repository Library system, depending on the model family.
-- Out of scope: production safety claims, benchmark claims beyond the bundled experiment config, or use outside the model's narrow training objective without task-specific validation.
+- 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
 
-This package was trained from the following declared datasets or corpus sources:
+The training inputs for this package were assembled from the following Repository Library data sources:
 
-- `source:arxiv_metadata`
-- `source:github_repos`
+- 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`
-- Batch size: `4`
 - 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: local experiment artifact mirrored for release; external benchmark reporting has not been standardized across the full model family yet.
+- Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack.
 
 ## Usage
 
@@ -73,14 +89,12 @@ model = PeftModel.from_pretrained(base, repo_id)
 
 ## Limitations
 
-- These model cards reflect the packaged experiment configs and mirrored checkpoint contents, not an independently audited benchmark sheet.
-- Some training datasets are local corpora or exported shards, so reproducibility may require access to the surrounding Repository Library data pipeline.
-- Models in this stack are narrow components of a larger paper-and-repository system and should be validated on downstream tasks before deployment.
+- 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
 
-Repository Library is a research system for indexing, retrieving, aligning, and reasoning over scientific papers, structured paper content, repositories, and cross-domain links between them.
-
-## Contact
-
-Published under `PeytonT` from the local `repository_library` build.
+- GitHub repository: `https://github.com/peytontolbert/research_library`
+- Model collection: `https://huggingface.co/collections/PeytonT/research-library-6a49c589ef4d763f7539b50d`
+- Publisher: `PeytonT`