research_library
Collection
Repository Library model stack mirrored from local research artifacts. • 41 items • Updated
YAML Metadata Warning:The pipeline tag "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
Unified Knowledge Model
Combines paper, repository, and metadata inputs into a shared reasoning model.
Model Details
- Artifact type: LoRA adapter
- Base model:
google/flan-t5-base - Backbone type:
encoder_decoder - Model ID:
U1 - Tier:
T6_unified - Role in stack: cross-domain reasoning component
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/unified-knowledge-model - 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/u1_unified_knowledge_model.json - Models directory:
https://github.com/peytontolbert/research_library/tree/main/models
Intended Use
- Primary use: Combines paper, repository, and metadata inputs into a shared reasoning model.
- 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
arxiv_pdfs_structured: structured PDF shards containing text, equations, figures, and tables. - Source
github_repos: repository graph and code chunk data exported from the Repository Library repo pipeline.
Training Procedure
- Sources:
arxiv_metadata, arxiv_pdfs_structured, github_repos - Input fields:
paper_context, repo_context, metadata - Target fields:
reasoning_output - Train/val/test split:
[0.9, 0.1, 0.0] - Max samples:
4000 - Batch size:
2 - Precision:
bf16 - Objective:
cross_entropy - Learning rate:
5e-05 - Max source tokens:
512 - Max target tokens:
256 - 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:
perplexity - Status: this card reflects the current tracked experiment configuration and packaged weights in the Repository Library model stack.
Usage
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from peft import PeftModel
repo_id = "PeytonT/unified-knowledge-model"
base_id = "google/flan-t5-base"
tokenizer = AutoTokenizer.from_pretrained(repo_id)
base = AutoModelForSeq2SeqLM.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
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Model tree for PeytonT/unified-knowledge-model
Base model
google/flan-t5-base