awesome-finetuning
github.com/mmarius/awesome-finetuning ↗A curated list of resources on fine-tuning language models.
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3 hours ago
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DisclaimerFine-tuning before transformersFine-tuning transformersTheoretical workSurveysMisc.
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Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me parameter-efficient fine-tuning resources from awesome-finetuning"
Installation instructions →What's inside
Fine-tuning transformers
- Adaptable AdaptersParameter-efficient fine-tuning
- AdapterDrop: On the Efficiency of Adapters in TransformersParameter-efficient fine-tuning
- AdapterFusion: Non-Destructive Task Composition for Transfer LearningParameter-efficient fine-tuning
- Adapting Language Models for Zero-shot Learning by Meta-tuning on Dataset and Prompt CollectionsPrompt-based fine-tuning
- Analyzing Commonsense Emergence in Few-shot Knowledge ModelsIntermediate task fine-tuning
- An Empirical Study on Robustness to Spurious Correlations using Pre-trained Language ModelsFine-tuning analysis
Theoretical work
Fine-tuning before transformers
- An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models
- How Transferable are Neural Networks in NLP Applications?
- Improving Neural Machine Translation Models with Monolingual Data
- Question Answering through Transfer Learning from Large Fine-grained Supervision Data
- Semi-supervised Sequence Learning
- Universal Language Model Fine-tuning for Text Classification
Surveys
Showing a sample of 113 resources. View the full list on GitHub →