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- ~30% Compression Of LLM (Flan-T5-Base) With Low Rank Decomposition Of Attention Weight Matrices
- Adapter Based Fine Tuning BART And T5-Flan-XXL For Single Word Spell Correction
- Revamping Dual Encoder Model Architecture: A layered approach to fuse multi-modal features and plug-and-play integration of Encoders
- Summary Of Adapter Based Performance Efficient Fine Tuning (PEFT) Techniques For Large Language Models
- Neural Ranking Architectures
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Tag Archives: large language model
~30% Compression Of LLM (Flan-T5-Base) With Low Rank Decomposition Of Attention Weight Matrices
Colab Link To Reproduce Experiment: LLM Compression Via Low Rank Decomposition.ipynb Context A neural network contains many dense layers which perform matrix multiplication. In the case of Transformers, Attention module has Key, Query, Value and Output matrices (along with the … Continue reading
Posted in Large Language Models, llm, machine learning
Tagged large language model, machine learning
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Adapter Based Fine Tuning BART And T5-Flan-XXL For Single Word Spell Correction
In this post I share results of a weekend project around fine tuning BART and T5 Flan models for sequence to sequence generation. I have used common misspellings in English language (single words) for training and evaluating the models. As … Continue reading
Posted in Uncategorized
Tagged large language model, llm, lora, machine learning, nlp, spell correction
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Summary Of Adapter Based Performance Efficient Fine Tuning (PEFT) Techniques For Large Language Models
The two most common transfer learning techniques in NLP were feature-based transfer (generating input text embedding from a pre-trained large model and using it as a feature in your custom model) and fine-tuning (fine tuning the pre-trained model on custom … Continue reading
Posted in performance efficient fine tuning, Uncategorized
Tagged adapters, gpt, large language model, llama, lora, machine learning, nlp, peft
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