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1. Improve LLMs with Proxy Tuning

   In this tutorial, learn how to improve the performance of large language models (LLMs) by utilizing a proxy tuning approach, which enables more efficient fine-tuning and better integration with the AI model.

   2024-05-11 Tags: llm, proxy, tuning, fine-tuning by klotz
2. Proxy Fine-Tuning LLMs

   - Proxy fine-tuning is a method to improve large pre-trained language models without directly accessing their weights.
   - It operates on top of black-box LLMs by utilizing only their predictions.
   - The approach combines elements of retrieval-based techniques, fine-tuning, and domain-specific adaptations.
   - Proxy fine-tuning can be used to achieve the performance of heavily-tuned large models by only tuning smaller models.

   2024-05-11 Tags: proxy, fine-tuning, llm, retrieval-augmented generation, domain-specific adaptations, data delivery, rag, catastrophic forgetting, drift by klotz
3. How to Generate Instruction Datasets from Any Documents for LLM Fine-Tuning

   Generate instruction datasets for fine-tuning Large Language Models (LLMs) using lightweight libraries and documents.

   2024-04-04 Tags: llm, fine-tuning, instruction datasets, towardsdatascience by klotz
4. Finetune LLMs on your own consumer hardware using tools from PyTorch and Hugging Face ecosystem | PyTorch

   efficient method for fine-tuning LLM using LoRA and QLoRA, making it possible to train them even on consumer hardware

   2024-01-12 Tags: llm, fine tuning, qlora, lora, peft, pytorch, hugging face, fine-tuning, llms by klotz

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