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11. End-to-end LLM Workflows Guide

    This guide demonstrates how to execute end-to-end LLM workflows for developing and productionizing LLMs at scale. It covers data preprocessing, fine-tuning, evaluation, and serving.

    2024-06-21 Tags: llm, workflows, data preprocessing, fine-tuning, evaluation, serving, ray, anyscale by klotz
12. Refusal in LLMs is mediated by a single direction

    This post discusses a study that finds that refusal behavior in language models is mediated by a single direction in the residual stream of the model. The study presents an intervention that bypasses refusal by ablating this direction, and shows that adding in this direction induces refusal. The study is part of a scholars program and provides more details in a forthcoming paper.

    2024-06-10 Tags: large language model, refusal, interpretability, ai alignment, safety, fine-tuning by klotz
13. Fine-Tuning LLM Models Course | freeCodeCamp.org

    This article announces a comprehensive course on fine-tuning large language models (LLMs) offered on the freeCodeCamp.org YouTube channel. The course, developed by Krish Naik, covers topics such as QLORA, LORA, quantization with LLama2, gradient, and Google Gemma Model, among others. The course aims to help learners deepen their understanding of machine learning and artificial intelligence.

    2024-05-24 Tags: freecodecamp, course, fine-tuning, llm, qlora, lora, quantization, llama2 by klotz
14. 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
15. 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
16. 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
17. 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
18. [2401.00908] DocLLM: A layout-aware generative language model for multimodal document understanding

    DocLLM is a lightweight extension to traditional LLMs for reasoning over visual documents, considering both textual semantics and spatial layout. It avoids expensive image encoders and focuses on bounding box information. It outperforms SotA LLMs on 14 out of 16 datasets across all tasks and generalizes well to previously unseen datasets.

    Keywords:

    2024-01-06 Tags: docllm, large language models, visual documents, textual semantics, spatial layout, pre-training, fine-tuning by klotz

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