Category: Large Language Models

Large Language Models, Python

Fine-tune Llama 3 with ORPO

ORPO is a new exciting fine-tuning technique that combines the traditional supervised fine-tuning and preference alignment stages into a single process. This reduces the computational resources and time required for training. Moreover, empirical results demonstrate that ORPO outperforms other alignment methods on various model sizes and benchmarks. In this article, we will fine-tune the new Llama 3 8B model using ORPO with the TRL library. The code is available on Google Colab and in the LLM Course on GitHub. ⚖️ […]

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Large Language Models, Python

Decoding Strategies in Large Language Models

In the fascinating world of large language models (LLMs), much attention is given to model architectures, data processing, and optimization. However, decoding strategies like beam search, which play a crucial role in text generation, are often overlooked. In this article, we will explore how LLMs generate text by delving into the mechanics of greedy search and beam search, as well as sampling techniques with top-k and nucleus sampling. By the conclusion of this article, you’ll not only understand these decoding […]

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Large Language Models, Python

Improve ChatGPT with Knowledge Graphs

ChatGPT has shown impressive capabilities in processing and generating human-like text. However, it is not without its imperfections. A primary concern is the model’s propensity to produce either inaccurate or obsolete answers, often called “hallucinations.” The New York Times recently highlighted this issue in their article, “Here’s What Happens When Your Lawyer Uses ChatGPT.” It presents a lawsuit where a lawyer leaned heavily on ChatGPT to assist in preparing a court filing for a client suing an airline. The model […]

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Large Language Models, Python

4-bit LLM Quantization with GPTQ

Recent advancements in weight quantization allow us to run massive large language models on consumer hardware, like a LLaMA-30B model on an RTX 3090 GPU. This is possible thanks to novel 4-bit quantization techniques with minimal performance degradation, like GPTQ, GGML, and NF4. In the previous article, we introduced naïve 8-bit quantization techniques and the excellent LLM.int8(). In this article, we will explore the popular GPTQ algorithm to understand how it works and implement it using the AutoGPTQ library. You […]

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Large Language Models, Python

A Beginner’s Guide to LLM Fine-Tuning

The growing interest in Large Language Models (LLMs) has led to a surge in tools and wrappers designed to streamline their training process. Popular options include FastChat from LMSYS (used to train Vicuna) and Hugging Face’s transformers/trl libraries (used in my previous article). In addition, each big LLM project, like WizardLM, tends to have its own training script, inspired by the original Alpaca implementation. In this article, we will use Axolotl, a tool created by the OpenAccess AI Collective. We […]

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Large Language Models, Python

ExLlamaV2: The Fastest Library to Run LLMs

Quantizing Large Language Models (LLMs) is the most popular approach to reduce the size of these models and speed up inference. Among these techniques, GPTQ delivers amazing performance on GPUs. Compared to unquantized models, this method uses almost 3 times less VRAM while providing a similar level of accuracy and faster generation. It became so popular that it has recently been directly integrated into the transformers library. ExLlamaV2 is a library designed to squeeze even more performance out of GPTQ. […]

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