llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
Learn to leverage Axolotl for LLM training and fine-tuning. This guide covers data preprocessing, quantization, and distributed scaling with a single config file.
Benefits of Using Unsloth for LLM Acceleration: A Technical Deep DiveDiscover Unsloth LLM acceleration benefits. Train 7B and 13B models utilizing up to 75% less VRAM with 4-bit quantization and LoRA on free Colab GPUs.
Using QLoRA for Efficient LLM Fine-Tuning: A Complete Implementation GuideMaster efficient LLM fine-tuning with QLoRA. This guide shows how to reduce GPU memory by 75% for 7B+ models on consumer GPUs with minimal performance loss. Learn implementation now.
Optimizing LLM Inference Speed and Performance with the LLM CourseLearn to optimize LLM inference speed and performance with the mlabonne/llm-course repository. Explore quantization, model merging, and speculative decoding for faster, high-quality results.
Designing and Implementing LLM Agents: A Practical Guide Using the LLM-Course RepositoryLearn to design and implement LLM agents that use tools and retrieval systems for autonomous tasks. Explore practical examples with LangChain and vector databases in the mlabonne/llm-course repository.
Building Retrieval-Augmented Generation (RAG) Systems: A Complete Guide Using the LLM Course RepositoryMaster building Retrieval-Augmented Generation RAG systems with the comprehensive mlabonne/llm-course. Learn via hands-on Colab notebooks and a structured curriculum.
Strategies for Running LLMs Efficiently: 8 Optimization Techniques from the LLM CourseDiscover 8 effective strategies for running LLMs efficiently. Optimize inference with quantization, KV caching, speculative decoding, and more to reduce latency and memory.
How Quantization Reduces LLM Memory Footprint and Improves Inference: A Technical GuideDiscover how LLM quantization slashes VRAM usage by converting weights to INT8 or INT4. Boost inference speed with this technical guide.
Methods for Evaluating the Performance of Large Language Models: 3 Essential ApproachesDiscover 3 essential methods for evaluating large language models automate benchmarks human evaluation and model based scoring for robust performance assessment
How to Align LLM Preferences Using DPO, GRPO, and PPOLearn how to align LLM preferences using DPO, GRPO, and PPO. Explore these RLHF methods for better language model control and understand their key differences for optimal model alignment.
Implementing Supervised Fine-Tuning (SFT) for LLMs: A Complete Guide with TRL, Unsloth, and AxolotlLearn Supervised Fine-Tuning SFT for LLMs with TRL Unsloth and Axolotl. Convert pre-trained models into instruction followers efficiently reducing VRAM.
Creating Post-Training Datasets for LLMs: 5 Proven Strategies for High-Quality Data CurationDiscover 5 proven strategies for creating high-quality post-training datasets for LLMs. Learn to standardize, generate, enrich, deduplicate, and filter data effectively.
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