Axolotl

LLM Quality Optimization Bootcamp: Thierry Moreau and Pedro Torruella

Feb 8, 2025 · 53:05

Thierry Moreau of OctoAI demonstrates how to fine-tune Llama 3 8B on a PII redaction task using OpenPipe and OctoAI, achieving 47% better accuracy and a 200x cost reduction (from $30 to $0.15 per million tokens) compared to GPT-4 Turbo. He explains that fine-tuning should follow prompt engineering and RAG, and works best for specialized tasks like function calling. The talk walks through building a fine-tuning dataset from the PI Masking 200k dataset, using OpenPipe to train a LoRA for $40, deploying it on OctoAI, and evaluating it to show the fine-tuned model scores 0.97 accuracy versus GPT-4’s 0.68. Moreau emphasizes that this continuous deployment cycle requires monitoring data drift and retraining, but tools like OpenPipe and OctoAI make it accessible even for teams without deep ML expertise.

AI Frontiers in Trust and Safety Combatting Multifaceted Harm on Tinder at Scale: Vibhor Kumar

Dec 2, 2024 · 14:36

Vibhor Kumar, senior AI engineer at Tinder, explains how the company uses open-source LLMs and LoRAX to detect a long tail of trust and safety violations at global scale. Facing challenges like content pollution and automated fraud from generative AI, Tinder leverages pre-trained models such as LLaMA and Mistral, fine-tuning them with LoRA and QLoRA on hybrid datasets generated by GPT-4 and manually verified. They serve dozens of fine-tuned adapters on a single GPU using LoRAX, achieving real-time inference (tens of QPS, ~100ms latency) for categories including hate speech, pig butchering scams, and underage users. The approach yields near 100% recall on simpler tasks and significant improvements over baselines, with better generalization that resists adversarial evasion. Future directions include visual language models for explicit image detection and automating retraining pipelines.

Everything you need to know about Fine-tuning and Merging LLMs: Maxime Labonne

Sep 25, 2024 · 17:52

Maxime Labonne from Liquid AI explains the LLM training lifecycle—pre-training, supervised fine-tuning (SFT), and preference alignment—and when to use fine-tuning over prompt engineering. He details SFT dataset creation (accuracy, diversity, complexity) and techniques like full fine-tuning, LoRA, and QLoRA, with key hyperparameters. The core of the talk is model merging: combining weights of fine-tuned models without GPU, using methods like SLERP (spherical linear interpolation for two models), TIES (pruning redundant parameters to merge many models), pass-through (concatenating layers, e.g., Meta LLaMA 3 120B Instruct by repeating layers gives strong creative writing), and Franken-MoE (extracting FFN layers from domain-specific models with a router). Labonne demonstrates these with his NeuralBeagle and Beyonder models, noting merged models dominate the OpenLLM leaderboard and that TIES merging often outperforms more experimental Mixture of Experts approaches.