Tagged: fine-tuning

3 articles on fine-tuning.

Fine-Tuning a 70B Model on a Consumer GPU: The Q-LoRA Practical Guide

Fine-Tuning a 70B Model on a Consumer GPU: The Q-LoRA Practical Guide

Q-LoRA + SFTTrainer + Flash Attention v2 means you can fine-tune a 70B parameter model on 24GB of VRAM. What that looks like end-to-end, what it costs in quality, and when to just use the API instead.

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Fine-Tuning LLMs Without the RLHF Headache: The DPO Approach

Fine-Tuning LLMs Without the RLHF Headache: The DPO Approach

RLHF is the right idea with the wrong implementation cost for most teams. DPO flips the math. How to align a healthcare AI model on clinician feedback without burning a month on reward model engineering.

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You Don't Need GPT-4 for That: Small Models and Edge Agents

You Don't Need GPT-4 for That: Small Models and Edge Agents

Frontier models aren't required for agentic function calling. For healthcare AI, assuming they are can also be a compliance liability. When a fine-tuned 7B model is the right architecture, and when it isn't.

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