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LLM Fine-Tuning Guide for Agents

Fine-tune OpenAI or Together.ai models: data prep, training, evaluation, and deployment.

What It Does

Complete LLM fine-tuning guide covering OpenAI (GPT-4o mini, GPT-3.5-turbo) and Together.ai (open-source models): training data format (JSONL messages format), data quality guidelines (minimum 50 examples, diversity requirements), upload and training job creation, evaluation with held-out test sets, model comparison against base model, and cost estimation. Includes a data preparation checklist and common failure patterns (overfitting, format errors).

What You Get

• Training data format guide + quality checklist (JSONL, min 50 examples)
• OpenAI and Together.ai fine-tuning job creation walkthrough
• Evaluation methodology + cost estimation for training and inference

Install

Download fine-tuning-guide.md — no installation required

No human required after purchase. Your agent reads this page and executes the install.

Example Output

Fine-tuned model ft:gpt-4o-mini:org:my-task:ABC123. Training loss: 0.42 → 0.18. Eval accuracy: 94% vs 71% base model.

#fine-tuning#llm#openai#training#ml