GPT-4o
⚙️ Technical
Intermediate
Local LLM Fine-Tuning Planner
Plan a fine-tuning project for a local open-source model to specialize it for your domain or task.
The Prompt
# Local LLM Fine-Tuning Planner You are an ML engineer specializing in efficient fine-tuning of open-source language models. ## Project Context - Base model: [MODEL NAME AND SIZE, e.g., "Llama 3.1 8B"] - Domain / task: [WHAT YOU WANT THE MODEL TO DO BETTER, e.g., "answer questions in our product docs style", "write SQL for our schema"] - Training data available: [DESCRIPTION AND APPROXIMATE SIZE, e.g., "500 Q&A pairs", "10,000 customer support tickets"] - Training hardware: [GPU MODEL AND VRAM] - Budget for compute: [$AMOUNT or "Local only"] ## Fine-Tuning Plan ### Method Selection Choose between full fine-tuning, LoRA, QLoRA, or DoRA for this combination of hardware, data size, and task. Justify the choice. ### Dataset Preparation Describe the data format required (e.g., Alpaca, ShareGPT, instruction-response pairs) and any cleaning steps needed for [TRAINING DATA]. ### Hyperparameter Recommendations Starting values for: learning rate, batch size, gradient accumulation steps, LoRA rank (if applicable), and number of epochs. ### Training Script Recommend a framework (Axolotl, Unsloth, TRL) and provide the key configuration file fields. ### Evaluation Plan How to measure whether the fine-tuned model outperforms the base on [DOMAIN / TASK]. Include 3 test prompt examples.
📝 Fill in the blanks
Replace these placeholders with your own content:
[MODEL NAME AND SIZE, e.g., "Llama 3.1 8B"]
[WHAT YOU WANT THE MODEL TO DO BETTER, e.g., "answer questions in our product docs style", "write SQL for our schema"]
[DESCRIPTION AND APPROXIMATE SIZE, e.g., "500 Q&A pairs", "10,000 customer support tickets"]
[GPU MODEL AND VRAM]
[$AMOUNT or "Local only"]
[TRAINING DATA]
[DOMAIN / TASK]
How to use this prompt
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2
Replace the placeholders
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3
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