LoRA Toolkit

Configure, estimate, and generate LoRA fine-tuning scripts for LLMs. Input: base model name, dataset size, GPU spec. Output: training config, PEFT script, cost estimate.

Configure and generate LoRA fine-tuning scripts for large language models. Supports Llama, Mistral, Qwen, Phi and other HuggingFace-compatible models.

Commands

config

Generate a LoRA training configuration for your model and hardware.

bash scripts/script.sh config --model llama3-8b --gpu 24gb --dataset 10000

Parameters:

• --model — base model (llama3-8b, mistral-7b, qwen2-7b, phi3-mini, llama3-70b)
• --gpu — VRAM size (8gb, 16gb, 24gb, 40gb, 80gb)
• --dataset — number of training samples

estimate

Estimate VRAM usage, training time, and cost before starting.

bash scripts/script.sh estimate --model mistral-7b --gpu 16gb --dataset 5000 --epochs 3

generate

Generate a ready-to-run Python training script using HuggingFace PEFT + TRL.

bash scripts/script.sh generate --model llama3-8b --output train.py

validate

Check dataset format compatibility (Alpaca / ShareGPT / OpenAI Chat format).

bash scripts/script.sh validate --file dataset.json --format alpaca

recommend

Recommend the best base model for your use case and hardware.

bash scripts/script.sh recommend --task chat --gpu 16gb --language en

help

Show all commands.

bash scripts/script.sh help

LoRA Parameters Reference

Model Size	Recommended Rank	Alpha	VRAM (4-bit)
7B	16–32	32–64	8–12 GB
13B	16	32	14–18 GB
70B	8–16	16–32	40–48 GB

Supported Dataset Formats

• Alpaca: {"instruction": "...", "input": "...", "output": "..."}
• ShareGPT: {"conversations": [{"from": "human", "value": "..."}, ...]}
• OpenAI Chat: {"messages": [{"role": "user", "content": "..."}, ...]}

Requirements

• Python 3.8+
• Optional: pip install transformers peft trl datasets for script execution

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