Fine-tune open-source LLMs with LoRA/QLoRA adapters and prepared datasets.
npx skills add https://github.com/mahg-es/araya --skill model-fine-tuning-mahg-es
Please help me install this Agent Skill. Skill: model-fine-tuning Source: https://github.com/mahg-es/araya/tree/main/skills/model-fine-tuning Command: npx skills add https://github.com/mahg-es/araya --skill model-fine-tuning-mahg-es
Fine-tune open-source LLMs to create domain-specific models using LoRA/QLoRA and dataset preparation.
Configure a LoRA-based fine-tuning workflow on a given dataset to adapt an open-source LLM.
High-intent search queries and answers about installing and using this skill.
To fine-tune an open-source LLM, you can configure a LoRA or QLoRA-based workflow to adapt the model on a specific dataset, optimizing parameters without full model retraining.
LoRA and QLoRA are adapter-based fine-tuning techniques that optimize parameters without full model retraining. QLoRA further reduces memory usage by quantizing the base model during training.
Dataset preparation for LLM fine-tuning involves formatting, augmenting, and evaluating your data to measure improvements against baselines before applying LoRA or QLoRA adapters.
Yes, you can deploy fine-tuned LoRA adapters locally or to edge deployments. The workflow provides deployment guidance for integrating tuned adapters into your target environment.
No, fine-tuning with LoRA or QLoRA adapters allows you to optimize parameters efficiently without the need for full model retraining, making scalable customization of base models possible.
skkeoriwOptimize LLM parameters and reduce VRAM usage during fine-tuning.
doanchienthangdevGuide LoRA and QLoRA finetuning with memory calculations and model merging.
dieu-donneeConfigure LoRA and QLoRA fine-tuning workflows with dataset validation and adapter deployment.
0xMerl99Guide fine-tuning of large language models with LoRA and QLoRA.
