Automates expert guidance for fast, memory-efficient model fine-tuning with Unsloth.
npx skills add https://github.com/gigagiova/hermes-agent --skill unsloth-gigagiova
Please help me install this Agent Skill. Skill: unsloth Source: https://github.com/gigagiova/hermes-agent/tree/main/skills/mlops/unsloth Command: npx skills add https://github.com/gigagiova/hermes-agent --skill unsloth-gigagiova
Unsloth provides structured guidance for fast and memory-efficient fine-tuning of large language models, helping practitioners implement, optimize, and debug advanced tuning workflows with LoRA/QLoRA.
Provide a concise Unsloth fine-tuning plan tailored to your model and dataset.
High-intent search queries and answers about installing and using this skill.
Fine-tune a LLM with LoRA to reduce VRAM usage by applying memory-efficient training techniques that maintain accuracy. This approach provides structured guidance and practical tips to optimize memory consumption during large language model training workflows.
The best way to speed up LLM fine-tuning for Llama and Gemma models is to apply automated expert guidance that delivers significant speedups. This approach optimizes training workflows specifically for these architectures, ensuring faster iteration during model development.
Yes, QLoRA can be used for reinforcement learning workflows with Qwen models. The framework provides guided fine-tuning workflows that support RL and GRPO scenarios specifically for Qwen, enabling efficient policy optimization and training.
Yes, you need PyTorch and Transformers installed as required dependencies to start fine-tuning. The environment also requires datasets, PEFT, and TRL libraries to properly execute the memory-efficient training and optimization workflows.
Large language model training runs out of memory during fine-tuning when standard methods consume excessive VRAM. Applying memory-efficient techniques with Unsloth and utilizing comprehensive debugging references can help identify and resolve these memory bottlenecks.
Debug and optimize GRPO training workflows for large language models by following comprehensive references and tutorials that support implementation. These resources provide best-practice guidance to identify issues and apply effective optimization strategies during RL training.
zhuangbiaoweiFine-tune LLMs with SFT, DPO, PPO, and GRPO using HuggingFace Transformers.
ScientiaCapitalExport fine-tuned models to GGUF and deploy with vLLM or Ollama.
davpatel605-beepConfigure Unsloth LoRA and QLoRA fine-tuning workflows for memory-efficient model training.
NITISH-gitbitOptimize LoRA and QLoRA model fine-tuning with unsloth libraries.
