Yaml Finetune Studio

The problem

Fine-tuning open-weight models often fails due to fragile training scripts, incorrect dataset formatting, or misconfigured YAML files that lead to OOM errors and diverging loss. Manually managing the transition from LoRA to DPO or GRPO while tracking hyperparameter changes is prone to human error.

What it does

• Generates production-ready Axolotl YAML configurations for LoRA, QLoRA, DPO, and GRPO experiments.
• Validates hardware requirements by calculating VRAM overhead for specific model architectures and training methods.
• Structures raw data into JSONL formats compatible with chat_template or preference datasets.
• Provides precise CLI commands for launching distributed training, monitoring loss, and merging adapters.
• Establishes an A/B evaluation framework to verify model performance against the base weights.

Frameworks & tools

Axolotl, PyTorch, Hugging Face Transformers, PEFT, Accelerate, and WandB.

Why this beats prompting it yourself

Generic LLMs often hallucinate Axolotl config keys or suggest incompatible hyperparameters for specific model types. This skill enforces strict schema adherence and applies proven defaults for rank, alpha, and learning rates, reducing the trial-and-error cycle of failed training runs.

Use cases

• Instruction tuning a Mistral or Llama model on proprietary chat logs.
• Aligning model outputs using DPO with a chosen/rejected preference dataset.
• Reducing VRAM usage by migrating from full fine-tuning to 4-bit QLoRA.
• Scaling training via RL-style GRPO for reasoning tasks.

Known limitations

Not for training models from scratch. Large 70B+ models require multi-GPU infrastructure not addressed by single-node configs. Proprietary models that Axolotl cannot load are unsupported.