How does it handle Out-of-Memory (OOM) errors?

The skill provides a step-by-step troubleshooting guide, including reducing batch sizes, increasing gradient accumulation, and utilizing sequence length optimizations.

Can I use Funsloth Local with multi-GPU setups?

Yes, the skill includes specific instructions for GPU selection using the CUDA_VISIBLE_DEVICES environment variable to target specific hardware.

What hardware is required for Funsloth Local?

It is designed for NVIDIA GPUs. Minimal requirements start at 8GB VRAM for 7B models (4-bit), though 24GB is recommended for larger models and faster training cycles.

Is Docker necessary for using this skill?

No, Docker is optional. You can install dependencies via pip directly on your host machine, but Docker is recommended for a pre-configured, isolated environment.

Can I resume training from a specific checkpoint?

Yes, Funsloth Local supports the resume_from_checkpoint argument in TrainingArguments, allowing you to pick up from the latest saved state automatically.

Funsloth Local GPU Training

Name: Funsloth Local GPU Training
Author: chrisvoncsefalvay

bychrisvoncsefalvay

•

Ciencia de Datos y ML

Manages local GPU fine-tuning workflows using Unsloth to optimize LLM training performance and resource utilization.

Acerca de

Funsloth Local provides a comprehensive toolkit for running Unsloth-powered LLM fine-tuning on local hardware. It automates critical environment checks like CUDA availability and VRAM capacity, offers flexible execution via Jupyter notebooks or Python scripts, and includes robust troubleshooting guides for common training issues like Out-of-Memory (OOM) errors. By streamlining GPU selection, monitoring, and checkpoint management, it ensures developers can efficiently train and test models directly on their own machines without complex manual setup.

Características Principales

4 GitHub stars
Automated CUDA and VRAM hardware validation
Built-in support for model saving, merging, and inference testing
Real-time training monitoring via terminal and WandB integration
Comprehensive troubleshooting for OOM and convergence issues
Pre-configured Docker environment for consistent training setups

Casos de Uso

Debugging and optimizing local LLM training runs for better efficiency
Fine-tuning 7B-14B models on local consumer GPUs like RTX 3090/4090
Setting up a standardized training environment using Docker containers

Acerca de

Características Principales

4 GitHub stars
Automated CUDA and VRAM hardware validation
Built-in support for model saving, merging, and inference testing
Real-time training monitoring via terminal and WandB integration
Comprehensive troubleshooting for OOM and convergence issues
Pre-configured Docker environment for consistent training setups

Casos de Uso

Debugging and optimizing local LLM training runs for better efficiency
Fine-tuning 7B-14B models on local consumer GPUs like RTX 3090/4090
Setting up a standardized training environment using Docker containers