Back to Projects
Scalable platform for fine-tuning large language models with custom datasets, LoRA adapters, and distributed training capabilities.
PyTorch
Transformers
PEFT
CUDA
MLflow
Ray
LLM Fine-tuning Platform
Overview
A comprehensive platform for fine-tuning large language models with enterprise-grade capabilities.
Key Features
- LoRA Adapters: Efficient fine-tuning with Low-Rank Adaptation
- Custom Datasets: Support for various data formats and preprocessing
- Distributed Training: Multi-GPU and multi-node training support
- Model Versioning: Complete MLOps pipeline with model versioning
- API Integration: Easy deployment and inference APIs
Technology Stack
- PyTorch, Transformers, PEFT
- Weights & Biases, MLflow
- Ray, Apache Airflow
- CUDA, Docker, Kubernetes
Features
-
Dataset Management
- Automated data validation
- Custom tokenization pipelines
- Data augmentation techniques
-
Training Pipeline
- Hyperparameter optimization
- Gradient checkpointing
- Mixed precision training
-
Evaluation Framework
- Comprehensive benchmarks
- A/B testing capabilities
- Performance analytics
Results
- 70% reduction in training time
- Support for models up to 70B parameters
- Automated deployment to production