ThousandML

ThousandML Training Factory enables efficient customization of powerful open-source language models. Key capabilities include: • LoRA adapter fine-tuning on 4-bit base models • Proprietary Coded Quantization (CQ) for quality preservation • Reduced GPU memory footprint for training • Calibration-free quantization method • Open-source toolkit for end-to-end workflow This platform significantly cuts the cost of model fine-tuning by utilizing high-quality, compressed base models. Unlike traditional methods that start with full-precision models, Training Factory leverages a 4-bit base, substantially reducing GPU memory requirements. The unique Coded Quantization (CQ) ensures that model quality is maintained near full precision without needing calibration data, making the savings tangible without sacrificing performance. Users can store a single CQ checkpoint and train multiple small LoRA adapters for different tasks, eliminating the need to store and serve a full fine-tuned copy for every use case. This optimizes resource utilization, allowing for longer sequences, bigger batches, or training on smaller, more affordable GPUs. The comprehensive open-source toolkit supports the entire process, from quantizing a model to fine-tuning its CQ checkpoint and serving it with CQ-enabled vLLM. Ideal for developers, researchers, and organizations looking to cost-effectively customize large language models for specific applications without compromising performance. It's perfect for projects requiring efficient model deployment and adaptation on resource-constrained hardware.