Sao10K/14B-Qwen2.5-Freya-x1

Sao10K/14B-Qwen2.5-Freya-x1: Multi-Step Fine-Tuning for Enhanced Performance

Sao10K/14B-Qwen2.5-Freya-x1 is a 14.8 billion parameter model based on the Qwen2.5 architecture, distinguished by its unique multi-step LoRA training approach. This methodology, inspired by multi-step training techniques, involves two distinct phases to enhance the model's capabilities.

Key Training Methodology

• Freya-S1: Initial LoRA training on approximately 1.1GB of cleaned literature and raw text, applied over the base Qwen/Qwen2.5-14B model. This phase focuses on broad language understanding and generation.
• Freya-S2: The LoRA from S1 is then applied over Qwen/Qwen2.5-14B-Instruct, followed by further training on instruction datasets. This stage refines the model's ability to follow instructions and engage in conversational tasks.

Noteworthy Features

• Extended Context: Supports a substantial context length of 131,072 tokens, enabling processing of long inputs.
• Optimized for ChatML: Recommended for use with the ChatML prompt format, with suggested temperature: 1+ and min_p: 0.05 for optimal output quality.
• Diverse Training Data: Utilizes a mix of completion datasets (e.g., eBooks, novels) and chat template datasets (e.g., 10k-amoral-full-fixed-sys.json, 44k-hespera-smartshuffle.json, 5k_rpg_adventure_instruct-sys.json) to build both creative and instructional capabilities.

Use Cases

This model is particularly well-suited for applications requiring detailed text generation, creative writing, and adherence to complex instructions in a conversational setting.