Delta-Vector/Austral-70B-Winton is a 70 billion parameter Llama-based language model fine-tuned by Delta-Vector. This model is specifically optimized for generalist roleplay and adventure scenarios, enhancing coherency and intelligence while maintaining creative capabilities. It utilizes KTO (Kahneman-Tversky Optimization) training to refine its performance, building upon the Austral-70B-Preview base model. With a 32768 token context length, it is designed for engaging and consistent narrative generation.
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Overview
Delta-Vector/Austral-70B-Winton is a 70 billion parameter language model developed by Delta-Vector, building on the Austral-70B-Preview. It is a Vulpecula finetune and Llama-based architecture, specifically enhanced with KTO (Kahneman-Tversky Optimization).
Key Capabilities
- Generalist Roleplay/Adventure: Optimized for generating engaging and coherent narratives in roleplay and adventure contexts.
- Enhanced Coherency and Intelligence: KTO training has improved the model's logical consistency and overall intelligence, reducing common 'slops' found in other models.
- Creative Generation: Maintains strong creative capabilities suitable for diverse storytelling.
- Llama-3 Instruct Chat Format: Utilizes the Llama-3 Instruct chat template for structured conversations.
Training Details
The model was initially fine-tuned on Sao's Vulpecula, trained as a 16-bit R128 LoRA for 2 epochs. The Winton version then underwent KTO training for 1 epoch using a mix of instruct and writing datasets to address coherency issues. The entire training process, including the base SFT and KTO, took approximately 48 hours on 8 x A100 GPUs.
Quantization
Delta-Vector/Austral-70B-Winton is available in various quantization formats for broader compatibility:
- GGUF: For use with LLama.cpp and its forks.
- EXL3: For use with TabbyAPI.
Top 3 parameter combinations used by Featherless users for this model. Click a tab to see each config.