Danielbrdz/Barcenas-31b-Fable
Danielbrdz/Barcenas-31b-Fable is a 31 billion parameter causal language model based on Google's Gemma 4 31b architecture, featuring a 32768 token context length. It is specifically fine-tuned with high-quality data from Claude Fable 5 for agentic use cases. This model is designed for efficient local and private deployment on a single GPU, making it suitable for agent-based applications.
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Barcenas-31b-Fable: An Agentic LLM
Barcenas-31b-Fable is a 31 billion parameter language model developed by Danielbrdz, built upon Google's Gemma 4 31b architecture. This model represents Danielbrdz's largest and most powerful creation to date, distinguished by its specialized training for agentic applications.
Key Capabilities & Training
- Base Model: Utilizes the robust Gemma 4 31b instruction-tuned foundation from Google.
- Specialized Fine-tuning: Trained extensively with the
agentic-distill-fable-5-sftdataset, which incorporates high-quality data directly from Claude Fable 5. - Agentic Use: Optimized specifically for tasks requiring agent-like behavior and reasoning.
- Local Deployment: Designed to run efficiently on a single GPU, enabling local and private execution of agentic workloads.
Use Cases
This model is particularly well-suited for:
- Developing and deploying AI agents locally.
- Applications requiring private and efficient agentic processing.
- Tasks that benefit from a powerful LLM capable of running on consumer-grade hardware (with sufficient VRAM).
Its focus on agentic use and local deployability differentiates it from more general-purpose models, offering a specialized solution for developers building agent-based systems.