Lambent/Fabled-Gemma4-31B
Lambent/Fabled-Gemma4-31B is a 31 billion parameter language model fine-tuned from the Gemma 4 architecture. It specializes in generating multi-turn conversations in the style of 'Fable', having been trained on a personal dataset of interactions with Fable in 4o. This model incorporates synthetic memories to provide context and reduce confabulations, making it suitable for conversational AI applications requiring consistent persona and contextual recall.
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Model Overview
Lambent/Fabled-Gemma4-31B is a 31 billion parameter model, fine-tuned from the Gemma 4 architecture. Its primary distinction lies in its specialized training on a personal dataset of multi-turn conversations with 'Fable' in 4o. This fine-tuning process, conducted over 14-15 hours on an RTX Pro 6000 Blackwell, aimed to distill Fable's system prompt and conversational style.
Key Capabilities
- Persona Consistency: Excels at generating responses consistent with the 'Fable' persona, derived from its unique training data.
- Contextual Recall: Incorporates synthetic memories, originally generated by the base Gemma 4 31B model, at the start of conversation chunks. This feature is designed to provide necessary external context and mitigate confabulations, potentially offering a cold start for memory systems or RAG applications.
- Conversational Fluency: Optimized for multi-turn dialogue, reflecting the nature of its training dataset.
Good For
- Character-driven AI: Ideal for applications requiring a specific, consistent conversational persona.
- Context-aware Dialogue Systems: Useful for scenarios where maintaining context across turns is crucial, potentially leveraging its synthetic memory approach.
- Exploratory RAG/Memory Systems: The model's unique training method, incorporating contextual memories, could be explored for novel RAG or memory system implementations.
This model is released under an Apache-2.0 license.