solvrays/solvrays-llm
Solvrays/solvrays-llm is a 2.5 billion parameter language model, fine-tuned from google/gemma-2b-it by Bibek Lama Singtan, specifically engineered for Zero-Hallucination Document Retrieval. It excels at analyzing complex, domain-specific documents (technical, legal, architectural) by strictly adhering to provided context and prioritizing 'Not Documented' over speculation. This model is optimized for grounded inference in professional settings, ensuring factual integrity and contextual continuity across document sections.
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Solvrays Llm: High Precision Document Analyst
This model, developed by Bibek Lama Singtan, is a specialized fine-tuning of google/gemma-2b-it designed for Zero-Hallucination Document Retrieval. It focuses on providing precise, factual responses strictly based on provided documentation, making it ideal for handling complex, domain-specific documents such as technical, legal, or architectural texts.
Key Capabilities & Design Objectives
- Factual Integrity: Prioritizes stating 'Not Documented' rather than generating speculative information.
- Contextual Continuity: Utilizes overlap-aware training (512 tokens with 128-token overlap) to prevent information loss across document sections.
- Domain Versatility: Capable of seamlessly adapting between various technical and non-technical document styles.
- Grounded Inference: Engineered for professional usage where strict adherence to context is critical.
Technical Specifications
- Base Model: google/gemma-2b-it
- Fine-tuning: QLoRA (4-bit quantization) with LoRA Rank 16 and Alpha 32.
- Training: 5 epochs with a context strategy of 512 tokens and 128-token overlap.
Limitations
- Context Window: Strictly limited to 512 tokens. For longer, multi-page queries, Retrieval Augmented Generation (RAG) is recommended.
- Bias: Reflects biases present in its training documentation.
- Accuracy: Critical technical numbers should always be verified against original sources.