dizza01/llama-3.1-8b-bib-grounded-sft-merged
The dizza01/llama-3.1-8b-bib-grounded-sft-merged model is an 8 billion parameter language model with an 8192 token context length. This model is a fine-tuned variant of the Llama 3.1 architecture, specifically designed for bib-grounded supervised fine-tuning. Its primary application is in tasks requiring responses grounded in provided bibliographic or factual information, enhancing accuracy and reducing hallucination in information retrieval and generation.
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Model Overview
The dizza01/llama-3.1-8b-bib-grounded-sft-merged is an 8 billion parameter language model based on the Llama 3.1 architecture, featuring an 8192 token context window. This model has undergone supervised fine-tuning (SFT) with a focus on "bib-grounded" tasks, meaning its training emphasizes generating responses that are directly supported by provided source material or bibliographic references.
Key Characteristics
- Architecture: Llama 3.1 base model.
- Parameter Count: 8 billion parameters.
- Context Length: Supports an 8192 token context, allowing for processing longer inputs and source documents.
- Training Focus: Supervised fine-tuning (SFT) specifically for "bib-grounded" applications, aiming to improve factual accuracy and reduce unsupported claims.
Intended Use Cases
This model is particularly well-suited for applications where factual accuracy and traceability to source information are critical. Potential use cases include:
- Information Retrieval with Citation: Generating answers or summaries that cite specific passages or documents.
- Fact-Checking Assistance: Aiding in verifying information by grounding responses in provided evidence.
- Academic Research Support: Summarizing papers or extracting information while referencing sources.
- Question Answering: Providing precise answers based on a given knowledge base or document set, minimizing hallucination.