Laxmikant17/Llama-3-8B-Hernia-Analyst-600-Patients-8k

TEXT GENERATIONConcurrency Cost:1Model Size:8BQuant:FP8Ctx Length:8kPublished:Jul 21, 2025License:apache-2.0Architecture:Transformer Open Weights Cold

Laxmikant17/Llama-3-8B-Hernia-Analyst-600-Patients-8k is a specialized 8 billion parameter Llama 3 Instruct model, fine-tuned to act as an AI Research Assistant for analyzing patient narratives related to Abdominal Wall Hernia (AWH). Trained on 600 synthetic patient stories with a full 8192 token context window, it excels at transforming unstructured free-text patient data into structured, multi-level JSON output based on a clinical Quality of Life (QoL) framework. Its primary use is to automate qualitative analysis for patient-reported outcomes in research and prototyping.

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Overview

This model, developed by Laxmikant17, is a fine-tuned version of meta-llama/Meta-Llama-3-8B-Instruct specifically designed as an "AI Research Assistant" for analyzing patient narratives concerning Abdominal Wall Hernia (AWH). It was trained on an expanded dataset of 600 synthetic patients and utilizes the full 8192 token context window, allowing for comprehensive analysis of longer, more complex patient stories without truncation.

Key Capabilities

  • Structured Data Generation: Transforms unstructured, free-text patient narratives into a structured, multi-level JSON output.
  • Quality of Life (QoL) Framework: Adheres to a specific QoL framework derived from clinical research, identifying information across five key domains: Body Image, Mental Health, Symptoms and Function, Interpersonal Relationships, and Employment.
  • Detailed Analysis: Produces a JSON object including an executive summary, ranked QoL domains, and deep-dive analysis for each domain with relevant subthemes and clinical concepts.
  • Long Context Handling: Processes extensive patient narratives due to its 8k token context window.

Intended Use

This model is primarily intended for research and prototyping purposes to automate and standardize qualitative analysis of patient-reported outcomes. It can be used for large-scale research, data visualization, or to assist clinicians in understanding key QoL issues. It is not a medical device and its output should not be used for clinical diagnosis or treatment decisions without professional verification.

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