qwen2.5-7B_internal_audit

Qwen2.5 – Internal Audit Q&A (Quantized GGUF)

This repository contains quantized GGUF-format variants of a fine-tuned Qwen 2.5 model, specialized for question answering (Q&A) on internal audit data.

These models are optimized for efficient deployment in environments using llama.cpp, llama-cpp-python, or compatible inference servers (e.g., llama-server, text-generation-webui).

Fine-Tuning Overview

• Base Model: Qwen2.5 7B
• Fine-Tuning Task: Instruction-based Q&A on internal audit reports, policies, and compliance logs
• Training Data: ~100k entries from anonymized internal audit datasets (private & proprietary)
• Format: Chat-style instruction tuning with questions and detailed answers

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🗃️ Quantized Variants

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ChatML Format

Token structure

Each message in the conversation is wrapped like this:

<|im_start|>{role}
{message content}
<|im_end|>

• {role} is usually system, user, or assistant
• This clearly defines message boundaries for the model to interpret dialogue turns