language, base_model, tags

language	base_model	tags
fr en ar	Qwen/Qwen2.5-7B-Instruct	gguf quantization qwen2.5 llama.cpp internal-audit question-answering fine-tuned

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

Filename	Quantization	Description
model-Q3_K_M.gguf	Q3_K_M	3-bit quantization — low memory footprint
model-Q4_K_M.gguf	Q4_K_M	4-bit — good performance and efficiency
model-Q5_K_M.gguf	Q5_K_M	5-bit — balance between performance and quality
model-Q6_K.gguf	Q6_K	6-bit — high quality, higher RAM usage
model-Q8_0.gguf	Q8_0	8-bit — near original model fidelity
model-fp16.gguf	FP16	Full precision — highest quality, requires GPU

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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