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Model: kturki/qwen2.5-7B_internal_audit
Source: Original Platform

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README.md

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+---
+language:
+- fr
+- en
+- ar
+base_model:
+- Qwen/Qwen2.5-7B-Instruct
+tags:
+  - 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](https://huggingface.co/Qwen/Qwen2.5-7B-instruct)
+- **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
+
+---
+
+## 🗃️ 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          |
+
+---
+
+##  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
+

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