60 lines
2.1 KiB
Markdown
60 lines
2.1 KiB
Markdown
---
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language:
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- fr
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- en
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- ar
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base_model:
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- Qwen/Qwen2.5-7B-Instruct
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tags:
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- gguf
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- quantization
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- qwen2.5
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- llama.cpp
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- internal-audit
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- question-answering
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- fine-tuned
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---
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# Qwen2.5 – Internal Audit Q&A (Quantized GGUF)
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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**.
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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`).
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## Fine-Tuning Overview
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- **Base Model**: [Qwen2.5 7B](https://huggingface.co/Qwen/Qwen2.5-7B-instruct)
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- **Fine-Tuning Task**: Instruction-based Q&A on internal audit reports, policies, and compliance logs
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- **Training Data**: ~100k entries from anonymized internal audit datasets (private & proprietary)
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- **Format**: Chat-style instruction tuning with questions and detailed answers
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---
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## 🗃️ Quantized Variants
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| Filename | Quantization | Description |
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|-----------------------|--------------|----------------------------------------------------------|
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| `model-Q3_K_M.gguf` | Q3_K_M | 3-bit quantization — low memory footprint |
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| `model-Q4_K_M.gguf` | Q4_K_M | 4-bit — good performance and efficiency |
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| `model-Q5_K_M.gguf` | Q5_K_M | 5-bit — balance between performance and quality |
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| `model-Q6_K.gguf` | Q6_K | 6-bit — high quality, higher RAM usage |
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| `model-Q8_0.gguf` | Q8_0 | 8-bit — near original model fidelity |
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| `model-fp16.gguf` | FP16 | Full precision — highest quality, requires GPU |
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---
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## ChatML Format
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### Token structure
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Each message in the conversation is wrapped like this:
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```
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<|im_start|>{role}
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{message content}
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<|im_end|>
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```
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- `{role}` is usually `system`, `user`, or `assistant`
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- This clearly defines message boundaries for the model to interpret dialogue turns
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