---
base_model: unsloth/Qwen2.5-3B-Instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---

# Uploaded finetuned  model

- **Developed by:** bqbbao6
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-3B-Instruct-bnb-4bit

This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

---

# Vietnamese Legal Qwen2.5 (3B Instruct - 4-bit Quantized)

**Model:** [bqbbao6/Qwen2.5-3B-legal-vn](https://huggingface.co/bqbbao6/Qwen2.5-3B-legal-vn)  

**Version 7B:** [bqbbao6/Qwen2.5-7B-legal-vn](https://huggingface.co/bqbbao6/Qwen2.5-7B-legal-vn)

---

## Model Description

This model is a fine-tuned version of **Qwen2.5-3B-Instruct**, optimized using Unsloth and quantized to **4-bit (bitsandbytes)**. 

The primary fine-tuning objective focuses on **Vietnamese Legal Domain Mastery** and **Context-Based Question Answering (RAG)**. Unlike general-purpose LLMs that reply strictly from pre-trained parametric memory, this model is specifically aligned to prioritize and reason directly over the provided context (legal articles, decrees, and circulars), drastically reducing hallucination and ensuring high-fidelity legal consulting.

### Key Enhancements:
* **Strict Context Adherence:** Tuned to extract facts and formulate arguments heavily based on the input context—making it perfect for Retrieval-Augmented Generation (RAG) pipelines.
* **Legal Formalism:** Adopts the authoritative, formal, and precise tone required in the Vietnamese administrative and legal sectors.
* **Hardware Efficiency:** Operates smoothly within a very low VRAM footprint during inference, leaving ample head-room for long contexts and tool-calling structures on mid-range GPUs.

---

## Model Details

| Property | Value |
|---|---|
| **Base Model** | Qwen/Qwen2.5-3B-Instruct |
| **Parameters** | 3 Billion |
| **Quantization** | 4-bit (bitsandbytes / bnb-4bit) |
| **Fine-tuning Method** | QLoRA (Rank 16, Alpha 32) |
| **Primary Task** | Context-Driven Legal Q&A / Legal Agent |

---

## Deployment & Inference (vLLM)

To host this model using **vLLM** on standard cloud environments.

### Start the vLLM Server:
```bash
!python -m vllm.entrypoints.openai.api_server \
    --model unsloth/Qwen2.5-3B-Instruct-bnb-4bit \
    --max-model-len 2048 \ # Can be changed
    --dtype float16 \
    --api-key 'your-api-key-here' \
    --max-num-seqs 16 \
    --trust-remote-code \
    --gpu-memory-utilization 0.85 \ # Can be changed
    --enforce-eager \
    --enable-auto-tool-choice \
    --tool-call-parser hermes \
    --port 8000 &