Model Description

This model is a fine-tuned version of Qwen2.5-7B-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 ~6GB VRAM footprint during inference, leaving ample head-room for long contexts and tool-calling structures.

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Model Details

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Deployment & Inference (vLLM)

To host this model using vLLM on standard cloud environments.

Start the vLLM Server:

bash
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!python -m vllm.entrypoints.openai.api_server \
    --model unsloth/Qwen2.5-7B-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 &