chuongdo1104/Qwen2.5-7B-Legal-VN

TEXT GENERATIONConcurrent Unit Cost:1Model Size:7.6BQuant:FP8Context Size:32kTool Calling:SupportedPublished:Aug 8, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The chuongdo1104/Qwen2.5-7B-Legal-VN is a 7.6 billion parameter Qwen2.5-based causal language model developed by chuongdo1104, fine-tuned from unsloth/Qwen2.5-7B-Instruct-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. With a 32768 token context length, it is specifically optimized for legal applications within the Vietnamese context.

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Model Overview

The chuongdo1104/Qwen2.5-7B-Legal-VN is a specialized large language model developed by chuongdo1104. It is a 7.6 billion parameter model, fine-tuned from the unsloth/Qwen2.5-7B-Instruct-bnb-4bit base model, leveraging the Qwen2.5 architecture. The model benefits from accelerated training, achieved by utilizing the Unsloth library in conjunction with Huggingface's TRL library, which reportedly makes training 2x faster.

Key Capabilities

  • Specialized Domain: This model is specifically fine-tuned for legal applications, particularly within the Vietnamese context, suggesting enhanced performance on legal texts and queries in Vietnamese.
  • Efficient Training: Developed with Unsloth, it highlights an efficient training methodology, which can be beneficial for further fine-tuning or deployment.
  • Context Length: Features a substantial context window of 32768 tokens, allowing it to process and understand longer legal documents and complex scenarios.

Good For

  • Vietnamese Legal Text Analysis: Ideal for tasks involving the analysis, summarization, or generation of legal documents and information in Vietnamese.
  • Legal Research and Assistance: Can be used as a foundation for AI-powered legal research tools or virtual assistants focused on Vietnamese legal frameworks.
  • Applications Requiring Long Context: Suitable for use cases where understanding extensive legal documents or conversations is critical due to its large context window.