MeakhelG/Qwen-Legal-SFT-Dicoding-Final

TEXT GENERATIONConcurrency Cost:1Model Size:1.5BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 5, 2026License:apache-2.0Architecture:Transformer Open Weights Cold

MeakhelG/Qwen-Legal-SFT-Dicoding-Final is a 1.5 billion parameter Qwen2 model developed by MeakhelG, fine-tuned for specific tasks. This model was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. It is designed for applications requiring a compact yet specialized language model, leveraging its 32768 token context length.

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Model Overview

MeakhelG/Qwen-Legal-SFT-Dicoding-Final is a specialized Qwen2 model with 1.5 billion parameters, developed by MeakhelG. This model was fine-tuned from unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit and utilizes a substantial 32768 token context length.

Key Characteristics

  • Architecture: Based on the Qwen2 model family.
  • Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
  • Context Length: Features a 32768 token context window, suitable for processing longer inputs.
  • Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.

Intended Use Cases

This model is particularly well-suited for applications where a compact, specialized language model with a large context window is beneficial. Its efficient fine-tuning process suggests it could be adapted for various domain-specific tasks, especially those requiring processing of extensive textual information.

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