hasbiiii/Qwen2.5-1.5B-Indo-Legal
The hasbiiii/Qwen2.5-1.5B-Indo-Legal model is a 1.5 billion parameter Qwen2.5-based causal language model developed by hasbiiii. Finetuned from unsloth/qwen2.5-1.5b-unsloth-bnb-4bit, it was trained using Unsloth and Huggingface's TRL library for accelerated finetuning. This model is specifically optimized for applications requiring a compact yet capable LLM, particularly for tasks related to Indonesian legal contexts.
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Model Overview
The hasbiiii/Qwen2.5-1.5B-Indo-Legal is a 1.5 billion parameter language model, developed by hasbiiii. It is finetuned from the unsloth/qwen2.5-1.5b-unsloth-bnb-4bit base model, leveraging the Qwen2.5 architecture. This model was trained with a focus on efficiency, utilizing Unsloth and Huggingface's TRL library, which enabled a 2x faster finetuning process.
Key Characteristics
- Architecture: Based on the Qwen2.5 model family.
- Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Finetuned using Unsloth, resulting in significantly faster training times.
- Context Length: Supports a context length of 32768 tokens.
Intended Use Cases
This model is particularly suitable for:
- Applications requiring a lightweight yet capable language model.
- Scenarios where rapid finetuning and deployment are critical.
- Tasks that benefit from the Qwen2.5 architecture's general language understanding capabilities.
While the model name suggests an "Indo-Legal" specialization, the provided README does not detail specific training data or benchmarks related to this domain. Users should evaluate its performance for specific legal tasks within the Indonesian context.