Alfikrah/qwen2.5-3b-legal-id-sft
TEXT GENERATIONConcurrent Unit Cost:1Model Size:3.1BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Aug 7, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
Alfikrah/qwen2.5-3b-legal-id-sft is a 3.1 billion parameter Qwen2.5 model, developed by Alfikrah, specifically fine-tuned for legal applications in Indonesia. This model leverages Unsloth and Huggingface's TRL library for efficient training, making it suitable for specialized legal text processing tasks. Its optimization focuses on delivering relevant performance for Indonesian legal contexts.
Loading preview...
Model Overview
Alfikrah/qwen2.5-3b-legal-id-sft is a 3.1 billion parameter language model developed by Alfikrah. It is a fine-tuned variant of the Qwen2.5 architecture, specifically adapted for legal applications within Indonesia. The model was trained using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
Key Capabilities
- Specialized Domain: Fine-tuned for Indonesian legal contexts, suggesting proficiency in legal terminology and structures relevant to Indonesia.
- Efficient Training: Utilizes Unsloth for accelerated training, indicating potential for rapid adaptation or deployment.
- Qwen2.5 Base: Built upon the Qwen2.5-3B-Instruct model, providing a strong foundation for instruction-following and general language understanding.
Good For
- Indonesian Legal Text Processing: Ideal for tasks involving legal documents, queries, or analysis specific to the Indonesian legal system.
- Domain-Specific Applications: Suitable for developers building applications that require a nuanced understanding of legal language in a specific regional context.
- Resource-Efficient Deployment: The 3.1 billion parameter size, combined with efficient training methods, suggests it could be deployed in environments with moderate computational resources.