rizalarfiyan/qwen-legal-grpo-dicoding
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 31, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The rizalarfiyan/qwen-legal-grpo-dicoding is a 1.5 billion parameter Qwen2 model developed by rizalarfiyan, fine-tuned from rizalarfiyan/qwen-legal-SFT-dicoding. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. It features a 32768 token context length and is optimized for legal domain applications.
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Model Overview
The rizalarfiyan/qwen-legal-grpo-dicoding is a 1.5 billion parameter Qwen2 language model developed by rizalarfiyan. It is a fine-tuned version of the rizalarfiyan/qwen-legal-SFT-dicoding model, specifically adapted for legal domain tasks.
Key Capabilities
- Efficient Training: This model was trained with Unsloth and Huggingface's TRL library, resulting in a 2x speedup during the fine-tuning process.
- Legal Domain Focus: As indicated by its lineage and naming, the model is specialized for applications within the legal sector.
- Context Length: It supports a substantial context window of 32768 tokens, allowing for processing longer legal documents or complex queries.
Good For
- Legal Text Analysis: Ideal for tasks requiring understanding and generation of legal language.
- Research and Development: Developers and researchers working on legal AI solutions can leverage its specialized fine-tuning.
- Efficient Deployment: The use of Unsloth for training suggests potential for more efficient inference, making it suitable for applications where speed is a factor.