Faishal-Anwar/qwen2.5-1.5b-pgabl-legal-grpo-faishal
TEXT GENERATIONPricing:Input $0.04 / Cached $0.008 / Output $0.08Concurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Sep 7, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold
The Faishal-Anwar/qwen2.5-1.5b-pgabl-legal-grpo-faishal model is a 1.5 billion parameter Qwen2.5-based causal language model developed by Faishal-Anwar. It is finetuned from a legal-specific model and optimized for group-related legal tasks. This model was trained using Unsloth and Huggingface's TRL library, enabling faster training for specialized legal applications.
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Model Overview
This model, developed by Faishal-Anwar, is a specialized Qwen2.5-based causal language model with 1.5 billion parameters and a context length of 32768 tokens. It is a finetuned version of the Faishal-Anwar/qwen2.5-1.5b-pgabl-legal-sft-faishal model, indicating a focus on legal domain applications.
Key Characteristics
- Architecture: Based on the Qwen2.5 family.
- Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
- Training Optimization: Leverages Unsloth and Huggingface's TRL library for accelerated training, resulting in a 2x speed improvement.
- Domain Specialization: Finetuned for legal applications, specifically targeting group-related legal tasks (implied by 'grpo' in the model name).
Good For
- Legal Text Analysis: Ideal for tasks requiring understanding and generation within a legal context, particularly those involving group dynamics or regulations.
- Specialized Legal AI: Suitable for developers building applications that need a compact yet domain-specific language model for legal research, document processing, or compliance checks.
- Efficient Deployment: Its 1.5 billion parameter size, combined with optimized training, makes it a candidate for scenarios where faster inference and lower resource consumption are critical.