Alicyabilqis/legalbot-exp1
TEXT GENERATIONConcurrency Cost:1Model Size:3.1BQuant:BF16Ctx Length:32kTool Calling:SupportedPublished:Jun 25, 2026License:apache-2.0Architecture:Transformer Open Weights Cold
Alicyabilqis/legalbot-exp1 is a 3.1 billion parameter Qwen2.5-Instruct model, fine-tuned by Alicyabilqis, with a 32768-token context length. This model was optimized for faster training using Unsloth and Huggingface's TRL library. It is designed for applications requiring a compact yet capable language model, likely focusing on legal or instruction-following tasks given its name and base model.
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Alicyabilqis/legalbot-exp1: A Fine-Tuned Qwen2.5-Instruct Model
Alicyabilqis/legalbot-exp1 is a 3.1 billion parameter language model, fine-tuned by Alicyabilqis from the unsloth/Qwen2.5-3B-Instruct-bnb-4bit base. This model leverages the Qwen2.5-Instruct architecture, known for its strong performance in instruction-following tasks, and features a substantial context length of 32768 tokens.
Key Capabilities
- Efficient Training: The model was fine-tuned with Unsloth and Huggingface's TRL library, enabling significantly faster training (2x speedup).
- Qwen2.5-Instruct Base: Benefits from the robust capabilities of the Qwen2.5-Instruct series, making it suitable for a wide range of instruction-based applications.
- Extended Context Window: A 32768-token context length allows for processing and generating longer texts, which is beneficial for complex tasks requiring extensive contextual understanding.
Good For
- Legal Applications: Given its name,
legalbot-exp1is likely intended for specialized legal text processing, analysis, or question-answering. - Instruction Following: Excels in tasks where precise adherence to instructions is critical, inherited from its Qwen2.5-Instruct foundation.
- Resource-Efficient Deployment: As a 3.1B parameter model, it offers a balance of capability and efficiency, making it suitable for deployment in environments with moderate computational resources.