razer23com/qwen2.5-legal-chatbot-dicoding

TEXT GENERATIONConcurrent Unit Cost:1Model Size:1.5BQuant:BF16Context Size:32kTool Calling:SupportedPublished:Jul 11, 2026License:apache-2.0Architecture:Transformer Open Weights Featherless Exclusive Cold

The razer23com/qwen2.5-legal-chatbot-dicoding is a 1.5 billion parameter Qwen2.5-based instruction-tuned causal language model developed by razer23com. This model was fine-tuned using Unsloth and Huggingface's TRL library, enabling faster training. It is designed for chatbot applications, specifically in the legal domain, leveraging its base model's capabilities for conversational tasks.

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Model Overview

The razer23com/qwen2.5-legal-chatbot-dicoding is a 1.5 billion parameter instruction-tuned language model, developed by razer23com. It is based on the Qwen2.5 architecture and was fine-tuned from unsloth/Qwen2.5-1.5B-Instruct-bnb-4bit.

Key Characteristics

  • Architecture: Qwen2.5-based causal language model.
  • Parameter Count: 1.5 billion parameters.
  • Context Length: Supports a context length of 32768 tokens.
  • Training Efficiency: Fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
  • License: Distributed under the Apache-2.0 license.

Intended Use Cases

This model is specifically designed for chatbot applications, particularly within the legal domain. Its instruction-tuned nature and base model capabilities make it suitable for:

  • Legal Chatbots: Engaging in conversational interactions related to legal queries or information.
  • Automated Legal Assistance: Providing preliminary responses or guidance in legal contexts.
  • Dicoding Projects: Potentially serving as a component in projects requiring a specialized legal chatbot.

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