OpenR1-Qwen-7B-Italian
| Property | Value |
|---|---|
| Base Model | Qwen2.5-Instruct |
| Training Data | WiroAI/dolphin-r1-Italian |
| Training Duration | 5 days on 8xA6000 ADA cluster |
| Max Sequence Length | 4096 tokens |
| Model URL | https://huggingface.co/WiroAI/OpenR1-Qwen-7B-Italian |
What is OpenR1-Qwen-7B-Italian?
OpenR1-Qwen-7B-Italian is a specialized language model fine-tuned for Italian language processing and reasoning. It addresses the challenge of improving performance on low-resource languages while maintaining sophisticated reasoning capabilities. The model is built upon Qwen2.5-Instruct and has been specifically optimized for Italian language understanding and generation.
Implementation Details
The model was trained for 2 epochs using a learning rate of 1e-5 and implements a cosine learning rate schedule with 10% warmup phase. It supports a maximum sequence length of 4096 tokens and has been optimized for extended reasoning tasks that require detailed thought processes.
- Fine-tuned on WiroAI/dolphin-r1-Italian dataset
- Implements cosine learning rate scheduling
- Optimized for long-form reasoning and detailed thought processes
- Supports chat-template formatting for structured interactions
Core Capabilities
- Enhanced Italian language reasoning and processing
- Step-by-step problem solving in Italian
- Extended token generation for comprehensive responses
- Improved cultural and linguistic alignment for Italian content
Frequently Asked Questions
Q: What makes this model unique?
This model specifically addresses the challenges of Italian language processing, offering improved reasoning capabilities compared to existing models. It's designed to think more clearly in Italian compared to other multilingual models that might default to Chinese or English reasoning.
Q: What are the recommended use cases?
The model is particularly well-suited for tasks requiring detailed reasoning in Italian, such as mathematical problem-solving, step-by-step explanations, and comprehensive analysis. It's optimized for generating longer responses (up to 4000 tokens) and should be allowed sufficient token generation for optimal performance.
