Zurich-7B-GCv2-100k
| Property | Value |
|---|---|
| Base Model | Qwen 2.5 7B Instruct |
| Parameter Count | 7.61B (6.53B Non-Embedding) |
| Architecture | Transformers with RoPE, SwiGLU, RMSNorm, GQA |
| Training Dataset | GammaCorpus v2-100k |
| License | Apache 2.0 |
What is Zurich-7B-GCv2-100k?
Zurich-7B-GCv2-100k is a sophisticated fine-tuned version of Alibaba's Qwen 2.5 7B Instruct model, specifically optimized using the GammaCorpus v2-100k dataset. This model represents a significant advancement in language model capabilities while maintaining a relatively compact size of 7.61B parameters.
Implementation Details
The model utilizes a state-of-the-art architecture combining several key technologies including RoPE (Rotary Position Embedding), SwiGLU activation functions, and RMSNorm layer normalization. It features 28 layers with a unique attention structure using 28 heads for queries and 4 heads for keys/values through Grouped Query Attention (GQA). The model was trained for 60 epochs using the Unsloth framework on a T4 GPU, with the entire training process taking approximately 70 minutes.
- Advanced architecture combining multiple modern techniques
- Efficient training implementation using Unsloth framework
- Optimized for both performance and practical deployment
- Trained on structured and filtered multi-turn conversations
Core Capabilities
- Natural language understanding and generation
- Multi-turn conversation handling
- Context-aware responses
- Efficient processing with optimized attention mechanism
Frequently Asked Questions
Q: What makes this model unique?
The model stands out due to its efficient fine-tuning on the GammaCorpus v2-100k dataset, combining the robust capabilities of Qwen 2.5 with optimized training procedures. The use of GQA and modern architecture components makes it particularly efficient for practical applications.
Q: What are the recommended use cases?
This model is well-suited for conversational AI applications, content generation, and general language understanding tasks. It's particularly effective in scenarios requiring structured dialogue and consistent responses, thanks to its training on the GammaCorpus dataset.
