calme-2.7-qwen2-7b
| Property | Value |
|---|---|
| Base Model | Qwen2-7B |
| Author | MaziyarPanahi |
| Model Size | 7B parameters |
| Hugging Face | Repository Link |
What is calme-2.7-qwen2-7b?
Calme-2.7-qwen2-7b is a fine-tuned variant of the Qwen2-7B language model, specifically engineered to enhance performance across multiple benchmarks. This model implements the ChatML prompt template and offers GGUF quantization options for improved efficiency.
Implementation Details
The model utilizes a structured ChatML format for interactions, with clear system, user, and assistant message demarcation. It can be easily implemented using either the Hugging Face pipeline API or direct model loading through AutoTokenizer and AutoModelForCausalLM.
- Supports GGUF quantization for efficient deployment
- Implements ChatML prompt template structure
- Available through both pipeline and direct model implementation
- Achieves notable benchmark scores, particularly in IFEval (35.92) and MMLU-PRO (30.06)
Core Capabilities
- Strong performance in inference tasks (IFEval: 35.92)
- Capable of complex reasoning (BBH: 28.91)
- Professional knowledge evaluation (MMLU-PRO: 30.06)
- Mathematical problem-solving abilities (MATH Lvl 5: 12.08)
- Multi-task reasoning capabilities (MuSR: 19.94)
Frequently Asked Questions
Q: What makes this model unique?
The model stands out for its balanced performance across various benchmarks, particularly excelling in inference tasks and professional knowledge evaluation. Its implementation of ChatML templating and GGUF quantization support makes it highly practical for real-world applications.
Q: What are the recommended use cases?
Given its benchmark performance, the model is well-suited for tasks requiring inference, professional knowledge application, and complex reasoning. It's particularly effective for applications needing structured chat-like interactions due to its ChatML implementation.
