MaziyarPanahi/calme-2.4-qwen2-7b
MaziyarPanahi/calme-2.4-qwen2-7b is a 7.6 billion parameter language model fine-tuned by MaziyarPanahi based on the Qwen2-7B architecture. This model aims to improve the base Qwen2-7B model's performance across various benchmarks, as indicated by its Open LLM Leaderboard evaluation results. It is designed for general language generation tasks, leveraging its fine-tuned capabilities for enhanced performance.
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Model Overview
MaziyarPanahi/calme-2.4-qwen2-7b is a fine-tuned version of the Qwen/Qwen2-7B base model, developed by MaziyarPanahi. This 7.6 billion parameter model focuses on enhancing the base model's performance across a range of benchmarks, as evidenced by its evaluation on the Open LLM Leaderboard.
Key Capabilities and Performance
The model's performance is highlighted by its scores on the Open LLM Leaderboard:
- Average Score: 22.52
- IFEval (0-Shot): 33.00
- BBH (3-Shot): 31.82
- MATH Lvl 5 (4-Shot): 18.35
- GPQA (0-shot): 4.47
- MuSR (0-shot): 14.43
- MMLU-PRO (5-shot): 33.08
These metrics suggest a balanced performance across different reasoning, knowledge, and instruction-following tasks, with a notable score in IFEval.
Technical Details
- Base Model: Qwen2-7B
- Parameter Count: 7.6 billion
- Context Length: 131,072 tokens
- Prompt Template: Uses the
ChatMLformat, which includessystem,user, andassistantroles for structured conversations.
Quantized Versions
Quantized GGUF versions of this model are available for more efficient deployment and inference, accessible at MaziyarPanahi/calme-2.4-qwen2-7b-GGUF.
Intended Use
This model is suitable for general-purpose language generation and understanding tasks where improved performance over the base Qwen2-7B model is desired, particularly in scenarios requiring instruction following and reasoning capabilities.