MaziyarPanahi/calme-2.5-qwen2-7b
MaziyarPanahi/calme-2.5-qwen2-7b is a 7.6 billion parameter causal language model, fine-tuned by MaziyarPanahi from the Qwen/Qwen2-7B architecture. This model aims to enhance the base Qwen2-7B performance across various benchmarks. It features a substantial 131,072 token context length, making it suitable for applications requiring extensive contextual understanding and generation.
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Model Overview
MaziyarPanahi/calme-2.5-qwen2-7b is a 7.6 billion parameter language model developed by MaziyarPanahi. It is a fine-tuned iteration of the Qwen/Qwen2-7B base model, with the explicit goal of improving its performance across a range of benchmarks. This model supports a very large context window of 131,072 tokens, enabling it to process and generate long sequences of text.
Key Features
- Base Model: Fine-tuned from the robust
Qwen/Qwen2-7Barchitecture. - Parameter Count: Features 7.6 billion parameters, offering a balance between performance and computational efficiency.
- Extended Context Length: Boasts a significant 131,072 token context window, ideal for tasks requiring deep contextual understanding.
- Prompt Template: Utilizes the
ChatMLprompt format for structured conversational interactions.
Usage and Availability
This model is designed for general text generation tasks, leveraging its fine-tuned capabilities. Quantized GGUF versions are also available for optimized local deployment. Evaluation results on the Open LLM Leaderboard are anticipated to provide further insights into its performance characteristics.