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  # MaziyarPanahi/calme-2.1-qwen2-72b
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- This is a fine-tuned version of the `Qwen/Qwen2-72B-Instruct` model. It aims to improve the base model across all benchmarks.
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- PS: This fine-tuned model was previously known as `MaziyarPanahi/Qwen2-72B-Instruct-v0.1`. It was renamed to avoid any confusion with the original model.
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  # ⚑ Quantized GGUF
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  model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-qwen2-72b")
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  ```
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  # MaziyarPanahi/calme-2.1-qwen2-72b
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+ This model is a fine-tuned version of the powerful `Qwen/Qwen2-72B-Instruct`, pushing the boundaries of natural language understanding and generation even further. My goal was to create a versatile and robust model that excels across a wide range of benchmarks and real-world applications.
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+ ## Model Details
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+ - **Base Model**: Qwen/Qwen2-72B-Instruct
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+ - **Training**: Fine-tuned on a diverse dataset to enhance performance
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+ - **Size**: 72 billion parameters
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+ - **Language**: Multilingual (primary focus on English and Chinese)
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+
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+ ## Key Features
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+ - πŸš€ Improved performance across all benchmarks
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+ - 🧠 Enhanced reasoning and analytical capabilities
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+ - 🌐 Better handling of complex, multi-turn conversations
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+ - πŸ“š Expanded knowledge base for more accurate and up-to-date information
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+ - 🎨 Increased creativity for open-ended tasks
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+ ## Use Cases
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+ This model is suitable for a wide range of applications, including but not limited to:
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+ - Advanced question-answering systems
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+ - Intelligent chatbots and virtual assistants
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+ - Content generation and summarization
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+ - Code generation and analysis
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+ - Complex problem-solving and decision support
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  # ⚑ Quantized GGUF
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  model = AutoModelForCausalLM.from_pretrained("MaziyarPanahi/calme-2.1-qwen2-72b")
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  ```
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+ # Ethical Considerations
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+ As with any large language model, users should be aware of potential biases and limitations. We recommend implementing appropriate safeguards and human oversight when deploying this model in production environments.