MaziyarPanahi
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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
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# β‘ 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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## Key Features
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- π Improved performance across all benchmarks
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- π§ Enhanced reasoning and analytical capabilities
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- π Better handling of complex, multi-turn conversations
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- π Expanded knowledge base for more accurate and up-to-date information
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- π¨ 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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# β‘ 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.
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