Shishir1807/M2_llama
Shishir1807/M2_llama is a 7 billion parameter causal language model fine-tuned from the Meta Llama 2 base model using H2O LLM Studio. This model is designed for general text generation tasks, providing a foundation for various natural language processing applications. It leverages the Llama 2 architecture, offering capabilities for conversational AI and content creation.
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Shishir1807/M2_llama: A Fine-Tuned Llama 2 Model
This model, developed by Shishir1807, is a fine-tuned version of the meta-llama/Llama-2-7b-hf base model. The training process was conducted using H2O LLM Studio, a platform designed for developing large language models.
Key Capabilities
- Base Architecture: Built upon the robust Llama 2 7B parameter architecture.
- Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Custom Training: Benefits from a fine-tuning process via H2O LLM Studio, suggesting potential optimizations for specific tasks or domains.
- Flexible Deployment: Supports integration with the
transformerslibrary, allowing for easy deployment on GPU-enabled machines. - Quantization Support: Can be loaded with 8-bit or 4-bit quantization for reduced memory footprint and faster inference.
- Sharding: Supports sharding across multiple GPUs for handling larger models or increased throughput.
Usage Considerations
Users should be aware of the standard disclaimers associated with large language models, including potential biases, limitations in generating accurate information, and the importance of ethical use. The model's prompt format requires specific <|prompt|> and <|answer|> tokens for optimal performance, as demonstrated in the usage examples.