Shishir1807/M1_llama
Shishir1807/M1_llama is a 7 billion parameter causal language model fine-tuned from the Meta Llama-2-7b-hf base model using H2O LLM Studio. This model is designed for general text generation tasks, leveraging the Llama 2 architecture for conversational and instructional applications. It is suitable for deployment on GPU-equipped machines, supporting quantization for efficient inference.
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Overview
Shishir1807/M1_llama is a 7 billion parameter language model built upon the robust meta-llama/Llama-2-7b-hf architecture. It was fine-tuned using H2O LLM Studio, a platform designed for training large language models.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Instruction Following: The model is structured to process prompts in a specific
<|prompt|>...</s><|answer|>format, indicating its suitability for instruction-tuned tasks. - Efficient Deployment: Supports
load_in_8bitandload_in_4bitquantization for reduced memory footprint and faster inference, as well as sharding across multiple GPUs usingdevice_map=auto.
Usage Considerations
This model is intended for general text generation. Users should be aware of potential biases inherent in models trained on diverse internet data. Critical evaluation of generated content is recommended, and responsible, ethical use is encouraged. The model's architecture is a standard LlamaForCausalLM with 32 decoder layers, 4096 hidden size, and 32000 vocabulary size.