rohanbalkondekar/yes-bank

Model Overview

The rohanbalkondekar/yes-bank model is a fine-tuned language model built upon the h2oai/h2ogpt-4096-llama2-7b base model. The training process was conducted using H2O LLM Studio, a platform for developing large language models.

Key Capabilities

• Text Generation: The model is capable of generating human-like text based on given prompts, as demonstrated by its usage examples.
• Llama Architecture: It utilizes a Llama-based architecture, featuring 32 LlamaDecoderLayer modules, each with self-attention and MLP blocks, indicating a robust structure for language processing.
• Flexible Deployment: Supports loading with transformers library, including options for torch_dtype="auto" and device_map for GPU utilization. It also allows for quantization (load_in_8bit or load_in_4bit) and sharding across multiple GPUs.

Usage Considerations

• Prompt Format: Users must adhere to the specific prompt format (<|prompt|>...</s><|answer|>) that the model was trained with to ensure optimal performance.
• Disclaimer: As with many large language models, users should be aware of potential biases, limitations, and ethical considerations. The model may produce incorrect or inappropriate responses, and users are responsible for critically evaluating its output.