Hugging Face Overview
Model Overview
FogTeams/experiment-105-model-consolidation-itr-1 is a 3.2 billion parameter language model built upon the Llama 3.2 base architecture. It was developed and fine-tuned using H2O LLM Studio, a platform for training large language models. The model is designed for general-purpose text generation and conversational AI, supporting a context length of 32,768 tokens.
Key Capabilities
- Instruction Following: The model is instruction-tuned, enabling it to respond to user prompts and engage in conversational exchanges.
- Efficient Inference: With 3.2 billion parameters, it offers a balance between performance and computational efficiency, suitable for various deployment scenarios.
- Flexible Deployment: Supports quantization (8-bit and 4-bit) and sharding across multiple GPUs for optimized resource utilization.
Good For
- General Text Generation: Creating coherent and contextually relevant text based on given prompts.
- Conversational AI: Developing chatbots and interactive agents that can maintain dialogue.
- Prototyping and Development: A lightweight yet capable model for experimenting with LLM applications where larger models might be overkill.