Model Architecture
Base Model: Llama 3.1 (8 billion parameters)
Quantization: 4-bit integer quantization for memory and computational efficiency
Framework: Fine-tuned with PyTorch, leveraging Hugging Face Transformers
PIM Optimization: Enhanced for PIM hardware to process data directly in memory, minimizing latency and maximizing throughput
Intended Use
Primary Use Cases:
Large-scale text generation
Summarization
Question answering
Conversational AI
Text classification
Research Focus:
This model is specifically designed for research and industrial applications that require efficient handling of large language models with constrained hardware resources.
