damfle/ornith-9b-custom
damfle/ornith-9b-custom is a 9 billion parameter causal language model fine-tuned by Damien FLETY from the Ornith-1.0-9B base model. It is specifically optimized for efficient 4-bit quantized inference and designed for integration into Retrieval-Augmented Generation (RAG) pipelines. The model utilizes Quantization-Aware Training (QAT) and the Muon optimizer for performance.
Loading preview...
Model Overview
damfle/ornith-9b-custom is a 9 billion parameter language model, fine-tuned by Damien FLETY from the Ornith-1.0-9B base model. This iteration focuses on optimizing for efficient inference, particularly through 4-bit quantization. The model is developed with Quantization-Aware Training (QAT) and uses the Muon optimizer, indicating a strong emphasis on computational efficiency.
Key Capabilities
- Quantized Inference: Designed for low-latency, high-throughput inference in a 4-bit quantized format.
- RAG Integration: Intended for seamless integration into Retrieval-Augmented Generation (RAG) workflows.
- Optimized Training: Utilizes Quantization-Aware Training (QAT) and the Muon optimizer for efficiency.
Intended Use Cases
- Efficient Deployment: Ideal for applications requiring fast and resource-light language model inference.
- Retrieval-Augmented Generation: Suitable for systems that combine information retrieval with text generation to produce more accurate and informed responses.
Future Development
Future plans for this model series include scaling to a 16B QAT 4-bit model and further development in merging embeddings for transformers architectures while maintaining separate embedding models for RAG.