Ornith 1.0 9B FP8 DYNAMIC

Ornith

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released June 2026context N/A

Ornith 1.0 9B FP8 DYNAMIC is a multimodal model that accepts both text and image inputs, producing text outputs. It runs in FP8 dynamic quantization, which reduces memory footprint compared to full-precision variants — a practical trade-off that may introduce minor quality differences. Details about its specific reasoning or task strengths are limited beyond its multimodal input support.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multimodal

Moderate

Factual Knowledge

Moderate

Instruction Following

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Ornith 1.0 9B FP8 DYNAMIC

Ornith

Open WeightModel weights are publicly available — can be downloaded and self-hosted

Released June 2026context N/A

Ornith 1.0 9B FP8 DYNAMIC is a multimodal model that accepts both text and image inputs, producing text outputs. It runs in FP8 dynamic quantization, which reduces memory footprint compared to full-precision variants — a practical trade-off that may introduce minor quality differences. Details about its specific reasoning or task strengths are limited beyond its multimodal input support.

Capabilities

Capability scores are AI-generated based on model documentation, benchmarks, and technical specifications. Learn more

Multimodal

Moderate

Factual Knowledge

Moderate

Instruction Following

Use Case Fit

Fit scores are AI-generated based on model capabilities, intended use, and technical specifications. Learn more

Glossary

Dynamic QuantizationA quantization approach that adjusts precision levels during inference based on the input data, optimizing the balance between speed and accuracy on-the-fly.FP8 Dynamic QuantizationA compression technique that reduces model size and speeds up inference by representing weights and activations using 8-bit floating-point numbers, with dynamic scaling adjusted per batch to maintain accuracy.FP8 Dynamic QuantizationA specific quantization method that uses 8-bit floating-point numbers and adjusts precision dynamically based on the data being processed, balancing speed and accuracy.Full-PrecisionA model using standard 32-bit floating-point numbers to represent weights, providing maximum accuracy but requiring more memory.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.MultimodalA model that can process and understand multiple types of input, such as both text and images.Multimodal InputThe ability to accept and process multiple types of input data simultaneously, such as both images and text in the same request.Multimodal ModelAn AI model that can process and understand multiple types of input data, such as video, images, and text together.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizationReducing a model's numerical precision (e.g., from 16-bit to 4-bit) to shrink memory usage and speed up inference.ReasoningThe model's ability to work through multi-step logical problems and provide justified answers rather than just pattern-matching.