Ornith 1.5 35B A3B NVFP4

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

Released August 2026context N/A

Ornith 1.5 35B A3B NVFP4 is a text-in, text-out open-weight model released under the MIT license in safetensors format. The NVFP4 designation suggests it uses 4-bit floating point quantization optimized for NVIDIA hardware, which typically trades a small amount of precision for significantly reduced memory footprint. Beyond its quantization approach and architecture size, specific capability details for this model are limited.

Capabilities

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

Instruction Following

Moderate

Reasoning & Logic

Moderate

Factual Knowledge

Use Case Fit

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

Ornith 1.5 35B A3B NVFP4

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

Released August 2026context N/A

Ornith 1.5 35B A3B NVFP4 is a text-in, text-out open-weight model released under the MIT license in safetensors format. The NVFP4 designation suggests it uses 4-bit floating point quantization optimized for NVIDIA hardware, which typically trades a small amount of precision for significantly reduced memory footprint. Beyond its quantization approach and architecture size, specific capability details for this model are limited.

Capabilities

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

Instruction Following

Moderate

Reasoning & Logic

Moderate

Factual Knowledge

Use Case Fit

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

Glossary

ArchitectureThe underlying structural design of a neural network that defines how data flows through layers and components.MIT LicenseA permissive open-source license that allows free use, modification, and distribution of software with minimal restrictions.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.Open-Weight ModelA model whose trained weights are publicly released, allowing anyone to download and run it locally.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.SafetensorsA safe, fast file format for storing model weights, designed to prevent code execution vulnerabilities.Safetensors FormatA secure and efficient file format for storing model weights that prioritizes safety and speed when loading models.Text-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.