Ornith 1.0 35B FP8

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

Released June 2026context N/A35B params

Ornith 1.0 35B FP8 is a text-in, text-out open-weight model from deepreinforce-ai, quantized to FP8 precision — a format that trades a small amount of numerical fidelity for reduced memory footprint and faster inference. At 35 billion parameters, it sits in a mid-to-large weight class. Specific capability details are limited, so concrete behavioral traits aren't well documented.

Capabilities

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

Reasoning & Logic

Moderate

Creative Writing

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 35B FP8

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

Released June 2026context N/A35B params

Ornith 1.0 35B FP8 is a text-in, text-out open-weight model from deepreinforce-ai, quantized to FP8 precision — a format that trades a small amount of numerical fidelity for reduced memory footprint and faster inference. At 35 billion parameters, it sits in a mid-to-large weight class. Specific capability details are limited, so concrete behavioral traits aren't well documented.

Capabilities

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

Reasoning & Logic

Moderate

Creative Writing

Moderate

Instruction Following

Use Case Fit

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

Glossary

FP8 PrecisionA data format that stores numbers using 8 bits instead of the standard 32 bits, significantly reducing memory requirements with minimal quality loss.FidelityThe degree to which a quantized or compressed model preserves the quality and accuracy of the original full-precision model.InferenceThe process of running a trained model to generate predictions or outputs from new inputs.Memory FootprintThe amount of RAM or storage space a model requires to run, which is critical for deployment on resource-constrained devices.Numerical FidelityThe accuracy and precision with which a model preserves mathematical calculations; lower fidelity means some precision is lost, often as a trade-off for smaller model size.Open-Weight ModelA model whose trained weights are publicly released, allowing anyone to download and run it locally.ParametersThe learned numerical values in a model — more parameters generally means more capacity but higher compute cost.PrecisionThe level of numerical detail a model uses to represent its internal values; higher precision means more accurate calculations but requires more memory.QuantizedA technique that reduces a model's size and memory usage by storing weights with lower precision (fewer bits), trading some accuracy for efficiency.Text-In, Text-OutA model that accepts text as input and produces text as output, without support for images, audio, or other data types.