Ornith 1.0 9B Alternatives

Ornith-1.0-35B is a 35-billion-parameter open-weight multimodal model hosted on Hugging Face. It features advanced vision and video understanding with a custom Jinja chat template. The model is designed for local inference using Transformers or compatible runtimes and supports multiple vision modalities.

Ornith-1.0-9B-GGUF is a 9B parameter open-source large language model designed for text generation and inference. It supports deployment via CLI, Docker, and APIs, making it accessible for AI researchers and developers seeking customizable LLM solutions. The model is distributed with open weights for flexible use.

Ornith-1.0-397B is a massive 397 billion parameter multimodal model released on Hugging Face. It supports text, image, and video inputs and uses advanced chat templates for vision-language tasks. The model is intended for researchers and developers with significant compute resources who want to experiment with state-of-the-art open-weight multimodal AI.

Ornith-1.0-35B-GGUF is an open-source large language model designed for advanced text generation and research applications. It supports multiple installation methods and is suitable for AI researchers and developers needing powerful NLP capabilities.

Ornith-1.0-397B-FP8 is a 397 billion parameter multimodal foundation model in FP8 format, supporting both image and video inputs alongside text. Developed by deepreinforce-ai and hosted on Hugging Face, it uses a sophisticated vision processing pipeline. The model is intended for advanced research and applications requiring large-scale multimodal reasoning.

Ornith-1.0-35B-NVFP4 is a large 35 billion parameter open-source multimodal model available on Hugging Face. It supports processing of text, images, and video content with specialized vision and video counting mechanisms in its chat template. The model is intended for advanced multimodal AI research and applications.

Ornith 1.0 9B is an AI-powered assistant that answers programming questions by streaming step-by-step reasoning and final solutions. It is designed for developers seeking detailed code explanations and support.

Ornith-1.0-9B-MTP-GGUF is a GGUF-quantized version of a 9B parameter multimodal model. It supports vision and text inputs and is optimized for local inference using tools like llama.cpp. The model is hosted on Hugging Face for easy download and use by developers.

Ornith 1.0 9B is an INT4 quantized version of a 9B parameter language model hosted on Hugging Face. The specific repository cyankiwi/Ornith-1.0-9B-AWQ-INT4 provides the model weights using AWQ quantization for reduced memory usage during inference. It belongs to the class of foundation models. The model includes a custom chat template implemented in Jinja that processes both text and vision inputs. The template tracks counts of images and videos separately, inserts special vision tokens such as vision_start, image_pad, and vision_end, and prevents images or videos from appearing in system messages. It also supports optional prefixes like "Picture N:" before visual content. The tokenizer configuration defines an end-of-text token as the pad token. Delivery occurs through the standard Hugging Face model hub, allowing download and local execution with compatible inference frameworks that support AWQ and the provided chat template. No pricing information appears for the model itself, as it follows the typical open-access distribution on the platform. No further capabilities, intended users, or performance claims are stated.

This is an FP8 dynamically quantized version of the Ornith 1.0 9B language model. It is designed for efficient local inference while preserving model quality. The model includes a chat template and can be used with standard Transformers-based inference frameworks and tools that support quantized models.

This repository hosts GGUF quantized files for the deepreinforce-ai Ornith-1.0 35B model. The model supports vision (image and video) inputs in addition to text and is optimized for local inference using llama.cpp and similar tools. It is designed for users who need high-capacity language and multimodal capabilities on consumer hardware.