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A dense 128B multimodal model with a 256k context window, configurable reasoning effort, and native function-calling for agentic workflows. Supports text+image input, multilingual output, and is released on Hugging Face under a Modified MIT license with revenue-based exceptions.
Multimodal image-text-to-text fork of Gemma 4 (31B) using a 'CRACK v2' abliteration — tuned for conversational vision inputs and thinking-mode support in JANG v2 safetensors format. Recommended to run in vMLX; published by dealignai.
An 8B-parameter, instruction-tuned long-context LLM optimized for instruction following, tool-calling, and multilingual dialogue — supports 131072-token context and common NLP tasks such as summarization, QA, code, and RAG.
A 30B-parameter, instruction-tuned language model built for long-context text generation, conversational agents, and tool-calling. It combines supervised fine-tuning and RL alignment, supports 131,072-token context, and is optimized for tasks like summarization, code, and RAG.
Generates text by iteratively denoising blocks of tokens with a two-tower design: a frozen autoregressive context tower and a trainable diffusion denoiser tower, trading minimal quality loss for higher wall-clock throughput.
Delivers an ultra-efficient, edge-friendly multimodal image-and-video-to-text model optimized for on-device deployment. Uses mixed 4x/16x visual token compression, a low-FLOPs visual encoder, and multiple quantized variants for mobile and embedded inference.
Clinical question-answering model for psychological support in obesity weight-management. Integrates UK Biobank population evidence to produce clinically interpretable, stigma-aware responses that help clinicians identify distress, prompt screening, and suggest appropriate referrals.
Unifies multimodal understanding, reasoning, and image generation in a single end-to-end architecture using the NEO-unify paradigm. Models pixels and words jointly without a separate visual encoder, and provides interleaved image–text generation, infographic editing, and GGUF/low‑VRAM inference options.
Generates expressive, prompt-driven text-to-speech audio with optional 10-second voice cloning; prompts control speaker identity, emotion, pauses and nonverbal sounds. An IC‑LoRA fine-tune of LTX‑2.3 that applies an imperceptible Resemble Perth watermark.
Generates English text matching pre-1931 style — a 13B language model trained on ~260B tokens of pre-1931 English, useful for historical-language generation and stylistic research. An instruction-tuned variant exists for interactive tasks.
Produces 384‑dim multilingual (and code) embeddings with up to 32,768 token context, optimized for low‑latency production retrieval. Compact 97M model with ONNX/OpenVINO and vLLM/GGUF deployment options for edge and high‑throughput use.
End-to-end multimodal model for native text↔image understanding, interleaved image-text generation, and image editing. Uses the NEO-Unify MoT architecture to avoid separate visual encoders/VAE. Suited for multimodal prototyping, demos, and research (Apache‑2.0).