Explorative modeling: Train on the best of K guesses

5 hours ago

Inkling-Small is a general-purpose multimodal model that accepts text, image, and audio inputs and generates text outputs.
It can be used with libraries like Transformers, vLLM, SGLang, and Docker, as well as through APIs and third-party providers.
The model uses a 42-layer decoder-only transformer with a sparse Mixture-of-Experts (MoE) architecture, totaling 276B parameters but activating only 12B per token.

Transformer Transformer is a unified model that generates a complete robot embodiment (links, joints, motors, inertia) optimized for a given manipulation demonstration.
The model uses RoboTokens, a unified tokenization of robot embodiments, states, and actions, enabling it to span diverse embodiment spaces like wheeled bimanual, quadrupeds, and humanoids.
It is a diffusion transformer trained with DDIM, and by masking different tokens it can act as a generator, cross-embodiment controller, or motion-conditioned designer.

Inkling-Small is a general-purpose multimodal model that accepts text, image, and audio inputs and generates text outputs.
It can be used with libraries like Transformers, vLLM, SGLang, and Docker, as well as through APIs and third-party providers.
The model uses a 42-layer decoder-only transformer with a sparse Mixture-of-Experts (MoE) architecture, totaling 276B parameters but activating only 12B per token.

Transformer Transformer is a unified model that generates a complete robot embodiment (links, joints, motors, inertia) optimized for a given manipulation demonstration.
The model uses RoboTokens, a unified tokenization of robot embodiments, states, and actions, enabling it to span diverse embodiment spaces like wheeled bimanual, quadrupeds, and humanoids.
It is a diffusion transformer trained with DDIM, and by masking different tokens it can act as a generator, cross-embodiment controller, or motion-conditioned designer.

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