Hugging Face

Provides multi-vector text embeddings optimized for compressed document retrieval.

Compresses document token embeddings through configurable hierarchical clustering and averaging.

Trains multi-vector embedding models to preserve retrieval quality after compression.

Identifies compatible decision models across architectures on the Hugging Face Hub.

Enables typed AI decisions, probability scoring, serving, and model training.

Extend robot policy execution using custom strategies and live diagnostics.

Connect external vision language models to plan robot subtasks.

Verifiable reinforcement learning environments derived from shared coding agent trajectories.

Rule-based proxy inspects agent tool calls and blocks reward hacking.

Harness-specific LoRA adapters improve coding agents through reinforcement learning.

Access robot learning datasets directly from browsers and Node.js.

Deploy robot policies across additional arms and humanoid configurations.

Quantify robot policy evaluation uncertainty using Wilson confidence intervals.

Evaluate robot policies through unified vector environments and isolated simulators.

Run large robot policies remotely while maintaining local hardware control.

Run additional vision language action architectures through unified robotics workflows.

Use additional world action models for predictive robot control.

Track robot model training experiments locally or through Spaces.

Optimize robot model training with precision controls and weight averaging.

Train large robot models with sharding and distributed checkpoints.

Accelerate robot policy training with GPU powered image augmentations.

Read robot training datasets from Lance tables without reencoding videos.

Train robot policies using structured language annotations and consistent recipes.

Robot policy combines language planning with flow matching action execution.

Control robot policies through live instructions, questions, and autonomous goals.