Transformers

Hugging Face is an AI and machine-learning collaboration platform that hosts and distributes models, datasets, and applications. Its Hub supports public model, dataset, and application repositories across text, image, video, audio, and 3D workloads, while its open-source stack includes Transformers, Diffusers, Safetensors, Tokenizers, TRL, Transformers.js, smolagents, and PEFT. The platform provides a unified API for accessing models from AI providers, GPU-based compute and Inference Endpoints, and paid team and enterprise features such as private datasets, access controls, single sign-on, audit logs, and dedicated support.

TensorFlow is an open-source, end-to-end machine-learning platform originally developed by Google Brain. It provides tools, libraries, community resources, and stable Python and C++ APIs for building, training, and deploying machine-learning applications, with support for CPU and GPU execution and additional device plugins. In TensorFlow-based deployment stacks, it provides serving infrastructure and the SavedModel format; JAX graphs can be converted to SavedModel for integration with TensorFlow serving. The project is distributed through PyPI packages, Docker containers, and source builds, including CPU-only and nightly packages.

Speech To Speech is an open-source, modular voice-agent pipeline from Hugging Face. It chains voice activity detection, speech-to-text, a language model, and text-to-speech as separate stages connected by queues; each stage has interchangeable backends. The language-model stage supports OpenAI-compatible protocols and can use hosted providers, Hugging Face Inference Providers, vLLM, or llama.cpp, while the system streams transcripts, generated text, tool calls, and synthesized audio. The project exposes the core OpenAI Realtime event set through WebSocket and WebRTC and includes commands for running a realtime server, a microphone-and-speaker client, or a local setup. It is distributed as a Python package and requires Python 3.10 or newer. The repository says the pipeline is used as the conversation backend for Reachy Mini robots.

KTransformers is an open-source research framework for efficient large-language-model inference and fine-tuning through heterogeneous CPU-GPU computing. Its user-facing capabilities are provided through the kt-kernel source tree: high-performance inference and supervised fine-tuning (SFT). For mixture-of-experts models, it uses heterogeneous expert scheduling, placing frequently used or “hot” experts on GPUs and other experts on CPUs with optimized AMX, AVX512, or AVX2 kernels and NUMA-aware memory management. It supports CPU-side INT4/INT8 quantized weights, GPU-side GPTQ, native BF16 and FP8 precision, and multiple CPU, GPU, and NPU backends. The SFT capability integrates with LlamaFactory for hybrid CPU-GPU fine-tuning of large MoE models, including LoRA and full-parameter training, with INT4/INT8 quantization options.

τ₀-VLA is an open-source hierarchical robot foundation model and reference implementation for long-horizon manipulation. A memory-augmented high-level policy generates the next subtask and uses world-model-guided test-time computation to search over alternatives when additional reasoning is needed; a generalist low-level policy then executes the selected subtask across robot embodiments. The low-level policy combines a Qwen3.5 vision-language backbone with a Mixture-of-Transformers action expert trained through conditional flow matching, using a unified 40-dimensional state/action space and multimodal co-training on tens of thousands of hours of heterogeneous real-world robot data. The repository provides LeRobot-format data loading, prompting, masking and normalization, embodiment-specific adapters, training and post-training recipes, deployment contracts, a policy server, and open-loop evaluation tools. It includes an AgiBot World example subset and templates for other datasets and robots. Public v1 serving supports joint-control checkpoints; native end-effector data can be used for training but end-effector serving is not supported in that release. The reference environment uses Python 3.11, CUDA 12.8, and PyTorch 2.7.1. Code and model weights are released under the Apache License 2.0.