Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
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Routes Distil-Whisper inference, PyTorch distillation training, and Flax reproduction workflows.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
Use this skill when the user wants to transcribe audio with Distil-Whisper, choose or compare checkpoints, run the PyTorch distillation stack under training/, or reproduce the original JAX/Flax stack under training/flax/.
references/model-overview.md to choose a checkpoint and decode path.scripts/check-env.py to verify the CPU inspection stack before deeper work.references/repo-provenance.md when deciding whether this skill still matches the current checkout.sub-skills/inference/SKILL.md for checkpoint usage, short-form transcription, long-form transcription, speculative decoding, and inference-speed trade-offs.sub-skills/pytorch-training/SKILL.md for pseudo-labelling, student initialization, distillation training, evaluation, and dataset mixing in the PyTorch stack.sub-skills/flax-reproduction/SKILL.md for the JAX/Flax package, conversion helpers, long-form transcription, and original reproduction workflows.This repo has two practical dependency surfaces:
training/flax/.training/.For a local checkout, install the Flax package in editable mode and then add the PyTorch training dependencies from the root training/ metadata. The verified inspection environment used a single CPU prefix with Python 3.11, a CPU-only torch wheel, JAX 0.4.18, Flax 0.7.2, SciPy 1.11.4, and the core Hugging Face audio stack. See references/repo-provenance.md for the exact verified versions.
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Routes 3D ResNets PyTorch video action-recognition workflows across training, inference, and data preparation.
中文简介正在准备中。当前显示原文说明。
Guide 3DDFA Python inference, geometry rendering, training/evaluation, and optional C++ ONNX workflows for 3D dense face alignment.
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Routes 3DDFA_V2 face-alignment setup, still-image demos, video tracking, and ONNX benchmarking workflows.
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Operate AB3DMOT 3D multi-object tracking workflows for KITTI and nuScenes data, tracking, evaluation, and visualization.
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Use Hugging Face Accelerate for PyTorch training-loop migration, distributed launch/configuration, DeepSpeed/FSDP/TPU backend setup, big-model inference/offload, checkpointing, tracking, and troubleshooting.
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Route Acme reinforcement-learning framework tasks across core loops, replay/data, JAX agents, and TensorFlow/Sonnet agents.
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