Scope

Metalhead ports what a checkpoint determines, and checks it against PyTorch. It doesn't reimplement the general-purpose machinery of timm, transformers, diffusers and sentence-transformers. A feature belongs in Metalhead when both of these hold:

The checkpoint determines it: through its configuration, its weights, or upstream code specific to its architecture.
Getting it wrong would silently change the results compared with the Python library, so it has to be validated against them.

In scope:

Architectures, their configurations and weights, and the feature interface of image and language models (forward_features, forward_intermediates, ...).
The inference preprocessing that a checkpoint's configuration sets: resizing, cropping, normalising, and the packing of inputs: the token indices and padding of a batch of texts, and what is specific to a model, such as image patches and image placeholder tokens.
The post-processing specific to a model, such as SAM 3's masks and boxes.
The schedulers and sampling loops of the ported diffusion pipelines. The sampler, not the denoiser alone, defines the image. A scheduler is ported along with a pipeline that uses it, not on its own.
The text embeddings of sentence-transformers models. sentence-transformers is a library of its own, but a checkpoint sets how its embeddings are made, and getting it wrong changes them silently: the pipeline of its modules.json (the pooling of the token embeddings, its mode, Dense projections and normalization), the truncation and lowercasing of sentence_bert_config.json, and the prompts and similarity function of config_sentence_transformers.json. Metalhead reads them strictly and computes the embeddings as sentence-transformers' encode does.
The key-value cache of language models (not implemented yet).

Out of scope, for other packages:

Tokenization algorithms. HuggingFaceTokenizers.jl tokenizes, and Metalhead uses it through an extension.
Chat templates, and decoding strategies such as beam search, logits processors and streaming.
Training: optimizers, learning-rate schedules, losses, weight averaging and training loops, which Flux, Optimisers.jl and ParameterSchedulers.jl provide. The models are ordinary differentiable Flux models.
Data augmentation, data loading and image I/O, which DataAugmentation.jl, MLUtils.jl and FileIO.jl provide.
Task APIs such as transformers' pipeline, quantization, LoRA and distributed training.
The rest of sentence-transformers: training and its losses, evaluators, semantic search and similarity utilities other than the similarity function a model sets, quantized embeddings, and multi-process encoding.

General-purpose code that matching PyTorch requires, such as PIL's resampling filters, stays internal rather than public. Architectures are added once a checkpoint validates them; the goal is not to cover each library's whole model collection.