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tao-finetune-clip

CLIP vision-language model for image-text retrieval, zero-shot classification, embedding extraction, ONNX export, and TensorRT deployment. Use when fine-tuning or training CLIP, running zero-shot classification, computing image embeddings, or deploying CLIP to ONNX/TensorRT. This is a single-action model skill; do not use it for an iterative weak-attribute improvement loop that keeps retraining and evaluating until progress stops, which belongs to tao-run-deft-pas.

skills/tao-finetune-clip/SKILL.md

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# CLIP

> **Standalone install?** Read [CLIP standalone setup](references/standalone-setup.md) and verify the bank checkout before running `tao-setup` or launching a CLIP action.

Contrastive Language-Image Pre-training model for zero-shot and fine-tuned image classification, image-text retrieval, and embedding extraction. Fine-tuning adapts CLIP's shared image-text embedding space to domain-specific image-caption data.

No default NGC pretrained checkpoint is required for spec construction, but unset checkpoint behavior is action-specific. In the validation-fixes PyTorch image, `export.checkpoint: null` exports the selected CLIP architecture and may initialize weights when pretrained weights are unavailable. Do not assume `inference.checkpoint: null` loads pretrained weights: `clip inference` currently calls the checkpoint loader with `None` and fails before embedding extraction. For PyTorch inference, checkpoint-backed evaluation/export, resume, and retrain flows, resolve and pass an exact checkpoint from the parent train output. For trusted TAO checkpoints produced by the current run or a known parent job, set `TORCH_FORCE_NO_WEIGHTS_ONLY_LOAD=1` on checkpoint-dependent PyTorch actions so PyTorch 2.6 can load the Lightning checkpoint metadata; do not set this for untrusted checkpoints.

Supported actions: `train`, `evaluate`, `inference`, `export`, `gen_trt_engine`.

## Train Action Policy

This model is AutoML-enabled at the model layer. Before handling any train-stage request, read `references/skill_info.yaml` and resolve the run override from either an explicit `automl_policy` value or the user's workflow request. Use `automl_policy: on` by default and only expose `on` / `off` in new launch prompts. Treat phrases like "turn off AutoML", "disable AutoML", "no HPO", or "plain training" as `automl_policy: off` for this run only. When `automl_policy: on`, `automl_enabled: true`, and both `schemas/train.schema.json` and `references/spec_template_train.yaml` are packaged, route the train action through `tao-skill-bank:tao-run-automl` by default with this model's `skill_dir`. Preserve workflow/application overrides for datasets, specs, output directories, GPU/platform settings, parent checkpoints, and `automl_policy`. Use direct model training only when `automl_policy: off` or the packaged train schema/template is missing; in the missing-schema case, report that AutoML is enabled but not runnable for this model until schemas are generated.