Optimum Model Enabler

huggingface/optimum-intel/.github/skills/optimum-model-enabler

Multi-file

codingOfficial

Official Provider SkillView repo

Add and validate support for a Hugging Face model architecture in the Optimum Intel OpenVINO backend, including exporter configuration, patching, repository tests, and documentation.

Files3 files

SKILL.md403 lines

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Skill details

Versionv1.0.0

AuthorHugging Face

Categorycoding

Skill IDhuggingface/optimum-intel/.github/skills/optimum-model-enabler

Files3 files

Related skills

Tiny Model CreatorCreate a small random Hugging Face model that preserves the original architecture and can serve as a local Optimum Intel repository test fixture.Custom BlocksUse when the user has written (or wants to write) a `ModularPipelineBlocks` subclass in a local Python file and needs to package it into a Hub-uploadable directory. Covers the workflow from a single `block.py` file to a published custom-block repo that consumers can load via `ModularPipeline.from_pretrained(<repo>, trust_remote_code=True)`.Diffusers CliUse when the user wants to run a diffusers pipeline from a terminal (one-off generation, batch jobs, smoke-testing a new model), run on HF Sandbox hardware via `--remote`, introspect a pipeline's input schema before calling it, or attach a LoRA at inference time. Prefer this over writing ad-hoc Python scripts for generation tasks.Model IntegrationUse when adding a new model or pipeline to diffusers, setting up file structure for a new model, converting a pipeline to modular format, or converting weights for a new version of an already-supported model.Peft Method ChangesUse when modifying an existing PEFT method (its forward method, parameters/buffers, state_dict handling, or config defaults) to ensure that existing checkpoints keep working.Trl TrainingPost-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.