Optimum Model Enabler

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

Çoklu dosya

codingResmi

Resmi Sağlayıcı Skill'iView 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.

Dosyalar3 dosya

SKILL.md403 satır

Loading editor…

Kurulum

Önerilen

Tek komut — ajanınız otomatik olarak devreye alır.

AI aracını seç

Kurulum komutunu görmek için yukarıdan bir AI aracı seçin.

veya

Manuel Kurulum

Daha fazla adım

Arşivi indirin ve dosyaları projenize manuel olarak ekleyin.

Skill detayları

Versiyonv1.0.0

YazarHugging Face

Kategoricoding

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

Dosyalar3 dosya

İlgili skill'ler

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.