How does this skill help with model performance?

It provides best practices for efficient model loading, including automatic device placement (device_map='auto') and utilizing torch.bfloat16 to reduce memory usage.

Does it provide instructions for fine-tuning models?

Yes, the skill includes comprehensive patterns for using the Trainer API, setting up TrainingArguments, and pushing fine-tuned models to the Hugging Face Hub.

Which modalities are supported by this Transformers skill?

This skill supports a wide range of modalities including text (generation, classification), vision (image classification, object detection), audio (speech-to-text), and multimodal tasks (VQA, document QA).

Can I use this for chat-based LLM implementations?

Absolutely. It covers the use of chat templates and AutoModelForCausalLM for building conversational AI features with models like Llama 3 or Qwen.

Hugging Face Transformers

Name: Hugging Face Transformers
Author: itsmostafa

byitsmostafa

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Data Science & ML

Loads and deploys state-of-the-art pretrained models for text, vision, and audio using the Hugging Face library.

About

This skill provides Claude with expert-level guidance for implementing the Hugging Face Transformers library, the industry standard for machine learning development. It facilitates the integration of pretrained models into applications by offering standardized patterns for model loading, inference via the Pipeline API, and advanced fine-tuning with the Trainer API. Whether you are building natural language processing tools, computer vision systems, or speech recognition features, this skill ensures best practices for device placement, memory optimization, and multimodal task handling.

Key Features

Standardized model and tokenizer loading using the Auto class pattern
10 GitHub stars
End-to-end fine-tuning workflows using the Trainer and TrainingArguments APIs
High-level inference for text, vision, audio, and multimodal tasks via Pipeline API
Advanced text generation strategies including chat templates and sampling
Performance optimization through device mapping and mixed-precision (bfloat16)

Use Cases

Implementing image classification or automated speech recognition features
Developing custom sentiment analysis or named entity recognition pipelines
Fine-tuning small language models (SLMs) on domain-specific datasets

About

Key Features

Standardized model and tokenizer loading using the Auto class pattern
10 GitHub stars
End-to-end fine-tuning workflows using the Trainer and TrainingArguments APIs
High-level inference for text, vision, audio, and multimodal tasks via Pipeline API
Advanced text generation strategies including chat templates and sampling
Performance optimization through device mapping and mixed-precision (bfloat16)

Use Cases

Implementing image classification or automated speech recognition features
Developing custom sentiment analysis or named entity recognition pipelines
Fine-tuning small language models (SLMs) on domain-specific datasets