# transformers-huggingface

A skill that steers your agent through Hugging Face Transformers: model loading, tokenization, Trainer fine-tuning and faster inference.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 758 installs · ★ 286
- Author: Mindrally
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/Mindrally/skills/tree/HEAD/transformers-huggingface/SKILL.md
- Updated: 2026-10-08
- HTML: https://affpapa.org/en/nevibecode/skills/transformers-huggingface

## What it does

The skill frames the agent as an expert in the Hugging Face ecosystem — Transformers, Datasets, Tokenizers and the libraries around them. Answers come as Python examples aimed at the task at hand: sequence classification, tokenization, text generation, vision-language models.

What's inside is a set of conventions plus code patterns. Loading through AutoModel and AutoTokenizer, configuration with AutoConfig, picking the right class per task, device placement across CPU, CUDA and MPS. Separate sections cover padding and truncation, attention masks, the Trainer API with TrainingArguments, dataset handling, LoRA and QLoRA, int8 and int4 quantization, Flash Attention.

The skill assumes a stack you install yourself: transformers, datasets, tokenizers, accelerate, peft for LoRA, bitsandbytes for quantization, safetensors, evaluate. Skip peft and bitsandbytes and the parameter-efficient training advice stays theoretical. One habit the skill pushes is pinning the model revision for reproducibility — worth adopting if you need runs to match.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill transformers-huggingface -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/transformers-huggingface/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/transformers-huggingface/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill transformers-huggingface -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/transformers-huggingface/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill transformers-huggingface -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/transformers-huggingface/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install Mindrally/skills transformers-huggingface
```

2. **Manually: put the files in place**
- File: `.github/skills/transformers-huggingface/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill transformers-huggingface -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/Mindrally/skills --path transformers-huggingface
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/transformers-huggingface/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add Mindrally/skills --skill transformers-huggingface -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/Mindrally/skills/tree/HEAD/transformers-huggingface/SKILL.md

## When to use it
- load a model and tokenizer for a specific task
- set up Trainer with metrics and checkpointing
- cut training memory with LoRA or QLoRA
- speed up inference with quantization and batching
- wire a pipeline for a vision-language model

## Example prompts
- Set up a Trainer for text classification with transformers, add compute_metrics and save the best model
- Show me how to fine-tune with QLoRA and gradient checkpointing on a single GPU
- Build batched inference with int8 quantization and measure throughput

## Source text · SKILL.md · Apache-2.0

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BEGIN THIRD-PARTY FILE edd46525db1bf0c1
````text
---
name: transformers-huggingface
description: Expert guidance for working with Hugging Face Transformers library for NLP, computer vision, and multimodal AI tasks.
metadata:
  maintainer: Mindrally
  source: https://github.com/Mindrally/skills
---

# Transformers and Hugging Face Development

You are an expert in the Hugging Face ecosystem, including Transformers, Datasets, Tokenizers, and related libraries for machine learning.

## Key Principles

- Write concise, technical responses with accurate Python examples
- Prioritize clarity, efficiency, and best practices in transformer workflows
- Use the Hugging Face API consistently and idiomatically
- Implement proper model loading, fine-tuning, and inference patterns
- Use descriptive variable names that reflect model components
- Follow PEP 8 style guidelines for Python code

## Model Loading and Configuration

- Use AutoModel and AutoTokenizer for flexible model loading
- Specify model revision/commit hash for reproducibility
- Handle model configuration properly with AutoConfig
- Use appropriate model classes for the task (ForSequenceClassification, ForTokenClassification, etc.)
- Implement proper device placement (CPU, CUDA, MPS)

## Tokenization Best Practices

- Use tokenizer's `__call__` method with appropriate parameters
- Handle padding and truncation consistently
- Use return_tensors parameter for framework compatibility
- Implement proper attention mask handling
- Handle special tokens correctly for each model family

```python
# Example tokenization pattern
inputs = tokenizer(
    texts,
    padding=True,
    truncation=True,
    max_length=512,
    return_tensors="pt"
)
```

## Fine-tuning with Trainer API

- Use the Trainer class for standard training workflows
- Implement custom TrainingArguments for configuration
- Use proper evaluation strategies and metrics
- Implement callbacks for logging and early stopping
- Handle checkpointing and model saving correctly

```python
# Example Trainer setup
training_args = TrainingArguments(
    output_dir="./results",
    evaluation_strategy="epoch",
    learning_rate=2e-5,
    per_device_train_batch_size=16,
    num_train_epochs=3,
    weight_decay=0.01,
    save_strategy="epoch",
    load_best_model_at_end=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=train_dataset,
    eval_dataset=eval_dataset,
    tokenizer=tokenizer,
    compute_metrics=compute_metrics,
)
```

## Dataset Handling

- Use the datasets library for efficient data loading
- Implement proper dataset mapping and batching
````
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