transformers-huggingface

mindrally

•

6155 months ago

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Expert guidance for working with Hugging Face Transformers library for NLP, computer vision, and multimodal AI tasks.

Quick Install

npxskills add mindrally/skills--skill transformers-huggingface

Instructions

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

# 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

# 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
Use dataset streaming for large datasets
Handle dataset caching appropriately
Implement custom data collators when needed

Efficient Fine-tuning Techniques

Use LoRA (Low-Rank Adaptation) for parameter-efficient fine-tuning
Implement QLoRA for memory-efficient training
Use gradient checkpointing to reduce memory usage
Apply mixed precision training (fp16/bf16)
Implement gradient accumulation for effective larger batch sizes

Inference Optimization

Use model.eval() and torch.no_grad() for inference
Implement batched inference for throughput
Use pipeline API for common tasks
Apply model quantization (int8, int4) for faster inference
Use Flash Attention when available

# Example inference pattern
model.eval()
with torch.no_grad():
    outputs = model(**inputs)
    predictions = outputs.logits.argmax(dim=-1)

Model Hub Integration

Use proper model card documentation
Implement model versioning with tags
Handle private models and authentication
Use push_to_hub for model sharing
Implement proper licensing and attribution

Text Generation

Use GenerationConfig for generation parameters
Implement proper stopping criteria
Use constrained generation when needed
Handle streaming generation for responsive UIs
Apply proper decoding strategies

# Example generation pattern
generation_config = GenerationConfig(
    max_new_tokens=100,
    do_sample=True,
    temperature=0.7,
    top_p=0.9,
    repetition_penalty=1.1,
)

outputs = model.generate(
    **inputs,
    generation_config=generation_config,
)

Multi-modal Models

Use appropriate processors for vision-language models
Handle image preprocessing correctly
Implement proper feature extraction
Use AutoProcessor for multi-modal inputs

Error Handling and Validation

Handle model loading errors gracefully
Validate tokenizer outputs before model inference
Implement proper OOM error handling
Use try-except for hub operations
Log warnings for deprecated features

Dependencies

transformers
datasets
tokenizers
accelerate
peft (for LoRA)
bitsandbytes (for quantization)
safetensors
evaluate

Key Conventions

Always specify model revision for reproducibility
Use appropriate dtype for model weights (float32, float16, bfloat16)
Handle padding side correctly for each model family
Document model requirements and limitations
Use consistent preprocessing across training and inference
Implement proper memory management for large models

Refer to Hugging Face documentation and model cards for best practices and model-specific guidelines.

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