---

name: training-patterns description: Templates and patterns for common ML training scenarios including text classification, text generation, fine-tuning, and PEFT/LoRA. Provides ready-to-use training configurations, dataset preparation scripts, and complete training pipelines. Use when building ML training pipelines, fine-tuning models, implementing classification or generation tasks, setting up PEFT/LoRA training, or when user mentions model training, fine-tuning, classification, generation, or parameter-efficient tuning. allowed-tools: Bash, Read, Write, Edit, Glob, Grep

ML Training Patterns

Purpose: Provide production-ready training templates, configuration files, and automation scripts for common ML training scenarios including classification, generation, fine-tuning, and PEFT/LoRA approaches.

Activation Triggers:

• Building text classification models (sentiment, intent, NER, etc.)
• Training text generation models (summarization, Q&A, chatbots)
• Fine-tuning pre-trained models for specific tasks
• Implementing PEFT (Parameter-Efficient Fine-Tuning) with LoRA
• Setting up training pipelines with HuggingFace Transformers
• Configuring training hyperparameters and optimization
• Preparing datasets for model training

Key Resources:

• scripts/setup-classification.sh - Classification training setup automation
• scripts/setup-generation.sh - Generation training setup automation
• scripts/setup-fine-tuning.sh - Full fine-tuning setup automation
• scripts/setup-peft.sh - PEFT/LoRA training setup automation
• templates/classification-config.yaml - Classification training configuration
• templates/generation-config.yaml - Generation training configuration
• templates/peft-config.json - PEFT/LoRA configuration
• examples/sentiment-classifier.md - Complete sentiment classification example
• examples/text-generator.md - Complete text generation example

Training Scenarios Overview

1. Text Classification

Use cases: Sentiment analysis, intent classification, topic categorization, spam detection, named entity recognition (NER)

Key characteristics:

• Input: Text → Output: Class label(s)
• Typically uses encoder models (BERT, RoBERTa, DistilBERT)
• Fast inference, suitable for production
• Requires labeled training data

Setup command:

./scripts/setup-classification.sh <project-name> <model-name> <num-classes>

Example:

./scripts/setup-classification.sh sentiment-model distilbert-base-uncased 3

2. Text Generation

Use cases: Summarization, question answering, chatbots, text completion, translation, code generation

Key characteristics:

• Input: Text (prompt) → Output: Generated text
• Uses decoder or encoder-decoder models (GPT-2, T5, BART)
• More computationally intensive
• Can be trained with or without labeled data

Setup command:

./scripts/setup-generation.sh <project-name> <model-name> <generation-type>

Example:

./scripts/setup-generation.sh qa-bot t5-small question-answering

3. Full Fine-Tuning

Use cases: When you have sufficient data and compute to retrain all model parameters

Key characteristics:

• Updates all model weights
• Requires significant compute (GPU with 16GB+ VRAM)
• Best for substantial domain adaptation
• Training time: hours to days

Setup command:

./scripts/setup-fine-tuning.sh <project-name> <model-name> <task-type>

Example:

./scripts/setup-fine-tuning.sh medical-classifier bert-base-uncased classification

4. PEFT (Parameter-Efficient Fine-Tuning)

Use cases: Limited compute resources, quick experimentation, domain adaptation with small datasets

Key characteristics:

• Only trains a small subset of parameters (LoRA adapters)
• 10-100x less memory than full fine-tuning
• Fast training (minutes to hours)
• Can fine-tune large models (7B+) on consumer GPUs
• Uses techniques like LoRA, QLoRA, Prefix Tuning, Adapter Layers

Setup command:

./scripts/setup-peft.sh <project-name> <model-name> <peft-method>

Example:

./scripts/setup-peft.sh efficient-classifier roberta-base lora

Classification Training Pattern

Configuration Template

File: templates/classification-config.yaml

Key parameters:

model:
  name: distilbert-base-uncased
  num_labels: 3
  task_type: classification

dataset:
  train_file: data/train.csv
  validation_file: data/val.csv
  test_file: data/test.csv
  text_column: text
  label_column: label

training:
  output_dir: ./outputs
  num_epochs: 3
  batch_size: 16
  learning_rate: 2e-5
  warmup_steps: 500
  weight_decay: 0.01
  evaluation_strategy: epoch
  save_strategy: epoch
  logging_steps: 100
  fp16: true  # Mixed precision training
  gradient_accumulation_steps: 1

optimizer:
  name: adamw
  betas: [0.9, 0.999]
  epsilon: 1e-8

Training Pipeline

1. Dataset Preparation:

from datasets import load_dataset

# Load from CSV
dataset = load_dataset('csv', data_files={
    'train': 'data/train.csv',
    'validation': 'data/val.csv',
    'test': 'data/test.csv'
})

# Preprocess
def preprocess(examples):
    return tokenizer(
        examples['text'],
        truncation=True,
        padding='max_length',
        max_length=512
    )

dataset = dataset.map(preprocess, batched=True)

2. Model Initialization:

from transformers import AutoModelForSequenceClassification

model = AutoModelForSequenceClassification.from_pretrained(
    model_name,
    num_labels=num_classes,
    id2label={0: 'negative', 1: 'neutral', 2: 'positive'},
    label2id={'negative': 0, 'neutral': 1, 'positive': 2}
)

3. Training:

from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(
    output_dir='./outputs',
    num_train_epochs=3,
    per_device_train_batch_size=16,
    per_device_eval_batch_size=32,
    learning_rate=2e-5,
    warmup_steps=500,
    weight_decay=0.01,
    evaluation_strategy='epoch',
    save_strategy='epoch',
    load_best_model_at_end=True,
    metric_for_best_model='accuracy',
    fp16=True,  # Enable mixed precision
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset['train'],
    eval_dataset=dataset['validation'],
    compute_metrics=compute_metrics,
)

trainer.train()

4. Evaluation:

from sklearn.metrics import accuracy_score, precision_recall_fscore_support

def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = predictions.argmax(axis=-1)

    accuracy = accuracy_score(labels, predictions)
    precision, recall, f1, _ = precision_recall_fscore_support(
        labels, predictions, average='weighted'
    )

    return {
        'accuracy': accuracy,
        'precision': precision,
        'recall': recall,
        'f1': f1
    }

# Evaluate on test set
results = trainer.evaluate(dataset['test'])
print(results)

Generation Training Pattern

Configuration Template

File: templates/generation-config.yaml

Key parameters:

model:
  name: t5-small
  task_type: generation
  generation_type: question-answering  # or summarization, translation, etc.

dataset:
  train_file: data/train.json
  validation_file: data/val.json
  input_column: question
  target_column: answer
  max_input_length: 512
  max_target_length: 128

training:
  output_dir: ./outputs
  num_epochs: 5
  batch_size: 8
  learning_rate: 3e-4
  warmup_steps: 1000
  weight_decay: 0.01
  evaluation_strategy: steps
  eval_steps: 500
  save_steps: 500
  logging_steps: 100
  fp16: true
  gradient_accumulation_steps: 2
  predict_with_generate: true

generation:
  max_length: 128
  min_length: 10
  num_beams: 4
  length_penalty: 2.0
  early_stopping: true
  no_repeat_ngram_size: 3

Training Pipeline

1. Dataset Preparation:

from datasets import load_dataset

# Load from JSON (question-answer pairs)
dataset = load_dataset('json', data_files={
    'train': 'data/train.json',
    'validation': 'data/val.json'
})

# Preprocess for seq2seq
def preprocess(examples):
    inputs = tokenizer(
        examples['question'],
        max_length=512,
        truncation=True,
        padding='max_length'
    )

    # Tokenize targets
    with tokenizer.as_target_tokenizer():
        targets = tokenizer(
            examples['answer'],
            max_length=128,
            truncation=True,
            padding='max_length'
        )

    inputs['labels'] = targets['input_ids']
    return inputs

dataset = dataset.map(preprocess, batched=True)

2. Model & Training:

from transformers import AutoModelForSeq2SeqLM, Seq2SeqTrainingArguments, Seq2SeqTrainer

model = AutoModelForSeq2SeqLM.from_pretrained('t5-small')

training_args = Seq2SeqTrainingArguments(
    output_dir='./outputs',
    num_train_epochs=5,
    per_device_train_batch_size=8,
    per_device_eval_batch_size=16,
    learning_rate=3e-4,
    predict_with_generate=True,
    generation_max_length=128,
    generation_num_beams=4,
    fp16=True,
)

trainer = Seq2SeqTrainer(
    model=model,
    args=training_args,
    train_dataset=dataset['train'],
    eval_dataset=dataset['validation'],
)

trainer.train()

3. Generation & Evaluation:

# Generate predictions
def generate_answer(question):
    inputs = tokenizer(question, return_tensors='pt', max_length=512, truncation=True)
    outputs = model.generate(
        **inputs,
        max_length=128,
        num_beams=4,
        length_penalty=2.0,
        early_stopping=True
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Test
question = "What is machine learning?"
answer = generate_answer(question)
print(f"Q: {question}\nA: {answer}")

PEFT/LoRA Training Pattern

Why PEFT/LoRA?

Traditional fine-tuning challenges:

• Requires updating all model parameters (millions to billions)
• High GPU memory requirements (often 40GB+ for 7B models)
• Slow training (hours to days)
• Risk of catastrophic forgetting

PEFT/LoRA benefits:

• Only trains ~0.1-1% of parameters (LoRA adapters)
• 10-100x less memory usage
• 3-10x faster training
• Can fine-tune 7B+ models on consumer GPUs (RTX 3090, 4090)
• Multiple task adapters for same base model

Configuration Template

File: templates/peft-config.json

{
  "peft_type": "LORA",
  "task_type": "SEQ_CLS",
  "inference_mode": false,
  "r": 8,
  "lora_alpha": 16,
  "lora_dropout": 0.1,
  "target_modules": [
    "query",
    "key",
    "value",
    "dense"
  ],
  "bias": "none",
  "modules_to_save": ["classifier"]
}

Key parameters:

• r: LoRA rank (lower = fewer parameters, typically 4-64)
• lora_alpha: Scaling factor (typically 2x rank)
• lora_dropout: Dropout for LoRA layers (0.05-0.1)
• target_modules: Which layers to apply LoRA (query, key, value, dense)

Training Pipeline

1. Install PEFT:

pip install peft

2. Setup PEFT Model:

from transformers import AutoModelForSequenceClassification
from peft import get_peft_model, LoraConfig, TaskType

# Load base model
base_model = AutoModelForSequenceClassification.from_pretrained(
    'roberta-base',
    num_labels=3
)

# Configure LoRA
peft_config = LoraConfig(
    task_type=TaskType.SEQ_CLS,
    inference_mode=False,
    r=8,
    lora_alpha=16,
    lora_dropout=0.1,
    target_modules=['query', 'key', 'value', 'dense']
)

# Apply PEFT
model = get_peft_model(base_model, peft_config)
model.print_trainable_parameters()
# Output: trainable params: 296,448 || all params: 124,940,546 || trainable%: 0.237%

3. Training:

from transformers import TrainingArguments, Trainer

training_args = TrainingArguments(
    output_dir='./peft_outputs',
    num_train_epochs=3,
    per_device_train_batch_size=16,  # Can use larger batch size!
    learning_rate=1e-3,  # Higher learning rate for PEFT
    fp16=True,
)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset['train'],
    eval_dataset=dataset['validation'],
)

trainer.train()

4. Save & Load Adapters:

# Save only LoRA adapters (tiny file, ~1-10MB)
model.save_pretrained('./lora_adapters')

# Load adapters later
from peft import PeftModel
base_model = AutoModelForSequenceClassification.from_pretrained('roberta-base', num_labels=3)
model = PeftModel.from_pretrained(base_model, './lora_adapters')

QLoRA (Quantized LoRA)

For even more memory efficiency with large models:

from transformers import BitsAndBytesConfig

# 4-bit quantization config
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

# Load model in 4-bit
model = AutoModelForCausalLM.from_pretrained(
    'meta-llama/Llama-2-7b-hf',
    quantization_config=bnb_config,
    device_map='auto'
)

# Apply LoRA on top of quantized model
model = prepare_model_for_kbit_training(model)
model = get_peft_model(model, peft_config)

# Now can fine-tune 7B model on 16GB GPU!

Setup Scripts Usage

Classification Setup

cd /home/gotime2022/.claude/plugins/marketplaces/ai-dev-marketplace/plugins/ml-training/skills/training-patterns
./scripts/setup-classification.sh my-classifier distilbert-base-uncased 3

Creates:

• Project directory structure
• Training script with Trainer API
• Configuration file (classification-config.yaml)
• Dataset preparation script
• Requirements.txt
• README with instructions

Arguments:

• project-name: Name of training project
• model-name: HuggingFace model identifier
• num-classes: Number of classification labels

Generation Setup

./scripts/setup-generation.sh my-generator t5-small summarization

Creates:

• Seq2Seq training pipeline
• Generation configuration
• Dataset processing for input-target pairs
• Evaluation with ROUGE/BLEU metrics
• Inference script

Arguments:

• project-name: Name of training project
• model-name: HuggingFace model identifier
• generation-type: summarization, question-answering, translation, etc.

Fine-Tuning Setup

./scripts/setup-fine-tuning.sh domain-model bert-base-uncased classification

Creates:

• Full fine-tuning pipeline
• GPU memory optimization configs
• Gradient checkpointing setup
• Mixed precision training
• Model checkpointing strategy

Arguments:

• project-name: Name of training project
• model-name: HuggingFace model identifier
• task-type: classification or generation

PEFT Setup

./scripts/setup-peft.sh efficient-trainer roberta-base lora

Creates:

• PEFT training pipeline with LoRA
• Adapter configuration
• Memory-efficient training setup
• Adapter save/load utilities
• Multi-adapter management

Arguments:

• project-name: Name of training project
• model-name: HuggingFace model identifier
• peft-method: lora, qlora, prefix-tuning, or adapter

Dataset Formats

Classification Dataset (CSV)

text,label
"This product is amazing!",positive
"Terrible experience",negative
"It's okay, nothing special",neutral

Generation Dataset (JSON)

[
  {
    "question": "What is the capital of France?",
    "answer": "The capital of France is Paris."
  },
  {
    "question": "How does photosynthesis work?",
    "answer": "Photosynthesis is the process where plants convert light energy into chemical energy..."
  }
]

HuggingFace Datasets Integration

from datasets import load_dataset

# Load from HuggingFace Hub
dataset = load_dataset('glue', 'sst2')  # Sentiment classification
dataset = load_dataset('squad')  # Question answering
dataset = load_dataset('cnn_dailymail', '3.0.0')  # Summarization

# Load local files
dataset = load_dataset('csv', data_files='data.csv')
dataset = load_dataset('json', data_files='data.json')

Training Best Practices

1. Hyperparameter Selection

Learning Rate:

• Full fine-tuning: 1e-5 to 5e-5
• PEFT/LoRA: 1e-4 to 1e-3 (can be higher)
• Rule of thumb: Start with 2e-5 for full, 3e-4 for PEFT

Batch Size:

• As large as GPU memory allows
• Use gradient accumulation if batch size limited
• Effective batch size = batch_size × gradient_accumulation_steps

Epochs:

• Classification: 3-5 epochs
• Generation: 5-10 epochs
• Watch for overfitting with validation metrics

Warmup Steps:

• Typically 10% of total training steps
• Helps stabilize training initially

2. GPU Memory Optimization

Techniques:

• Mixed precision (fp16/bf16): 2x memory reduction
• Gradient checkpointing: 30-50% memory reduction (slower training)
• Gradient accumulation: Simulate larger batch sizes
• PEFT/LoRA: 10-100x memory reduction
• 8-bit/4-bit quantization: 2-4x memory reduction

Example:

from transformers import TrainingArguments

training_args = TrainingArguments(
    fp16=True,  # Mixed precision
    gradient_checkpointing=True,  # Memory optimization
    gradient_accumulation_steps=4,  # Effective batch size × 4
    per_device_train_batch_size=4,  # Small batch per GPU
)

3. Monitoring Training

Track these metrics:

• Training loss (should decrease steadily)
• Validation loss (should decrease, not increase)
• Validation accuracy/F1/ROUGE (should increase)
• Learning rate schedule
• GPU memory usage

Use Weights & Biases:

training_args = TrainingArguments(
    report_to='wandb',
    run_name='my-training-run',
)

4. Early Stopping

from transformers import EarlyStoppingCallback

trainer = Trainer(
    callbacks=[EarlyStoppingCallback(early_stopping_patience=3)]
)

5. Model Checkpointing

training_args = TrainingArguments(
    save_strategy='epoch',  # Save after each epoch
    save_total_limit=3,  # Keep only best 3 checkpoints
    load_best_model_at_end=True,  # Load best after training
    metric_for_best_model='f1',  # Choose best by F1 score
)

Common Training Patterns

Pattern 1: Quick Experimentation (PEFT)

When: Testing ideas, limited compute, small datasets Approach: LoRA with small rank (r=4-8) Time: Minutes to 1 hour Memory: Can fine-tune 7B models on 16GB GPU

Pattern 2: Production Classification (Full Fine-Tuning)

When: Production deployment, sufficient labeled data Approach: Full fine-tuning with early stopping Time: 1-6 hours Memory: 16GB GPU for base models (110M-340M params)

Pattern 3: Domain Adaptation (PEFT + Full)

When: Adapting to specific domain, then task-specific fine-tuning Approach:

1. PEFT on domain data (unlabeled or weakly labeled)
2. Full fine-tuning on task data (labeled) Time: 2-12 hours total Memory: 16-40GB GPU

Pattern 4: Multi-Task Learning (Multiple LoRA Adapters)

When: One model for multiple tasks Approach: Train separate LoRA adapters per task, swap at inference Time: 1-3 hours per task Memory: 16GB GPU, adapters are tiny (1-10MB each)

Troubleshooting

Out of Memory (OOM) Errors:

• Reduce batch size
• Enable gradient checkpointing
• Use gradient accumulation
• Switch to PEFT/LoRA
• Use 8-bit quantization

Training Not Converging:

• Lower learning rate
• Increase warmup steps
• Check data quality and preprocessing
• Verify labels are correct
• Try different optimizer (AdamW vs SGD)

Overfitting:

• Add dropout
• Use weight decay
• Get more training data
• Use data augmentation
• Early stopping

Slow Training:

• Enable fp16 mixed precision
• Increase batch size
• Use gradient accumulation less
• Remove gradient checkpointing
• Use faster model variant (distilbert vs bert)

Poor Evaluation Metrics:

• Check data distribution (train vs val vs test)
• Verify preprocessing is consistent
• Try different model architecture
• Increase model size or training time
• Check for label imbalance

---

Supported Models:

• Classification: BERT, RoBERTa, DistilBERT, ALBERT, DeBERTa
• Generation: T5, BART, GPT-2, Llama-2, Mistral, Phi
• PEFT: Compatible with all transformer models

Requirements:

• Python 3.11+
• PyTorch 2.0+
• Transformers 4.30+
• PEFT 0.7+ (for LoRA)
• Datasets 2.14+

Best Practice: Start with PEFT/LoRA for quick iteration, switch to full fine-tuning only when necessary