VLA Model Fine-tuning Pipeline

Overview

This project provides a complete pipeline for fine-tuning Vision-Language-Action (VLA) models like SmolVLA for specific robotic tasks. It includes data preparation, training infrastructure, and evaluation frameworks.

Key Features

1. Data Preparation

• Dataset conversion tools
• Data augmentation
• Quality checking
• Format validation

2. Training Infrastructure

• Distributed training support
• Mixed precision training
• Gradient checkpointing
• Checkpoint management

3. Model Architectures

• SmolVLA fine-tuning
• LoRA adaptation
• Custom model heads
• Multi-modal fusion

4. Evaluation Framework

• Real-time inference
• Performance metrics
• Visualization tools
• A/B testing

VLA Models

SmolVLA

A small but powerful Vision-Language-Action model:

• Vision Encoder: SigLIP (400M parameters)
• Language Model: SmolLM (135M parameters)
• Action Decoder: Transformer decoder
• Total Size: ~500M parameters

Supported Tasks

• Pick and Place: Object manipulation
• Tool Use: Using tools to complete tasks
• Multi-step Tasks: Complex manipulation sequences
• Language-conditioned: Following natural language instructions

Installation

# Install dependencies
pip install torch torchvision transformers
pip install accelerate peft
pip install wandb  # Optional for experiment tracking

# Clone the repository
git clone https://github.com/1905185430/vla-finetune.git
cd vla-finetune
pip install -e .

Quick Start

1. Prepare Dataset

# Convert dataset to VLA format
python scripts/prepare_dataset.py \
    --input-dir /path/to/raw/data \
    --output-dir /path/to/vla/dataset \
    --task "pick_and_place"

2. Fine-tune Model

# Fine-tune SmolVLA with LoRA
python scripts/train.py \
    --model-name "smolvla" \
    --dataset-path "/path/to/vla/dataset" \
    --output-dir "outputs/smolvla_finetuned" \
    --lora-rank 16 \
    --batch-size 8 \
    --num-epochs 10

3. Evaluate Model

# Evaluate fine-tuned model
python scripts/evaluate.py \
    --model-path "outputs/smolvla_finetuned" \
    --test-dataset "/path/to/test/data" \
    --output-dir "evaluation_results"

Configuration

Training Configuration

# config/training.yaml
model:
  name: "smolvla"
  pretrained: "HuggingFaceTB/SmolVLA-Base"
  lora:
    rank: 16
    alpha: 32
    dropout: 0.1

dataset:
  path: "/path/to/dataset"
  task: "pick_and_place"
  augmentation: true

training:
  batch_size: 8
  num_epochs: 10
  learning_rate: 1e-4
  warmup_steps: 100
  weight_decay: 0.01
  gradient_checkpointing: true
  mixed_precision: "bf16"

evaluation:
  batch_size: 4
  num_episodes: 10
  metrics: ["success_rate", "completion_time", "path_efficiency"]

Usage Examples

Custom Training Script

from vla_finetune import VLATrainer, SmolVLA
from vla_finetune.dataset import VLADataset

# Load model
model = SmolVLA.from_pretrained("HuggingFaceTB/SmolVLA-Base")

# Load dataset
dataset = VLADataset(
    path="/path/to/dataset",
    task="pick_and_place"
)

# Create trainer
trainer = VLATrainer(
    model=model,
    dataset=dataset,
    config={
        "batch_size": 8,
        "learning_rate": 1e-4,
        "num_epochs": 10
    }
)

# Start training
trainer.train()

Inference Script

from vla_finetune import SmolVLA
import torch

# Load fine-tuned model
model = SmolVLA.from_pretrained("outputs/smolvla_finetuned")
model.eval()

# Prepare input
image = torch.randn(1, 3, 224, 224)  # Camera image
language = "Pick up the red cube"     # Language instruction

# Run inference
with torch.no_grad():
    actions = model.generate(
        image=image,
        language=language,
        max_length=10
    )

print(f"Predicted actions: {actions}")

Performance Optimization

Memory Optimization

• Gradient Checkpointing: Reduce memory usage by 60%
• Mixed Precision: Use BF16 for faster training
• LoRA: Train only 0.1% of parameters

Training Speed

• Distributed Training: Multi-GPU support
• Data Loading: Parallel data loading
• Compilation: PyTorch 2.0 compilation

Results

Benchmark Performance

| Model | Task | Success Rate | Training Time | |——-|——|————–|—————| | SmolVLA-Base | Pick and Place | 45% | - | | SmolVLA-LoRA | Pick and Place | 78% | 2 hours | | SmolVLA-LoRA | Tool Use | 65% | 3 hours |

Real-world Deployment

• Inference Time: 50ms per action
• Memory Usage: 2GB GPU memory
• Success Rate: 70-80% on simple tasks

Troubleshooting

Common Issues

1. Out of memory errors

   # Reduce batch size
   python scripts/train.py --batch-size 4
      
   # Enable gradient checkpointing
   python scripts/train.py --gradient-checkpointing
   

2. Training instability

   # Reduce learning rate
   python scripts/train.py --learning-rate 5e-5
      
   # Increase warmup steps
   python scripts/train.py --warmup-steps 200
   

3. Poor convergence
   • Check data quality
   • Verify task alignment
   • Adjust LoRA rank
   • Increase training epochs

Contributing

We welcome contributions! Please see our Contributing Guide for details.

License

This project is licensed under the MIT License.

Acknowledgments

• HuggingFace Team for SmolVLA and Transformers
• LeRobot Community for dataset tools
• Dian Team for testing and validation

For more information, visit the GitHub repository.