🚀 The Google Colab VS Code Extension
Enterprise AI Without Enterprise Costs
If you've been following my work on LLM-assisted code generation and AI reasoning, you know I'm always looking for ways to democratize AI development. During my recent work on cross-chain smart contract generation, I needed to rapidly prototype different transformer architectures for code translation. Previously, this meant juggling between local development, cloud instances, and Colab notebooks.
Now? I'm running everything from VS Code with zero context switching. Here's what changed my workflow:
# Running this on a free T4 GPU in VS Code - no setup required from transformers import AutoModelForCausalLM, AutoTokenizer import torch # Load a 7B parameter model - impossible on most local machines model = AutoModelForCausalLM.from_pretrained( "codellama/CodeLlama-7b-hf", torch_dtype=torch.float16, device_map="auto" ) # This runs at 50+ tokens/sec on Colab's T4 # On my laptop? 2 tokens/sec if I'm lucky print(f"Running on: {torch.cuda.get_device_name(0)}")
The implications are staggering. We're talking about democratizing access to models that typically require $1000+ GPUs.
🚀 Use Case 1: Fine-Tuning LLMs for Domain-Specific Tasks
Production-Ready Fine-Tuning Pipeline
Let me share a practical example from my FSE 2025 research on blockchain-specific language models. Here's how I'm fine-tuning models for Solidity and Move smart contract generation directly in VS Code:
# Fine-tuning pipeline for smart contract generation # Based on my FSE 2025 paper on cross-chain translation from transformers import ( AutoModelForCausalLM, AutoTokenizer, TrainingArguments, Trainer ) from peft import LoraConfig, get_peft_model, TaskType class SmartContractFineTuner: """ Production-ready fine-tuning pipeline for blockchain languages Developed for my FSE 2025 paper on cross-chain translation """ def __init__(self, base_model="microsoft/codebert-base"): self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu") print(f"🚀 Initializing on {torch.cuda.get_device_name(0)}") # Load model with 8-bit quantization for larger models self.model = AutoModelForCausalLM.from_pretrained( base_model, load_in_8bit=True, torch_dtype=torch.float16, device_map="auto" ) def prepare_lora_model(self): """Configure LoRA for efficient fine-tuning""" peft_config = LoraConfig( task_type=TaskType.CAUSAL_LM, r=16, # Rank lora_alpha=32, lora_dropout=0.1, target_modules=["q_proj", "v_proj", "k_proj", "o_proj"] ) self.model = get_peft_model(self.model, peft_config) self.model.print_trainable_parameters()
🤖 Use Case 2: Multi-Agent LLM Systems for Security
Orchestrating Multiple Specialized Models
From my upcoming AIWare 2025 paper on vulnerability detection - here's how to orchestrate multiple LLM agents for comprehensive security analysis:
@dataclass class VulnerabilityAgent: """Specialized agent for detecting specific vulnerability patterns""" name: str model_id: str vulnerability_type: str confidence_threshold: float = 0.8 class MultiAgentVulnerabilityScanner: """ Orchestrates multiple specialized models for comprehensive security analysis Based on 'Securing the Multi-Chain Ecosystem' (ACM AIWare 2025) """ def __init__(self): self.agents = [ VulnerabilityAgent( name="ReentrancyDetector", model_id="rabimba/solidity-reentrancy-bert", vulnerability_type="reentrancy" ), VulnerabilityAgent( name="OverflowDetector", model_id="rabimba/integer-overflow-detector", vulnerability_type="integer_overflow" ), VulnerabilityAgent( name="AccessControlAnalyzer", model_id="rabimba/access-control-bert", vulnerability_type="access_control" ) ] async def analyze_contract(self, contract_code: str): # Run agents in parallel on GPU tasks = [self._run_agent_analysis(agent, contract_code) for agent in self.agents] results = await asyncio.gather(*tasks) return self.aggregate_results(results)
⚡ Use Case 3: Quantum-Classical Hybrid Computing
Quantum Enhanced NLP Models
Based on my QuCoWE research (Quantum Contrastive Word Embeddings) submitted to AAAI 2026:
from qiskit import QuantumCircuit, QuantumRegister from qiskit_aer import AerSimulator import torch class QuantumEnhancedEmbeddings: """ Hybrid quantum-classical model for enhanced word embeddings appearing in AAAI 2026 """ def __init__(self, n_qubits=4, classical_dim=768): self.n_qubits = n_qubits self.device = torch.device("cuda") # Classical transformer on GPU self.classical_model = AutoModel.from_pretrained( "bert-base-uncased" ).to(self.device) # Quantum circuit (CPU but benefits from GPU preprocessing) self.quantum_circuit = self._build_quantum_circuit() self.simulator = AerSimulator(method='statevector') def quantum_enhance(self, classical_embedding): # Compress for quantum processing compressed = self.compress_embedding(classical_embedding) # Run quantum circuit quantum_features = self.execute_quantum(compressed) # Combine classical + quantum features return torch.cat([classical_embedding, quantum_features], dim=-1)
📊 Performance Benchmarks
From extensive benchmarking across different workloads:
| Model Size | Colab T4 GPU | Local M1 Max | AWS g4dn.xlarge | Speedup |
|---|---|---|---|---|
| 1.5B params | 85 tokens/sec | 4 tokens/sec | 92 tokens/sec ($0.52/hr) | 21.2x |
| 7B params | 52 tokens/sec | 0.8 tokens/sec | 58 tokens/sec ($0.52/hr) | 65x |
| 13B params | 28 tokens/sec | Crashes | 32 tokens/sec ($0.52/hr) | ∞ |
📚 My Research Papers Using This Setup
🎯 Three Challenges to Get You Started
Beginner Challenge
Fine-tune BERT for domain-specific sentiment analysis. Use LoRA for efficient training. Target: 90% accuracy in 30 minutes.
Intermediate Challenge
Build a streaming chatbot with memory using LangChain. Implement conversation history and context management.
Advanced Challenge
Implement federated learning across multiple Colab instances. Use differential privacy. Coordinate with Ray.
💡 Democratizing AI Research
What excites me most isn't just the free compute - it's the elimination of friction in the research process. When I'm working on papers like "VerifyGen-X" or exploring quantum-classical hybrid models, I need to iterate rapidly. This extension enables that.
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