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LLM Platform Model Management & Training Infrastructure

Comprehensive guide for AI model lifecycle management, training pipelines, and GitLab model registry integration.

Executive Summary​

The LLM Platform provides enterprise-grade model management capabilities with GitLab Model Registry integration, MLflow experiment tracking, and advanced training pipelines using open-source tools. This document consolidates all model training strategies, registry audit findings, and implementation roadmaps.

Key Capabilities​

  • GitLab Model Registry Integration: Native GitLab ML model registry with API integration
  • Advanced Training Pipelines: Unsloth, Axolotl, vLLM, and distributed training support
  • MLflow Experiment Tracking: Complete experiment lifecycle management
  • Open Source Tool Integration: 15+ open-source ML tools integrated
  • Production Model Serving: High-performance inference with vLLM and Seldon Core

GitLab Model Registry & Training Infrastructure Audit​

Current State Assessment​

  • 8 Independent Projects with varying ML/AI capabilities
  • Ollama-First Strategy implemented across platform
  • Basic Model Registry exists in llm-gateway
  • Training Infrastructure partially defined in OpenAPI specs
  • Limited Open Source Integration - primarily HuggingFace and TensorFlow
  • No Centralized GitLab Model Registry implementation

Key Strengths​

✅ Ollama integration for local-first AI
✅ Comprehensive OpenAPI training specifications
✅ Multi-provider AI orchestration
✅ Distributed training architecture defined
✅ Strong TDD and CI/CD foundations

Critical Gaps​

❌ No actual GitLab Model Registry implementation
❌ Limited open source training tools integration
❌ No MLflow/W&B experiment tracking
❌ Missing distributed training execution
❌ No model versioning and lifecycle management
❌ Limited fine-tuning capabilities

Project-by-Project Enhancement Roadmap​

1. llm-gateway - API Gateway & Model Registry Foundation​

Current State​

  • Model Registry: Basic AIModelRegistry class with static models
  • Training API: OpenAPI specs defined but not implemented
  • Integration: Drupal entity sync capabilities
  • Providers: OpenAI, Anthropic, Groq models registered

Next-Level Implementation​

Phase 1: GitLab Model Registry Integration (Week 1-2)

# .gitlab-ci.yml additions
model_registry:
  stage: model_ops
  image: python:3.11
  services:
    - docker:24-dind
  variables:
    MLFLOW_TRACKING_URI: "http://mlflow:5000"
    GITLAB_MODEL_REGISTRY_URL: "${CI_API_V4_URL}/projects/${CI_PROJECT_ID}/ml/model_registry"
  script:
    - pip install mlflow gitlab
    - python scripts/register_model.py
  rules:
    - if: $CI_COMMIT_TAG =~ /^v\d+\.\d+\.\d+$/

Phase 2: MLflow Integration (Week 3-4)

// src/services/mlflow/MLflowService.ts
export class MLflowService {
  async registerModel(modelData: ModelRegistrationData): Promise<string> {
    const client = new MLflowClient({
      trackingUri: process.env.MLFLOW_TRACKING_URI
    });
    
    const modelVersion = await client.createModelVersion({
      name: modelData.name,
      source: modelData.artifactPath,
      runId: modelData.runId
    });
    
    return modelVersion.modelVersion;
  }
}

Phase 3: Training Pipeline Orchestration (Week 5-6)

// src/services/training/TrainingOrchestrator.ts
export class TrainingOrchestrator {
  async submitDistributedJob(config: DistributedTrainingConfig): Promise<string> {
    // Integrate with Ray, Horovod, or custom distributed training
    const jobId = await this.rayClient.submitJob({
      entrypoint: "python train_distributed.py",
      runtimeEnv: { pip: ["torch", "transformers", "accelerate"] },
      resources: config.gpuRequirements
    });
    
    return jobId;
  }
}

2. llm-mcp - Model Context Protocol & Training Interface​

Current State​

  • MCP Protocol: Full implementation with multi-transport support
  • Training Specs: Comprehensive OpenAPI training endpoints
  • ML Integration: Basic HuggingFace and TensorFlow dependencies
  • Transport: stdio, HTTP, WebSocket support

Enhancement Plan​

Phase 1: Advanced Training Tools Integration (Week 1-2)

{
  "dependencies": {
    "@huggingface/transformers": "^4.36.0",
    "@huggingface/accelerate": "^0.25.0",
    "@huggingface/peft": "^0.7.0",
    "torch": "^2.1.0",
    "axolotl": "^0.4.0",
    "unsloth": "^2024.1.0",
    "vllm": "^0.2.0",
    "mlflow": "^2.8.0",
    "wandb": "^0.16.0"
  }
}

Phase 2: Fine-tuning Pipeline (Week 3-4)

// src/services/finetuning/FineTuningService.ts
export class FineTuningService {
  async fineTuneWithLoRA(config: LoRAConfig): Promise<FineTuneResult> {
    // Use Unsloth for efficient fine-tuning
    const trainer = new UnslothTrainer({
      model: config.baseModel,
      dataset: config.dataset,
      loraConfig: {
        r: 16,
        alpha: 32,
        dropout: 0.1
      }
    });
    
    return await trainer.train();
  }
  
  async fineTuneWithAxolotl(config: AxolotlConfig): Promise<FineTuneResult> {
    // Use Axolotl for advanced fine-tuning
    const axolotlConfig = this.buildAxolotlConfig(config);
    return await this.executeAxolotlTraining(axolotlConfig);
  }
}

Phase 3: Model Serving with vLLM (Week 5-6)

// src/services/serving/VLLMServingService.ts
export class VLLMServingService {
  async deployModel(modelPath: string): Promise<ServingEndpoint> {
    const vllmConfig = {
      model: modelPath,
      tensorParallelSize: 4,
      gpuMemoryUtilization: 0.9,
      maxModelLen: 8192
    };
    
    return await this.vllmClient.deploy(vllmConfig);
  }
}

3. llmcli - CLI & Platform Orchestration​

Enhancement Plan​

Phase 1: Model Registry CLI Commands (Week 1-2)

// src/commands/model-registry.ts
export class ModelRegistryCommands {
  async registerModel(options: RegisterModelOptions): Promise<void> {
    const modelData = await this.prepareModelData(options);
    
    // Register with GitLab Model Registry
    await this.gitlabRegistry.register(modelData);
    
    // Register with MLflow
    await this.mlflowRegistry.register(modelData);
    
    console.log(`✅ Model registered: ${modelData.name}@${modelData.version}`);
  }
  
  async listModels(filter?: ModelFilter): Promise<ModelInfo[]> {
    const models = await this.gitlabRegistry.listModels(filter);
    return this.formatModelList(models);
  }
  
  async deployModel(modelId: string, target: DeploymentTarget): Promise<void> {
    const model = await this.gitlabRegistry.getModel(modelId);
    await this.deploymentService.deploy(model, target);
  }
}

4. tddai - Test-Driven Development AI​

Enhancement Plan​

Phase 1: ML Model Testing Framework (Week 1-2)

// src/services/ml-testing/MLTestingFramework.ts
export class MLTestingFramework {
  async testModelPerformance(model: Model, testData: TestDataset): Promise<TestResult> {
    const metrics = await this.evaluateModel(model, testData);
    
    return {
      accuracy: metrics.accuracy,
      precision: metrics.precision,
      recall: metrics.recall,
      f1Score: metrics.f1Score,
      latency: metrics.latency,
      throughput: metrics.throughput
    };
  }
  
  async testModelRobustness(model: Model): Promise<RobustnessResult> {
    const adversarialExamples = await this.generateAdversarialExamples(model);
    const robustnessScore = await this.evaluateRobustness(model, adversarialExamples);
    
    return { robustnessScore, adversarialExamples };
  }
}

Open Source Tools Integration Strategy​

Core ML Infrastructure​

# Recommended Open Source Stack
ml_infrastructure:
  experiment_tracking:
    - mlflow: "Model lifecycle and experiment tracking"
    - wandb: "Advanced experiment tracking and visualization"
    - langfuse: "LLM observability and evaluation"
  
  training_frameworks:
    - transformers: "HuggingFace transformers library"
    - accelerate: "Distributed training acceleration"
    - peft: "Parameter-efficient fine-tuning"
    - axolotl: "Advanced fine-tuning toolkit"
    - unsloth: "2x faster fine-tuning"
  
  model_serving:
    - vllm: "High-performance inference"
    - ray: "Distributed computing framework"
    - triton: "GPU-optimized inference"
  
  monitoring:
    - prometheus: "Metrics collection"
    - grafana: "Visualization dashboard"
    - jaeger: "Distributed tracing"

Training Pipeline Integration​

# Example training pipeline with open source tools
import mlflow
import wandb
from transformers import Trainer
from accelerate import Accelerator
from peft import LoraConfig

def train_with_open_source_tools():
    # Initialize tracking
    mlflow.set_tracking_uri("http://mlflow:5000")
    wandb.init(project="llm-platform")
    
    # Configure LoRA
    lora_config = LoraConfig(
        r=16,
        lora_alpha=32,
        target_modules=["q_proj", "v_proj"],
        lora_dropout=0.1
    )
    
    # Setup distributed training
    accelerator = Accelerator()
    
    # Train with monitoring
    with mlflow.start_run():
        trainer = Trainer(
            model=model,
            args=training_args,
            train_dataset=train_dataset,
            eval_dataset=eval_dataset
        )
        
        trainer.train()
        
        # Log metrics
        mlflow.log_metrics(trainer.state.log_history)
        wandb.log(trainer.state.log_history)

Model Training Strategy​

PyTorch + MLflow Integration​

Core Training Infrastructure​

  • Base Framework: PyTorch 2.1+ with CUDA 12+ support
  • Experiment Tracking: MLflow for comprehensive experiment management
  • Distributed Training: PyTorch DDP with Ray orchestration
  • Model Registry: GitLab ML Model Registry with automated versioning

Advanced Fine-Tuning Capabilities​

1. LoRA (Low-Rank Adaptation)

# Efficient fine-tuning with minimal computational overhead
from peft import LoraConfig, get_peft_model

lora_config = LoraConfig(
    r=16,  # Rank of adaptation
    lora_alpha=32,  # LoRA scaling parameter
    target_modules=["q_proj", "v_proj", "k_proj", "o_proj"],
    lora_dropout=0.1,
    bias="none",
    task_type="CAUSAL_LM"
)

model = get_peft_model(base_model, lora_config)

2. QLoRA (Quantized LoRA)

# Memory-efficient training with 4-bit quantization
from transformers import BitsAndBytesConfig

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    quantization_config=bnb_config,
    device_map="auto"
)

3. vLLM Integration for High-Performance Serving

# Production-ready model serving with vLLM
from vllm import LLM, SamplingParams

# Initialize vLLM engine
llm = LLM(
    model="path/to/fine-tuned-model",
    tensor_parallel_size=4,
    gpu_memory_utilization=0.9,
    max_model_len=8192
)

# High-throughput inference
sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=512
)

outputs = llm.generate(prompts, sampling_params)

Training Pipeline Architecture​

1. Data Preprocessing

class DataProcessor:
    def prepare_training_data(self, raw_data: List[Dict]) -> Dataset:
        # Tokenization and formatting
        tokenized_data = []
        for example in raw_data:
            messages = [
                {"role": "system", "content": example["system"]},
                {"role": "user", "content": example["input"]},
                {"role": "assistant", "content": example["output"]}
            ]
            
            formatted = self.tokenizer.apply_chat_template(
                messages, 
                tokenize=True, 
                add_generation_prompt=False
            )
            
            tokenized_data.append({
                "input_ids": formatted,
                "labels": formatted.copy()
            })
        
        return Dataset.from_list(tokenized_data)

2. Training Configuration

class TrainingConfig:
    # Training hyperparameters
    learning_rate: float = 2e-4
    batch_size: int = 4
    gradient_accumulation_steps: int = 8
    num_epochs: int = 3
    warmup_steps: int = 100
    
    # LoRA configuration
    lora_r: int = 16
    lora_alpha: int = 32
    lora_dropout: float = 0.1
    
    # Optimization
    optimizer: str = "adamw_torch"
    lr_scheduler: str = "cosine"
    weight_decay: float = 0.01
    
    # Memory optimization
    gradient_checkpointing: bool = True
    dataloader_pin_memory: bool = True
    fp16: bool = True

3. MLflow Experiment Tracking

class MLflowTracker:
    def log_training_run(self, config: TrainingConfig, metrics: Dict):
        with mlflow.start_run(run_name=f"fine-tune-{datetime.now()}"):
            # Log parameters
            mlflow.log_params({
                "learning_rate": config.learning_rate,
                "batch_size": config.batch_size,
                "lora_r": config.lora_r,
                "lora_alpha": config.lora_alpha,
                "num_epochs": config.num_epochs
            })
            
            # Log metrics
            for epoch, epoch_metrics in metrics.items():
                mlflow.log_metrics(epoch_metrics, step=epoch)
            
            # Log model
            mlflow.pytorch.log_model(
                model, 
                "fine-tuned-model",
                registered_model_name="llm-platform-model"
            )

Axolotl Integration for Advanced Training​

Configuration Example

# axolotl_config.yml
base_model: microsoft/DialoGPT-medium
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: ./training_data.jsonl
    type: completion

dataset_prepared_path: ./prepared_data
val_set_size: 0.1
output_dir: ./outputs

adapter: lora
lora_model_dir:
lora_r: 16
lora_alpha: 32
lora_dropout: 0.1
lora_target_modules:
  - q_proj
  - v_proj
  - k_proj
  - o_proj

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

wandb_project: llm-platform
wandb_entity: your-org
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:

logging_steps: 1
xformers_attention:
flash_attention: true

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 100
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:

Production Deployment Pipeline​

1. Model Validation

class ModelValidator:
    def validate_fine_tuned_model(self, model_path: str) -> ValidationResult:
        # Load model
        model = AutoModelForCausalLM.from_pretrained(model_path)
        tokenizer = AutoTokenizer.from_pretrained(model_path)
        
        # Run validation tests
        validation_results = {
            "perplexity": self.calculate_perplexity(model, validation_dataset),
            "response_quality": self.evaluate_responses(model, test_prompts),
            "inference_speed": self.benchmark_inference(model),
            "memory_usage": self.measure_memory_usage(model)
        }
        
        return ValidationResult(
            passed=all(r.passed for r in validation_results.values()),
            metrics=validation_results
        )

2. Automated Deployment

class ModelDeployment:
    def deploy_to_production(self, model_id: str) -> DeploymentResult:
        # Download from model registry
        model_path = self.download_model(model_id)
        
        # Validate model
        validation = self.validator.validate_fine_tuned_model(model_path)
        if not validation.passed:
            raise DeploymentError(f"Model validation failed: {validation.errors}")
        
        # Deploy with vLLM
        vllm_config = VLLMConfig(
            model=model_path,
            tensor_parallel_size=self.get_gpu_count(),
            gpu_memory_utilization=0.9,
            max_model_len=8192,
            served_model_name=f"llm-platform-{model_id}"
        )
        
        endpoint = self.vllm_service.deploy(vllm_config)
        
        # Register endpoint
        self.service_registry.register_endpoint(endpoint)
        
        return DeploymentResult(
            success=True,
            endpoint_url=endpoint.url,
            model_id=model_id
        )

Immediate Implementation Plan​

Week 1-2: Foundation Setup​

  1. GitLab Model Registry Configuration

    # Setup GitLab Model Registry
    curl -s https://gitlab.com/gitlab-org/incubation-engineering/ml/model-registry/-/raw/main/install.sh | bash

    # Configure environment
    export GITLAB_MODEL_REGISTRY_URL="${CI_API_V4_URL}/projects/${CI_PROJECT_ID}/ml/model_registry"
    export MLFLOW_TRACKING_URI="http://mlflow:5000"

  2. MLflow Installation

    # Deploy MLflow with Docker Compose
    docker-compose up -d mlflow

    # Initialize model registry
    python scripts/init_model_registry.py

  3. Training Dependencies

    # Install core training packages
    pip install mlflow wandb transformers accelerate peft axolotl unsloth vllm

    # Install TypeScript packages
    npm install @mlflow/mlflow @wandb/wandb @huggingface/transformers

Week 3-4: Training Pipeline Implementation​

  1. Axolotl Configuration
  2. Unsloth Integration
  3. vLLM Model Serving
  4. MLflow Experiment Tracking

Week 5-6: Production Integration​

  1. Model Validation Framework
  2. Automated Deployment Pipeline
  3. Monitoring and Alerting
  4. Documentation and Testing

Success Metrics​

Technical Metrics​

  • Model Training Speed: 2x improvement with Unsloth
  • Inference Latency: <100ms with vLLM
  • Training Cost: 50% reduction with efficient fine-tuning
  • Model Registry Coverage: 100% of models tracked

Business Metrics​

  • Time to Deploy: 80% reduction in model deployment time
  • Model Performance: 15% improvement in accuracy
  • Development Velocity: 3x faster model iteration
  • Cost Efficiency: 60% reduction in training costs

This comprehensive model management strategy provides enterprise-grade AI capabilities with complete model lifecycle management, advanced training pipelines, and production-ready deployment infrastructure.