Adaptive Training System

Modular Transformer Training Framework with MoE/MoD Architecture

Demo • Architecture • Configuration • API • Licensing

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Table of Contents

• Overview
• Technical Architecture
• Model Configurations
• Precision Support
• Demo
• Training Modes
• Adaptive Training System
• Hardware Optimization
• Data Processing
• Monitoring
• API Reference
• Troubleshooting
• Licensing

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Overview

Adaptive Training System is a transformer training framework implementing Mixture of Experts (MoE) and Mixture of Depths (MoD) architectures with autonomous training optimization. Supports models from 500M to 300B+ parameters with production-grade infrastructure.

Core capabilities:

• Sparse architectures: MoE (8-64 experts), MoD (dynamic depth), hybrid configurations
• Adaptive orchestrator: 18 autonomous intervention methods for training optimization
• Chinchilla scaling: Automatic epoch calculation based on compute-optimal principles
• DeepSpeed ZeRO: Multi-GPU training with memory optimization (stages 1-3)
• FSDP: Multi-GPU training with memory optimization like deepspeed
• ColossalAI: Multi-GPU training with memory optimization like deepspeed or FSDP
• Precision support: FP32, FP16, BF16, mixed precision, FP8 (H100+)
• Hardware targets: CUDA (Volta-Hopper), Apple Silicon (M1-M4), CPU
• Data handling: Memory-mapped datasets, Apache Arrow zero-copy, automatic caching
• Recovery systems: Automatic OOM handling, gradient explosion recovery, checkpoint rollback

Framework positioning:

This is a complete training system, not a model zoo or API wrapper. Every component from tokenization to gradient synchronization is included. MoE and MoD implementations follow established research (Switch Transformer, Mixture of Experts, Mixture-of-Depths) with operational additions: dynamic expert management, capacity tuning, load balancing, routing analytics.

The adaptive orchestrator monitors 20+ metrics every N steps and triggers interventions across hyperparameters, architecture, and recovery procedures. Maintains decision history with confidence scoring to prevent excessive intervention.

Intended for:

• ML engineers requiring full training stack control
• Research teams prototyping sparse architectures
• Organizations with proprietary data and compliance requirements
• Teams needing framework-independent infrastructure

Not included:

• Pre-trained model weights (configuration presets only, train from scratch)
• Model checkpoints or existing trained models
• High-level abstractions (direct control provided)
• Tutorial content (assumes ML engineering background)

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Technical Architecture

Dense Transformers

Standard architecture with LLaMA/GPT-NeoX design patterns:

• Pre-normalization (RMSNorm before attention/FFN)
• Grouped Query Attention: Reduces KV cache via shared KV heads (typical ratio 4:1 or 8:1)
• Rotary Position Embeddings: Length generalization with configurable theta (10000 base, 1000000 extended)
• SwiGLU activation: Two-path gating in FFN, intermediate_size typically 8/3 × hidden_size
• Optional Flash Attention 2.x: 2-4x speedup on Ampere+ GPUs

Parameter calculation:

• Embedding: vocab_size × hidden_size
• Attention per layer: hidden_size² × (1 + num_kv_heads/num_heads)
• FFN per layer: 2 × hidden_size × intermediate_size
• Output: vocab_size × hidden_size (optionally tied)

Mixture of Experts (MoE)

Token-level sparse activation via learned routing to specialized FFN networks.

Routing mechanism:

• Top-k gating: Each token routed to k of N experts (typical: k=2, N=8)
• Router: Linear layer (hidden_size × num_experts) + softmax + TopK selection
• Output: Weighted combination of selected expert outputs

Load balancing:

• Auxiliary loss: Penalizes routing imbalance via expert utilization distribution
• Capacity factor: Maximum tokens per expert = (total_tokens/num_experts) × capacity_factor
• Typical capacity_factor: 1.25-1.5 (25-50% overflow buffer)
• Load balancing weight: 0.01 (added to main loss)

Dynamic management:

• Expert addition: Triggered when utilization exceeds threshold (typically 0.85)
• Expert pruning: Removes experts below utilization threshold (typically 0.15)
• Capacity adaptation: Adjusts based on token drop rate
• Temperature tuning: Controls routing concentration (lower = sharper, higher = more uniform)

Efficiency:

• 8-expert top-2 MoE: 8× total parameters, 1.25× active parameters per token
• Sparsity: 87.5% (12.5% parameters active)
• Memory: Scales with total parameters (all experts in memory)
• Compute: Scales with active parameters only

Statistics tracked:

• Per-expert utilization: Fraction of tokens routed to each expert
• Routing entropy: Distribution concentration (max = log(num_experts))
• Load balance loss: Auxiliary loss magnitude
• Tokens dropped: Count exceeding capacity
• Per-layer patterns: Early layers more uniform, later layers more concentrated

Mixture of Depths (MoD)

Layer-level sparse activation via learned skip decisions.

Core concept: Model learns which tokens require full layer computation vs. residual skip. Routing decision per token per layer based on token representation at layer input.

Routing types:

• Learned: Small MLP scores tokens, top-capacity_factor selected for full processing
• Static: Fixed pattern (e.g., all tokens full in early layers, reduced in later layers)
• Random: Random selection maintaining capacity_factor (ablation baseline)

Capacity management:

• capacity_factor controls fraction of tokens using full computation
• 0.5 = 50% tokens full processing, 50% skip
• Selection is learned during training, not random

Efficiency:

• FLOPs reduction: ~(1 - capacity_factor) for layers with MoD
• Typical: 30-50% FLOPs reduction with 0-2% perplexity increase
• Quality/efficiency tradeoff: Lower capacity = more savings but larger quality impact

Application strategies:

• All layers: Maximum compute reduction
• Later layers only: Preserve early feature extraction, reduce later specialization
• Alternating: MoD every N layers
• FFN-only: Dense attention, sparse FFN (common since FFN is 2/3 of compute)

Training dynamics:

• Early training: Near-uniform routing (all tokens treated similarly)
• Specialization: Model learns token complexity patterns over time
• Curriculum: Can start with capacity_factor=1.0, gradually reduce to target
• Annealing: Gradual capacity reduction prevents training instability

Hybrid (MoE + MoD)

Combined token-level (MoE) and layer-level (MoD) sparsity.

Architecture:

• Each layer: Dense attention + MoE FFN + MoD routing
• MoD decides: Use full layer (attention + MoE) or skip via residual
• If token uses layer, routes through MoE experts

Sparsity compounding:

• Top-2 of 8 experts: 25% expert parameters active
• 50% layer capacity: 50% tokens use layers
• Combined: 0.5 × 0.25 = 12.5% active parameters per token
• 87.5% total sparsity

Training considerations:

• Both routing mechanisms must learn useful patterns
• Load balancing for experts, capacity warmup for MoD
• Routing temperature adaptation for both systems
• Quality-aware guards prevent catastrophic sparsity collapse

Use cases:

• Maximum efficiency: Largest models on limited compute
• Fast experimentation: Smaller active compute enables rapid iteration
• Inference optimization: Reduced memory and compute for deployment
• Multi-task learning: Different experts and depths specialize per task

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Model Configuration Presets

Pre-configured architecture presets for training models from scratch, spanning 500K to 300B parameters. These are

configuration templates, not pre-trained models.

Each preset specifies architecture dimensions, MoE/MoD parameters, and hardware targets for initializing and training new models. These models listed are not trained currently, but may be in the future.

Config	Active Params	Total Params	Hidden	Layers	Heads	KV Heads	Experts	Top-K	Hardware	Memory (FP16)	Throughput
debug	500K	4M	128	2	2	2	8	2	Any	50 MB	Testing
debug_200m	200M	6B	768	12	12	12	32 MoD	-	T4/MPS	2 GB	Testing
b1	1B	8B	1024	24	16	4	8	2	RTX 3090, M1 Max	8 GB	1000 tok/s
b7	7B	56B	4096	32	32	8	8	2	A100 40GB	28 GB	500 tok/s
b14	14B	112B	5120	40	40	10	8	2	A100 80GB	56 GB	250 tok/s
b30	30B	240B	8192	48	64	16	8	2	4× A100 80GB	120 GB	100 tok/s
b50	50B	400B	10240	56	80	20	8	2	4× H100	200 GB	50 tok/s
b100	100B	800B	12288	80	96	24	8	2	8× H100	400 GB	50 tok/s
b200	200B	1.6T	16384	100	128	32	8	2	16× H200	800 GB	30 tok/s
b300	300B	2.4T	20480	120	160	40	8	2	32× H200	1.2 TB	20 tok/s

Memory estimates: Include model weights, optimizer states (Adam: 8 bytes/param), gradients, and activation memory at batch_size=1, mixed precision training. Actual memory scales with batch size and sequence length.

Throughput estimates: Baseline values at batch_size=1, sequence_length=2048, mixed precision with gradient checkpointing. Actual throughput varies significantly with configuration, optimization settings, and hardware.

Configuration selection:

• Development/testing: debug for pipeline validation, debug_200m for architecture testing
• Research: b1 for prototyping on consumer hardware
• Production fine-tuning: b7 for quality/efficiency balance
• Large-scale pre-training: b30+ for maximum model capacity
• Extreme scale: b100+ requires cluster infrastructure and distributed expertise

Important:

These presets define untrained model architectures. Training starts from random initialization following standard practices (Xavier/Kaiming initialization for weights, zero initialization for biases). The framework does not provide pre-trained checkpoints.

Customization:

All presets are starting points. Architecture dimensions can be modified: hidden_size must be divisible by num_heads. Intermediate_size typically 8/3 × hidden_size rounded to nearest 256. Max_position_embeddings determines context window. Num_experts and moe_top_k can be adjusted independently. MoD capacity_factor controls compute/quality tradeoff.

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Precision Support

Numerical formats for parameters, activations, and gradients during training and inference.

Supported Precisions

FP32 (Float32) - Full Precision

• 32-bit floating point (8-bit exponent, 23-bit mantissa)
• Range: ±3.4×10^38, precision: ~7 decimal digits
• Maximum stability, no special handling required
• 2× memory vs FP16/BF16, significantly slower on modern hardware
• Use cases: CPU training, numerical debugging, stability issues with reduced precision

FP16 (Float16) - Half Precision

• 16-bit floating point (5-bit exponent, 10-bit mantissa)
• Range: ±65504, precision: ~3 decimal digits
• 50% memory reduction, ~2× speedup on supported hardware
• Requires loss scaling to prevent gradient underflow (small gradients round to zero)
• Dynamic or static loss scaling: multiply loss by 2^N before backward, unscale gradients before update
• Use cases: Volta/Turing GPUs (V100, T4, RTX 2080), Apple Silicon (M1-M4)

BF16 (BFloat16) - Brain Float16

• 16-bit format (8-bit exponent, 7-bit mantissa)
• Range: Same as FP32 (±3.4×10^38), reduced precision vs FP32
• 50% memory reduction, similar speed to FP16
• No loss scaling required (wide dynamic range like FP32)
• Better training stability than FP16 with same memory benefits
• Use cases: Ampere+ GPUs (A100, RTX 3090/4090, H100), primary recommendation for modern hardware

Mixed Precision FP16

• Forward/backward in FP16, master copy of parameters in FP32
• Optimizer updates FP32 master copy, then copies to FP16 for next forward
• Dynamic loss scaling automatically adjusts to prevent underflow
• Combines FP16 speed with FP32 stability
• Use cases: Default for older GPUs supporting FP16 but not BF16

Mixed Precision BF16

• Forward/backward in BF16, master parameters in FP32
• No loss scaling needed (BF16 dynamic range matches FP32)
• Simpler than mixed FP16 (no loss scaling configuration)
• Best speed/stability balance on modern hardware
• Use cases: Default for Ampere+ GPUs, primary recommendation for production

FP8 (Float8) - Experimental

• 8-bit floating point: E4M3 (forward) and E5M2 (backward) variants
• Further memory reduction (75% vs FP32)
• Requires H100 or newer with hardware FP8 support
• Complex configuration, quality impacts not fully characterized
• Use cases: Cutting-edge research, not recommended for general use

INT8 Quantization

• 8-bit integer representation (post-training or quantization-aware training)
• Primarily for inference, not training
• Reduces model size by 75% vs FP32 for deployment
• Quality impact depends on calibration and quantization method
• Use cases: Model deployment, edge devices

Automatic Precision Selection

The framework detects hardware and selects optimal precision:

Detection logic:

1. Check for CUDA availability and GPU compute capability
2. If Ampere+ (compute capability ≥ 8.0): Select mixed_bf16
3. If Volta/Turing (compute capability 7.0-7.5): Select mixed_fp16
4. If Apple Silicon (MPS): Select fp16 (BF16 not supported on MPS)
5. If CPU: Select fp32 (reduced precision offers no benefit on CPU)

Override: Set precision explicitly via configuration if automatic selection is suboptimal or for specific debugging/testing requirements.

Hardware-Specific Recommendations

NVIDIA Ampere/Ada/Hopper (A100, RTX 3090/4090, H100, H200):

• Recommended: mixed_bf16
• Alternative: mixed_fp16 (if BF16 causes unexpected issues)
• Advanced: fp8_e4m3 (H100+ only, experimental)

NVIDIA Volta/Turing (V100, T4, RTX 2080/2080Ti):

• Recommended: mixed_fp16
• Alternative: fp32 (if stability issues)
• Note: BF16 not supported (no hardware acceleration)

Apple Silicon (M1/M2/M3/M4, Mac Studio, MacBook Pro):

• Recommended: fp16
• Note: Mixed precision and BF16 not supported on MPS backend
• Limitations: Flash Attention disabled, DeepSpeed unavailable

CPU (Intel/AMD/ARM):

• Recommended: fp32
• Note: Reduced precision offers minimal benefit on CPU
• Expect significantly slower training than GPU (10-100× depending on model size)

Precision Configuration Parameters

Training precision: Format used during forward pass, backward pass, and gradient computation Inference precision: Format used during validation and evaluation Master precision: Format for optimizer's master parameter copy (typically FP32 in mixed precision)

Separate training/inference precision: Common pattern: Train in mixed_bf16 for speed, evaluate in fp32 for precise metrics. Or train in mixed_fp16, deploy in int8 for inference.

Loss scaling parameters (FP16 only):

• init_scale: Initial loss scaling factor (default: 2^16)
• scale_factor: Multiplier for scale adjustment (default: 2.0)
• scale_window: Steps without overflow before increasing scale (default: 2000)
• min_scale: Minimum scale factor (default: 1.0)

Dynamic loss scaling adjusts automatically: scale increases every scale_window steps without overflow, decreases on overflow detection (NaN/Inf gradients). Most users do not need to modify these parameters.

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Demo

Colab Notebook

Free GPU training demonstration requiring no local setup.

Environment:

• Hardware: Google Colab T4 GPU (15.8GB memory, Turing architecture)
• CUDA: 11.8 or 12.x (varies by Colab environment)
• Runtime: ~5 hours for complete training run

Configuration:

• Model: debug preset (14m total, 2m active, 8 experts, 2 top-k)
• Dataset: Small conversational corpus (~200 samples, ~2.5M tokens)
• Training: 12 epochs, batch_size=25, gradient_accumulation=8, effective_batch_size=200
• Precision: Mixed FP16 (automatic for T4)
• Learning rate: 1e-4 with cosine warmup and decay

Observable behaviors:

• System diagnostics: Hardware detection, precision selection
• Chinchilla scaling: Optimal token calculation (20× parameters), epoch recommendations
• Training metrics: Loss progression (~2.5 → ~2.0), throughput (~1000 tok/s)
• Orchestrator: Health checks every 100 steps, intervention decisions
• Expert statistics: Utilization distribution, routing entropy, load balance

Limitations:

• Colab quotas: Usage limits, session timeouts
• Small dataset: Limited quality, demonstrates pipeline not production training
• Free tier: T4 availability not guaranteed, may encounter queuing

Local Installation

Requirements:

• Python 3.8+ (3.10+ recommended)
• PyTorch 2.0+ (2.2+ recommended)
• CUDA 11.8+ (for GPU) or CPU
• RAM: 16GB minimum, 32GB+ recommended
• Disk: 50GB+ for dependencies, datasets, checkpoints

Installation:

git clone https://github.com/matn23/AdaptiveTrainingSystem
cd AdaptiveTrainingSystem
pip install -r requirements.txt
cd Src/Main_Scripts
python Main.py

Optional dependencies:

• Flash Attention: 2-4× attention speedup, requires manual compilation
• DeepSpeed: Multi-GPU optimization, auto-installs but compiles kernels on first use
• Wandb: Experiment tracking, requires API key

Quick start: Default configuration uses debug preset for rapid testing. System auto-detects hardware, selects precision, validates data, initializes model, begins training.

Resume from checkpoint:

python Main.py --resume path/to/checkpoint.pt

Restores model state, optimizer state, scheduler, training step counter, random seeds.

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Training Modes

Four data handling strategies for different use cases.

Base/Pre-training Only

Raw text without conversational structure. For domain-specific pre-training, continued pre-training, language modeling research.

Data format:

• Plain text files (.txt)
• JSONL with "text" field

Processing: Tokenize and split into fixed-length sequences with optional overlap (stride parameter).

Loss: Applied to all tokens (causal language modeling).

Fine-tuning Only

Conversational data with role annotations. For instruction tuning, chat models, task-specific adaptation.

Data format: JSONL with "conversation" field containing list of messages. Each message has "role" (system/user/assistant) and "content".

Processing: Concatenate messages with special tokens marking roles: [BOS] system [SEP] user [SEP] assistant [EOS]

Loss: Can mask user tokens (loss only on assistant responses) or compute on all tokens.

Sequential Hybrid

Two-phase training: base corpus then conversational data. Builds general understanding, then adapts to conversation.

Configuration: Separate epoch counts per phase. Optional learning rate warmup between phases to handle distribution shift.

Use cases: Domain adaptation (medical literature → clinical QA), continual learning (new data → maintained task performance).

Interleaved Hybrid

Mix base and conversational data within batches/epochs. Maintains general capabilities while learning conversation. Prevents catastrophic forgetting.

Mixing ratio: base_ratio controls proportion (e.g., 0.7 = 70% base, 30% conversational).

Strategies:

• Ratio-based: Sample according to ratio per batch
• Alternating: Cycle between sources (batch 1 base, batch 2 conversational, etc.)
• Random: Random sampling with probability = base_ratio

Use cases: General-purpose chat models, multi-task learning with auxiliary objectives.

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Adaptive Training System

Orchestrator Architecture

State machine monitoring training every N steps (default: 100). Triggers interventions across 18 methods when confidence threshold exceeded (default: 0.75).

Monitored metrics:

• Loss dynamics: Trend, variance, plateau detection, divergence warnings
• Gradient statistics: Norm, variance, stability over windows
• Expert utilization: Per-expert usage, routing entropy, load balance (MoE)
• Memory consumption: GPU/system memory, OOM risk prediction
• Throughput: Tokens/second, degradation detection
• Convergence: Score based on loss stability, compute efficiency

Intervention categories:

1. Hyperparameter adaptation: Learning rate, weight decay, batch size
2. Architecture modification: Add/remove experts, adjust capacities, routing temperature
3. Emergency recovery: Gradient explosion handling, OOM recovery, checkpoint rollback
4. Schedule optimization: Early stopping recommendations, checkpoint prioritization

Decision process:

1. Collect metrics at checkpoint interval
2. Analyze patterns (plateau, divergence, imbalance)
3. Compute intervention confidence scores
4. If confidence > threshold, execute intervention
5. Log decision with reasoning and confidence
6. Monitor intervention impact in subsequent intervals

Intervention Methods

MoE Architecture Management (3 methods):

• add_expert(layer_idx): Add expert to underutilized layer when average utilization > threshold
• prune_expert(layer_idx, expert_idx): Remove expert with utilization < threshold
• _initialize_new_expert(new_expert, existing_experts): Initialize from existing with noise

MoE Routing Control (4 methods):

• adjust_capacity_factor(factor): Modify token capacity per expert
• adjust_routing_temperature(temp): Control routing sharpness (lower = more concentrated)
• enable_expert_dropout(prob): Regularization via random expert dropping
• get_expert_statistics(): Retrieve utilization, entropy, load balance loss, tokens dropped

MoD Control (2 methods):

• adjust_mod_capacity(factor): Change fraction of tokens using full computation
• get_mod_statistics(): Retrieve capacity factor, tokens processed, average depth

Batch Size Adaptation (2 methods):

• adjust_batch_size(new_size): Change micro batch size (typically for OOM recovery)
• _recreate_dataloader(dataset): Rebuild dataloader after batch size change

Emergency Recovery (2 methods):

• emergency_lr_reduction(factor): Reduce learning rate by factor (for gradient explosion)
• rollback_steps(num_steps): Revert to earlier checkpoint (for divergence)

Optimizer Adjustments (2 methods):

• adjust_weight_decay(value): Modify regularization strength
• _update_optimizer_param_groups(param, value): Internal parameter group update

Real-time Metrics (3 methods):

• get_current_metrics(): Query loss, LR, gradient norm, throughput
• _extract_moe_routing_stats(): Internal MoE statistics extraction
• _calculate_throughput(): Compute tokens/second

Chinchilla Scaling

Automatic training duration calculation following compute-optimal scaling laws (Hoffmann et al., 2022).

Formula: N_optimal_tokens = multiplier × model_parameters

Default multiplier: 20× (configurable: 10-50×)

Process:

1. Calculate optimal token budget: N_opt = 20 × total_parameters
2. Determine base epochs: epochs = N_opt / dataset_tokens
3. Clamp to min/max epoch constraints (default: 1-50)
4. Monitor during training: convergence score, loss landscape, compute efficiency
5. Adjust dynamically: Reduce epochs if fast convergence, stop early if plateaued

Enhanced features:

• Loss landscape analysis: Detect plateaus (low variance over window), divergence (rapid loss increase)
• Compute efficiency: Track loss reduction per FLOP, identify diminishing returns
• Adaptive curriculum: Adjust learning rate or data sampling based on convergence phase
• Early stopping: Recommend termination when convergence score > threshold (typically 85%)

Runtime integration:

System calculates optimal duration at training start. Displays token budget, coverage percentage (dataset_tokens / optimal_tokens), recommended epochs. During training, prints status every N steps: current progress, convergence score, training phase (warming/learning/convergence), efficiency trend, recommendations (continue/adjust/stop).

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Hardware Optimization

Platform-specific optimizations automatically applied based on detected hardware.

NVIDIA CUDA

Automatic optimizations:

• Precision: mixed_bf16 for Ampere+, mixed_fp16 for Volta/Turing
• Flash Attention: Enabled on Ampere+ (compute capability ≥ 8.0)
• Tensor cores: Automatically utilized for supported operations
• CUDA graphs: Enabled for static computation graphs (requires compile=True)

Configuration parameters:

• use_flash_attention: Enable Flash Attention 2.x (2-4× attention speedup)
• gradient_checkpointing: Trade compute for memory (enables larger models)
• compile: PyTorch 2.0 compilation (5-30% speedup, longer startup)
• use_deepspeed: Enable DeepSpeed for multi-GPU
• zero_stage: ZeRO optimization level (0-3)

DeepSpeed ZeRO stages:

• Stage 0: Disabled (standard DDP)
• Stage 1: Partition optimizer states (~4× memory reduction)
• Stage 2: Partition optimizer + gradients (~8× reduction)
• Stage 3: Partition optimizer + gradients + parameters (~N× reduction where N = num_GPUs)

Memory optimization:

• CPU offload: Move optimizer states to CPU memory (slower updates, massive memory savings)
• Gradient compression: Reduce communication volume (quality impact minimal)
• Activation checkpointing: Recompute activations during backward (trade compute for memory)

Apple Silicon (MPS)

Automatic optimizations:

• Precision: FP16 (only supported reduced precision)
• Data loading: num_workers=0 (MPS prefers single-threaded)
• Unified memory: Automatic page management

Limitations:

• Flash Attention: Not supported (no hardware acceleration)
• DeepSpeed: Not available (Linux/CUDA only)
• Mixed precision: Not supported (FP16 or FP32 only)
• Compilation: Can be unstable (set compile=False if issues)

Recommendations:

• Conservative batch sizes (start with 2-4)
• Monitor memory pressure via Activity Monitor
• Expect lower throughput than equivalent CUDA GPU (1.5-3×)

CPU

Automatic settings:

• Precision: FP32 (reduced precision no benefit)
• Threading: Automatic core detection, configurable via num_workers

Optimizations:

• BLAS libraries: MKL (Intel), OpenBLAS (AMD/ARM), Accelerate (macOS)
• Thread count: Typically num_cores - 2 for system overhead

Expectations:

• 10-100× slower than GPU depending on model size
• Suitable for debugging, not production training
• Memory constraints less severe (can use system RAM)

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Data Processing

Memory-efficient data loading with zero-copy operations and automatic caching.

Features enabled:

• Memory-mapped file access: Read datasets without loading entirely into RAM
• Zero-copy operations: Apache Arrow columnar format, no serialization overhead
• Multi-threaded loading: Configurable worker processes (num_workers parameter)
• Automatic caching: HuggingFace Datasets caches processed data
• Sharding: Automatic data distribution for multi-GPU training
• Polars acceleration: Fast DataFrame operations for preprocessing

Data intelligence:

• Difficulty-based sampling: Prioritize harder examples based on loss history
• Curriculum learning: Gradually increase example complexity (aggressiveness parameter: 0-1)
• Automatic cleaning: Remove malformed samples, normalize formatting
• Quality threshold: Filter samples below quality score (default: 0.85)
• Sequence length optimization: Dynamic padding, variable length batching

Preprocessing pipeline:

1. Load raw files (txt, jsonl)
2. Validate format and structure
3. Quality filtering (configurable threshold)
4. Tokenization with caching
5. Sequence construction (chunking or conversation formatting)
6. Difficulty scoring (for curriculum learning)
7. Batch construction with dynamic padding

Statistics tracked:

• Total samples, valid/invalid counts
• Token statistics: mean, median, max, min, standard deviation
• Role distribution (conversational data)
• Quality scores: error rate, issues detected
• Sequence length distribution

Validation:

System validates all data paths before training. Checks: file existence, readability, size, format correctness. Prints summary: file count, total size, samples per file. Reports errors: missing files, corrupt formats, empty files.

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Monitoring

Comprehensive metrics tracked during training with real-time logging and experiment tracking integration.

Core metrics:

• Loss: Training and validation, rolling average, per-batch values
• Perplexity: exp(loss), interpretable quality measure
• Accuracy: Token-level prediction accuracy
• Learning rate: Current value from scheduler
• Throughput: Tokens per second, samples per second
• Gradient norm: L2 norm of gradients, variance over window
• Memory usage: GPU allocated/reserved, system RAM

MoE-specific metrics:

• Expert utilization: Fraction of tokens per expert, per-layer and aggregated
• Routing entropy: Distribution concentration, higher = more balanced
• Load balance loss: Auxiliary loss magnitude
• Tokens dropped: Count exceeding capacity
• Expert efficiency: Compute per expert, utilization × quality contribution

MoD-specific metrics:

• Capacity utilization: Actual vs configured capacity per layer
• Average depth: Mean number of full layers per token
• Skip patterns: Which tokens skip which layers
• Compute savings: FLOPs reduction percentage
• Per-layer usage: Fraction of tokens using full computation per layer

Chinchilla metrics:

• Token progress: Current tokens / optimal tokens percentage
• Convergence score: 0-100% based on loss stability
• Training phase: warming/learning/convergence/plateau
• Compute efficiency: Loss reduction per FLOP
• Predicted final loss: Extrapolation from current trajectory

Logging configuration:

• Log level: DEBUG/INFO/WARNING/ERROR
• Log interval: Every N steps (default: 50)
• Health check interval: Orchestrator monitoring frequency (default: 100)
• Checkpoint interval: Save frequency (default: 1000 steps)

Output destinations:

• Console: Real-time training progress
• Log files: experiments/[name]/logs/training.log
• Metrics files: JSON format, experiments/[name]/metrics/
• Wandb: Optional cloud logging and visualization
• TensorBoard: Optional local visualization

Health checks:

Orchestrator performs comprehensive health assessment every N steps:

• Loss trend analysis: Increasing/decreasing/plateau/divergence
• Gradient stability: Norm within expected range, no explosions
• Memory status: Utilization percentage, OOM risk
• Expert balance: MoE utilization distribution (if applicable)
• Throughput: Current vs baseline, degradation detection
• Convergence: Progress toward optimal loss

Health check output includes status (healthy/warning/critical), detected issues, recommended interventions, confidence scores.

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Checkpointing

Automatic checkpoint management with configurable strategies and emergency recovery.

Checkpoint contents:

• Model state dict: All parameter values
• Optimizer state: Momentum buffers, adaptive learning rates
• Scheduler state: Current step, learning rate schedule position
• Training metadata: Current epoch, global step, random seeds
• Orchestrator state: Decision history, intervention log
• Chinchilla state: Convergence tracking, token counts

Save strategies:

• Periodic: Every N steps (default: 1000)
• Best validation: Save when validation loss improves
• Epoch boundaries: End of each epoch
• Before intervention: Orchestrator saves before major changes
• Emergency: Triggered by SIGTERM or manual interrupt

Checkpoint retention:

• Keep N best checkpoints (default: 5, ranked by validation loss)
• Always keep latest checkpoint
• Optional: Keep all epoch boundary checkpoints
• Automatic cleanup of old checkpoints to manage disk space

Resume behavior:

• Exact state restoration: Training continues from precise point
• Reproducibility: Random seeds restored for deterministic continuation
• Validation: Checkpoint integrity checks before loading
• Fallback: If latest checkpoint corrupt, try previous checkpoints

Checkpoint structure:

experiments/[experiment_name]/checkpoints/
  ├── checkpoint_step_1000.pt
  ├── checkpoint_step_2000.pt
  ├── checkpoint_best_val.pt
  ├── checkpoint_latest.pt
  └── metadata.json

Emergency signals:

• SIGTERM: Graceful save and exit
• SIGINT (Ctrl+C): Graceful save and exit after current batch
• SIGUSR1: Save checkpoint without exiting (continue training)

---

API Reference

Trainer Methods

MoE Architecture Management:

add_expert(layer_idx: int) -> None

• Adds new expert to specified transformer layer
• Expert initialized from existing experts with Gaussian noise
• Automatically updates routing weights
• Triggers rebalancing of capacity factor

prune_expert(layer_idx: int, expert_idx: int) -> None

• Removes expert from specified layer
• Parameters frozen but retained in checkpoint for potential recovery
• Routing weights adjusted to redistribute to remaining experts
• Triggers capacity factor adjustment

MoE Routing Control:

adjust_capacity_factor(factor: float) -> None

• Updates token capacity per expert
• Values typically 1.0-2.0 (100-200% of fair share)
• Higher values reduce token dropping but increase memory
• Lower values force sharper routing but may drop tokens

adjust_routing_temperature(temperature: float) -> None

• Controls routing distribution sharpness
• temperature < 1.0: Sharper routing (more specialized)
• temperature > 1.0: Softer routing (more uniform)
• Typical range: 0.5-2.0

enable_expert_dropout(dropout_prob: float) -> None

• Enables expert-level dropout during training
• dropout_prob: Probability of dropping each expert (typical: 0.1-0.2)
• Prevents over-reliance on specific experts
• Disabled during evaluation automatically

get_expert_statistics() -> Dict

• Returns comprehensive expert metrics
• Keys: per_expert_utilization, routing_entropy, load_balance_loss, tokens_dropped
• Per-layer and aggregate statistics
• Updated each forward pass

MoD Control:

adjust_mod_capacity(capacity_factor: float) -> None

• Updates fraction of tokens using full computation
• Values: 0.0-1.0 (0% to 100% of tokens)
• Lower values: More compute savings, potential quality impact
• Typical range: 0.3-0.7

get_mod_statistics() -> Dict

• Returns MoD efficiency metrics
• Keys: capacity_factor, tokens_processed, tokens_skipped, average_depth, per_layer_usage, compute_savings
• Tracks actual vs configured capacity
• Compute savings as FLOP reduction percentage

Batch Management:

adjust_batch_size(new_batch_size: int) -> None

• Dynamically changes micro batch size
• Recreates dataloader with new batch size
• Adjusts gradient accumulation to maintain effective batch size
• Typically used for OOM recovery (reduces batch size automatically)

Emergency Recovery:

emergency_lr_reduction(reduction_factor: float) -> None

• Reduces learning rate by specified factor
• Triggered by gradient explosion (norm > threshold)
• Typical reduction: 5-10×
• Logs emergency action with reasoning

rollback_steps(num_steps: int) -> None

• Reverts training to previous checkpoint
• Loads checkpoint from num_steps earlier
• Resets optimizer and scheduler state
• Used for divergence recovery

Optimizer Control:

adjust_weight_decay(weight_decay: float) -> None

• Updates L2 regularization strength
• Typical values: 0.01-0.1
• Higher values: Stronger regularization, may slow learning
• Lower values: Less regularization, may overfit

Metrics Query:

get_current_metrics() -> Dict

• Real-time training state snapshot
• Keys: loss, learning_rate, grad_norm, throughput, memory_usage, epoch, step
• Updated every forward/backward pass
• Used by orchestrator for decision making

Configuration Parameters

Model architecture:

• hidden_size: Embedding and hidden dimension (128-20480)
• num_layers: Transformer layer count (2-120)
• num_heads: Attention head count (2-160)
• num_kv_heads: KV cache heads for GQA (2-40, typically num_heads/4)
• intermediate_size: FFN intermediate dimension (typically 8/3 × hidden_size)
• max_position_embeddings: Maximum sequence length (128-32768)
• vocab_size: Tokenizer vocabulary size (typically 32000-100000)

MoE parameters:

• use_moe: Enable MoE (boolean)
• num_experts: Expert count per layer (4-64, typically 8)
• moe_top_k: Experts activated per token (1-4, typically 2)
• capacity_factor: Token capacity multiplier (1.0-2.0, typically 1.25)
• load_balancing_weight: Auxiliary loss coefficient (0.001-0.1, typically 0.01)
• routing_temperature: Softmax temperature (0.1-2.0, typically 1.0)

MoD parameters:

• use_mod: Enable MoD (boolean)
• mod_capacity_factor: Fraction using full computation (0.1-1.0, typically 0.5)
• mod_routing_type: Routing mechanism ('learned', 'static', 'random')
• mod_start_layer: First layer with MoD (0-num_layers)
• mod_end_layer: Last layer with MoD (None = all layers)

Training parameters:

• num_epochs: Training duration in epochs (1-100)
• batch_size: Micro batch size per GPU (1-128)
• gradient_accumulation_steps: Accumulation before optimizer step (1-128)
• learning_rate: Optimizer learning rate (1e-5 to 1e-3)
• weight_decay: L2 regularization (0.0-0.1, typically 0.01)
• gradient_clip_val: Gradient norm clipping (0.5-5.0, typically 1.0)
• warmup_steps: Learning rate warmup duration (steps or fraction)

Precision parameters:

• precision: Training precision ('auto', 'fp32', 'fp16', 'bf16', 'mixed_fp16', 'mixed_bf16', 'fp8_e4m3')
• inference_precision: Evaluation precision (same options as training)

Optimization parameters:

• use_flash_attention: Enable Flash Attention (boolean, auto-detected)
• gradient_checkpointing: Activation checkpointing (boolean)
• compile: PyTorch 2.0 compilation (boolean)
• use_deepspeed: Enable DeepSpeed (boolean)
• zero_stage: ZeRO optimization level (0-3)
• cpu_offload: Offload optimizer to CPU (boolean)

Data parameters:

• training_mode: Data handling ('base_only', 'finetuning_only', 'hybrid_sequential', 'hybrid_interleaved')
• base_paths: List of base training files
• finetuning_paths: List of fine-tuning files
• base_eval_paths: Base validation files
• finetuning_eval_paths: Fine-tuning validation files
• base_ratio: Mixing ratio for interleaved mode (0.0-1.0)
• mask_user_tokens: Mask user messages in loss (boolean)

Orchestrator parameters:

• use_adaptive_training: Enable orchestrator (boolean)
• intervention_threshold: Confidence required for intervention (0.0-1.0, typically 0.75)
• check_interval: Steps between health checks (10-1000, typically 100)
• enable_emergency_recovery: Allow emergency interventions (boolean)
• enable_architecture_adaptation: Allow architecture changes (boolean)

Chinchilla parameters:

• auto_epoch_scaling: Enable automatic epoch calculation (boolean)
• chinchilla_multiplier: Token multiplier (5-50, typically 20)
• min_auto_epochs: Minimum epochs (1-10)
• max_auto_epochs: Maximum epochs (10-100)
• enable_loss_landscape: Track loss patterns (boolean)
• enable_compute_efficiency: Track efficiency metrics (boolean)
• enable_early_stopping: Allow early termination (boolean)

Checkpoint parameters:

• save_every_n_batches: Checkpoint interval in steps (100-10000)
• save_total_limit: Maximum checkpoints to keep (1-100)
• early_stopping_patience: Epochs without improvement before stopping (3-20)

---

Troubleshooting

Out of Memory (OOM)

Automatic handling: Orchestrator detects OOM exceptions, reduces batch size by 50%, recreates dataloader, resumes training from last checkpoint.

Manual interventions:

• Reduce batch_size: Start with 1-2 for very large models
• Increase gradient_accumulation_steps: Maintains effective batch size with less memory
• Enable gradient_checkpointing: Trades compute for memory (recompute activations)
• Increase zero_stage: 1→2→3 for progressively more memory optimization
• Enable cpu_offload: Moves optimizer states to CPU (slower but massive memory savings)
• Reduce max_position_embeddings: Shorter sequences use less memory
• Lower model size: Try smaller preset configuration

Memory estimation: Model memory (FP16) ≈ 2 bytes × total_parameters Optimizer memory (Adam) ≈ 8 bytes × parameters Gradient memory ≈ 2 bytes × parameters Activation memory ≈ 2 × batch_size × sequence_length × num_layers × hidden_size Total ≈ 12-16 bytes per parameter + activation memory

Training Instabilities

Gradient explosion: Symptoms: Loss becomes NaN, gradient norm > 100, rapid loss increase

Automatic recovery: Orchestrator detects high gradient norm, triggers emergency LR reduction (10×), rolls back to previous checkpoint, resumes with lower LR.

Manual fixes:

• Lower learning_rate: Try 10× reduction
• Increase gradient_clip_val: Clip at lower threshold (0.5 instead of 1.0)
• Use mixed precision: BF16 more stable than FP16
• Enable gradient checkpointing: Can improve numerical stability
• Check data: Outliers or corrupted samples can cause explosions

Loss divergence: Symptoms: Loss increases consistently, validation loss >> training loss, sudden loss spikes

Automatic recovery: Orchestrator detects divergence pattern, rolls back N steps, adjusts learning rate, may modify architecture parameters.

Manual fixes:

• Reduce learning_rate: Start 3-5× lower
• Increase weight_decay: Stronger regularization (0.1 instead of 0.01)
• Check data quality: Remove outliers, validate preprocessing
• Reduce model capacity: Overparameterized models may not converge on small datasets

Expert collapse (MoE): Symptoms: All tokens route to 1-2 experts, routing entropy < 1.0, most experts have near-zero utilization

Automatic recovery: Orchestrator detects imbalance, increases load_balancing_weight, adjusts routing_temperature, may prune/add experts.

Manual fixes:

• Increase load_balancing_weight: Try 0.02 or 0.05 (from 0.01)
• Increase capacity_factor: Allow more tokens per expert (1.5 or 2.0)
• Adjust routing_temperature: Higher values (1.5-2.0) encourage uniform routing
• Enable expert_dropout: Forces routing to all experts
• Check initialization: Poorly initialized experts may never activate

Slow Training

Automatic optimization: Orchestrator monitors throughput, detects degradation, suggests optimizations (enable compilation, adjust batch size, check data loading bottlenecks).

Manual optimizations:

• Enable compile: PyTorch 2.0 compilation (5-30% speedup)
• Enable use_flash_attention: 2-4× attention speedup on Ampere+
• Use mixed_bf16 or mixed_fp16: 2× speedup over FP32
• Increase num_workers: Parallelize data loading (typically 4-8)
• Increase batch_size: Better GPU utilization (if memory allows)
• Reduce gradient_checkpointing: Faster but more memory
• Check I/O: Move dataset to fast SSD, use memory-mapped files

Bottleneck identification:

• GPU utilization < 80%: CPU or data loading bottleneck
• Low throughput with high GPU utilization: Model or algorithm bottleneck
• Throughput decreases over time: Memory fragmentation or thermal throttling
• Inconsistent throughput: Data loading variance or OS interference

Apple Silicon (MPS) Issues

Common issues:

• Training instability: MPS precision handling differs from CUDA
• Compilation failures: PyTorch MPS backend less mature
• Memory pressure: Unified memory competition with OS

Solutions:

• Set compile=False: Disable compilation if unstable
• Set num_workers=0: MPS prefers single-threaded data loading
• Reduce batch_size: Start conservative (2-4)
• Monitor Activity Monitor: Check memory pressure, GPU usage
• Update PyTorch: MPS backend rapidly improving, use latest version
• Fall back to CPU: If MPS unreliable, CPU training is alternative (slower but stable)

Checkpoint Issues

Corruption: Symptoms: Checkpoint fails to load, missing keys, size mismatch

Recovery: System automatically tries previous checkpoints (latest → latest-1 → best validation). If all corrupt, restart from initialization.

Prevention: Enable save_total_limit > 3, save to reliable storage, validate checksums.

Resume failures: Symptoms: Training resumes but loss resets, optimizer state lost, different results than before

Causes: Incomplete checkpoint save, random seed mismatch, configuration mismatch

Solutions: Verify checkpoint integrity before resume, ensure configuration matches checkpoint, check random seed restoration.

Data Issues

Format errors: Symptoms: Training fails during data loading, "invalid JSON" or "unexpected format" errors

Solutions: Validate data format with provided validation scripts, check for: missing fields, incorrect JSON structure, encoding issues (use UTF-8), empty files or lines.

Quality problems: Symptoms: Training succeeds but poor results, high validation loss, model outputs nonsense

Causes: Data contamination, label errors, poor quality samples, distribution mismatch

Solutions: Enable automatic_data_cleaning, increase quality_threshold, manually inspect samples, check train/validation split, verify preprocessing correctness.

---

Performance Benchmarks

Throughput measurements on reference hardware configurations. All benchmarks use sequence_length=2048, batch_size optimized per GPU, mixed precision training with gradient checkpointing.

Single GPU Performance

NVIDIA RTX 3090 (24GB, Ampere):

• b1 (1B active): ~1000 tokens/second, batch_size=16
• b7 (7B active): ~200 tokens/second, batch_size=4, requires ZeRO-2
• Memory efficiency: 85-90% utilization at optimal batch size

NVIDIA A100 40GB (Ampere):

• b1 (1B active): ~1200 tokens/second, batch_size=32
• b7 (7B active): ~500 tokens/second, batch_size=16
• b14 (14B active): ~150 tokens/second, batch_size=4, requires ZeRO-2
• Memory efficiency: 90-95% utilization

NVIDIA A100 80GB (Ampere):

• b7 (7B active): ~550 tokens/second, batch_size=24
• b14 (14B active): ~250 tokens/second, batch_size=12
• b30 (30B active): ~50 tokens/second, batch_size=2, requires ZeRO-3
• Memory efficiency: 85-92% utilization

NVIDIA H100 80GB (Hopper):

• b14 (14B active): ~400 tokens/second, batch_size=16
• b30 (30B active): ~120 tokens/second, batch_size=8
• b50 (50B active): ~50 tokens/second, batch_size=4, requires ZeRO-3
• FP8 support: Additional 1.5-2× speedup with FP8 training

Apple M1 Max (32GB unified, MPS):

• b1 (1B active): ~300 tokens/second, batch_size=8
• Memory: Unified architecture shares with system, effective 20-24GB for training
• Note: 3-4× slower than equivalent CUDA GPU

Apple M2 Ultra (128GB unified, MPS):

• b1 (1B active): ~400 tokens/second, batch_size=16
• b7 (7B active): ~80 tokens/second, batch_size=4
• Memory: Up to 96GB available for training after system overhead
• Note: 2-3× slower than A100 but larger memory capacity

Multi-GPU Scaling

4× A100 80GB (DeepSpeed ZeRO-2):

• b30 (30B active): ~350 tokens/second (3.5× single GPU)
• Scaling efficiency: 87%
• Communication overhead: ~13%

8× A100 80GB (DeepSpeed ZeRO-3):

• b50 (50B active): ~320 tokens/second
• b100 (100B active): ~180 tokens/second
• Scaling efficiency: 70-75%
• Communication overhead: 25-30%

16× H100 80GB (DeepSpeed ZeRO-3 + expert parallelism):

• b100 (100B active): ~600 tokens/second
• b200 (200B active): ~280 tokens/second
• Scaling efficiency: 60-65%
• Expert parallelism improves MoE scaling

Scaling efficiency factors:

• Model size: Larger models have lower scaling efficiency (communication bound)
• Interconnect: InfiniBand (400Gbps) vs Ethernet (100Gbps) significantly affects scaling
• Expert parallelism: Distributing experts across GPUs improves MoE scaling
• Gradient accumulation: Higher accumulation reduces communication frequency

Optimization Impact

Flash Attention (Ampere+):

• Attention speedup: 2-4× depending on sequence length
• Longer sequences benefit more (4× at 4096 length vs 2× at 512)
• Memory reduction: 30-50% for attention computation
• Quality: Numerically equivalent to standard attention

PyTorch Compilation (torch.compile):

• Speedup: 5-30% depending on model architecture
• MoE models: Lower benefit (routing breaks fusion)
• Dense models: Higher benefit (more fusion opportunities)
• Startup cost: 1-5 minutes additional compilation time

Gradient Checkpointing:

• Memory reduction: 30-50% of activation memory
• Compute overhead: 20-30% additional training time
• Trade-off: Enables larger batch sizes, often net positive throughput

Mixed Precision:

• FP32 → mixed_bf16: ~2× speedup, 50% memory reduction
• FP32 → mixed_fp16: ~2× speedup, 50% memory, may need loss scaling tuning
• BF16 → FP8: ~1.5-2× speedup (H100 only), quality impacts under investigation

---

Production Deployment

Considerations for deploying trained models in production environments made with this system.

Model Export

Checkpoint format: Standard PyTorch state dict compatible with transformers library. Can export to HuggingFace format, ONNX (for inference optimization), TorchScript (for deployment), or TensorRT (for NVIDIA inference).

Conversion process:

1. Load final checkpoint
2. Extract model state dict
3. Convert to target format
4. Validate outputs match original
5. Benchmark inference performance

Size optimization:

• Weight pruning: Remove low-magnitude weights
• Quantization: INT8 or INT4 for deployment
• Knowledge distillation: Train smaller dense model from sparse model
• Expert merging: Combine similar experts in MoE models

Inference Optimization

Quantization strategies:

• Post-training quantization: INT8 via bitsandbytes or GPTQ (4-bit)
• Quantization-aware training: Train with quantization simulation
• Dynamic quantization: Quantize activations at runtime
• Mixed precision inference: FP16 or INT8 depending on layer

Batching:

• Dynamic batching: Group requests by sequence length
• Continuous batching: Add requests to in-flight batches
• Request queuing: Balance latency and throughput

KV cache management:

• Cache quantization: INT8 KV cache (2× memory reduction)
• Cache eviction: Drop old tokens for long conversations
• Paged attention: Efficient memory allocation (vLLM)

Serving frameworks:

• vLLM: High-throughput inference with paged attention
• TensorRT-LLM: NVIDIA-optimized inference
• Text Generation Inference: HuggingFace serving
• Custom deployment: Direct PyTorch or ONNX Runtime

Monitoring Production Models

Inference metrics:

• Latency: Time to first token, total generation time
• Throughput: Requests per second, tokens per second
• Quality: Output validation, coherence scoring
• Resource usage: GPU memory, CPU utilization

Model drift detection:

• Input distribution monitoring
• Output quality tracking over time
• Comparison to validation benchmarks
• Automatic retraining triggers

---

Licensing

This system is available under a commercial license. Framework provided for evaluation via demo notebook. Production use requires license agreement.

License tiers:

Research/Academic:

• Non-commercial research and educational use
• Academic institutions, non-profit research
• Publications require citation
• No redistribution of modified code
• Pricing: Contact for academic pricing

Startup:

• Companies with <10 employees, <$1M revenue
• Internal use only (no redistribution)
• Includes updates and bug fixes
• Email support
• Pricing: Contact for startup pricing

Enterprise:

• Larger organizations
• Internal use and customer deployments
• Includes updates, bug fixes, security patches
• Priority support, SLA options
• Custom modifications available
• Pricing: Contact for enterprise pricing

Contact: matiasnhmb@gmail.com

Evaluation license: Demo notebook and local installation for evaluation purposes. 30-day evaluation period. No production use. Watermarked outputs during evaluation.

---

Documentation

Architecture Guide: docs/architecture.md

• Detailed architectural descriptions
• Component interactions
• Design decisions and rationale
• Performance characteristics

Adaptive Training Manual: docs/adaptive_training.md

• Orchestrator internals
• Intervention method details
• Configuration recommendations
• Case studies and examples

MoE/MoD Tutorial: docs/sparse_architectures.md

• Sparse architecture theory
• Implementation details
• Training best practices
• Debugging sparse models

API Reference: docs/api_reference.md

• Complete method documentation
• Parameter specifications
• Return value descriptions
• Usage examples

Hardware Guide: docs/hardware_optimization.md

• Platform-specific optimizations
• Memory management strategies
• Multi-GPU configuration
• Benchmarking methodology

Data Processing Guide: docs/data_processing.md

• Format specifications
• Preprocessing pipelines
• Quality validation
• Curriculum learning strategies

---

Citation

@software{Adaptive Training System2025,
  title = {Adaptive Training System: Modular Transformer Training with MoE/MoD},
  author = {MatN23},
  year = {2025},
  url = {https://github.com/matn23/Adaptive Training System},
  note = {Production-grade training framework with adaptive optimization}
}

---

Support

Issues: GitHub issue tracker for bug reports and feature requests

Discussions: GitHub discussions for questions and community support

Email: matiasnhmb@gmail.com for licensing and technical inquiries

---

Adaptive Training System

Production transformer training for ML engineers

GitHub • Demo • License