LLM Training

Master Large Language Model training including pre-training, fine-tuning, and alignment techniques with modern optimization strategies.

LLM Training Pipeline

The complete LLM training pipeline consists of multiple stages:

1. Pre-training (Foundation Models)
- Causal Language Modeling: Next-token prediction on massive text corpora
- Masked Language Modeling: BERT-style bidirectional training (less common for modern LLMs)
- Mixture of Experts (MoE): Scale model capacity while maintaining computational efficiency
- Scale Laws: Optimal compute allocation following Chinchilla scaling laws

2. Supervised Fine-Tuning (SFT)
- Train on high-quality instruction-response pairs
- Format data as conversation turns or prompt-completion pairs
- Preserve pre-trained knowledge while learning task-specific behaviors
- Typical dataset sizes: 10K-100K examples

3. Alignment Training
- Reinforcement Learning from Human Feedback (RLHF): Optimize for human preferences
- Direct Preference Optimization (DPO): Simpler alternative to RLHF
- Constitutional AI: Self-improvement through critique and revision
- Red Team Testing: Evaluate safety and robustness

4. Deployment Optimization
- Model Quantization: INT8/INT4 precision for inference speedup
- Knowledge Distillation: Transfer knowledge to smaller models
- Speculative Decoding: Accelerate generation with draft models

Training Paradigms & Architectures

Modern LLM training leverages advanced architectures and training strategies:

Transformer Architectures:
- Decoder-Only: GPT-style autoregressive models (GPT-4, LLaMA, PaLM)
- Encoder-Decoder: T5-style sequence-to-sequence models
- Encoder-Only: BERT-style models for understanding tasks
- Mixture of Experts (MoE): Switch Transformer, GLaM, PaLM-2

Training Strategies:
- Autoregressive Training: Next-token prediction with teacher forcing
- Prefix LM: Bidirectional encoding + autoregressive generation
- GLM: General Language Model with blank infilling objectives
- PaLM: Pathways Language Model with sparsely activated experts

Scaling Techniques:
- Data Parallelism: Distribute batches across multiple GPUs/TPUs
- Model Parallelism: Split model layers across devices
- Pipeline Parallelism: Stage model execution across pipeline stages
- Tensor Parallelism: Distribute individual operations across devices
- 3D Parallelism: Combine data, model, and pipeline parallelism

Memory Optimization:
- ZeRO: Zero Redundancy Optimizer for distributed training
- Gradient Checkpointing: Trade computation for memory
- Mixed Precision: FP16/BF16 training with loss scaling
- CPU Offloading: Move optimizer states and gradients to CPU

Advanced Optimization for LLMs

State-of-the-art optimization techniques for efficient LLM training:

Modern Optimizers:
- AdamW: Weight decay decoupled from gradient-based update
- Lion: Evolved sign momentum optimizer with better generalization
- Adafactor: Memory-efficient adaptive learning rate with factorized second moments
- 8-bit Adam: Memory-efficient optimizer using 8-bit statistics
- Sophia: Second-order optimizer designed for language model pre-training

Learning Rate Scheduling:
- Cosine Annealing: Smooth decay following cosine curve
- Linear Warmup: Gradual increase from 0 to max LR over warmup steps
- Inverse Square Root: Decay proportional to 1/√step used in Transformer training
- Polynomial Decay: Polynomial learning rate schedule with configurable power
- OneCycleLR: Single cycle with peak learning rate in middle of training

Gradient Management:
- Gradient Clipping: Clip by norm (typically 1.0) to prevent exploding gradients
- Gradient Accumulation: Simulate larger batch sizes across multiple micro-batches
- Gradient Checkpointing: Recompute activations during backward pass to save memory
- Stochastic Weight Averaging (SWA): Average model weights over training trajectory

Memory Optimization:
- DeepSpeed ZeRO: Partition optimizer states, gradients, and parameters
- Fully Sharded Data Parallel (FSDP): PyTorch's native ZeRO implementation
- CPU Offloading: Move inactive parameters and optimizer states to CPU
- Activation Checkpointing: Store subset of activations and recompute rest

LLM Training Monitoring & Evaluation

Comprehensive monitoring for large-scale language model training:

Language Modeling Metrics:
- Perplexity: Primary metric for autoregressive language models (lower is better)
- Cross-Entropy Loss: Standard training objective for next-token prediction
- Bits per Character/Byte: Information-theoretic measure of compression
- BLEU/ROUGE Scores: Evaluation on downstream generation tasks
- Token Accuracy: Percentage of correctly predicted tokens

Training Dynamics:
- Learning Rate Schedule: Track adaptive learning rate changes
- Gradient Norm: Monitor gradient magnitudes to detect vanishing/exploding gradients
- Parameter Updates: Ratio of update magnitude to parameter magnitude
- Loss Spikes: Detect and recover from training instabilities
- Throughput: Tokens per second, samples per second, FLOPs utilization

Resource Utilization:
- GPU Memory: Track peak memory usage and memory fragmentation
- Communication Overhead: Time spent on gradient synchronization
- Pipeline Efficiency: Utilization across pipeline stages
- I/O Bottlenecks: Data loading and checkpointing performance

Model Quality Assessment:
- Downstream Task Performance: Evaluation on held-out benchmarks
- Human Evaluation: Quality ratings from human annotators
- Safety Metrics: Toxicity, bias, and harmful content detection
- Calibration: Confidence score alignment with actual correctness

Full Examples

Pre-training LLM from Scratch

python

1import langtrain
2from langtrain.models import LlamaForCausalLM
3from langtrain.data import PretrainingDataset
4import torch
5
6# Configure model architecture
7config = langtrain.LlamaConfig(
8    vocab_size=32000,
9    hidden_size=4096,
10    intermediate_size=11008,
11    num_hidden_layers=32,
12    num_attention_heads=32,
13    max_position_embeddings=4096,
14    rms_norm_eps=1e-6,
15    rope_theta=10000.0,
16    attention_dropout=0.0,
17    hidden_dropout=0.0
18)
19
20# Initialize model with proper weight initialization
21model = LlamaForCausalLM(config)
22model.apply(lambda m: langtrain.init_weights(m, config))
23
24# Prepare pre-training dataset
25dataset = PretrainingDataset(
26    data_path="path/to/tokenized_data",
27    seq_length=4096,
28    tokenizer=tokenizer,
29    pack_sequences=True,  # Pack multiple documents
30    shuffle_buffer_size=10000
31)
32
33# Configure training with modern optimizations
34training_args = langtrain.TrainingArguments(
35    output_dir="./llama-7b-pretrain",
36    per_device_train_batch_size=8,
37    gradient_accumulation_steps=16,  # Effective batch size: 8*16*num_gpus
38    max_steps=100000,
39    learning_rate=3e-4,
40    weight_decay=0.1,
41    warmup_steps=2000,
42    lr_scheduler_type="cosine",
43    bf16=True,  # Use bfloat16 for numerical stability
44    dataloader_num_workers=4,
45    gradient_checkpointing=True,
46    optim="adamw_torch_fused",  # Fused AdamW for efficiency
47    logging_steps=10,
48    save_steps=5000,
49    max_grad_norm=1.0
50)
51
52# Initialize trainer with distributed support
53trainer = langtrain.Trainer(
54    model=model,
55    args=training_args,
56    train_dataset=dataset,
57    tokenizer=tokenizer
58)
59
60# Start pre-training
61trainer.train()

Distributed Training with DeepSpeed

python

1import deepspeed
2from langtrain.distributed import setup_distributed_training
3
4# DeepSpeed ZeRO configuration
5ds_config = {
6    "train_batch_size": 512,
7    "train_micro_batch_size_per_gpu": 4,
8    "gradient_accumulation_steps": 32,
9    
10    "optimizer": {
11        "type": "AdamW",
12        "params": {
13            "lr": 3e-4,
14            "betas": [0.9, 0.95],
15            "eps": 1e-8,
16            "weight_decay": 0.1
17        }
18    },
19    
20    "scheduler": {
21        "type": "WarmupDecayLR",
22        "params": {
23            "warmup_min_lr": 0,
24            "warmup_max_lr": 3e-4,
25            "warmup_num_steps": 2000,
26            "total_num_steps": 100000
27        }
28    },
29    
30    "zero_optimization": {
31        "stage": 2,  # ZeRO-2: shard gradients and optimizer states
32        "offload_optimizer": {
33            "device": "cpu",  # Offload optimizer to CPU
34            "pin_memory": True
35        },
36        "allgather_partitions": True,
37        "reduce_scatter": True,
38        "overlap_comm": True,
39        "contiguous_gradients": True
40    },
41    
42    "fp16": {
43        "enabled": True,
44        "auto_cast": True,
45        "loss_scale": 0,
46        "initial_scale_power": 16,
47        "loss_scale_window": 1000,
48        "hysteresis": 2,
49        "min_loss_scale": 1
50    },
51    
52    "gradient_clipping": 1.0,
53    "wall_clock_breakdown": False
54}
55
56# Initialize distributed training
57setup_distributed_training()
58
59# Initialize DeepSpeed engine
60model_engine, optimizer, _, _ = deepspeed.initialize(
61    model=model,
62    config=ds_config,
63    model_parameters=model.parameters()
64)
65
66# Training loop with DeepSpeed
67for step, batch in enumerate(dataloader):
68    loss = model_engine(batch)
69    model_engine.backward(loss)
70    model_engine.step()
71    
72    if step % 100 == 0:
73        print(f"Step {step}, Loss: {loss.item():.4f}")
74        
75    if step % 5000 == 0:
76        model_engine.save_checkpoint("./checkpoints", step)
77
78# Train with real-time monitoring
79for epoch in trainer.train_epochs():
80    print(f"Epoch {epoch.number}: Loss={epoch.loss:.4f}")
81    
82    # Adjust parameters if needed
83    if epoch.loss > 0.5:
84        trainer.adjust_learning_rate(0.8)  # Reduce by 20%

Advanced Training Configuration

python

1# Advanced training with custom configuration
2training_config = langtrain.TrainingConfig(
3    # Optimization
4    optimizer="adamw",
5    learning_rate=2e-5,
6    weight_decay=0.01,
7    
8    # Scheduling
9    lr_scheduler="cosine",
10    warmup_steps=1000,
11    
12    # Efficiency
13    mixed_precision=True,
14    gradient_checkpointing=True,
15    dataloader_num_workers=8,
16    
17    # Monitoring
18    eval_steps=500,
19    save_steps=1000,
20    logging_steps=100
21)
22
23trainer = langtrain.Trainer(
24    model=model,
25    config=training_config,
26    callbacks=[
27        langtrain.EarlyStoppingCallback(patience=3),
28        langtrain.ModelCheckpointCallback(save_best=True),
29        langtrain.WandbCallback(project="my-project")
30    ]
31)
32
33results = trainer.train()

Distributed Training

bash

1# Multi-GPU training
2langtrain train \
3    --config config.yaml \
4    --distributed \
5    --num-gpus 4 \
6    --backend nccl
7
8# Multi-node training
9langtrain train \
10    --config config.yaml \
11    --distributed \
12    --num-nodes 2 \
13    --node-rank 0 \
14    --master-addr "192.168.1.100" \
15    --master-port 29500

Initializing Studio...

LLM Training

Master Large Language Model training including pre-training, fine-tuning, and alignment techniques with modern optimization strategies.

LLM Training Pipeline

Training Paradigms & Architectures

Advanced Optimization for LLMs

LLM Training Monitoring & Evaluation

Full Examples

Pre-training LLM from Scratch

python

1import langtrain
2from langtrain.models import LlamaForCausalLM
3from langtrain.data import PretrainingDataset
4import torch
5
6# Configure model architecture
7config = langtrain.LlamaConfig(
8    vocab_size=32000,
9    hidden_size=4096,
10    intermediate_size=11008,
11    num_hidden_layers=32,
12    num_attention_heads=32,
13    max_position_embeddings=4096,
14    rms_norm_eps=1e-6,
15    rope_theta=10000.0,
16    attention_dropout=0.0,
17    hidden_dropout=0.0
18)
19
20# Initialize model with proper weight initialization
21model = LlamaForCausalLM(config)
22model.apply(lambda m: langtrain.init_weights(m, config))
23
24# Prepare pre-training dataset
25dataset = PretrainingDataset(
26    data_path="path/to/tokenized_data",
27    seq_length=4096,
28    tokenizer=tokenizer,
29    pack_sequences=True,  # Pack multiple documents
30    shuffle_buffer_size=10000
31)
32
33# Configure training with modern optimizations
34training_args = langtrain.TrainingArguments(
35    output_dir="./llama-7b-pretrain",
36    per_device_train_batch_size=8,
37    gradient_accumulation_steps=16,  # Effective batch size: 8*16*num_gpus
38    max_steps=100000,
39    learning_rate=3e-4,
40    weight_decay=0.1,
41    warmup_steps=2000,
42    lr_scheduler_type="cosine",
43    bf16=True,  # Use bfloat16 for numerical stability
44    dataloader_num_workers=4,
45    gradient_checkpointing=True,
46    optim="adamw_torch_fused",  # Fused AdamW for efficiency
47    logging_steps=10,
48    save_steps=5000,
49    max_grad_norm=1.0
50)
51
52# Initialize trainer with distributed support
53trainer = langtrain.Trainer(
54    model=model,
55    args=training_args,
56    train_dataset=dataset,
57    tokenizer=tokenizer
58)
59
60# Start pre-training
61trainer.train()

Distributed Training with DeepSpeed

python

1import deepspeed
2from langtrain.distributed import setup_distributed_training
3
4# DeepSpeed ZeRO configuration
5ds_config = {
6    "train_batch_size": 512,
7    "train_micro_batch_size_per_gpu": 4,
8    "gradient_accumulation_steps": 32,
9    
10    "optimizer": {
11        "type": "AdamW",
12        "params": {
13            "lr": 3e-4,
14            "betas": [0.9, 0.95],
15            "eps": 1e-8,
16            "weight_decay": 0.1
17        }
18    },
19    
20    "scheduler": {
21        "type": "WarmupDecayLR",
22        "params": {
23            "warmup_min_lr": 0,
24            "warmup_max_lr": 3e-4,
25            "warmup_num_steps": 2000,
26            "total_num_steps": 100000
27        }
28    },
29    
30    "zero_optimization": {
31        "stage": 2,  # ZeRO-2: shard gradients and optimizer states
32        "offload_optimizer": {
33            "device": "cpu",  # Offload optimizer to CPU
34            "pin_memory": True
35        },
36        "allgather_partitions": True,
37        "reduce_scatter": True,
38        "overlap_comm": True,
39        "contiguous_gradients": True
40    },
41    
42    "fp16": {
43        "enabled": True,
44        "auto_cast": True,
45        "loss_scale": 0,
46        "initial_scale_power": 16,
47        "loss_scale_window": 1000,
48        "hysteresis": 2,
49        "min_loss_scale": 1
50    },
51    
52    "gradient_clipping": 1.0,
53    "wall_clock_breakdown": False
54}
55
56# Initialize distributed training
57setup_distributed_training()
58
59# Initialize DeepSpeed engine
60model_engine, optimizer, _, _ = deepspeed.initialize(
61    model=model,
62    config=ds_config,
63    model_parameters=model.parameters()
64)
65
66# Training loop with DeepSpeed
67for step, batch in enumerate(dataloader):
68    loss = model_engine(batch)
69    model_engine.backward(loss)
70    model_engine.step()
71    
72    if step % 100 == 0:
73        print(f"Step {step}, Loss: {loss.item():.4f}")
74        
75    if step % 5000 == 0:
76        model_engine.save_checkpoint("./checkpoints", step)
77
78# Train with real-time monitoring
79for epoch in trainer.train_epochs():
80    print(f"Epoch {epoch.number}: Loss={epoch.loss:.4f}")
81    
82    # Adjust parameters if needed
83    if epoch.loss > 0.5:
84        trainer.adjust_learning_rate(0.8)  # Reduce by 20%

Advanced Training Configuration

python

1# Advanced training with custom configuration
2training_config = langtrain.TrainingConfig(
3    # Optimization
4    optimizer="adamw",
5    learning_rate=2e-5,
6    weight_decay=0.01,
7    
8    # Scheduling
9    lr_scheduler="cosine",
10    warmup_steps=1000,
11    
12    # Efficiency
13    mixed_precision=True,
14    gradient_checkpointing=True,
15    dataloader_num_workers=8,
16    
17    # Monitoring
18    eval_steps=500,
19    save_steps=1000,
20    logging_steps=100
21)
22
23trainer = langtrain.Trainer(
24    model=model,
25    config=training_config,
26    callbacks=[
27        langtrain.EarlyStoppingCallback(patience=3),
28        langtrain.ModelCheckpointCallback(save_best=True),
29        langtrain.WandbCallback(project="my-project")
30    ]
31)
32
33results = trainer.train()

Distributed Training

bash

1# Multi-GPU training
2langtrain train \
3    --config config.yaml \
4    --distributed \
5    --num-gpus 4 \
6    --backend nccl
7
8# Multi-node training
9langtrain train \
10    --config config.yaml \
11    --distributed \
12    --num-nodes 2 \
13    --node-rank 0 \
14    --master-addr "192.168.1.100" \
15    --master-port 29500