N150 N300 T3K P100 P150 P300C Galaxy 90 min Blocked

Training from Scratch

Build a tiny transformer (10-20M parameters) from random initialization. See a model learn language from nothing on Tenstorrent hardware.

What You'll Learn

Time: 60-90 minutes (30-60 min training) | Prerequisites: CT-1 through CT-7

Why Train from Scratch?

You've Fine-Tuned, Now Build

In CT-4, you fine-tuned TinyLlama (1.1B params) - adjusting pre-trained weights.

Training from scratch means:

When to Train from Scratch

Fine-tuning is better when:

Training from scratch is better when:

graph LR
    A[Model Training Decision] --> B{Have pre-trainedmodel for task?}
    B -->|Yes| C[Fine-TuningCT-4]
    B -->|No| D{Need generalknowledge?}

    D -->|Yes| C
    D -->|No| E{Large datasetavailable?}

    E -->|Yes| F[Train from ScratchCT-8]
    E -->|No| C

    C --> G[Result: Specialized1.1B paramsHours to train]
    F --> H[Result: Custom11M paramsMinutes to train]

    style C fill:#7B68EE,stroke:#333,stroke-width:2px
    style F fill:#50C878,stroke:#333,stroke-width:2px
    style G fill:#4A90E2,stroke:#333,stroke-width:2px
    style H fill:#E85D75,stroke:#333,stroke-width:2px

Meet Nano-Trickster

Architecture Overview

Nano-Trickster: A tiny but complete transformer designed for learning.

nano-trickster:
  vocab_size: 256        # Character-level (simple!)
  hidden_dim: 256        # Small but workable
  num_layers: 6          # Shallow (6× faster than TinyLlama's 22)
  num_heads: 8           # Decent parallelism
  mlp_dim: 768           # 3× hidden_dim
  max_seq_len: 512       # Short context
  total_params: ~11M     # 100× smaller than TinyLlama!

graph TD
    A[Nano-Trickster11M Parameters] --> B[Input: Charactersvocab_size: 256]
    B --> C[Token Embedding256 → 25665K params]
    C --> D[6 Transformer Blocks10.8M params total]

    D --> E[Block 1-6 Each Contains:1.8M params]
    E --> F[Multi-Head Attention8 heads, 32 dims each]
    E --> G[Feed-Forward Network256 → 768 → 256]
    E --> H[RMSNorm × 2Stabilization]

    D --> I[Output Projection256 → 25665K params shared]
    I --> J[Output: Next CharacterProbability distribution]

    style A fill:#4A90E2,stroke:#333,stroke-width:2px
    style D fill:#50C878,stroke:#333,stroke-width:2px
    style E fill:#7B68EE,stroke:#333,stroke-width:2px
    style J fill:#E85D75,stroke:#333,stroke-width:2px

Why This Size Works

Trade-offs:

Aspect Nano-Trickster (11M) TinyLlama (1.1B)
Training time (N150) 30-60 minutes Many hours
Memory ~200MB ~17GB
Iterations/sec ~100 ~10
Learns Basic patterns Complex language
Use case Learning, prototyping Production

Perfect for:

Not for:

Dataset: Tiny Shakespeare

What Is It?

Tiny Shakespeare: 1.1MB of Shakespeare plays (1M characters)

Why Shakespeare?

Dataset stats:

Character-Level Tokenization

Unlike TinyLlama's BPE (32,000 tokens), we use characters:

graph LR
    A["Text: 'ROMEO:'"] --> B[Tokenization]

    B --> C[Character-levelNano-Trickster]
    C --> D["['R', 'O', 'M', 'E', 'O', ':']6 tokens"]

    B --> E[BPETinyLlama]
    E --> F["['ROM', 'EO', ':']3 tokens ish"]

    D --> G[Pros:- Simple vocab 256- No training needed- Handles any text]
    D --> H[Cons:- Longer sequences- Less semantic info]

    F --> I[Pros:- Shorter sequences- Semantic chunks- More efficient]
    F --> J[Cons:- Large vocab 32K- Training required- Out-of-vocab issues]

    style C fill:#50C878,stroke:#333,stroke-width:2px
    style E fill:#7B68EE,stroke:#333,stroke-width:2px
    style G fill:#6C757D,stroke:#333,stroke-width:2px
    style I fill:#6C757D,stroke:#333,stroke-width:2px

For learning, characters are perfect:

Part 1: Setup

Install Dependencies

tt-metal version: v0.66.0-rc5 or later (v0.67.0+ or latest RC recommended)

Check your version:

cd $TT_METAL_HOME && git describe --tags
# Should show v0.66.0-rc5 or later
# Recommended: v0.67.0 or later for latest improvements

⚠️ Version Notes:

Install ttml (if not already done from CT-4):

cd $TT_METAL_HOME/tt-train
pip install -e .

Verify installation:

python -c "import ttml; print('✅ ttml available')"

Prepare Dataset

Step 1: Download Shakespeare Text

Use the automated script:

cd ~/tt-scratchpad/training/data
python prepare_shakespeare.py --output . --split

What this does:

Expected output:

✅ Downloaded 1,115,394 characters to shakespeare.txt
✅ Created train split: 1,003,854 chars → shakespeare_train.txt
✅ Created val split: 111,540 chars → shakespeare_val.txt

Manual alternative (if script unavailable):

# Download
wget https://raw.githubusercontent.com/karpathy/char-rnn/master/data/tinyshakespeare/input.txt -O shakespeare.txt

# Create 90/10 split
head -n 32000 shakespeare.txt > shakespeare_train.txt
tail -n 8000 shakespeare.txt > shakespeare_val.txt

Step 2: Preprocess to PyTorch Tensors

Convert text files to tensors for training:

cd ~/tt-scratchpad/training/data
python preprocess_shakespeare.py

What this does:

Expected output:

✅ Saved train.pt (1,003,854 tokens)
✅ Saved val.pt (111,540 tokens)
✅ Saved tokenizer.pt (vocab_size=65)

Files created:

Verify:

ls -lh *.txt *.pt
# Should show text files + PyTorch tensors

Part 2: Understanding the Architecture

Model Code Overview

The nano_trickster.py file contains:

  1. RMSNorm - Fast normalization (replaces LayerNorm)
  2. RotaryPositionalEmbedding - Better position encoding (RoPE)
  3. MultiHeadAttention - Context learning (8 heads)
  4. SwiGLU - Modern activation (replaces ReLU)
  5. TransformerBlock - Combines attention + FFN + norms
  6. NanoTrickster - Complete model
graph TD
    A[Input: Character IDs] --> B[Token Embedding256 → 256 vectors]
    B --> C[Transformer Block 1]
    C --> D[Transformer Block 2]
    D --> E[... 4 more blocks ...]
    E --> F[Transformer Block 6]
    F --> G[Final RMSNorm]
    G --> H[Output Projection256 → vocab_size]
    H --> I[Softmax]
    I --> J[Next Character Probabilities]

    K[Each Transformer Block] --> L[RMSNorm 1]
    L --> M[Multi-Head AttentionQuery/Key/Value + RoPE]
    M --> N[Residual Add]
    N --> O[RMSNorm 2]
    O --> P[SwiGLU FFN256 → 768 → 256]
    P --> Q[Residual Add]

    style B fill:#4A90E2,stroke:#333,stroke-width:2px
    style C fill:#50C878,stroke:#333,stroke-width:2px
    style F fill:#50C878,stroke:#333,stroke-width:2px
    style K fill:#7B68EE,stroke:#333,stroke-width:2px
    style M fill:#E85D75,stroke:#333,stroke-width:2px
    style P fill:#DDA0DD,stroke:#333,stroke-width:2px

Test the Model

Let's verify it works:

cd ~/tt-scratchpad/training
python nano_trickster.py

Expected output:

Nano-Trickster initialized: 11,234,816 trainable params

Parameter breakdown:
  Total: 11,234,816
  Trainable: 11,234,816
  Embedding: 65,536
  Transformer blocks: 10,878,464
  Per block: 1,813,077
  Output layer: 65,536 (weight-tied)

Test forward pass:
  Input shape: torch.Size([4, 64])
  Logits shape: torch.Size([4, 64, 256])
  Loss: 5.5452

Test generation:
  Prompt shape: torch.Size([1, 10])
  Generated shape: torch.Size([1, 30])

Key observations:

Part 3: Training Configuration

Review the Config

Open configs/nano_trickster.yaml:

# Key settings:
model_config:
  vocab_size: 256
  hidden_dim: 256
  num_layers: 6
  num_heads: 8
  mlp_dim: 768
  max_seq_len: 512

training_config:
  batch_size: 16
  max_steps: 10000        # ~30-60 minutes on N150
  learning_rate: 0.0003   # 3e-4 (standard for small models)
  warmup_steps: 1000      # Gradual LR increase
  grad_clip: 1.0          # Prevent exploding gradients

graph TD
    A[Training Process] --> B[Step 0-1000Warmup Phase]
    B --> C[LR increases linearly0 → 3e-4]

    A --> D[Step 1000-10000Main Training]
    D --> E[LR decays via cosine3e-4 → 3e-5]

    A --> F[Every 50 stepsLog loss]
    A --> G[Every 500 stepsEvaluate on val]
    A --> H[Every 1000 stepsSave checkpoint]

    B --> I[Why warmup?Prevents early instability]
    D --> J[Why cosine decay?Smooth convergence]

    style B fill:#4A90E2,stroke:#333,stroke-width:2px
    style D fill:#50C878,stroke:#333,stroke-width:2px
    style F fill:#7B68EE,stroke:#333,stroke-width:2px
    style G fill:#E85D75,stroke:#333,stroke-width:2px
    style H fill:#DDA0DD,stroke:#333,stroke-width:2px

Hardware Variants

N150 (single chip):

N300 (dual chips with DDP):

To use N300: Update config:

device_config:
  enable_ddp: True
  mesh_shape: [1, 2]  # 1 row, 2 columns

training_config:
  batch_size: 32
  gradient_accumulation_steps: 1

Part 4: Launch Training

Start Training

cd ~/tt-scratchpad/training
python train_from_scratch.py --config configs/nano_trickster.yaml

You'll see:

============================================================
Training Nano-Trickster from Scratch
============================================================

Config: configs/nano_trickster.yaml

Device: cuda
Loaded 900,000 tokens from data/train.pt
Loaded 100,000 tokens from data/val.pt

Model architecture:
  Total parameters: 11,234,816
  Per block: 1,813,077
  Vocabulary size: 256

Dataset:
  Train batches: 1,758
  Val batches: 195

Training:
  Max steps: 10,000
  Warmup steps: 1,000
  Learning rate: 0.0003
  Gradient clip: 1.0
  Output: output/nano_trickster

============================================================
Starting training...
============================================================

Training:   0%|          | 0/10000 [00:00<?, ?it/s]

What's Happening?

graph TD
    A[Training Loop] --> B[1. Get Batch16 sequences × 512 chars]
    B --> C[2. Forward PassCompute predictions]
    C --> D[3. Calculate LossCross-entropy]
    D --> E[4. Backward PassCompute gradients]
    E --> F[5. Clip GradientsPrevent explosions]
    F --> G[6. Optimizer StepUpdate weights]
    G --> H[7. Update LRWarmup/decay schedule]
    H --> B

    I[Every 50 steps] --> J[Log train loss]
    I --> K[Update progress bar]

    L[Every 500 steps] --> M[Evaluate on val set]
    M --> N[Generate sample text]
    N --> O[Check if best model]
    O --> P[Save checkpoint if best]

    style B fill:#4A90E2,stroke:#333,stroke-width:2px
    style D fill:#E85D75,stroke:#333,stroke-width:2px
    style E fill:#7B68EE,stroke:#333,stroke-width:2px
    style M fill:#50C878,stroke:#333,stroke-width:2px
    style P fill:#DDA0DD,stroke:#333,stroke-width:2px

Part 5: Monitoring Progress

Understanding the Loss

Initial loss (~5.5):

After 1000 steps (~3 minutes):

Step 1000:
  Train loss: 2.456
  Val loss: 2.489
  Val perplexity: 12.05

  Sample generation:
  --------------------------------------------------------
  ROMEO:
  Thit the stook to tean the couse,
  And the beep the me the shoun,
  --------------------------------------------------------

What we see:

After 5000 steps (~15 minutes):

Step 5000:
  Train loss: 1.234
  Val loss: 1.287
  Val perplexity: 3.62

  Sample generation:
  --------------------------------------------------------
  ROMEO:
  What is the world and the man that shall be
  The heart of my soul, and the world is the world
  That is the blood of my heart.
  --------------------------------------------------------

What we see:

After 10000 steps (~30-60 minutes):

Step 10000:
  Train loss: 0.876
  Val loss: 0.934
  Val perplexity: 2.54

  Sample generation:
  --------------------------------------------------------
  ROMEO:
  I will not speak of this, my lord,
  For I have done the worst of all my love,
  And yet I cannot speak of what I know.
  I have a heart that will not be content
  To make me think of this.
  --------------------------------------------------------

What we see:

graph LR
    A[Step 0Loss: 5.5] --> B[Step 1000Loss: 2.5]
    B --> C[Step 5000Loss: 1.3]
    C --> D[Step 10000Loss: 0.9]

    A --> E[Random gibberishNo patterns]
    B --> F[Letter patternsSpaces, caps]
    C --> G[Word patternsGrammar emerges]
    D --> H[Sentence patternsCoherent Shakespeare]

    style A fill:#FF6B6B,stroke:#333,stroke-width:2px
    style B fill:#4A90E2,stroke:#333,stroke-width:2px
    style C fill:#50C878,stroke:#333,stroke-width:2px
    style D fill:#7B68EE,stroke:#333,stroke-width:2px

Loss Curves

Typical training progression:

xychart-beta
    title "Nano-Trickster Training Loss"
    x-axis [0, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000]
    y-axis "Loss" 0 --> 6
    line [5.5, 3.2, 2.5, 2.1, 1.8, 1.5, 1.3, 1.1, 1.0, 0.95, 0.88]

Phases:

  1. 0-1000 steps (Warmup): Rapid initial learning, loss drops quickly
  2. 1000-5000 steps (Main): Steady improvement, patterns emerge
  3. 5000-10000 steps (Refinement): Slower gains, quality increases

Part 6: Testing Your Model

Generate Text

After training completes, test generation:

cd ~/tt-scratchpad/training

python -c "
import torch
from nano_trickster import NanoTrickster

# Load model
model = NanoTrickster()
model.load_state_dict(torch.load('output/nano_trickster/final_model.pt'))
model.eval()

# Load tokenizer
tokenizer = torch.load('data/tokenizer.pt')
stoi = tokenizer['stoi']
itos = tokenizer['itos']

# Encode prompt
prompt = 'ROMEO:'
input_ids = torch.tensor([[stoi.get(c, 0) for c in prompt]])

# Generate
with torch.no_grad():
    generated = model.generate(input_ids, max_new_tokens=200, temperature=0.8)

# Decode
text = ''.join([itos.get(int(t), '?') for t in generated[0]])
print(text)
"

Try different prompts:

Compare to Random

To prove learning occurred, compare to a freshly initialized model:

python -c "
import torch
from nano_trickster import NanoTrickster

# Create random model (no training)
model = NanoTrickster()
model.eval()

# Load tokenizer
tokenizer = torch.load('data/tokenizer.pt')
stoi = tokenizer['stoi']
itos = tokenizer['itos']

# Encode prompt
prompt = 'ROMEO:'
input_ids = torch.tensor([[stoi.get(c, 0) for c in prompt]])

# Generate
with torch.no_grad():
    generated = model.generate(input_ids, max_new_tokens=200, temperature=0.8)

# Decode
text = ''.join([itos.get(int(t), '?') for t in generated[0]])
print('RANDOM MODEL OUTPUT:')
print(text)
"

Expected random output:

RANDOM MODEL OUTPUT:
ROMEO:xJ#*8dK...mnoP@!qrs...

Comparison:

Model Output Quality Loss
Random Complete gibberish, no structure ~5.5
Trained (1K steps) Letter patterns, some spaces ~2.5
Trained (5K steps) Words, grammar ~1.3
Trained (10K steps) Coherent Shakespeare ~0.9

This proves the model learned!

Part 7: Understanding What Was Learned

Learned Patterns

graph TD
    A[What Nano-Trickster Learned] --> B[Character Level]
    B --> C[Letters form words'a', 'n', 'd' → 'and']
    B --> D[Spaces separate wordsNot random placement]
    B --> E[Punctuation rulesPeriods end sentences]

    A --> F[Word Level]
    F --> G[Common words'the', 'is', 'and', 'of']
    F --> H[Shakespeare vocab'thou', 'thy', 'hath']
    F --> I[Word order matters'I am' not 'am I']

    A --> J[Sentence Level]
    J --> K[Grammar structureSubject-verb-object]
    J --> L[Poetic phrasingIambic patterns]
    J --> M[Emotional toneLove, tragedy, honor]

    A --> N[Discourse Level]
    N --> O[Character voicesRomeo vs Juliet style]
    N --> P[Dialogue formatNAME: speech]
    N --> Q[Scene structureBack-and-forth]

    style B fill:#4A90E2,stroke:#333,stroke-width:2px
    style F fill:#7B68EE,stroke:#333,stroke-width:2px
    style J fill:#50C878,stroke:#333,stroke-width:2px
    style N fill:#E85D75,stroke:#333,stroke-width:2px

What It DIDN'T Learn

Limitations of 11M parameters:

This is expected! We built a tiny model to learn fundamentals, not production system.

Part 8: Scaling Up

From Nano to Production

Want a more capable model? Scale up the config:

# Nano-Trickster: 11M params, 30-60 min (N150)
nano:
  hidden_dim: 256
  num_layers: 6
  mlp_dim: 768

# Mini-Trickster: 50M params, 2-3 hours (N150)
mini:
  hidden_dim: 512    # 2× larger
  num_layers: 8      # 33% deeper
  mlp_dim: 1536      # 3× hidden_dim

# Midi-Trickster: 200M params, 8-10 hours (N300)
midi:
  hidden_dim: 768    # 3× nano
  num_layers: 12     # 2× nano
  mlp_dim: 2304      # 3× hidden_dim

# Mega-Trickster: 1.1B params, days (T3K/Galaxy)
mega:
  hidden_dim: 2048   # Same as TinyLlama
  num_layers: 22     # Same as TinyLlama
  mlp_dim: 5632      # Same as TinyLlama

graph LR
    A[Nano11M30-60 min] --> B[Mini50M2-3 hours]
    B --> C[Midi200M8-10 hours]
    C --> D[Mega1.1Bdays]

    A --> E[Learn fundamentalsN150 sufficient]
    B --> F[Simple tasksN150 OK, N300 better]
    C --> G[Production qualityN300/T3K recommended]
    D --> H[SOTA performanceT3K/Galaxy required]

    style A fill:#4A90E2,stroke:#333,stroke-width:2px
    style B fill:#7B68EE,stroke:#333,stroke-width:2px
    style C fill:#50C878,stroke:#333,stroke-width:2px
    style D fill:#E85D75,stroke:#333,stroke-width:2px

Scaling Laws

Rule of thumb:

Training cost ∝ num_params × num_tokens × context_length

Practical guide:

Model Size Params Hardware Training Time Use Case
Nano 11M N150 30-60 min Learning, prototyping
Mini 50M N150/N300 2-3 hours Simple tasks
Midi 200M N300/T3K 8-10 hours Production (niche)
Mega 1.1B T3K/Galaxy Days Production (general)

Key insight: Start small! Iterate quickly. Scale up once you understand the patterns.

Part 9: Next Steps

Experiment Ideas

Easy (10-30 minutes):

  1. Try different prompts - "JULIET:", "KING:", "GHOST:"
  2. Adjust temperature - 0.5 (conservative) to 1.5 (creative)
  3. Longer generation - max_new_tokens=500 or 1000
  4. Different datasets - Try poetry, code, Wikipedia

Medium (1-2 hours):

  1. Extend training - Run to 20K steps, see if loss improves
  2. Tune hyperparameters - Learning rate, batch size, warmup
  3. Add regularization - Increase dropout, try weight decay
  4. Multi-device - If you have N300, enable DDP

Advanced (3-5 hours):

  1. Scale up architecture - Try 50M or 200M params
  2. Better tokenization - Train BPE tokenizer (like TinyLlama)
  3. Longer context - Increase max_seq_len to 1024 or 2048
  4. Different loss - Try label smoothing or focal loss

What You've Accomplished

🎉 Congratulations! You just:

  1. ✅ Designed a transformer architecture from scratch
  2. ✅ Trained a model from random initialization
  3. ✅ Watched it learn language patterns in real-time
  4. ✅ Compared trained vs random to prove learning
  5. ✅ Generated coherent Shakespeare text
  6. ✅ Understood the full training pipeline
  7. ✅ Learned how to scale from 11M → 1B+ params

You now understand:

Troubleshooting

"Data file not found"

Error:

FileNotFoundError: Data file not found: data/train.pt
Run: python data/prepare_shakespeare.py

Fix:

cd ~/tt-scratchpad/training/data
python prepare_shakespeare.py --output shakespeare.txt --split

Then process the data:

cd ~/tt-scratchpad/training

python -c "
from prepare_shakespeare import create_train_val_split
create_train_val_split('data/shakespeare.txt')
"

"Loss is NaN"

Causes:

Fixes:

  1. Lower learning rate: 0.0003 → 0.0001
  2. Enable gradient clipping: grad_clip: 1.0
  3. Reduce batch size: 16 → 8
  4. Add mixed precision: --fp16 flag

"Loss not decreasing"

If loss stays at ~5.5 after 1000 steps:

Check:

  1. Is data loading correctly? (Check dataset size)
  2. Is optimizer stepping? (Check LR schedule)
  3. Are gradients flowing? (Print gradient norms)
  4. Is model too small? (Try hidden_dim=512)

Debug:

# Check dataset
python -c "import torch; data = torch.load('data/train.pt'); print(len(data))"

# Check learning rate
grep "lr:" logs/training.log | head -20

# Print model size
python nano_trickster.py

"Out of memory"

If training crashes with OOM:

Reduce memory:

  1. Smaller batch size: 16 → 8 → 4
  2. Shorter sequences: max_seq_len: 512 → 256
  3. Fewer layers: num_layers: 6 → 4
  4. Smaller hidden: hidden_dim: 256 → 128

For N150: Nano-Trickster (11M) should work easily. If not, check:

Beyond This Lesson: From Nano to Production

You've trained nano-trickster (11M params) from random initialization. But what can you build when you scale up these fundamentals? Let's explore how training from scratch unlocks possibilities fine-tuning can't reach.

What Developers Have Trained from Scratch

Real models trained from zero by teams who understood the fundamentals:

🚀 "SQL Query Generator" (DevTools startup)

🔬 "Chemical Formula Parser" (Pharma research lab)

💼 "Contract Clause Generator" (LegalTech SaaS)

🎮 "Game Quest Generator" (Mid-size game studio)

The Scaling Path: Nano → Mini → Midi → Mega

How developers scale from prototype to production:

📈 Stage 1: Nano (11M params, 30-60 min on N150) Purpose: Validate the idea

📈 Stage 2: Mini (50M params, 2-3 hours on N150/N300) Purpose: Production prototype

📈 Stage 3: Midi (200M params, 8-10 hours on N300/T3K) Purpose: Production quality

📈 Stage 4: Mega (1B+ params, days on T3K/Galaxy) Purpose: State-of-the-art in niche

Real Scaling Stories

🎯 "Medical Coding Assistant"

💡 "Code Documentation Generator"

🚀 "Financial Report Parser"

From Shakespeare to Your Domain

What you learned with Shakespeare:

✅ Character-level modeling (simple, universal)

✅ Loss progression (5.5 → <1.0)

✅ Architecture design (11M params, 6 layers, 256 hidden)

✅ Scaling principles (11M → 50M → 200M → 1B)

What you can build:

🎯 Code Models (Your Codebase)

📊 Document Models (Your Industry)

🔬 Scientific Models (Your Domain)

🎨 Creative Models (Your Style)

The Economics of Training from Scratch

Why it's more accessible than you think:

💰 Hardware Investment (Scaling Path)

But consider the alternative:

ROI Example (Legal Contract Generator):

💡 "Code Review Bot" Economics

Your Training from Scratch Journey

Month 1 (Learning - This lesson):

Month 2 (Applying - Your domain):

Month 3 (Scaling - Production prototype):

Month 6+ (Optimizing - Full production):

When Training from Scratch Wins

Choose training from scratch when:

✅ Specialized vocabulary (medical terms, code, formulas)

✅ Deployment constraints (edge, real-time, cost)

✅ Data privacy (can't send to APIs)

✅ Cost at scale (millions of inferences)

✅ Novel architecture (research, experimentation)

Choose fine-tuning when:

⚠️ Broad knowledge needed (general Q&A, reasoning)

⚠️ Limited data (<10K examples)

⚠️ Time to market (ship in days, not weeks)

Imagine: Your Specialized Model

You now know how to:

What will you build?

🎯 Industry-Specific Models

🚀 Deployment-Optimized Models

🔬 Research & Innovation

💼 Commercial Products

The Transformation

From fine-tuning to training from scratch:

Fine-tuning taught you:

Training from scratch teaches you:

Together, they give you:

The question isn't "Should I train from scratch or fine-tune?"

The question is "What specialized model will create the most value?"

Imagine:

From 11M parameters learning Shakespeare...

...to production models transforming industries.

You have the knowledge. What will you build?

Key Takeaways

✅ Training from scratch gives you full control - architecture, size, specialization

✅ Start small (11M), scale up (1B+) - iterate quickly, learn patterns, then scale

✅ Character-level is simple and effective - no tokenizer training, works for any language

✅ Loss curves tell the story - rapid initial learning, then refinement

✅ Compare to random to prove learning - baseline is critical

✅ Hardware scales linearly - N150 → N300 → T3K = 2-4× faster each step

✅ Tiny models teach fundamentals - understanding > performance for learning

Additional Resources

Papers

Code References

Next Steps

🎭 You've completed the Custom Training series! You now know how to:

  1. Understand transformer fundamentals (CT-1, CT-7)
  2. Create datasets (CT-2)
  3. Configure training (CT-3)
  4. Fine-tune existing models (CT-4)
  5. Scale to multiple devices (CT-5)
  6. Track experiments (CT-6)
  7. Design architectures (CT-7)
  8. Train from scratch (CT-8)

Next: Build production systems with vLLM (Lesson 7) or explore creative applications (Lessons 9-12)!