Nano Language Models

How to train lightweight Small Language Models using teacher–student learning.

🚀 Introduction — Why Distillation Still Matters

Even in 2025, when quantization and fine-tuning dominate the headlines, distillation remains the single most powerful way to transfer intelligence from a big model to a small one.

It’s the foundation behind models like:

• Phi-3 Mini (distilled from GPT-4-level instruction data)
• TinyLlama (distilled from LLaMA-2 7B)
• Gemma 2B (refined from PaLM and Gemini data pipelines)

Distillation lets developers train faster, deploy smaller, and retain quality — all while staying within hardware limits.

🧠 Step 1: What Is Knowledge Distillation?

Knowledge distillation trains a smaller “student” model to replicate the outputs of a larger “teacher” model.

The process focuses on soft targets — the probabilities or logits of the teacher’s predictions — rather than hard labels.

Simple Concept:

Teacher: Large model (e.g., GPT-3.5)
Student: Small model (e.g., TinyLlama 1.1B)

The student learns to match the teacher’s “thinking process,” not just the final answer.

It’s not imitation — it’s compression of intelligence.

⚙️ Step 2: The Two Types of Distillation

In modern SLM training, response distillation dominates — often via synthetic datasets generated by large models (GPT-4, Claude, Gemini).

🧩 Step 3: Building a Dataset

You’ll need pairs of (prompt, response) examples from the teacher model.

Example Python script:

import openai, json

prompts = [
  "Explain the concept of gradient descent simply.",
  "What are the benefits of model quantization?"
]

data = []
for prompt in prompts:
    completion = openai.ChatCompletion.create(
        model="gpt-4",
        messages=[{"role": "user", "content": prompt}]
    )
    data.append({"prompt": prompt, "response": completion["choices"][0]["message"]["content"]})

json.dump(data, open("teacher_responses.json", "w"))

✅ Result: Your synthetic “teacher” dataset — ready for student training.

⚡ Step 4: Fine-Tuning the Student

You can fine-tune a smaller model (e.g., TinyLlama or Phi-3 Mini) on the teacher-generated data.

from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments

model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

dataset = load_dataset("json", data_files="teacher_responses.json")

training_args = TrainingArguments(
    output_dir="./distilled_tinyllama",
    per_device_train_batch_size=2,
    num_train_epochs=3,
    fp16=True,
    save_steps=200,
)

trainer = Trainer(model=model, args=training_args, train_dataset=dataset)
trainer.train()

✅ After 2–3 epochs, you’ll have a student model that captures much of the teacher’s reasoning — at a fraction of the compute cost.

⚙️ Step 5: Evaluation Loop

Use lm-eval-harness to benchmark the student:

lm_eval \
  --model hf \
  --model_args pretrained=./distilled_tinyllama \
  --tasks lambada,hellaswag,gsm8k

Compare results with the teacher model to measure distillation efficiency.

🧮 Step 6: Key Metrics

Example:

🧠 Step 7: Advanced Techniques

1. Progressive Distillation — Multiple teacher layers train intermediate student checkpoints.
2. Self-Distillation — A model teaches itself iteratively (used in Phi-3 pipeline).
3. Data Weighting — Weight samples by teacher confidence for stable training.
4. Mixture-of-Teachers — Combine responses from GPT-4 + Claude for diverse generalization.

Modern pipelines often combine these for the best balance of accuracy and size.

⚡ Step 8: Real-World Pipelines

All use synthetic response distillation — now the dominant trend in SLM production.

🧱 Step 9: Tools to Streamline Distillation

You can build a full open-source distillation stack with these tools — no proprietary APIs required.

🔮 Step 10: The Future — Self-Distilling Ecosystems

The next wave of SLMs will distill themselves continuously via:

• Automated teacher rotation (use strongest models for hardest data)
• Self-feedback loops (reinforcement through evaluator models)
• Dataset refinement via reward modeling

Distillation isn’t just a training trick anymore — it’s becoming a learning philosophy.

Follow NanoLanguageModels.com for hands-on engineering guides to build, train, and optimize your own small models — from distillation to deployment. ⚙️