# PyCoder-300M: The "Cargo Cult" Coder 🚀

**A 300M parameter Python coding model built entirely from scratch on Kaggle TPUs**

| Checkpoint | HumanEval Score | What It Knows |
|------------|----------------|---------------|
| Stage 1 (Base Model) | 0.00% | Perfect Python syntax, zero logic |
| Stage 2 (Instruct Model)| 0.00% | Perfect instruction formatting, beautiful reasoning blocks... still zero logic |

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## 📖 The Story

I spent three weeks building this model from absolute scratch—not fine-tuning a pretrained model, but building the entire pipeline: custom tokenizer, custom architecture, distributed TPU training, everything. 

**Stage 1:** I trained it on 8 billion tokens of Python code scraped from GitHub. It learned perfect formatting—pristine indentation, beautiful docstrings, proper type hints. But the logic? Complete hallucination. It would see `def two_sum(nums, target):` and confidently return `len(nums)`. Every. Single. Time.

It learned Python the way a parrot learns language: perfect mimicry, zero comprehension. 

**Stage 2:** I thought, *"Maybe it just needs to learn how to think!"* So, I instruction-tuned it on 59k high-quality reasoning problems generated by Qwen2.5-Coder-32B. I taught it a strict format: read the instruction, write out the reasoning, then write the code. 

**The Result:** It learned the exact format! It now outputs a beautiful `# REASONING:` block where it confidently hallucinates absolute nonsense, followed by flawlessly indented code that completely fails the unit tests. 

**HumanEval score: Still 0.00%.** This is the brutal reality of the Data Scaling Wall. To get a 300M parameter model to actually *reason* zero-shot, you need Trillions of high-quality tokens. 

But as an engineering project? It was a massive success. With the help of AI (Gemini and Claude), I tried to build this from the ground up using what I know about Transformers, and the distributed XLA pipeline works perfectly.

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## 🏗️ Technical Architecture

**Model Specifications:**
- **Parameters:** 300M (24 layers × 1024 hidden dim)
- **Attention:** MLA (Multi-Head Latent Attention) with QK-Norm
- **Positional Encoding:** RoPE (Rotary Position Embeddings)
- **FFN:** SwiGLU activation
- **Context Length:** 4096 tokens
- **Tokenizer:** Custom 32k BPE trained purely on Python

**Training Infrastructure:**
- **Hardware:** 8× TPU v5e cores (Kaggle free tier)
- **Optimizers:** Muon (for weight matrices) + AdamW (for biases/norms)
- **Precision:** bfloat16 with FP32-safe RMSNorm
- **Stage 1:** 127k steps on 8B tokens → PPL 3.0
- **Stage 2:** 690 steps (3 epochs) on 59k instructions → PPL 5.2

**Key Design Choices:**
- Used `# INSTRUCTION:` and `# REASONING:` format (optimal for Python BPE tokenizer)
- Masked loss: only trained on model's reasoning + code, not user instruction
- 10x lower learning rate for Stage 2 vs Stage 1 (prevents catastrophic forgetting)

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## 📂 What's In This Repo

| File | Size | Description |
|------|------|-------------|
| `checkpoint before instruction traning.pt` | 3.05 GB | **Stage 1 Base Model** - 127k steps, 0% HumanEval |
| `after instruction trannig.pt` | 1.4 GB | **Stage 2 Instruct Model** - After 59k instruction tuning |
| `tokenizer_100_python.json` | 2.21 MB | Custom BPE tokenizer (required!) |
| `vocab.json` / `merges.txt` | 748 KB | Tokenizer vocab files |
| `stage1.75 and hman eval after that all code.ipynb` | 58.8 KB | Stage 1 training loop + HumanEval code |
| `training of 59k instruction set traning stage 2.ipynb` | 72.8 KB | Stage 2 instruction tuning code |

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## 💻 How to Load and Use

### ⚠️ Important Note
This is a **custom architecture** built from scratch. You **cannot** use `transformers.AutoModel.from_pretrained()`. You must load the model class from the Jupyter notebook.

### Step 1: Load the Tokenizer
```python
from tokenizers import Tokenizer

# Load the custom tokenizer
tokenizer = Tokenizer.from_file("tokenizer_100_python.json")

# Test it
text = "def fibonacci(n):"
encoded = tokenizer.encode(text)
print(f"Tokens: {encoded.ids}")
print(f"Decoded: {tokenizer.decode(encoded.ids)}")