Yaya-125M

A 129M parameter causal language model trained from scratch in PyTorch — no HuggingFace Transformers dependency.

Model Details

Property	Value
Parameters	128,994,048 (~129M)
Architecture	Transformer (decoder-only)
Layers	12
Hidden size	768
FFN size	3,072
Attention heads	12 (GQA: 4 KV heads)
Vocab size	32,768 (SentencePiece)
Max sequence length	1,024
Positional encoding	RoPE
Activation	SwiGLU
Tied embeddings	Yes

Training

• Hardware: Kaggle T4 GPU (float16)
• SFT: 40,000 steps on ~205K examples (GSM8K + MetaMath + OpenHermes + custom Q&A)
• DPO: 2,500 steps on 4,225 preference pairs
• Optimizer: AdamW (lr=2e-5, β₁=0.9, β₂=0.95)
• Batch size: 32 effective (4 × 8 grad accum)

Benchmark Results

Checkpoint	Overall	Arithmetic	Word Problems	Facts	Identity	Reasoning	Language
Step 15k	29%	50%	33%	25%	25%	20%	0%
Step 30k	23%	25%	17%	13%	50%	0%	50%
DPO final	26%	38%	50%	13%	50%	0%	0%

Usage

import torch
from src.model.yaya_model import YayaForCausalLM
from src.utils.config import ModelConfig
from src.tokenizer.tokenizer import YayaTokenizer
from src.inference.generator import TextGenerator, GenerationConfig

# Load
tokenizer = YayaTokenizer("data/tokenizer/yaya_tokenizer.model")
model = YayaForCausalLM(ModelConfig())
state = torch.load("checkpoint/model.pt", map_location="cpu")
model.load_state_dict(state["model"])
model.eval()

gen = TextGenerator(model, tokenizer)
cfg = GenerationConfig(max_new_tokens=200, temperature=0.7, repetition_penalty=1.5)

response = gen.generate("What is 2 + 2?", config=cfg)
print(response)  # "4"

Repo Structure

yaya-ai/
├── src/
│   ├── model/          # Transformer architecture
│   ├── tokenizer/      # SentencePiece wrapper
│   ├── training/       # Trainer, DPO trainer
│   ├── inference/      # TextGenerator
│   └── data/           # Dataset classes
├── scripts/
│   ├── kaggle_run_sft.py       # Main Kaggle SFT runner (40k steps, DONE)
│   ├── kaggle_run_recovery.py  # Recovery fine-tune (anti-list-format)
│   ├── train_dpo.py            # DPO alignment (DONE)
│   ├── benchmark.py            # 35-question eval suite
│   ├── chat.py                 # CLI chat
│   ├── web_ui.py               # Gradio web UI
│   ├── quantize.py             # int8 quantization (492MB → 219MB)
│   └── update_dashboard.py     # Regenerate dashboard from benchmark data
├── configs/
│   ├── model/yaya_125m.yaml
│   └── training/milestones.yaml
└── docs/
    ├── dashboard.html           # Training progress dashboard
    └── benchmark_results.jsonl

Notes

• Built entirely from scratch — no HuggingFace Transformers dependency
• Token format: <|system|>, </|user|>, </|assistant|>
• Checkpoints pushed to HF Hub every 90s during Kaggle training
• See docs/dashboard.html for training progress visualization