NanoChat Base Training Report | Generated by AI
Here’s the comprehensive training summary:
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NANOCHAT BASE TRAINING SUMMARY
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MODEL ARCHITECTURE — GPT-2 style, 286M params
Layers: 12
Heads: 6 (GQA with n_kv_head=6)
Embed dim: 768, Head dim: 128
Vocab: 32,768
Context: 2,048 tokens
Total params: 286.3M (261.1M non-embedding)
Notable: RoPE, QK norm, ReLU² MLP, no bias,
untied embed/lm_head, Value Embeddings
on odd layers (1,3,5,7,9,11) w/ learned gate
TRAINING SCHEDULE
Phase 1 (steps 0-10k): fineweb-edu-d12, from scratch
Phase 2 (steps 10k-87k): rtx4070-d12-chinchilla, resumed
Total steps: 87,000
Batch size: 65,536 tokens/step (device_bs=8 × seq=2048)
Total tokens: ~5.7B
Data ratio: 12 (Chinchilla-optimal: 5.7B / 286M ≈ 20x)
GPU: RTX 4070 12GB, bf16, peak 9.4 GB VRAM
OPTIMIZER
Type: MuonAdamW (custom)
Matrix LR: 0.02, Scalar LR: 0.5
Embed LR: 0.3, Unembed LR: 0.008
Weight decay: 0.28
Warmup: 40 steps
Warmdown: starts at 65% of training (step ~56,550)
Final LR frac: 0.05
LOSS PROGRESSION
┌─────────┬───────────┬────────────┬──────────┬──────────┐
│ Step │ Val bpb │ Train Loss │ Delta │ Time │
├─────────┼───────────┼────────────┼──────────┼──────────┤
│ 10,000 │ 0.9349 │ 2.979 │ (base) │ 3.2h │
│ 20,000 │ 1.0155* │ 3.271* │ spike │ 6.5h │
│ 30,000 │ 1.0019 │ 3.234 │ -0.014 │ 9.8h │
│ 40,000 │ 0.9780 │ 3.136 │ -0.024 │ 13.1h │
│ 50,000 │ 0.9533 │ 3.042 │ -0.025 │ 16.3h │
│ 60,000 │ 0.9299 │ 2.954 │ -0.023 │ 19.6h │
│ 70,000 │ 0.9022 │ 2.885 │ -0.028 │ 22.9h │
│ 80,000 │ 0.8799 │ 2.850 │ -0.022 │ 26.1h │
│ 87,000 │ 0.8658 │ 2.748 │ -0.014 │ 28.4h │
└─────────┴───────────┴────────────┴──────────┴──────────┘
* Step 20k spike: optimizer state resume + config change
Phase 2 net improvement: 1.0155 → 0.8658 bpb (-14.7%)
From scratch to finish: 0.9349 → 0.8658 bpb (-7.4%)
PERFORMANCE
Throughput: 55,670 tok/sec (constant throughout)
Step time: 1,177 ms/step
Total time: 1,706 min (28.4 hours)
Checkpoints: 9 saved (every 10k steps + final)
Model size: ~756 MB per checkpoint
Optim size: ~1.2 GB per checkpoint
KEY OBSERVATIONS
1. Loss never plateaued — still decreasing at final step.
The warmdown (LR decay) was just beginning to take effect.
More steps would likely yield further gains.
2. Phase 2 resume caused a temporary loss spike (0.93→1.02),
likely from optimizer state mismatch or data distribution
change. Recovered within ~20k steps.
3. Val bpb tracked smoothly — no overfitting. Train loss
stayed above val bpb throughout (expected for smoothed
vs. point estimates).
4. RTX 4070 handled this well — 9.4 GB peak VRAM, 100% util,
66°C, 211W throughout. No OOM, no crashes.
NEXT STEPS
Evaluate: python -m scripts.base_eval --device-batch-size=8
Chat: python -m scripts.chat_cli -p 'Why is the sky blue?'
SFT: python -m scripts.chat_sft (fine-tune for chat)
RL: python -m scripts.chat_rl (RLHF/DPO alignment)
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The model is in great shape — loss was still decreasing at finish, never plateaued, no overfitting. The 286M param model with value embeddings and Chinchilla-optimal data ratio performed well on a single RTX 4070. The next natural step would be running base_eval to get benchmark scores, then SFT/RL for chat capability.