stale
super_chat_20260306140049_x706
55.77M parameter nanochat model — bpe-chat-4k tokenizer, 16L/512D/8H
Overview
55.77M
Parameters
6.8133
Final Loss
-
Best Val Loss
909.9
Perplexity
917,504
Tokens Processed
0.0
Tokens/Param
1,108 tok/s
Avg Throughput
3m 44s
Training Time
Training Progress448 / 20,000 steps (2.2%)
Loss reduced by 18.9% from initial 8.3968
Dataset & Training
Domainnanochat
Tokenizerbpe-chat-4k
Total Iterations20,000
Batch Size4
Context Length512 tokens
Tokens per Batch2,048
Dataset Passes~2
Effective Tokens917,504
Training Pipeline
Warmupsteps 1–1
Learning rate warmup — model weights adjusting to data distribution
Loss: 8.397 → 8.397Linear LR warmup, gradient clipping
Training Metrics
Loss Curve
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Smoothed Loss
Perplexity
Learning Rate
Gradient Norm
Throughput (tok/s)
Timing Breakdown
No Telemetry
Model Architecture
Model Configuration
ArchitectureGPT (decoder-only transformer)
Parameters55.77M
Layers16
Embedding Dim512
Attention Heads8
Head Dim64
FFN Dim2048
FFN Activationswiglu
Vocab Size4,000
Context Length512 tokens
Dropout0
Training Configuration
Optimizeradamw
Learning Rate0.0006
LR Min0.00006
LR ScheduleCosine decay
Warmup Steps500
Batch Size4
Grad Accum Steps1
Effective Batch4
Grad Clip1
Weight Decay0.1
Backendhelios
Tokenizerbpe-chat-4k
Seed42
Layer Structure
Token Embed
4,000×512
Pos Embed
512×512
Block 0
Attn+FFN
Block 1
Attn+FFN
Block 2
Attn+FFN
Block 3
Attn+FFN
Block 4
Attn+FFN
Block 5
Attn+FFN
LayerNorm
512
LM Head
512×4,000
Generated Samples
No samples generated yet. Samples appear at configured intervals during training.
Checkpoints
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