stale
super_chat_20260306130743_e3ca
55.77M parameter nanochat model — bpe-chat-4k tokenizer, 16L/512D/8H
Overview
55.77M
Parameters
6.7689
Final Loss
6.8867
Best Val Loss
870.3
Perplexity
2,537,472
Tokens Processed
0.0
Tokens/Param
960 tok/s
Avg Throughput
43m 47s
Training Time
Training Progress1,239 / 20,000 steps (6.2%)
Loss reduced by 19.3% from initial 8.3828
Dataset & Training
Domainnanochat
Tokenizerbpe-chat-4k
Total Iterations20,000
Batch Size4
Context Length512 tokens
Tokens per Batch2,048
Dataset Passes~6
Effective Tokens2,537,472
Training Pipeline
Warmupsteps 1–200
Learning rate warmup — model weights adjusting to data distribution
Loss: 8.383 → 6.824Linear 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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