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super_chat_20260306151707_utoo
55.77M parameter nanochat model — bpe-chat-4k tokenizer, 16L/512D/8H
Overview
55.77M
Parameters
6.5955
Final Loss
-
Best Val Loss
731.8
Perplexity
495,616
Tokens Processed
0.0
Tokens/Param
1,037 tok/s
Avg Throughput
7m 58s
Training Time
Training Progress242 / 20,000 steps (1.2%)
Loss reduced by 21.5% from initial 8.3985
Dataset & Training
Domainnanochat
Tokenizerbpe-chat-4k
Total Iterations20,000
Batch Size4
Context Length512 tokens
Tokens per Batch2,048
Dataset Passes~1
Effective Tokens495,616
Training Pipeline
Warmupsteps 1–200
Learning rate warmup — model weights adjusting to data distribution
Loss: 8.399 → 6.589Linear 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 Rate0.0003
LR Min0.00003
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
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Checkpoints
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