completed
super_chat_20260306125603_gy0d
55.77M parameter nanochat model — bpe-chat-4k tokenizer, 16L/512D/8H
Overview
55.77M
Parameters
6.5033
Final Loss
-
Best Val Loss
667.4
Perplexity
102,400
Tokens Processed
0.0
Tokens/Param
796 tok/s
Avg Throughput
2m 9s
Training Time
Training Progress100 / 100 steps (100.0%)
Loss reduced by 22.2% from initial 8.3586
Dataset & Training
Domainnanochat
Tokenizerbpe-chat-4k
Total Iterations100
Batch Size2
Context Length512 tokens
Tokens per Batch1,024
Dataset Passes~0
Effective Tokens102,400
Training Pipeline
Warmupsteps 1–1
Learning rate warmup — model weights adjusting to data distribution
Loss: 8.359 → 8.359Linear LR warmup, gradient clipping
Rapid Descentsteps 1–31
Steepest loss reduction — model learning primary patterns
Loss: 8.359 → 7.452Cosine LR schedule, AdamW optimization
Refinementsteps 31–71
Diminishing returns — model fine-tuning subtler patterns
Loss: 7.452 → 6.671Lower LR, gradient accumulation
Convergencesteps 71–100
Approaching minimum — model capacity saturation
Loss: 6.671 → 6.503Minimum LR, weight decay regularization
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 Size2
Grad Accum Steps1
Effective Batch2
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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