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Alpha
stale

super_chat_20260306121219_r926

55.77M parameter nanochat model — bpe-chat-4k tokenizer, 16L/512D/8H

Overview

55.77M
Parameters
7.4852
Final Loss
-
Best Val Loss
1781.4
Perplexity
63,488
Tokens Processed
0.0
Tokens/Param
887 tok/s
Avg Throughput
1m 6s
Training Time
Training Progress31 / 500 steps (6.2%)
Loss reduced by 10.9% from initial 8.3966

Dataset & Training

Domainnanochat
Tokenizerbpe-chat-4k
Total Iterations500
Batch Size4
Context Length512 tokens
Tokens per Batch2,048
Dataset Passes~0
Effective Tokens63,488

Training Pipeline

Warmupsteps 15

Learning rate warmup — model weights adjusting to data distribution

Loss: 8.3978.277Linear 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
...10 more
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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