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fine_corpus_clean_v9
20.51M parameter fine_corpus model — bpe-8k tokenizer, 8L/384D/6H
Overview
20.51M
Parameters
7.5216
Final Loss
7.3502
Best Val Loss
1847.6
Perplexity
4,452,352
Tokens Processed
0.2
Tokens/Param
7,424 tok/s
Avg Throughput
10m 1s
Training Time
Training Progress2,174 / 150,000 steps (1.4%)
Loss reduced by 16.8% from initial 9.0448
Dataset & Training
Domainfine_corpus
Tokenizerbpe-8k
Total Iterations150,000
Batch Size4
Context Length512 tokens
Tokens per Batch2,048
Dataset Passes~6
Effective Tokens4,452,352
Training Pipeline
Warmupsteps 1–1,500
Learning rate warmup — model weights adjusting to data distribution
Loss: 9.045 → 8.010Linear 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)
Parameters20.51M
Layers8
Embedding Dim384
Attention Heads6
Head Dim64
FFN Dim1536
FFN Activationgelu
Vocab Size8,000
Context Length512 tokens
Dropout0
Training Configuration
Optimizeradamw
Learning Rate0.0001
LR Min0.00001
LR ScheduleCosine decay
Warmup Steps2,000
Batch Size4
Grad Accum Steps1
Effective Batch4
Grad Clip1
Weight Decay0.1
Backendhelios
Tokenizerbpe-8k
Seed42
Layer Structure
Token Embed
8,000×384
Pos Embed
512×384
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
384
LM Head
384×8,000
Generated Samples
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Checkpoints
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