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concordance-clean-v1
122.35M parameter concordance model — bpe-64k tokenizer, 12L/768D/12H
Overview
122.35M
Parameters
8.4351
Final Loss
-
Best Val Loss
4605.9
Perplexity
409,600
Tokens Processed
0.0
Tokens/Param
1,804 tok/s
Avg Throughput
3m 48s
Training Time
Training Progress50 / 100,000 steps (0.1%)
Loss reduced by 17.8% from initial 10.2599
Dataset & Training
Domainconcordance
Tokenizerbpe-64k
Total Iterations100,000
Batch Size16
Context Length512 tokens
Tokens per Batch8,192
Dataset Passes~0
Effective Tokens409,600
Training Pipeline
Warmupsteps 1–1
Learning rate warmup — model weights adjusting to data distribution
Loss: 10.260 → 10.260Linear 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)
Parameters122.35M
Layers12
Embedding Dim768
Attention Heads12
Head Dim64
FFN Dim3072
FFN Activationswiglu
Vocab Size24,076
Context Length512 tokens
Dropout0
Training Configuration
Optimizeradamw
Learning Rate0.0006
LR Min0.00006
LR ScheduleCosine decay
Warmup Steps500
Batch Size16
Grad Accum Steps1
Effective Batch16
Grad Clip1
Weight Decay0.1
Backendhelios
Tokenizerbpe-64k
Seed42
Layer Structure
Token Embed
24,076×768
Pos Embed
512×768
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
768
LM Head
768×24,076
Generated Samples
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Checkpoints
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