completed
historic_chat_v2_20260309194951_rpoe
34.16M parameter concordance-chat model — bpe-8k-chat tokenizer, 8L/512D/8H
Overview
34.16M
Parameters
1.8364
Final Loss
1.8835
Best Val Loss
6.3
Perplexity
184,320,000
Tokens Processed
5.4
Tokens/Param
5,719 tok/s
Avg Throughput
4h 22m
Training Time
Training Progress22,500 / 22,500 steps (100.0%)
Loss reduced by 9.2% from initial 2.0229
Dataset & Training
Domainconcordance-chat
Tokenizerbpe-8k-chat
Total Iterations22,500
Batch Size16
Context Length512 tokens
Tokens per Batch8,192
Dataset Passes~230
Effective Tokens184,320,000
Training Pipeline
Warmupsteps 17,001–17,001
Learning rate warmup — model weights adjusting to data distribution
Loss: 2.023 → 2.023Linear 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)
Parameters34.16M
Layers8
Embedding Dim512
Attention Heads8
Head Dim64
FFN Dim2048
FFN Activationswiglu
Vocab Size8,000
Context Length512 tokens
Dropout0
Training Configuration
Optimizeradamw
Learning Rate0.00005
LR Min0.000005
LR ScheduleCosine decay
Warmup Steps50
Batch Size16
Grad Accum Steps2
Effective Batch32
Grad Clip1
Weight Decay0.01
Backendhelios
Tokenizerbpe-8k-chat
Seed42
Layer Structure
Token Embed
8,000×512
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
512×8,000
Generated Samples
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Checkpoints
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