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super_chat_20260306235606_gtkf
6.84M parameter super_chat model — bpe-chat-4k tokenizer, 6L/256D/8H
Overview
6.84M
Parameters
6.2601
Final Loss
-
Best Val Loss
523.3
Perplexity
332,800
Tokens Processed
0.0
Tokens/Param
30,767 tok/s
Avg Throughput
22s
Training Time
Training Progress325 / 50,000 steps (0.7%)
Loss reduced by 25.0% from initial 8.3452
Dataset & Training
Domainsuper_chat
Tokenizerbpe-chat-4k
Total Iterations50,000
Batch Size4
Context Length256 tokens
Tokens per Batch1,024
Dataset Passes~1
Effective Tokens332,800
Training Pipeline
Warmupsteps 1–1
Learning rate warmup — model weights adjusting to data distribution
Loss: 8.345 → 8.345Linear 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)
Parameters6.84M
Layers6
Embedding Dim256
Attention Heads8
Head Dim32
FFN Dim1024
FFN Activationgelu
Vocab Size4,000
Context Length256 tokens
Dropout0
Training Configuration
Optimizeradamw
Learning Rate0.0006
LR Min0.00006
LR ScheduleCosine decay
Warmup Steps2,000
Batch Size4
Grad Accum Steps2
Effective Batch8
Grad Clip1
Weight Decay0.1
Backendhelios
Tokenizerbpe-chat-4k
Seed42
Layer Structure
Token Embed
4,000×256
Pos Embed
256×256
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
256
LM Head
256×4,000
Generated Samples
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Checkpoints
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