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minipile_1g_20260310192446_hpkq
97.65M parameter unknown model — bpe-8k-chat tokenizer, 12L/768D/12H
Overview
97.65M
Parameters
4.5053
Final Loss
4.7986
Best Val Loss
90.5
Perplexity
8,499,200
Tokens Processed
0.1
Tokens/Param
2,043 tok/s
Avg Throughput
4h 37m
Training Time
Training Progress2,075 / 10,000 steps (20.8%)
Loss reduced by 50.7% from initial 9.1416
Dataset & Training
Domainunknown
Tokenizerbpe-8k-chat
Total Iterations10,000
Batch Size8
Context Length512 tokens
Tokens per Batch4,096
Dataset Passes~11
Effective Tokens8,499,200
Training Pipeline
Warmupsteps 1–100
Learning rate warmup — model weights adjusting to data distribution
Loss: 9.142 → 7.220Linear 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)
Parameters97.65M
Layers12
Embedding Dim768
Attention Heads12
Head Dim64
FFN Dim3072
FFN Activationgelu
Vocab Size8,000
Context Length512 tokens
Dropout0
Training Configuration
Optimizeradamw
Learning Rate0.0003
LR Min0.00003
LR ScheduleCosine decay
Warmup Steps500
Batch Size8
Grad Accum Steps4
Effective Batch32
Grad Clip1
Weight Decay0.1
Backendhelios
Tokenizerbpe-8k-chat
Seed42
Layer Structure
Token Embed
8,000×768
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
768×8,000
Generated Samples
No samples generated yet. Samples appear at configured intervals during training.
Checkpoints
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