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FrontD.11m completed session 1

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README.md ADDED
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+ ---
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+ license: mit
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+ library_name: pytorch
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+ pipeline_tag: text-generation
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+ tags:
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+ - text-generation
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+ - pretraining
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+ - decoder-only
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+ - from-scratch
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+ - 11m
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+ - 1096-context
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+ - rope
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+ - rmsnorm
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+ - swiglu
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+ ---
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+
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+ # FrontD.11m
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+
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+ FrontD.11m is an approximately 11-million-parameter
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+ decoder-only language model trained completely from scratch.
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+
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+ ## Architecture
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+
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+ - Parameters: 10,996,608
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+ - Context: 1096
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+ - Vocabulary: 16000
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+ - Hidden size: 256
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+ - Layers: 6
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+ - Attention heads: 16
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+ - FFN size: 1152
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+ - RoPE
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+ - RMSNorm
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+ - SwiGLU
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+ - Tied input/output embeddings
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+
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+ ## Pretraining
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+
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+ Dataset:
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+
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+ `Plans11/Organized_PreTrain_1k_Context`
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+
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+ Training is performed in 20,000-example sessions.
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+
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+ A persistent SHA-256 ledger prevents completed examples
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+ from being selected again.
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+
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+ Interrupted sessions resume from their latest checkpoint.
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+
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+ ## Training from scratch
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+
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+ No pretrained model weights are imported.
config.json ADDED
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+ {
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+ "model_type": "frontd",
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+ "architectures": [
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+ "FrontD11M"
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+ ],
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+ "model_name": "FrontD.11m",
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+ "vocab_size": 16000,
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+ "context_length": 1096,
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+ "hidden_size": 256,
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+ "num_hidden_layers": 6,
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+ "num_attention_heads": 16,
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+ "intermediate_size": 1152,
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+ "position_embedding_type": "rope",
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+ "rope_theta": 10000.0,
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+ "normalization": "rmsnorm",
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+ "activation_function": "silu",
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+ "attention_bias": false,
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+ "mlp_bias": false,
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+ "tie_word_embeddings": true,
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+ "dropout": 0.0,
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+ "bos_token": "<bos>",
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+ "eos_token": "<eos>",
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+ "pad_token": "<pad>",
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+ "unk_token": "<unk>",
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+ "parameter_target": "~11M",
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+ "parameter_count": 10996608,
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+ "dataset": "Plans11/Organized_PreTrain_1k_Context",
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+ "session_examples": 20000,
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+ "training_from_scratch": true,
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+ "license": "mit"
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+ }
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session_000001.json ADDED
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+ {
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+ "dataset": "Plans11/Organized_PreTrain_1k_Context",
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+ "examples_per_session": 20000,
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+ "uploaded_at": "2026-08-19 06:22:30 UTC"
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