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Raghav-Singhal/1pp-1b-raw-base
1pp-1b-raw-base is a text generation model from Raghav-Singhal. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as apache-2.0.
One Persona Pretraining (1PP) experiment model: 0.98B parameters, pretraining condition original documents.
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From the Hugging Face model README
One Persona Pretraining (1PP) experiment model: 0.98B parameters, pretraining condition original documents.
Part of a 3 × 3 study: three sizes (0.5B, 1B, 1.7B) × three pretraining conditions on the same 47.8M source documents in the same order (original documents; rewritten conversations with loss on assistant turns; rewritten conversations with loss on user and assistant turns). Every run saw the identical batch sequence, so the conditions differ only in the document text and the loss mask. Models are grouped in the 1pp collection.
Llama-style decoder, 24 layers, hidden 1,536, FFN 6,144 (SwiGLU), attention heads / KV heads 12 / 4 (head dim 128), RMSNorm, RoPE base 10,000, untied embeddings, no biases, no QK-norm, sequence length 4,096. Tokenizer: SmolLM2 vocabulary (49,152) plus <|pad|>; <|endoftext|> is the end-of-document token.
Data: the original DCLM-edu documents (raw baseline); loss on all document tokens and on <|endoftext|>. One pass over 47.8M documents (66.2B tokens of original documents; 63.0B tokens as conversations), 31,777 steps at global batch 512 × 4,096 tokens, cross-document attention masking, best-fit packing with step-aligned document assignment. Optimizer: Muon (shape scaling, matrix LR 0.005) with Adam for embeddings and norms, warmup 2,000 steps, constant, linear decay over the last 10% to 1/100, weight decay 0.1, bf16.
Validation loss (per token, 2,433 held-out documents, final checkpoint):
| assistant text | user text | document text |
|---|---|---|
| 2.574 | 2.620 | 2.458 |
ChatML without a system turn (the models never saw one):
<|im_start|>user\n{message}<|im_end|>\n<|im_start|>assistant\n{reply}<|im_end|>\n
The bundled chat_template renders exactly this. Generation stops at <|im_end|> (id 2) or <|endoftext|> (id 0); both are listed in eos_token_id. This is a base model; the conversation conditions produce chat-formatted text, the raw baseline plain text.
The HF weights were checked against the Megatron checkpoint by recomputing validation losses with this model:
| set | HF loss | Megatron reference | abs. diff |
|---|---|---|---|
| val50m segments [3] | 2.5726 | 2.5744 | 0.0018 |
| raw_val50m segments [8] | 2.4600 | 2.4582 | 0.0018 |