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Raghav-Singhal/1pp-1.7b-ua-seed42-sft

sourceHugging Faceapache-2.0updated 16d agoView on Hugging Face

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1pp-1.7b-ua-seed42-sft

One Persona Pretraining (1PP) experiment model: 1.66B parameters, pretraining condition rewritten conversations, user+assistant loss, followed by supervised fine-tuning (SFT).

Seed replicate of `1pp-1.7b-ua-sft`: the same recipe and the same batch sequence, with --seed 42 instead of 28 for the parameter initialization (pretraining and SFT). It measures the initialization variance of the recipe: the two seeds differ by 0.001 to 0.003 nats in pretraining validation loss at 1.7B.

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.

Architecture

Llama-style decoder, 24 layers, hidden 2,048, FFN 8,192 (SwiGLU), attention heads / KV heads 16 / 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.

Pretraining

Data: the 1PP conversations rewritten from those documents; loss on user and assistant turns (no loss 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
1.433	1.339	3.123

Supervised fine-tuning

One epoch over a 400k-conversation mix: jkminder/model-raising-pb-100k-3c-mt-sft (98.5k multi-turn, constitution-cited track), dlab-spp/sp-sft-normal-300k minus prompts duplicated in the first set (271.6k), and a 30k sample of dlab-spp/sp-sft-safety-180k. Same stack as pretraining (Megatron, Muon, ChatML without a system turn, loss on assistant turns only). Matrix LR 0.002 selected per model from {0.0005, 0.001, 0.002, 0.005} by held-out loss (on the seed-28 model; the replicate reuses it); global batch 128 × 4,096, linear decay to 1/10 after 3% warmup.

Held-out SFT loss (assistant tokens, 1,998 held-out conversations): 1.832

Chat format

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.

Verification

The HF weights were checked against the Megatron checkpoint by recomputing validation losses with this model: