ArchSpace-Collection/OLMo3-1B-SiameseNorm-DepthAttention-stage3

OLMo 3 with SiameseNorm and DepthAttention

This repository is one of five standalone OLMo 3 checkpoints trained with SiameseNorm and DepthAttention. All model, configuration, tokenizer, and custom modeling files are stored directly at the repository root.

Stage 3 and Stage 4 apply YaRN only to Full-attention layers. Sliding-window attention layers retain the original RoPE and a 4,096-token window.

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Set repo_id to the desired repository from the table above:

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "ArchSpace-Collection/OLMo3-SiameseNorm-DepthAttention-<stage>"

tokenizer = AutoTokenizer.from_pretrained(
    repo_id,
    trust_remote_code=True,
    fix_mistral_regex=False,
)
model = AutoModelForCausalLM.from_pretrained(
    repo_id,
    trust_remote_code=True,
    dtype=torch.bfloat16,
    attn_implementation="sdpa",
)

fix_mistral_regex=False preserves the tokenizer behavior used during training. SDPA is the recommended and release-validated BF16 inference backend.

Architecture

• —16 transformer layers
• —hidden size 2,048
• —intermediate size 8,192
• —16 query heads and 16 key/value heads
• —128-dimensional attention heads
• —3:1 sliding-window/full-attention pattern
• —4,096-token sliding window
• —reordered RMSNorm, SiameseNorm, and DepthAttention