ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage4-think

OLMo 3 3B SiameseNorm + DepthAttention — Stage 4 Think SFT

This repository is the Hugging Face export of o3sd3b-think-sft-dolci-s32768-g32-m1-ga2-tp2-cp8-dp16-h16-b2-lr2e5-min1e6-wd5e2-wu10pct-2ep-256npu-ptdata-20260904t154044z-s4v1 at iteration 43224. This model preserves the trained SiameseNorm + DepthAttention architecture through bundled Hugging Face remote code. Load it with trust_remote_code=True.

• —Training sequence length: 32,768
• —Model context capacity: 65,536
• —Sliding-window size: 4,096
• —Attention pattern: [SWA, SWA, SWA, Full]
• —Vocabulary: 100,278 real tokens; 74 Megatron padding-only rows removed

Stage 3/4 use the frozen 65,536-token configuration. YaRN applies to the Full Attention layers; SWA layers retain their original RoPE and 4,096-token local window.

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Use transformers>=4.57.6,<5.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage4-think"
tokenizer = AutoTokenizer.from_pretrained(
    repo_id,
    trust_remote_code=True,
    use_fast=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 exact tokenizer behavior used during training. Conversion provenance, per-tensor hashes, and CPU validation results are included in conversion_manifest.json, SHA256SUMS, and hf_validation_report.json.