OLMo 3 3B SiameseNorm + DepthAttention — Stage 4 Instruct SFT
This repository is the Hugging Face export of o3sd3b-instruct-sft-dolci-s32768-g32-m1-ga2-tp2-cp8-dp16-h16-b2-lr3e5-min0-wd5e2-wu10pct-2ep-256npu-ptdata-20260904t154044z-s4v1 at
iteration 3252. 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-instruct"
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.