OLMo 3 3B Baseline — Stage 4 Think SFT
This repository is the Hugging Face export of o3b3b-think-sft-dolci-s32768-g32-m1-ga1-tp2-cp8-dp32-h32-b2-lr2e5-min1e6-wd5e2-wu10pct-2ep-512npu-share-20260906t095032z-s4v2 at
iteration 43224. This is the matched pure OLMo 3 baseline. It uses Transformers' official Olmo3ForCausalLM implementation and does not require remote code.
- 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-stage4-think"
tokenizer = AutoTokenizer.from_pretrained(
repo_id,
use_fast=True,
fix_mistral_regex=False,
)
model = AutoModelForCausalLM.from_pretrained(
repo_id,
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.