---
library_name: transformers
pipeline_tag: text-generation
tags:
- olmo3
- safetensors
- sliding-window-attention
- trust_remote_code
- siamese-norm
- depth-attention
---

# OLMo 3 3B SiameseNorm + DepthAttention — Stage 2 Mid-training

This repository is the Hugging Face export of `o3sd3b-s2-s8192-g256-m1-ga2-tp2-cp1-dp128-h16-b8-mc2-lr2p071e4-256npu-arch-0903100156-s2v2` at
iteration `47684`. This model preserves the trained SiameseNorm + DepthAttention architecture through bundled Hugging Face remote code. Load it with `trust_remote_code=True`.

- Training sequence length: 8,192
- Model context capacity: 8,192
- 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.

## Loading

Use `transformers>=4.57.6,<5`.

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage2"
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`.