Instructions to use ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3

SGLang

How to use ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3 with Docker Model Runner:
```
docker model run hf.co/ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3
```

OLMo 3 3B SiameseNorm + DepthAttention — Stage 3 Long-context Training

This repository is the Hugging Face export of o3sd3b-s3-s65536-g64-m1-ga2-tp2-cp8-dp32-h32-b2-lr2p5e4-w200-save1000-512npu-ptdata-0904062425-s3v1 at iteration 11921. This model preserves the trained SiameseNorm + DepthAttention architecture through bundled Hugging Face remote code. Load it with trust_remote_code=True.

Training sequence length: 65,536
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.

Loading

Use transformers>=4.57.6,<5.

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "ArchSpace-Collection/OLMo3-3B-SiameseNorm-DepthAttention-stage3"
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.

Downloads last month: 6

Safetensors

Model size

4B params

Tensor type

BF16