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

Libraries

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

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ArchSpace-Collection/OLMo3-3B-stage3")
model = AutoModelForCausalLM.from_pretrained("ArchSpace-Collection/OLMo3-3B-stage3", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArchSpace-Collection/OLMo3-3B-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-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-stage3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

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

SGLang

How to use ArchSpace-Collection/OLMo3-3B-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-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-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-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-stage3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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

OLMo3-3B-stage3 / hf_validation_report.json

arch-space

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	{
	"checksummed_files": 9,
	"forward": {
	"cache_no_cache_max_abs": 0.0,
	"logits_finite": true,
	"logits_shape": [
	1,
	4,
	100278
	],
	"parameter_count": 3503016192,
	"post_8192_yarn_finite": true
	},
	"model_dir": "/data/PonderLM/fcsong/olmo3_hf_remote_exports_3b_20260907_v1/olmo3-base-3b-stage3-step11921",
	"parameter_count": 3503016192,
	"schema": "olmo3.3b.hf_validation/v1",
	"stage": "stage3",
	"status": "pass",
	"tensor_count": 179,
	"tokenizer_size": 100278,
	"torch_version": "2.6.0+cpu",
	"transformers_version": "4.57.6",
	"variant": "baseline",
	"weight_files": [
	"model.safetensors"
	]
	}