Instructions to use ArchSpace-Collection/OLMo3-1B-stage1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArchSpace-Collection/OLMo3-1B-stage1 with Transformers:

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

pipe = pipeline("text-generation", model="ArchSpace-Collection/OLMo3-1B-stage1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ArchSpace-Collection/OLMo3-1B-stage1")
model = AutoModelForCausalLM.from_pretrained("ArchSpace-Collection/OLMo3-1B-stage1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArchSpace-Collection/OLMo3-1B-stage1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ArchSpace-Collection/OLMo3-1B-stage1"
# 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/OLMo3-1B-stage1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ArchSpace-Collection/OLMo3-1B-stage1

SGLang

How to use ArchSpace-Collection/OLMo3-1B-stage1 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/OLMo3-1B-stage1" \
    --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/OLMo3-1B-stage1",
		"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/OLMo3-1B-stage1" \
        --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/OLMo3-1B-stage1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ArchSpace-Collection/OLMo3-1B-stage1 with Docker Model Runner:
```
docker model run hf.co/ArchSpace-Collection/OLMo3-1B-stage1
```

OLMo3-1B-stage1

File size: 1,490 Bytes

5afa639

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  "_name_or_path": "",
  "architectures": [
    "Olmo3ForCausalLM"
  ],
  "attention_bias": false,
  "attention_dropout": 0.0,
  "bos_token_id": null,
  "eos_token_id": 100257,
  "hidden_act": "silu",
  "hidden_dropout": 0.0,
  "hidden_size": 2048,
  "initializer_range": 0.02,
  "intermediate_size": 8192,
  "layer_types": [
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  "max_position_embeddings": 8192,
  "max_sequence_length": 8192,
  "mlp_bias": false,
  "model_type": "olmo3",
  "num_attention_heads": 16,
  "num_hidden_layers": 16,
  "num_key_value_heads": 16,
  "olmo3_weight_decay": true,
  "pad_token_id": 100277,
  "padded_vocab_size": 100352,
  "qk_norm": true,
  "qk_norm_mode": "full_projection",
  "rms_norm_eps": 1e-06,
  "rope_full_precision": true,
  "rope_scaling": null,
  "rope_theta": 500000.0,
  "scale_embeds": false,
  "scale_output_layer_init": false,
  "sliding_window": 4096,
  "tie_word_embeddings": false,
  "torch_dtype": "bfloat16",
  "transformers_version": "4.57.6",
  "true_vocab_size": 100278,
  "truncated_normal_factor": 3.0,
  "use_cache": true,
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  "vocab_z_loss_coeff": 1e-05
}