Instructions to use ArchSpace-Collection/OLMo3-1B-SiameseNorm-DepthAttention-stage3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ArchSpace-Collection/OLMo3-1B-SiameseNorm-DepthAttention-stage3 with Transformers:

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

pipe = pipeline("text-generation", model="ArchSpace-Collection/OLMo3-1B-SiameseNorm-DepthAttention-stage3", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("ArchSpace-Collection/OLMo3-1B-SiameseNorm-DepthAttention-stage3", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ArchSpace-Collection/OLMo3-1B-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/OLMo3-1B-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/OLMo3-1B-SiameseNorm-DepthAttention-stage3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ArchSpace-Collection/OLMo3-1B-SiameseNorm-DepthAttention-stage3

SGLang

How to use ArchSpace-Collection/OLMo3-1B-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/OLMo3-1B-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/OLMo3-1B-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/OLMo3-1B-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/OLMo3-1B-SiameseNorm-DepthAttention-stage3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ArchSpace-Collection/OLMo3-1B-SiameseNorm-DepthAttention-stage3 with Docker Model Runner:
```
docker model run hf.co/ArchSpace-Collection/OLMo3-1B-SiameseNorm-DepthAttention-stage3
```

OLMo3-1B-SiameseNorm-DepthAttention-stage3 / config.json

arch-space

Publish standalone stage3 checkpoint

3e210ec verified 2 days ago

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	{
	"_name_or_path": "",
	"architectures": [
	"Olmo3SiameseDepthForCausalLM"
	],
	"attention_bias": false,
	"attention_dropout": 0.0,
	"auto_map": {
	"AutoConfig": "configuration_olmo3_siamese_depth.Olmo3SiameseDepthConfig",
	"AutoModel": "modeling_olmo3_siamese_depth.Olmo3SiameseDepthModel",
	"AutoModelForCausalLM": "modeling_olmo3_siamese_depth.Olmo3SiameseDepthForCausalLM"
	},
	"bos_token_id": null,
	"depth_attention_recent_window": 0,
	"depth_attention_stride": 8,
	"eos_token_id": 100257,
	"hidden_act": "silu",
	"hidden_dropout": 0.0,
	"hidden_size": 2048,
	"initializer_range": 0.02,
	"intermediate_size": 8192,
	"layer_types": [
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"full_attention",
	"sliding_attention",
	"sliding_attention",
	"sliding_attention",
	"full_attention"
	],
	"max_position_embeddings": 65536,
	"max_sequence_length": 65536,
	"mlp_bias": false,
	"model_type": "olmo3_siamese_depth",
	"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": {
	"beta_fast": 32.0,
	"beta_slow": 1.0,
	"factor": 8.0,
	"original_max_position_embeddings": 8192,
	"rope_type": "yarn"
	},
	"rope_theta": 500000.0,
	"scale_embeds": false,
	"scale_output_layer_init": false,
	"siamese_norm_variant": "hybrid_pre",
	"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,
	"use_depth_attention": true,
	"use_siamese_norm": true,
	"vocab_size": 100278,
	"vocab_z_loss_coeff": 1e-05
	}