Instructions to use Atomic-Germ/NuExtract3-4B-NPU2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Atomic-Germ/NuExtract3-4B-NPU2 with Transformers:

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

pipe = pipeline("text-generation", model="Atomic-Germ/NuExtract3-4B-NPU2")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Atomic-Germ/NuExtract3-4B-NPU2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Atomic-Germ/NuExtract3-4B-NPU2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Atomic-Germ/NuExtract3-4B-NPU2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Atomic-Germ/NuExtract3-4B-NPU2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Atomic-Germ/NuExtract3-4B-NPU2

SGLang

How to use Atomic-Germ/NuExtract3-4B-NPU2 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 "Atomic-Germ/NuExtract3-4B-NPU2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Atomic-Germ/NuExtract3-4B-NPU2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "Atomic-Germ/NuExtract3-4B-NPU2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Atomic-Germ/NuExtract3-4B-NPU2",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Atomic-Germ/NuExtract3-4B-NPU2 with Docker Model Runner:
```
docker model run hf.co/Atomic-Germ/NuExtract3-4B-NPU2
```

NuExtract3-4B-NPU2 / config.json

Atomic-Germ

Upload config.json with huggingface_hub

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	{
	"attention_bias": false,
	"attention_dropout": 0.0,
	"attn_output_gate": true,
	"dtype": "bfloat16",
	"eos_token_id": 248044,
	"full_attention_interval": 4,
	"head_dim": 256,
	"hidden_act": "silu",
	"hidden_size": 2560,
	"initializer_range": 0.02,
	"intermediate_size": 9216,
	"layer_types": [
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention",
	"linear_attention",
	"linear_attention",
	"linear_attention",
	"full_attention"
	],
	"linear_conv_kernel_dim": 4,
	"linear_key_head_dim": 128,
	"linear_num_key_heads": 16,
	"linear_num_value_heads": 32,
	"linear_value_head_dim": 128,
	"max_position_embeddings": 262144,
	"mlp_only_layers": [],
	"model_type": "qwen3_5_text",
	"mtp_num_hidden_layers": 1,
	"mtp_use_dedicated_embeddings": false,
	"num_attention_heads": 16,
	"num_hidden_layers": 32,
	"num_key_value_heads": 4,
	"rms_norm_eps": 1e-06,
	"tie_word_embeddings": true,
	"use_cache": true,
	"vocab_size": 248320,
	"mamba_ssm_dtype": "float32",
	"rope_parameters": {
	"mrope_interleaved": true,
	"mrope_section": [
	11,
	11,
	10
	],
	"rope_type": "default",
	"rope_theta": 10000000,
	"partial_rotary_factor": 0.25
	},
	"addr_qk": 53248,
	"addr_kv": 53536,
	"flm_version": "1.0.0",
	"vision_config": {
	"vision_mm_engine_xclbin_name": "vision_mm.xclbin",
	"vision_mha_engine_xclbin_name": "vision_attn.xclbin",
	"QWEN3_5_PATCH_SIZE": 16,
	"QWEN3_5_IMAGE_MERGE_SIZE": 2,
	"QWEN3_5_SPATIAL_MERGE_SIZE": 2,
	"QWEN3_5_SHORTEST_EDGE": 65536,
	"QWEN3_5_LONGEST_EDGE": 16777216,
	"QWEN3_5_VISION_RESCALE_FACTOR": 0.00392156862745098,
	"QWEN3_5_VISION_RESCALE_IMAGE_MEAN": 0.5,
	"QWEN3_5_VISION_RESCALE_IMAGE_STD": 0.5,
	"QWEN3_5_TEMPORAL_PATCH_SIZE": 2,
	"QWEN3_5_VISION_EMBED_DIM": 1024,
	"QWEN3_5_VISION_NUM_HEADS": 16,
	"QWEN3_5_VISION_HEAD_DIM": 64,
	"QWEN3_5_VISION_MLP_INTERMEDIATE_SIZE": 4096,
	"QWEN3_5_VISION_NUM_POSITION_EMBEDDINGS": 2304,
	"QWEN3_5_VISION_NUM_LAYERS": 24,
	"QWEN3_5_VISION_LAYER_NORM_EPSILON": 1e-06,
	"QWEN3_5_VISION_OUT_HIDDEN_SIZE": 2560,
	"VISION_MM_TILE_M": 128,
	"VISION_MM_TILE_K": 512,
	"VISION_MM_TILE_N": 64
	}
	}