Instructions to use nvidia/LocateAnything-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use nvidia/LocateAnything-3B with Transformers:

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

pipe = pipeline("image-text-to-text", model="nvidia/LocateAnything-3B", trust_remote_code=True)
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("nvidia/LocateAnything-3B", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use nvidia/LocateAnything-3B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "nvidia/LocateAnything-3B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "nvidia/LocateAnything-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/nvidia/LocateAnything-3B

SGLang

How to use nvidia/LocateAnything-3B 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 "nvidia/LocateAnything-3B" \
    --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": "nvidia/LocateAnything-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

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 "nvidia/LocateAnything-3B" \
        --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": "nvidia/LocateAnything-3B",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use nvidia/LocateAnything-3B with Docker Model Runner:
```
docker model run hf.co/nvidia/LocateAnything-3B
```

LocateAnything-3B / batch_utils /README.md

ShihaoW

Add batch inference and LA Flash runtime

8d21b77 verified 21 days ago

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	# Batch Utils

	`batch_utils` contains the optional batched hybrid generation runtime for
	LocateAnything. It keeps the model loading, tokenization, image feature caching,
	sampling, and scheduler code used by `batch_infer.py` and the detection
	experiments.

	## Runtime Modes

	- `LA_FLASH_ATTN=sdpa`: stock PyTorch SDPA path.
	- `LA_FLASH_ATTN=eager`: eager attention path for debugging.
	- `LA_FLASH_ATTN=magi`: MagiAttention path when MagiAttention is installed.
	- `LA_FLASH_ATTN=la_flash`: LA Flash sparse range backend
	from `kernel_utils`.

	## Common Knobs

	\| Variable \| Default \| Meaning \|
	\| --- \| --- \| --- \|
	\| `LA_FLASH_MODEL` \| `nvidia/LocateAnything-3B` \| HF model id or local model directory. \|
	\| `LA_FLASH_ATTN` \| `sdpa` \| LLM attention backend. \|
	\| `LA_FLASH_VISION_ATTN` \| `auto` \| Vision encoder attention: `auto`, `flash_attention_2`, `sdpa`, or `eager`. \|
	\| `LA_FLASH_STRICT_ATTN` \| `0` \| Set `1` to fail instead of falling back to SDPA. \|
	\| `LA_FLASH_HYBRID_SCHEDULER` \| `eager` \| Hybrid decode scheduler. \|
	\| `LA_FLASH_HYBRID_GROUP_SIZE` \| `0` \| Scheduler group size; `0` lets the runtime decide. \|
	\| `LA_FLASH_VISION_ENCODE_BATCH_SIZE` \| `8` \| Maximum images per MoonViT encode micro-batch. \|
	\| `LA_FLASH_KV_PACK_TOKEN_BUDGET` \| `0` \| Optional KV packing memory cap for long-tail batches. \|
	\| `LA_FLASH_DENSE_BACKEND` \| `sdpa` \| Dense worker/prefill attention backend. Keep this as `sdpa`; LA Flash is used for sparse range plans. \|
	\| `LA_FLASH_SEGMENT_FASTPATH` \| `auto` \| Sparse MTP decode uses FlashAttention varlen multi-segment merge by default. \|

	## CLI Example

	```bash
	python batch_infer.py \
	--model nvidia/LocateAnything-3B \
	--attn la_flash \
	--scheduler pipeline \
	--batch-size 4 \
	--image /path/to/image.jpg \
	--query "person</c>car"
	```

	For JSONL input, each row should contain:

	```json
	{"image": "/path/to/image.jpg", "query": "person</c>car"}
	```

	## Training Boundary

	This package is for inference and evaluation. Training remains on the
	MagiAttention backend; the batched sparse-plan decode runtime does not support
	the `labels` training path.