Instructions to use shreethar/Latent-Student-Spatial-Forcing with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shreethar/Latent-Student-Spatial-Forcing with Transformers:

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

pipe = pipeline("image-text-to-text", model="shreethar/Latent-Student-Spatial-Forcing")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("shreethar/Latent-Student-Spatial-Forcing")
model = AutoModelForMultimodalLM.from_pretrained("shreethar/Latent-Student-Spatial-Forcing", device_map="auto")
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?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shreethar/Latent-Student-Spatial-Forcing with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shreethar/Latent-Student-Spatial-Forcing"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shreethar/Latent-Student-Spatial-Forcing",
		"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/shreethar/Latent-Student-Spatial-Forcing

SGLang

How to use shreethar/Latent-Student-Spatial-Forcing 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 "shreethar/Latent-Student-Spatial-Forcing" \
    --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": "shreethar/Latent-Student-Spatial-Forcing",
		"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 "shreethar/Latent-Student-Spatial-Forcing" \
        --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": "shreethar/Latent-Student-Spatial-Forcing",
		"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 shreethar/Latent-Student-Spatial-Forcing with Docker Model Runner:
```
docker model run hf.co/shreethar/Latent-Student-Spatial-Forcing
```

Latent Student Spatial Forcing

This is the standalone Stage 4 inference package for the Latent Student. The Stage 4 LoRA adapter has been merged into shreethar/LatentStudent-ckpt-400.

Included components

Merged Qwen3.5 vision-language model weights
Processor and tokenizer
spatial_parameters.pt: five learned spatial-slot embeddings and the Stage 4 waypoint MLP
latent_student_config.json: packaging and provenance metadata
stage4_config.json: training configuration, when present in the checkpoint

VGGT and the Spatial Forcing projection head were training-only supervision components. They are not needed for waypoint inference.

Provenance

Stage 2 model: shreethar/LatentStudent-ckpt-400
Stage 4 checkpoint: stage4_partial_run_2/step_002650
Checkpoint selection: best_checkpoint.json
Checkpoint step: 002650
Loss weights: alpha=1.0, beta=3.0, gamma=0.025

Loading for waypoint inference

Use the project's LatentStudent wrapper so the spatial slots and waypoint head are restored alongside the merged VLM:

from transformers import AutoTokenizer
from train.stage4.checkpointing import load_latent_student_checkpoint

repo_id = "shreethar/Latent-Student-Spatial-Forcing"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
end_think_token_id = tokenizer.convert_tokens_to_ids("</think>")

student = load_latent_student_checkpoint(
    checkpoint=repo_id,
    end_think_token_id=end_think_token_id,
    trainable=False,
    M=6,
    K=5,
)
student.eval()

Loading only with AutoModelForImageTextToText restores the merged VLM but not the external spatial slots or waypoint head. Use the wrapper above for the complete Latent Student behavior.

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Safetensors

Model size

5B params

Tensor type

BF16

Model tree for shreethar/Latent-Student-Spatial-Forcing

Base model

shreethar/LatentStudent-ckpt-400

Finetuned

(1)

this model