Instructions to use manycore-research/SpatialLM-Llama-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use manycore-research/SpatialLM-Llama-1B with Transformers:

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

pipe = pipeline("text-generation", model="manycore-research/SpatialLM-Llama-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("manycore-research/SpatialLM-Llama-1B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use manycore-research/SpatialLM-Llama-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "manycore-research/SpatialLM-Llama-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "manycore-research/SpatialLM-Llama-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/manycore-research/SpatialLM-Llama-1B

SGLang

How to use manycore-research/SpatialLM-Llama-1B 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 "manycore-research/SpatialLM-Llama-1B" \
    --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": "manycore-research/SpatialLM-Llama-1B",
		"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 "manycore-research/SpatialLM-Llama-1B" \
        --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": "manycore-research/SpatialLM-Llama-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use manycore-research/SpatialLM-Llama-1B with Docker Model Runner:
```
docker model run hf.co/manycore-research/SpatialLM-Llama-1B
```

Why tagged as 'Text Generation'?

by Impulse2000 - opened Mar 24, 2025

Discussion

Impulse2000

Mar 24, 2025

•

edited Mar 24, 2025

Hello, cool project, i just wondered why this model was tagged as text generation because the description says it's purpose is solely for 3D/Vision based use cases.

If it doesn't output any plain text, I would recommend using a tag in the 'Computer Vision' category, perhaps 'Mask Generation', as the video demo shows Video Segmentation and Masking of sorts.

'Text Generation' implies that has the ability to output plain text, which is not cited as a feature in the model card.

Anyway, Great work!! Just wanted to understand the reasoning, i am new to this area of research, especially vision/multimodal models, so i may have misunderstood.

Many thanks,
James Clarke

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