Instructions to use percepta-ai/spotlight-vm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use percepta-ai/spotlight-vm with Transformers:

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

pipe = pipeline("text-generation", model="percepta-ai/spotlight-vm", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("percepta-ai/spotlight-vm", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use percepta-ai/spotlight-vm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "percepta-ai/spotlight-vm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "percepta-ai/spotlight-vm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/percepta-ai/spotlight-vm

SGLang

How to use percepta-ai/spotlight-vm 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 "percepta-ai/spotlight-vm" \
    --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": "percepta-ai/spotlight-vm",
		"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 "percepta-ai/spotlight-vm" \
        --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": "percepta-ai/spotlight-vm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use percepta-ai/spotlight-vm with Docker Model Runner:
```
docker model run hf.co/percepta-ai/spotlight-vm
```

spotlight-vm / examples /euler3.py

ctzamos

Spotlight VM: the model and the programs from the post

e0c043c verified about 1 month ago

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237 Bytes

# https://projecteuler.net/problem=3
# Find the largest prime factor of 600851475143.
def euler3(n=600851475143):
    i = 2
    while i * i <= n:
        while n % i == 0:
            n //= i
        i += 1
    return n

print(euler3())

	# https://projecteuler.net/problem=3
	# Find the largest prime factor of 600851475143.
	def euler3(n=600851475143):
	i = 2
	while i * i <= n:
	while n % i == 0:
	n //= i
	i += 1
	return n

	print(euler3())