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
```

ctzamos commited on 14 days ago

Commit

26bcc5a

verified ·

1 Parent(s): e0c043c

Move the tokenizer auto_map to tokenizer_config.json

Browse files

Files changed (2) hide show

config.json +1 -1
tokenizer_config.json +1 -0

config.json CHANGED Viewed

	@@ -1 +1 @@
1	- {"model_type":"spotlight","architectures":["SpotlightForCausalLM"],"auto_map":{"AutoConfig":"spotlight.SpotlightConfig","AutoModelForCausalLM":"spotlight.SpotlightForCausalLM"~~,"AutoTokenizer":["spotlight.SpotlightTokenizer",null]~~}}


1	+ {"model_type":"spotlight","architectures":["SpotlightForCausalLM"],"auto_map":{"AutoConfig":"spotlight.SpotlightConfig","AutoModelForCausalLM":"spotlight.SpotlightForCausalLM"}}

tokenizer_config.json ADDED Viewed

	@@ -0,0 +1 @@


1	+ {"tokenizer_class":"SpotlightTokenizer","auto_map":{"AutoTokenizer":["spotlight.SpotlightTokenizer",null]}}