Instructions to use Executespec/ganesh-review with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Executespec/ganesh-review with Transformers:

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

pipe = pipeline("image-text-to-text", model="Executespec/ganesh-review")
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("Executespec/ganesh-review")
model = AutoModelForMultimodalLM.from_pretrained("Executespec/ganesh-review", 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 Executespec/ganesh-review with vLLM:

Install from pip and serve model

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

SGLang

How to use Executespec/ganesh-review 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 "Executespec/ganesh-review" \
    --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": "Executespec/ganesh-review",
		"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 "Executespec/ganesh-review" \
        --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": "Executespec/ganesh-review",
		"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 Executespec/ganesh-review with Docker Model Runner:
```
docker model run hf.co/Executespec/ganesh-review
```

ganesh-review

File size: 1,188 Bytes

9c4b5c1

"""Verify original release artifacts without importing a model framework."""
import hashlib
from pathlib import Path

HASHES = {
    "adapter.safetensors": "39a20fb38e31c307351fc4eb8035c1f50833f687ff9d3da2e0282120a7cbafd9",
    "model-config.json": "dfeaa5769ef784456ed522ef6a6ff96ed82b76298b5aedbcebb90d3c5cc5b093",
    "inference-config.json": "81f65e76e54cea15e16d3b139c418fecd18ad5f863cd7f9923beb2a85247150c",
    "training-public.json": "5f9ec6b362eae7c8fc2d92bec34c050a42aa15e1d14f8c3dfdc15c174cdaf82a",
}


def sha256(path):
    digest = hashlib.sha256()
    with Path(path).open("rb") as stream:
        for chunk in iter(lambda: stream.read(1024 * 1024), b""):
            digest.update(chunk)
    return digest.hexdigest()


def verify(root=None):
    root = Path(root) if root is not None else Path(__file__).parent / "artifacts"
    for name, expected in HASHES.items():
        path = root / name
        if path.is_symlink() or not path.is_file() or sha256(path) != expected:
            raise ValueError(f"Artifact identity mismatch: {name}")
    return dict(HASHES)


if __name__ == "__main__":
    for name, digest in verify().items():
        print(f"{digest}  {name}")