Image-Text-to-Text
Transformers
Safetensors
English
qwen3_5
lora
structured-prediction
experimental
conversational
Instructions to use Executespec/ganesh-review with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
Download src/verify_artifacts.py from Executespec/ganesh-review: direct link, hf CLI and curl.
- Browser
- Download file 1.19 kB
-
https://huggingface.co/Executespec/ganesh-review/resolve/main/src/verify_artifacts.py
- Command line
-
hf download hf://Executespec/ganesh-review/src/verify_artifacts.py
-
curl -L -o verify_artifacts.py https://huggingface.co/Executespec/ganesh-review/resolve/main/src/verify_artifacts.py
1.19 kB
| """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}") | |