Instructions to use luoruipu1/Volcano-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use luoruipu1/Volcano-7b with Transformers:

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

pipe = pipeline("text-generation", model="luoruipu1/Volcano-7b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("luoruipu1/Volcano-7b", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use luoruipu1/Volcano-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "luoruipu1/Volcano-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "luoruipu1/Volcano-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/luoruipu1/Volcano-7b

SGLang

How to use luoruipu1/Volcano-7b 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 "luoruipu1/Volcano-7b" \
    --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": "luoruipu1/Volcano-7b",
		"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 "luoruipu1/Volcano-7b" \
        --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": "luoruipu1/Volcano-7b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use luoruipu1/Volcano-7b with Docker Model Runner:
```
docker model run hf.co/luoruipu1/Volcano-7b
```

Add model card and metadata

by nielsr HF Staff - opened Mar 11, 2025

base: refs/heads/main

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from: refs/pr/1

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+---
+pipeline_tag: image-text-to-text
+library_name: transformers
+license: mit # Please verify license in the repository
+---
+# VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models
+This model, VolCano, is presented in the paper [VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models](https://arxiv.org/abs/2405.16919) and is designed for multi-step visually grounded reasoning.
+Code and further details are available at: https://github.com/RupertLuo/VoCoT
+## Quick Start
+This example demonstrates basic usage. For more details, please refer to the project's GitHub repository.
+```python
+from model.load_model import load_model, infer
+from PIL import Image
+# loading the model
+model_path = 'luoruipu1/Volcano-7b'
+model, preprocessor = load_model(model_path, precision='fp16')
+# perform reasoning, activate VoCoT by passing cot=True
+input_image = Image.open('figs/sample_input.jpg')
+response = infer(model, preprocessor, input_image, 'Describe the image.', cot=True)
+print('response: ', response[0])
+```