Instructions to use aimagelab/LLaVA_MORE-gemma_2_9b-finetuning with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use aimagelab/LLaVA_MORE-gemma_2_9b-finetuning with Transformers:

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

pipe = pipeline("image-text-to-text", model="aimagelab/LLaVA_MORE-gemma_2_9b-finetuning")
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)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("aimagelab/LLaVA_MORE-gemma_2_9b-finetuning", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use aimagelab/LLaVA_MORE-gemma_2_9b-finetuning with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "aimagelab/LLaVA_MORE-gemma_2_9b-finetuning"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "aimagelab/LLaVA_MORE-gemma_2_9b-finetuning",
		"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/aimagelab/LLaVA_MORE-gemma_2_9b-finetuning

SGLang

How to use aimagelab/LLaVA_MORE-gemma_2_9b-finetuning 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 "aimagelab/LLaVA_MORE-gemma_2_9b-finetuning" \
    --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": "aimagelab/LLaVA_MORE-gemma_2_9b-finetuning",
		"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 "aimagelab/LLaVA_MORE-gemma_2_9b-finetuning" \
        --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": "aimagelab/LLaVA_MORE-gemma_2_9b-finetuning",
		"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 aimagelab/LLaVA_MORE-gemma_2_9b-finetuning with Docker Model Runner:
```
docker model run hf.co/aimagelab/LLaVA_MORE-gemma_2_9b-finetuning
```

Improve model card for LLaVA_MORE-gemma_2_9b-finetuning

by nielsr HF Staff - opened Aug 1, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+167

-133

nielsr

Aug 1, 2025

This PR significantly enhances the model card for aimagelab/LLaVA_MORE-gemma_2_9b-finetuning by providing comprehensive details and improving discoverability.

Key updates include:

Metadata: Added pipeline_tag: image-text-to-text, a comprehensive set of tags, base_model (google/gemma-2-9b-it), and datasets (liuhaotian/LLaVA-Pretrain, liuhaotian/LLaVA-Instruct-150K) for better categorization and searchability. The license is explicitly set to Apache 2.0.
Model Details & Description: Replaced placeholder content with a detailed overview of the LLaVA-MORE family, including its purpose, LLM and visual backbone variations explored, and specific details for this model variant (Gemma-2 9B + CLIP).
Model Sources: Added direct links to the paper on Hugging Face Papers, the official GitHub repository, the project page, the Hugging Face collection, and a general Hugging Face Space demo.
Usage Example: Included a ready-to-use Python code snippet for inference, specifically tailored for this model variant, with guidance on handling out-of-memory issues.
Performance Benchmarks: Integrated the detailed performance table and plot from the original GitHub repository, showcasing the model's evaluation results.
Training Details: Provided information on the two-stage training process, including datasets and procedure.
Checkpoints: Included the full table of all LLaVA-MORE checkpoints with their respective Hugging Face links.
Latest Updates: Included the "Latest Updates" section from the GitHub repository to keep users informed about project milestones.
Citation & Acknowledgments: Ensured the BibTeX citation is present and retained the acknowledgments section.

This update aims to make the model card a much richer, more informative, and user-friendly resource on the Hugging Face Hub.

Improve model card for LLaVA_MORE-gemma_2_9b-finetuning939ed4f5

readmecfaf2a76

fede97 changed pull request status to merged Aug 2, 2025

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