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---
language:
- en
license_name: gemma-terms
license_link: https://ai.google.dev/gemma/terms
---
# LLaVA-Gemma Model Card
_This model card corresponds to the 2B version of the model with the CLIP-based vision encoder._
Preprint: [arxiv.org/abs/2404.01331](https://arxiv.org/abs/2404.01331)
## Overview
`llava-gemma-2b` is a large multimodal model (LMM) trained using the [LLaVA-v1.5 framework](https://arxiv.org/abs/2310.03744) with the 2-billion parameter `google/gemma-2b-it` model as language backbone.
## Uses
The model has been finetuned for multimodal benchmark evaluations, but can also be used as a multimodal chatbot.
## Bias, Risks, and Limitations
This model has not been assessed for harm or biases, and should not be used for sensitive applications where it may cause harm.
## How to Get Started with the Model
Currently using `llava-gemma` requires a [modified preprocessor](https://huggingface.co/Intel/llava-gemma-2b/blob/main/processing_llavagemma.py).
_We are currently working on modifying the `LlavaProcessor` class to streamline usage (see [PR #30030](https://github.com/huggingface/transformers/pull/30030)), expect updates soon._
For current usage, see [`usage.py`](/usage.py) or the following code block:
```python
import requests
from PIL import Image
from transformers import (
LlavaForConditionalGeneration,
AutoTokenizer,
CLIPImageProcessor
)
from processing_llavagemma import LlavaGemmaProcessor # This is in this repo
checkpoint = "Intel/llava-gemma-2b"
# Load model
model = LlavaForConditionalGeneration.from_pretrained(checkpoint)
processor = LlavaGemmaProcessor(
tokenizer=AutoTokenizer.from_pretrained(checkpoint),
image_processor=CLIPImageProcessor.from_pretrained(checkpoint)
)
# Prepare inputs
# Use gemma chat template
prompt = processor.tokenizer.apply_chat_template(
[{'role': 'user', 'content': "What's the content of the image?<image>"}],
tokenize=False,
add_generation_prompt=True
)
url = "https://www.ilankelman.org/stopsigns/australia.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=prompt, images=image, return_tensors="pt")
inputs = {k: v.to('cuda') for k, v in inputs.items()}
# Generate
generate_ids = model.generate(**inputs, max_length=30)
output = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(output)
```
## Training Details
The `llava-gemma-2b` model was trained on 8 Gaudi 2 accelerators.
### Training Data
The model was trained using the LLaVA-v1.5 data mixture.
This is listed as follows:
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
- 158K GPT-generated multimodal instruction-following data.
- 450K academic-task-oriented VQA data mixture.
- 40K ShareGPT data.
## Evaluation
| LM Backbone | Vision Model | Pretrained Connector | GQA | MME cognition | MME perception | MM-Vet | POPE accuracy | POPE F1 | VQAv2 | TextVQA | ScienceQA Image | MMVP |
| ----------- | ------------ | -------------------- | ----- | ------------- | -------------- | ------ | ------------- | ------- | ----- | ------- | --------------- | ----- |
| gemma-2b-it | CLIP | Yes | 0.531 | 236.071 | 1130.492 | 17.706 | 0.850 | 0.839 | 70.65 | 28.06 | 0.564 | 0.287 |
| gemma-2b-it | CLIP | No | 0.481 | 247.857 | 934.611 | 13.119 | 0.784 | 0.762 | 61.74 | | 0.549 | 0.180 |
| gemma-7b-it | CLIP | Yes | 0.472 | 253.571 | 894.910 | 18.165 | 0.848 | 0.829 | 68.7 | | 0.625 | 0.327 |
| gemma-7b-it | CLIP | No | 0.472 | 278.214 | 857.274 | 19.083 | 0.782 | 0.734 | 65.09 | | 0.636 | 0.240 |
| gemma-2b-it | DinoV2 | Yes | 0.587 | 307.143 | 1132.970 | 19.128 | 0.853 | 0.838 | 71.37 | 12.53 | 0.555 | 0.227 |
| gemma-2b-it | DinoV2 | No | 0.501 | 308.929 | 959.351 | 14.541 | 0.793 | 0.772 | 61.65 | 11.1 | 0.568 | 0.180 |
|