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metadata
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.

Overview

llava-gemma-2b is a large multimodal model (LMM) trained using the LLaVA-v1.5 framework 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.

For example usage, see usage.py or the following code block:

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​