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