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--- |
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license: mit |
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datasets: |
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- liuhaotian/LLaVA-Pretrain |
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- liuhaotian/LLaVA-Instruct-150K |
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language: |
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- en |
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- zh |
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library_name: transformers |
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--- |
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# WORK IN PROGRESS |
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We present TinyLLaVA, a small vision-language chatbot (1.4B) that reaches comparable performances with contemporary vision language models on common benchmarks, using less parameters. |
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TinyLLaVA was trained by finetuning [TinyLlama](https://huggingface.co/PY007/TinyLlama-1.1B-Chat-v0.3) on the [LLaVA-1.5](https://github.com/haotian-liu/LLaVA) dataset, following the training recipe of [LLaVA-1.5](https://github.com/haotian-liu/LLaVA). For more details, please refer to the [LLaVA-1.5 paper](https://arxiv.org/abs/2310.03744). |
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## Model Performance |
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We have evaluated TinyLLaVA on [GQA](https://cs.stanford.edu/people/dorarad/gqa/about.html), [VizWiz](https://www.vizwiz.com/), [VQAv2](https://visualqa.org/), [TextVQA](https://textvqa.org/) and [SQA](https://github.com/lupantech/ScienceQA). |
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| Model | VQAv2 | GQA | SQA | TextVQA | VizWiz | |
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| -------------------- | :------------: | :------------: | :------------: | :------------: | :------------: | |
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| TinyLLaVA-v1-1.4B | 73.41 | 57.54 | 59.40 | 46.37 | 49.56 | |
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| BLIP-2 | 41.00 | 41.00 | 61.00 | 42.50 | 19.60 | |
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| LLaVA-v1.5-7B | 78.50 | 62.00 | 66.80 | 61.3 | 50 | |
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| LLaVA-v1.5-13B | 80.00 | 63.30 | 71.60 | 61.3 | 53.6 | |
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| Qwen-VL-7B | 78.80 | 59.30 | 67.10 | 63.8 | 35.2 | |
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| Qwen-VL-13B | 78.20 | 57.50 | 68.20 | 61.5 | 38.9 | |
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More evaluations are ongoing. |
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## Model Preparations |
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#### - Transformers Version |
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Make sure to have `transformers >= 4.35.3`. |
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#### - Prompt Template |
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The model supports multi-image and multi-prompt generation. When using the model, make sure to follow the correct prompt template (`USER: <image>xxx\nASSISTANT:`), where `<image>` token is a place-holding special token for image embeddings. |
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## Model Inference from `pipeline` and `transformers` |
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#### - Using `pipeline`: |
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Below we used [`"bczhou/tiny-llava-v1-hf"`](https://huggingface.co/bczhou/tiny-llava-v1-hf) checkpoint. |
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```python |
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from transformers import pipeline |
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from PIL import Image |
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import requests |
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model_id = "bczhou/tiny-llava-v1-hf" |
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pipe = pipeline("image-to-text", model=model_id) |
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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prompt = "USER: <image>\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT:" |
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outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200}) |
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print(outputs[0]) |
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>>> {"generated_text': 'USER: \nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT: The label 15 represents lava, which is a type of volcanic rock."} |
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``` |
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#### - Using pure `transformers`: |
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Below is an example script to run generation in `float16` precision on a GPU device: |
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```python |
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import requests |
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from PIL import Image |
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import torch |
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from transformers import AutoProcessor, LlavaForConditionalGeneration |
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model_id = "bczhou/tiny-llava-v1-hf" |
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prompt = "USER: <image>\nWhat are these?\nASSISTANT:" |
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image_file = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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model = LlavaForConditionalGeneration.from_pretrained( |
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model_id, |
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torch_dtype=torch.float16, |
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low_cpu_mem_usage=True, |
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).to(0) |
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processor = AutoProcessor.from_pretrained(model_id) |
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raw_image = Image.open(requests.get(image_file, stream=True).raw) |
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inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16) |
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output = model.generate(**inputs, max_new_tokens=200, do_sample=False) |
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print(processor.decode(output[0][2:], skip_special_tokens=True)) |
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``` |
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## Contact |
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This model was trained by [Baichuan Zhou](https://baichuanzhou.github.io/), from Beihang Univerisity, under the supervision of [Prof. Lei Huang](https://huangleibuaa.github.io/). |
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