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--- |
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language: |
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- en |
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tags: |
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- llava |
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- multimodal |
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- qwen |
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license: apache-2.0 |
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pipeline_tag: image-text-to-text |
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--- |
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# nanoLLaVA-1.5 - Improved sub 1B Vision-Language Model |
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<p align="center"> |
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<img src="https://i.postimg.cc/d15k3YNG/nanollava.webp" alt="Logo" width="350"> |
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</p> |
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## Description |
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nanoLLaVA-1.5 is a "small but mighty" 1B vision-language model designed to run efficiently on edge devices. This is an update from the v1.0 version [qnguyen3/nanoLLaVA](https://huggingface.co/qnguyen3/nanoLLaVA) |
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- **Base LLM**: [Quyen-SE-v0.1](https://huggingface.co/vilm/Quyen-SE-v0.1) (Qwen1.5-0.5B) |
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- **Vision Encoder**: [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) |
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| Model | **VQA v2** | **TextVQA** | **ScienceQA** | **POPE** | **MMMU (Test)** | **MMMU (Eval)** | **GQA** | **MM-VET** | |
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|---------|--------|---------|-----------|------|-------------|-------------|------|--------| |
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| nanoLLavA-1.0 | 70.84 | 46.71 | 58.97 | 84.1 | 28.6 | 30.4 | 54.79| 23.9 | |
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| nanoLLavA-1.5 | TBD | TBD | TBD | TBD | TBD | TBD | TBD| TBD | |
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## Training Data |
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Training Data will be released later as I am still writing a paper on this. Expect the final final to be much more powerful than the current one. |
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## Finetuning Code |
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Coming Soon!!! |
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## Usage |
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You can use with `transformers` with the following script: |
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```bash |
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pip install -U transformers accelerate flash_attn |
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``` |
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```python |
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import torch |
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import transformers |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from PIL import Image |
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import warnings |
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# disable some warnings |
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transformers.logging.set_verbosity_error() |
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transformers.logging.disable_progress_bar() |
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warnings.filterwarnings('ignore') |
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# set device |
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torch.set_default_device('cuda') # or 'cpu' |
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model_name = 'qnguyen3/nanoLLaVA-1.5' |
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# create model |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype=torch.float16, |
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device_map='auto', |
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trust_remote_code=True) |
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tokenizer = AutoTokenizer.from_pretrained( |
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model_name, |
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trust_remote_code=True) |
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# text prompt |
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prompt = 'Describe this image in detail' |
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messages = [ |
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{"role": "user", "content": f'<image>\n{prompt}'} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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print(text) |
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text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')] |
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input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0) |
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# image, sample images can be found in images folder |
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image = Image.open('/path/to/image.png') |
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image_tensor = model.process_images([image], model.config).to(dtype=model.dtype) |
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# generate |
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output_ids = model.generate( |
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input_ids, |
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images=image_tensor, |
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max_new_tokens=2048, |
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use_cache=True)[0] |
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print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip()) |
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``` |
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## Prompt Format |
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The model follow the ChatML standard, however, without `\n` at the end of `<|im_end|>`: |
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``` |
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<|im_start|>system |
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Answer the question<|im_end|><|im_start|>user |
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<image> |
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What is the picture about?<|im_end|><|im_start|>assistant |
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``` |
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<!-- --- |
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| Image | Example | |
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|--------------------------------------|---------------------------------------------------------------------------------------------| |
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|  | **What is the text saying?** <br> "Small but mighty". <br>**How does the text correlate to the context of the image?** <br> The text seems to be a playful or humorous representation of a small but mighty figure, possibly a mouse or a mouse toy, holding a weightlifting bar. | |
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--- --> |
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Model is trained using a modified version from [Bunny](https://github.com/BAAI-DCAI/Bunny/tree/main/bunny) |
