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license: creativeml-openrail-m |
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<h1 align='center' style='font-size: 36px; font-weight: bold;'>Sparrow</h1> |
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<h3 align='center' style='font-size: 24px;'>Blazzing Fast Tiny Vision Language Model</h3> |
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<p align="center"> |
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<img src="https://cdn-uploads.huggingface.co/production/uploads/650c7fbb8ffe1f53bdbe1aec/DTjDSq2yG-5Cqnk6giPFq.jpeg" width="50%" height="auto"/> |
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</p> |
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<p align='center', style='font-size: 16px;' >A Custom 3B parameter Model Enhanced for Educational Contexts: This specialized model integrates slide-text pairs from machine learning classes, leveraging a unique training approach. It connects a frozen pre-trained vision encoder (SigLip) with a frozen language model (Phi-2) through an innovative projector. The model employs attention mechanisms and language modeling loss to deeply understand and generate educational content, specifically tailored to the context of machine learning education. Built by <a href="https://www.linkedin.com/in/manishkumarthota/">@Manish</a> The model is released for research purposes only, commercial use is not allowed. </p> |
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## How to use |
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**Install dependencies** |
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```bash |
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pip install transformers # latest version is ok, but we recommend v4.31.0 |
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pip install -q pillow accelerate einops |
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``` |
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You can use the following code for model inference. The format of text instruction is similar to [LLaVA](https://github.com/haotian-liu/LLaVA). |
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```Python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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from PIL import Image |
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torch.set_default_device("cuda") |
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#Create model |
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model = AutoModelForCausalLM.from_pretrained( |
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"ManishThota/Sparrow", |
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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("ManishThota/Sparrow", trust_remote_code=True) |
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#function to generate the answer |
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def predict(question, image_path): |
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#Set inputs |
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text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{question}? ASSISTANT:" |
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image = Image.open(image_path) |
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input_ids = tokenizer(text, return_tensors='pt').input_ids.to('cuda') |
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image_tensor = model.image_preprocess(image) |
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#Generate the answer |
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output_ids = model.generate( |
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input_ids, |
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max_new_tokens=25, |
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images=image_tensor, |
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use_cache=True)[0] |
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return tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip() |
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``` |
