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--- |
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license: mit |
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language: |
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- en |
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pipeline_tag: image-to-text |
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--- |
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# git_20 |
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<!-- Provide a quick summary of what the model is/does. --> |
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This model is fine-tuned with LLaMA with 8 Nvidia A100-80G GPUs using 3,000,000 groups of conversations in the context of mathematics by students and facilitators on Algebra Nation (https://www.mathnation.com/). Llama-mt-lora consists of 32 layers and over 7 billion parameters, consuming up to 13.5 gigabytes of disk space. Researchers can experiment with and finetune the model to help construct math conversational AI that can effectively respond generation in a mathematical context. |
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### Here is how to use it with texts in HuggingFace |
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```python |
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from transformers import AutoModelForCausalLM |
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from transformers import AutoProcessor |
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from PIL import Image |
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model = AutoModelForCausalLM.from_pretrained("Fan21/git_20") |
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processor = AutoProcessor.from_pretrained("Fan21/git_20") |
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image_path ='Please enter the image address here' |
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image = Image.open(image_path) |
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width, height = image.size |
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display(image.resize((int(1 * width), int(1 * height)))) |
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pixel_values = processor(images=image, return_tensors="pt").pixel_values |
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with torch.no_grad(): |
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outputs = model.generate(pixel_values=pixel_values, max_length=50) |
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answer = tokenizer.decode(outputs[0], skip_special_tokens=True) |
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``` |
