Delete LLaVa.py

LLaVa.py

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-#!/usr/bin/env python
-# coding: utf-8
-
-# # Set-up environment
-
-# In[2]:
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-
-get_ipython().system('pip install --upgrade -q accelerate bitsandbytes')
-
-
-# In[ ]:
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-get_ipython().system('rm -r transformers')
-get_ipython().system('git clone -b llava_improvements https://github.com/NielsRogge/transformers.git')
-get_ipython().system('cd transformers')
-get_ipython().system('pip install -q ./transformers')
-
-
-# In[ ]:
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-get_ipython().system('pip install git+https://github.com/huggingface/transformers.git')
-
-
-# ## Load model and processor
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-# In[ ]:
-
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-from transformers import AutoProcessor, LlavaForConditionalGeneration
-from transformers import BitsAndBytesConfig
-import torch
-
-quantization_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_compute_dtype=torch.float16
-)
-
-
-model_id = "llava-hf/llava-1.5-7b-hf"
-
-processor = AutoProcessor.from_pretrained(model_id)
-model = LlavaForConditionalGeneration.from_pretrained(model_id, quantization_config=quantization_config, device_map="auto")
-
-
-# ## Prepare image and text for the model
-
-# In[ ]:
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-
-import requests
-from PIL import Image
-
-image1 = Image.open('data/clock.jpeg')
-display(image1)
-
-
-# In the prompt, you can refer to images using the special \<image> token. To indicate which text comes from a human vs. the model, one uses USER and ASSISTANT respectively. The format looks as follows:
-# 
-# ```bash
-# USER: <image>\n<prompt>\nASSISTANT:
-# ```
-
-# In other words, you always need to end your prompt with `ASSISTANT:`. Here we will perform batched generation (i.e generating on several prompts).
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-# In[ ]:
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-caption = 'an old fashioned clock sitting on top of a table'
-
-user_input = "This is an intricately crafted old-fashioned clock created by a skilled Moroccan artisan back in 1988 from Chefchaoune.. it reminds me of my mother."
-
-prompts = [
-            f"USER: <image>\nBased on the caption '{caption}' and the following user input: '{user_input}', generate a detailed product name and description for this Moroccan artisanal item; the description should be minimal yet it gives the essence of the product and convinces people to buy or express their interest in it.\nASSISTANT:"
-          # f"""
-          # USER: <image>\nBased on the image caption '{caption}' and the following background information: '{user_input}', generate an attention-grabbing yet concise product name and description for this authentic Moroccan artisanal item. The description should:
-          # Highlight the key features and unique selling points that make this product exceptional and desirable.
-          # Convey the cultural significance, craftsmanship, and rich heritage behind the item's creation.
-          # Use evocative language that resonates with potential buyers and piques their interest in owning this one-of-a-kind piece.
-          # Be concise, direct, and persuasive, leaving the reader eager to learn more or acquire the product.
-
-          # Your response should follow this format:
-          # Product Name: [Compelling and relevant product name]
-          # Product Description: [Concise yet captivating description addressing the points above]
-          # ASSISTANT:"""
-
-]
-
-inputs = processor(prompts, images=[image1], padding=True, return_tensors="pt").to("cuda")
-for k,v in inputs.items():
-  print(k,v.shape)
-
-
-# ## Autoregressively generate completion
-# 
-# Finally, we simply let the model predict the next tokens given the images + prompt. Of course one can adjust all the [generation parameters](https://huggingface.co/docs/transformers/v4.35.2/en/main_classes/text_generation#transformers.GenerationMixin.generate). By default, greedy decoding is used.
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-# In[ ]:
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-output = model.generate(**inputs, max_new_tokens=200)
-generated_text = processor.batch_decode(output, skip_special_tokens=True)
-for text in generated_text:
-  print(text.split("ASSISTANT:")[-1])
-
-
-# ## Pipeline API
-# 
-# Alternatively, you can leverage the [pipeline](https://huggingface.co/docs/transformers/main_classes/pipelines) API which abstracts all of the logic above away for the user. We also provide the quantization config to make sure we leverage 4-bit inference.
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-# In[ ]:
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-from transformers import pipeline
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-pipe = pipeline("image-to-text", model=model_id, model_kwargs={"quantization_config": quantization_config})
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-# In[ ]:
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-max_new_tokens = 200
-prompt = "USER: <image>\nWhat are the things I should be cautious about when I visit this place?\nASSISTANT:"
-
-outputs = pipe(image1, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
-
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-# In[ ]:
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-print(outputs[0]["generated_text"])
-
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-# In[ ]:
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