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Update README.md

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@@ -58,11 +58,11 @@ You are Dolphin, a helpful AI assistant.<|im_end|>
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  ```python
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  from huggingface_hub import hf_hub_download
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- hf_hub_download(repo_id="visheratin/LLaVA-3b", filename="configuration_llava.py", local_dir="./", force_download=True)
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- hf_hub_download(repo_id="visheratin/LLaVA-3b", filename="configuration_phi.py", local_dir="./", force_download=True)
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- hf_hub_download(repo_id="visheratin/LLaVA-3b", filename="modeling_llava.py", local_dir="./", force_download=True)
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- hf_hub_download(repo_id="visheratin/LLaVA-3b", filename="modeling_phi.py", local_dir="./", force_download=True)
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- hf_hub_download(repo_id="visheratin/LLaVA-3b", filename="processing_llava.py", local_dir="./", force_download=True)
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  ```
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  **Create a model**
@@ -71,7 +71,7 @@ hf_hub_download(repo_id="visheratin/LLaVA-3b", filename="processing_llava.py", l
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  from modeling_llava import LlavaForConditionalGeneration
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  import torch
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- model = LlavaForConditionalGeneration.from_pretrained("visheratin/LLaVA-3b", torch_dtype=torch.float16)
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  model = model.to("cuda")
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  ```
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@@ -81,7 +81,7 @@ model = model.to("cuda")
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  from transformers import AutoTokenizer
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  from processing_llava import LlavaProcessor, OpenCLIPImageProcessor
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- tokenizer = AutoTokenizer.from_pretrained("visheratin/LLaVA-3b")
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  image_processor = OpenCLIPImageProcessor(model.config.preprocess_config)
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  processor = LlavaProcessor(image_processor, tokenizer)
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  ```
@@ -123,27 +123,3 @@ import torch
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  with torch.inference_mode():
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  output = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.4, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id)
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  ```
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-
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- ## Benchmarks
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-
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- - TextVQA - 38.59%
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- - GQA - 49.6%
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- - VQAv2 - 64.24%
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- - VizWiz - 24.88%
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- - POPE - 80.59%
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- - V*-bench - 52.25% (OCR - 46.66%, GPT4V-hard - 41.17%, direct attributes - 43.48%, relative position - 65.79%)
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-
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- ## Examples
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-
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- <a target="_blank" href="https://colab.research.google.com/drive/1sXDvVl5s9fTcE0N2bQGOlXhnNlKEdeun">
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- <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
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- </a>
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-
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- ## License
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-
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- The model is licensed under MIT license, but since the data used for model training is largely synthetic, you should also follow OpenAI and Google Gemini terms of service.
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- Which means don't create competitor models for them.
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-
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- ## Acknowledgments
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-
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- Thanks to [ML Collective](https://mlcollective.org/) for providing credits for computing resources.
 
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  ```python
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  from huggingface_hub import hf_hub_download
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+ hf_hub_download(repo_id="OEvortex/HelpingAI-Vision", filename="configuration_llava.py", local_dir="./", force_download=True)
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+ hf_hub_download(repo_id="OEvortex/HelpingAI-Vision", filename="configuration_phi.py", local_dir="./", force_download=True)
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+ hf_hub_download(repo_id="OEvortex/HelpingAI-Vision", filename="modeling_llava.py", local_dir="./", force_download=True)
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+ hf_hub_download(repo_id="OEvortex/HelpingAI-Vision", filename="modeling_phi.py", local_dir="./", force_download=True)
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+ hf_hub_download(repo_id="OEvortex/HelpingAI-Vision", filename="processing_llava.py", local_dir="./", force_download=True)
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  ```
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  **Create a model**
 
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  from modeling_llava import LlavaForConditionalGeneration
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  import torch
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+ model = LlavaForConditionalGeneration.from_pretrained("OEvortex/HelpingAI-Vision", torch_dtype=torch.float16)
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  model = model.to("cuda")
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  ```
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  from transformers import AutoTokenizer
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  from processing_llava import LlavaProcessor, OpenCLIPImageProcessor
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+ tokenizer = AutoTokenizer.from_pretrained("OEvortex/HelpingAI-Vision")
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  image_processor = OpenCLIPImageProcessor(model.config.preprocess_config)
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  processor = LlavaProcessor(image_processor, tokenizer)
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  ```
 
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  with torch.inference_mode():
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  output = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.4, pad_token_id=tokenizer.eos_token_id, eos_token_id=tokenizer.eos_token_id)
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  ```