shi-labs
/

vpt_OLA-VLM-CLIP-ConvNeXT-Llama3-8b

@@ -1,199 +1,102 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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 ---
 library_name: transformers
+license: apache-2.0
+language:
+- en
+base_model:
+- meta-llama/Meta-Llama-3-8B-Instruct
+pipeline_tag: image-text-to-text
 ---
+# vpt_OLA-VLM-CLIP-ConvNeXT-Llama3-8b Model Card
+OLA-VLM distills target visual information into the intermediate representations of the LLM  from a set of target encoders. It adopts a predictive embedding optimization approach at selected LLM layers during training to minimize the embedding losses along with the next token prediction (NTP) objective,  resulting in a vision-centric approach to training the Multimodal Large Language Model.
+- **GitHub Repo:** [https://github.com/SHI-Labs/OLA-VLM](https://github.com/SHI-Labs/OLA-VLM)
+- **Project Page:** [https://praeclarumjj3.github.io/ola_vlm/](https://praeclarumjj3.github.io/ola_vlm/)
+<p align="center">
+    <img src="https://praeclarumjj3.github.io/ola_vlm/teaser.png" width="90%" class="center"/>
+</p>
+## Get Started with the Model
+Clone the repository and follow the [setup instructions](https://github.com/SHI-Labs/OLA-VLM#installation-instructions):
+```bash
+git lfs install
+git clone https://github.com/SHI-Labs/OLA-VLM
+cd OLA-VLM
+```
+After setup, you can use OLA-VLM with the following code:
+```python
+import gradio as gr
+import os
+import torch
+import numpy as np
+from ola_vlm.constants import DEFAULT_IMAGE_TOKEN
+from ola_vlm.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN
+from ola_vlm.conversation import conv_templates, SeparatorStyle
+from ola_vlm.model.builder import load_pretrained_model
+from ola_vlm.mm_utils import tokenizer_image_token, get_model_name_from_path, process_images
+model_path = "shi-labs/vpt_OLA-VLM-CLIP-ConvNeXT-Llama3-8b"
+conv_mode = "llava_llama_3"
+image_path = "/path/to/OLA-VLM/assets/pb.jpg"
+input_prompt = "Describe this image."
+# load model
+model_name = get_model_name_from_path(model_path)
+tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name)
+# prepare prompt
+input_prompt = DEFAULT_IMAGE_TOKEN + '\n' + input_prompt
+conv = conv_templates[conv_mode].copy()
+conv.append_message(conv.roles[0], input_prompt)
+conv.append_message(conv.roles[1], None)
+prompt = conv.get_prompt()
+# load and preprocess image
+image = Image.open(image_path).convert('RGB')
+image_tensor = process_images([image], image_processor, model.config)[0]
+input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt')
+input_ids = input_ids.to(device='cuda', non_blocking=True)
+image_tensor = image_tensor.to(dtype=torch.float16, device='cuda', non_blocking=True)
+# run inference
+with torch.inference_mode():
+    output_ids = model.generate(
+        input_ids.unsqueeze(0),
+        images=image_tensor.unsqueeze(0),
+        image_sizes=[image.size],
+        do_sample=True,
+        temperature=0.2,
+        top_p=0.5,
+        num_beams=1,
+        max_new_tokens=256,
+        use_cache=True)
+outputs = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip()
+print(f"Image:{image_path} \nPrompt:{input_prompt} \nOutput:{outputs}")
+```
+For more information, please refer to [https://github.com/SHI-Labs/OLA-VLM](https://github.com/SHI-Labs/OLA-VLM).
+## Citation
+If you found our work useful in your research, please consider starring ⭐ us on [GitHub](https://github.com/SHI-Labs/OLA-VLM) and citing 📚 us in your research!
+```
+@article{jain2024ola_vlm,
+    title={{OLA-VLM: Elevating Visual Perception in Multimodal LLMs with Auxiliary Embedding Distillation}},
+    author={Jitesh Jain and Zhengyuan Yang and Humphrey Shi and Jianfeng Gao and Jianwei Yang},
+    journal={arXiv},
+    year={2024}
+}
+```