Update README.md

README.md

@@ -1,3 +1,164 @@
----
-license: apache-2.0
----
+---
+language:
+- en
+pipeline_tag: image-to-text
+inference: false
+arxiv: 2408.03326
+license: apache-2.0
+tags:
+- vision
+- image-text-to-text
+datasets:
+- lmms-lab/LLaVA-OneVision-Data
+---
+# LLaVA-Onevision Model Card
+
+![image/png](llava_onevision_arch.png)
+
+Below is the model card of 0.5B LLaVA-Onevision model which is copied from the original LLaVA-Onevision model card that you can find [here](https://huggingface.co/lmms-lab/llava-onevision-qwen2-0.5b-si).
+
+
+
+## Model details
+
+**Model type:**
+LLaVA-Onevision is an open-source multimodal LLM trained by fine-tuning Qwen2 on GPT-generated multimodal instruction-following data.
+LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer
+vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning
+across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario
+capabilities are demonstrated through task transfer from images to videos.
+
+**Model date:**
+LLaVA-Onevision-0.5-si was added in August 2024.
+
+**Paper or resources for more information:**
+https://llava-vl.github.io/
+
+- **Architecture:** SO400M + Qwen2
+- **Pretraining Stage:** LCS-558K, 1 epoch, projector
+- **Mid Stage:** A mixture of 4.7M high-quality synthetic data, 1 epoch, full model
+- **Final-Image Stage:** A mixture of 3.6M single-image data, 1 epoch, full model
+- **OneVision Stage:** A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model
+- **Precision:** bfloat16
+
+
+## How to use the model
+
+First, make sure to have `transformers >= 4.35.3`. 
+The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template by applyong chat template:
+
+### Using `pipeline`:
+
+Below we used [`"llava-hf/llava-onevision-qwen2-0.5b-si-hf"`](https://huggingface.co/llava-hf/llava-onevision-qwen2-0.5b-si-hf) checkpoint.
+
+```python
+from transformers import pipeline
+from PIL import Image    
+import requests
+
+model_id = "llava-hf/llava-onevision-qwen2-0.5b-si-hf"
+pipe = pipeline("image-to-text", model=model_id)
+url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
+image = Image.open(requests.get(url, stream=True).raw)
+
+# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
+# Each value in "content" has to be a list of dicts with types ("text", "image") 
+conversation = [
+    {
+
+      "role": "user",
+      "content": [
+          {"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"},
+          {"type": "image"},
+        ],
+    },
+]
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+
+outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
+print(outputs)
+>>> {"generated_text": "user\n\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nassistant\nLava"}
+```
+
+### Using pure `transformers`:
+
+Below is an example script to run generation in `float16` precision on a GPU device:
+
+```python
+import requests
+from PIL import Image
+
+import torch
+from transformers import AutoProcessor, LlavaNextForConditionalGeneration
+
+model_id = "llava-hf/llava-onevision-qwen2-0.5b-si-hf"
+model = LlavaNextForConditionalGeneration.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
+    low_cpu_mem_usage=True, 
+).to(0)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
+# Each value in "content" has to be a list of dicts with types ("text", "image") 
+conversation = [
+    {
+
+      "role": "user",
+      "content": [
+          {"type": "text", "text": "What are these?"},
+          {"type": "image"},
+        ],
+    },
+]
+prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
+
+image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
+raw_image = Image.open(requests.get(image_file, stream=True).raw)
+inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
+
+output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+print(processor.decode(output[0][2:], skip_special_tokens=True))
+```
+
+### Model optimization
+
+#### 4-bit quantization through `bitsandbytes` library
+
+First make sure to install `bitsandbytes`, `pip install bitsandbytes` and make sure to have access to a CUDA compatible GPU device. Simply change the snippet above with: 
+
+```diff
+model = LlavaNextForConditionalGeneration.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
+    low_cpu_mem_usage=True,
++   load_in_4bit=True
+)
+```
+
+#### Use Flash-Attention 2 to further speed-up generation
+
+First make sure to install `flash-attn`. Refer to the [original repository of Flash Attention](https://github.com/Dao-AILab/flash-attention) regarding that package installation. Simply change the snippet above with: 
+
+```diff
+model = LlavaNextForConditionalGeneration.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
+    low_cpu_mem_usage=True,
++   use_flash_attention_2=True
+).to(0)
+```
+
+# Citation
+```
+@misc{li2024llavaonevisioneasyvisualtask,
+      title={LLaVA-OneVision: Easy Visual Task Transfer}, 
+      author={Bo Li and Yuanhan Zhang and Dong Guo and Renrui Zhang and Feng Li and Hao Zhang and Kaichen Zhang and Yanwei Li and Ziwei Liu and Chunyuan Li},
+      year={2024},
+      eprint={2408.03326},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2408.03326}, 
+}
+```