Update README.md

README.md

@@ -1,3 +1,106 @@
----
-license: llama3
----
+---
+license: llama3
+tags:
+- vision
+- image-text-to-text
+---
+
+# LLaVa-Next, leveraging [liuhaotian/llava-v1.6-vicuna-7b](https://huggingface.co/liuhaotian/llava-v1.6-vicuna-7b) as LLM
+
+The LLaVA-NeXT model was proposed in [LLaVA-NeXT: Stronger LLMs Supercharge Multimodal Capabilities in the Wild
+](https://llava-vl.github.io/blog/2024-05-10-llava-next-stronger-llms/) by Bo Li, Kaichen Zhang, Hao Zhang, Dong Guo, Renrui Zhang, Feng Li, Yuanhan Zhang, Ziwei Liu, Chunyuan Li.
+These LLaVa-NeXT series improves upon [LLaVa-1.6](https://llava-vl.github.io/blog/2024-01-30-llava-next/) by training with stringer language backbones, improving the
+performance.
+
+Disclaimer: The team releasing LLaVa-NeXT did not write a model card for this model so this model card has been written by the Hugging Face team.
+
+## Model description
+
+LLaVa combines a pre-trained large language model with a pre-trained vision encoder for multimodal chatbot use cases. LLaVA NeXT Llama3 improves on LLaVA 1.6 BY:
+- More diverse and high quality data mixture
+- Better and bigger language backbone
+
+Base LLM: [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
+  
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/62441d1d9fdefb55a0b7d12c/FPshq08TKYD0e-qwPLDVO.png)
+
+## Intended uses & limitations
+
+You can use the raw model for tasks like image captioning, visual question answering, multimodal chatbot use cases. See the [model hub](https://huggingface.co/models?search=llava-hf) to look for
+other versions on a task that interests you.
+
+### How to use
+
+Here's the prompt template for this model:
+```
+"A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: <image>\nWhat is shown in this image? ASSISTANT:"
+```
+You can load and use the model like following:
+```python
+from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
+import torch
+from PIL import Image
+import requests
+
+processor = LlavaNextProcessor.from_pretrained("llava-hf/llama3-llava-next-8b-hf")
+model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llama3-llava-next-8b-hf", torch_dtype=torch.float16, device_map="auto") 
+
+# prepare image and text prompt, using the appropriate prompt template
+url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
+image = Image.open(requests.get(url, stream=True).raw)
+prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: <image>\nWhat is shown in this image? ASSISTANT:"
+
+inputs = processor(prompt, image, return_tensors="pt").to(model.device)
+
+# autoregressively complete prompt
+output = model.generate(**inputs, max_new_tokens=100)
+
+print(processor.decode(output[0], 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)
+```
+### Training Data
+- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
+- 158K GPT-generated multimodal instruction-following data.
+- 500K academic-task-oriented VQA data mixture.
+- 50K GPT-4V data mixture.
+- 40K ShareGPT data.
+
+
+### BibTeX entry and citation info
+
+```bibtex
+@misc{li2024llavanext-strong,
+    title={LLaVA-NeXT: Stronger LLMs Supercharge Multimodal Capabilities in the Wild},
+    url={https://llava-vl.github.io/blog/2024-05-10-llava-next-stronger-llms/},
+    author={Li, Bo and Zhang, Kaichen and Zhang, Hao and Guo, Dong and Zhang, Renrui and Li, Feng and Zhang, Yuanhan and Liu, Ziwei and Li, Chunyuan},
+    month={May},
+    year={2024}
+}
+```