Update README.md

README.md

@@ -1,3 +1,58 @@
----
-license: llama3.1
----
+---
+license: apache-2.0
+language:
+- en
+base_model:
+- meta-llama/Meta-Llama-3.1-8B-Instruct
+---
+license: llama3.1
+# 🦙 Llama3.1-8b-instruct-vision Model Card
+
+## Model Details
+
+This repository contains a reproduced version of the [LLaVA](https://github.com/haotian-liu/LLaVA) model from the [Llama 3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct) foundation model using the [PKU-Alignment/align-anything](https://github.com/PKU-Alignment/align-anything) library.
+
+> **NOTE:** The reproduced version of LLaVA has some different implementation details than the original [LLaVA](https://github.com/haotian-liu/LLaVA) model.
+>
+> 1. The reproduced LLaVA uses a different conversation template than the original [LLaVA](https://github.com/haotian-liu/LLaVA) model.
+> 2. The initial model weights are loaded from Llama 3.1 8B Instruct model ([meta-llama/Llama 3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct)) rather than [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5).
+
+- **Developed by:** the [PKU-Alignment](https://github.com/PKU-Alignment) Team.
+- **Model Type:** An auto-regressive language model based on the transformer architecture.
+- **License:** Non-commercial license.
+- **Fine-tuned from model:** [meta-llama/Llama 3.1-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct).
+
+## Model Sources
+
+- **Repository:** <https://github.com/PKU-Alignment/align-anything>
+- **Dataset:**
+  - <https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K>
+  - <https://huggingface.co/datasets/OpenGVLab/ShareGPT-4o>
+  - <https://huggingface.co/datasets/HuggingFaceM4/A-OKVQA>
+  - <https://huggingface.co/datasets/Multimodal-Fatima/OK-VQA_train>
+  - <https://huggingface.co/datasets/howard-hou/OCR-VQA>
+  - <https://huggingface.co/datasets/HuggingFaceM4/VQAv2>
+
+## How to use model (reprod.)
+
+- Using transformers
+
+```python
+from transformers import (
+    LlavaForConditionalGeneration,
+    AutoProcessor,
+)
+from PIL import Image
+
+path = <path_to_model_dir>
+processor = AutoProcessor.from_pretrained(path)
+model = LlavaForConditionalGeneration.from_pretrained(path)
+
+prompt = "<|start_header_id|>user<|end_header_id|>: <image> Give an overview of what's in the image.\n<|start_header_id|>assistant<|end_header_id|>: "
+image_path = "align-anything/assets/test_image.webp"
+image = Image.open(image_path)
+
+inputs = processor(text=prompt, images=image, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=1024)
+print(processor.decode(outputs[0], skip_special_tokens=True))
+```