Update README.md

README.md

@@ -1,3 +1,71 @@
----
-license: mit
----
+---
+license: mit
+datasets:
+- liuhaotian/LLaVA-Pretrain
+- liuhaotian/LLaVA-Instruct-150K
+language:
+- en
+metrics:
+- accuracy
+- precision
+- recall
+- f1
+base_model:
+- apple/aimv2-large-patch14-224
+- apple/OpenELM
+pipeline_tag: image-text-to-text
+tags:
+- cpu
+- nano
+- small
+- tiny
+- llava
+model_size: 0.6B parameters
+---
+
+**<center><span style="font-size:2em;">Tiny Llava 4 CPU 🐛</span></center>**
+
+---
+
+### 🚀 **Model Overview**
+`tiny-llava-open-elm-aimv2` is a lightweight image-text-to-text model that combines **[OpenELM 270M - INSTRUCT](https://huggingface.co/apple/OpenELM-270M-Instruct)** as the LLM backbone and **[AIMv2-Large-Patch14-224-distilled (309M)](https://huggingface.co/apple/aimv2-large-patch14-224-distilled)** as the vision encoder. The model has been fine-tuned using **LoRA (Low-Rank Adaptation)** for efficient training. It was developed using the **[TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory)** codebase, which provides a modular framework for lightweight multi-modal models. 
+
+The model is designed to run efficiently on **CPU**, making it ideal for resource-constrained environments. It is trained and evaluated on **POPE** and **TextVQA** benchmarks. The total model size is **0.6B parameters**.
+
+---
+
+### Usage
+Execute the following test code:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+hf_path = 'cpu4dream/llava-small-OpenELM-AIMv2-0.6B-auto'
+model = AutoModelForCausalLM.from_pretrained(hf_path, trust_remote_code=True)
+model.cuda()
+config = model.config
+tokenizer = AutoTokenizer.from_pretrained(hf_path, use_fast=False, model_max_length = config.tokenizer_model_max_length,padding_side = config.tokenizer_padding_side)
+prompt="What are these?"
+image_url="http://images.cocodataset.org/test-stuff2017/000000000001.jpg"
+output_text, genertaion_time = model.chat(prompt=prompt, image=image_url, tokenizer=tokenizer)
+print('model output:', output_text)
+print('runing time:', genertaion_time)
+```
+
+---
+
+### 📊 **Performance**
+
+|                          Model Name                          | VQAv2 | GQA   | SQA   | TextVQA | MM-VET | POPE  | MME    | MMMU  |
+|:-----------------------------------------------------------:|:-----:|:-----:|:-----:|:-------:|:------:|:-----:|:------:|:-----:|
+| [LLaVA-1.5-7B](https://huggingface.co/llava-hf/llava-1.5-7b-hf) | 78.5  | 62.0  | 66.8  |   58.2  |  30.5  | 85.9  | 1510.7 |   -   |
+| [bczhou/TinyLLaVA-3.1B](https://huggingface.co/bczhou/TinyLLaVA-3.1B) | 79.9  | 62.0  | 69.1  |   59.1  |  32.0  | 86.4  | 1464.9 |   -   |
+| [tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B](https://huggingface.co/tinyllava/TinyLLaVA-Gemma-SigLIP-2.4B) | 78.4  | 61.6  | 64.4  |   53.6  |  26.9  | 86.4  | 1339.0 |  31.7 |
+| [tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B](https://huggingface.co/tinyllava/TinyLLaVA-Phi-2-SigLIP-3.1B) | 80.1  | 62.1  | 73.0  |   60.3  |  37.5  | 87.2  | 1466.4 |  38.4 |
+| cpu4dream/llava-small-OpenELM-AIMv2-0.6B                                   |   -   |   -   |   -   |  39.68  |   -    | 83.93 |   -    |   -   |
+
+---
+
+### 🔗 **References**
+- [OpenELM](https://huggingface.co/apple/OpenELM)
+- [AIMv2-Large-Patch14-224](https://huggingface.co/apple/aimv2-large-patch14-224)
+- [TinyLLaVA Factory](https://github.com/TinyLLaVA/TinyLLaVA_Factory)
+- [LoRA Paper (arXiv:2402.14289)](https://arxiv.org/pdf/2402.14289)