Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+library_name: transformers
+pipeline_tag: image-text-to-text
+tags:
+- multimodal
+- aria
+---
+<!-- <p align="center">
+  <br>Aria</br>
+</p>  -->
+
+
+# Aria-Base-64K Model Card
+
+<p align="center">
+🔗 <a href="https://rhymes.ai/" target="_blank"> Try Aria!</a> · 📖 <a href="https://www.rhymes.ai/blog-details/aria-first-open-multimodal-native-moe-model" target="_blank">Blog</a> · 📌 <a href="https://arxiv.org/pdf/2410.05993" target="_blank">Paper</a> 
+ · ⭐ <a href="https://github.com/rhymes-ai/Aria" target="_blank">GitHub</a> · 🟣 <a href="https://discord.com/invite/u8HxU23myj" target="_blank"> Discord </a>
+</p> 
+
+
+This checkpoint is one of base models of [Aria](https://huggingface.co/rhymes-ai/Aria), designed for research purposes as well as continue training. Specifically, Aria-Base-64K corresponds to the model checkpoint after the long-context pre-training stage (boxed in purple).
+
+<img src="./aria-stages.png" alt="Aria Training Stages" style="width: 75%;">
+
+Aria-Base-64K is fine-tuned from [Aria-Base-8K](https://huggingface.co/teowu/Aria-Base-8K).
+
+<!-- 
+- Aria is the **first open multimodal native MoE** model, capable of seamlessly handling various input modalities within a MoE architecture.
+- Aria performs **on par with GPT-4o mini and Gemini 1.5 Flash** across a range of multimodal tasks while maintaining strong performance on **text**-only tasks.
+- Compared to similar or even larger models, Aria boasts **faster speeds** and **lower costs**. This high efficiency stems from its ability to activate only 3.9B parameters during inference – the **fewest** among models with comparable performance.
+ -->
+ 
+## Aria-Base-8K
+
+- **Base Model After Long-Context Pre-training**: This model corresponds to the model checkpoint after the long-context pre-training stage, with 33B tokens (21B multimodal, 12B language, 69% in long-form) trained in this stage. This stage lasts 1,000 iterations, with all sequences packed to 65536 with Megatron-LM, with global batch size 512. During this training stage, the learning rate keeps constant at `3.5e-5`.
+- **Appropriate for Video and Long-document Fine-tuning**: This model is recommended for long-form continue pre-training or fine-tuning, e.g. on video QA datasets or long-document QA datasets. While resource is limited, it is also possible to post-train this model with short instruction tuning datasets and transfer to long-form QA scenarios.
+- **Understanding on Hundreds of Images**: This model is capable of understanding up to 250 high-resolution images or up to 500 mid-resolution images.
+- **Strong Base Performance on Language and Multimodal Scenarios**: This model retains strong base performance as [Aria-Base-8K](https://huggingface.co/teowu/Aria-Base-8K).
+- ***Limited Chat Template Availability***: This model is trained with a very low percentage of data (around 3%) re-formatted with the chat template. Hence, it might not be optimal to be directly tested with various benchmarks.
+
+<!-- # Model Info
+
+| Model  | Download  | Parameter | Context Length |
+| :---- | :------- | :------------ | :------ |
+| Aria | < HF link - TBD> | • Activation: 3.9B (3.5B MoE + 0.4B Visual Encoder) <br> • Total: 25.3B | 64K           | -->
+
+## Benchmark
+
+N/A.
+
+## Quick Start
+### Installation
+```
+pip install transformers==4.45.0 accelerate==0.34.1 sentencepiece==0.2.0 torchvision requests torch Pillow
+pip install flash-attn --no-build-isolation
+
+# For better inference performance, you can install grouped-gemm, which may take 3-5 minutes to install
+pip install grouped_gemm==0.1.6
+```
+
+### Inference
+
+You can use the same method as the final Aria model to load this checkpoint. However, as the base model, it might not be able to yield optimal chat performance.
+
+```python
+import requests
+import torch
+from PIL import Image
+from transformers import AutoModelForCausalLM, AutoProcessor
+
+model_id_or_path = "teowu/Aria-Base-64K"
+
+model = AutoModelForCausalLM.from_pretrained(model_id_or_path, device_map="auto", torch_dtype=torch.bfloat16, trust_remote_code=True)
+
+processor = AutoProcessor.from_pretrained(model_id_or_path, trust_remote_code=True)
+
+image_path = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/cat.png"
+
+image = Image.open(requests.get(image_path, stream=True).raw)
+
+messages = [
+    {
+        "role": "user",
+        "content": [
+            {"text": None, "type": "image"},
+            {"text": "what is the image?", "type": "text"},
+        ],
+    }
+]
+
+text = processor.apply_chat_template(messages, add_generation_prompt=True)
+inputs = processor(text=text, images=image, return_tensors="pt")
+inputs["pixel_values"] = inputs["pixel_values"].to(model.dtype)
+inputs = {k: v.to(model.device) for k, v in inputs.items()}
+
+with torch.inference_mode(), torch.cuda.amp.autocast(dtype=torch.bfloat16):
+    output = model.generate(
+        **inputs,
+        max_new_tokens=500,
+        stop_strings=["<|im_end|>"],
+        tokenizer=processor.tokenizer,
+        do_sample=True,
+        temperature=0.9,
+    )
+    output_ids = output[0][inputs["input_ids"].shape[1]:]
+    result = processor.decode(output_ids, skip_special_tokens=True)
+
+print(result)
+```
+
+### Advanced Inference and Fine-tuning
+
+We provide a [codebase](https://github.com/rhymes-ai/Aria) for more advanced usage of Aria,
+including vllm inference, cookbooks, and fine-tuning on custom datasets.
+
+As it shares the same structure with the final model, 
+you may just replace the `rhymes-ai/Aria` to this model path for any advanced inference and fine-tuning.
+
+
+## Citation
+If you find our work helpful, please consider citing.
+```
+@article{aria,
+  title={Aria: An Open Multimodal Native Mixture-of-Experts Model}, 
+  author={Dongxu Li and Yudong Liu and Haoning Wu and Yue Wang and Zhiqi Shen and Bowen Qu and Xinyao Niu and Guoyin Wang and Bei Chen and Junnan Li},
+  year={2024},
+  journal={arXiv preprint arXiv:2410.05993},
+}
+```