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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+  Bunny-v1_0-3B - bnb 4bits
+  - Model creator: https://huggingface.co/BAAI/
+  - Original model: https://huggingface.co/BAAI/Bunny-v1_0-3B/
+  
+
+
+
+  Original model description:
+  ---
+inference: false
+license: apache-2.0
+---
+
+# Model Card
+
+<p align="center">
+  <img src="./icon.png" alt="Logo" width="350">
+</p>
+
+📖 [Technical report](https://arxiv.org/abs/2402.11530) | 🏠 [Code](https://github.com/BAAI-DCAI/Bunny) | 🐰 [Demo](https://wisemodel.cn/spaces/baai/Bunny)
+
+This is the merged weights of [bunny-phi-2-siglip-lora](https://huggingface.co/BAAI/bunny-phi-2-siglip-lora).
+
+Bunny is a family of lightweight but powerful multimodal models. It offers multiple plug-and-play vision encoders, like EVA-CLIP, SigLIP and language backbones, including Llama-3-8B, Phi-1.5, StableLM-2, Qwen1.5, MiniCPM and Phi-2. To compensate for the decrease in model size, we construct more informative training data by curated selection from a broader data source. Remarkably, our Bunny-v1.0-3B model built upon SigLIP and Phi-2 outperforms the state-of-the-art MLLMs, not only in comparison with models of similar size but also against larger MLLM frameworks (7B), and even achieves performance on par with 13B models.
+
+The model is pretrained on LAION-2M and finetuned on Bunny-695K.
+More details about this model can be found in [GitHub](https://github.com/BAAI-DCAI/Bunny).
+
+![comparison](comparison.png)
+
+# Quickstart
+
+Here we show a code snippet to show you how to use the model with transformers.
+
+Before running the snippet, you need to install the following dependencies:
+
+```shell
+pip install torch transformers accelerate pillow
+```
+
+If the CUDA memory is enough, it would be faster to execute this snippet by setting `CUDA_VISIBLE_DEVICES=0`.
+
+Users especially those in Chinese mainland may want to refer to a HuggingFace [mirror site](https://hf-mirror.com). 
+
+
+```python
+import torch
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from PIL import Image
+import warnings
+
+# disable some warnings
+transformers.logging.set_verbosity_error()
+transformers.logging.disable_progress_bar()
+warnings.filterwarnings('ignore')
+
+# set device
+device = 'cuda'  # or cpu
+torch.set_default_device(device)
+
+# create model
+model = AutoModelForCausalLM.from_pretrained(
+    'BAAI/Bunny-v1_0-3B',
+    torch_dtype=torch.float16, # float32 for cpu
+    device_map='auto',
+    trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(
+    'BAAI/Bunny-v1_0-3B',
+    trust_remote_code=True)
+
+# text prompt
+prompt = 'Why is the image funny?'
+text = f"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: <image>\n{prompt} ASSISTANT:"
+text_chunks = [tokenizer(chunk).input_ids for chunk in text.split('<image>')]
+input_ids = torch.tensor(text_chunks[0] + [-200] + text_chunks[1], dtype=torch.long).unsqueeze(0).to(device)
+
+# image, sample images can be found in images folder
+image = Image.open('example_2.png')
+image_tensor = model.process_images([image], model.config).to(dtype=model.dtype, device=device)
+
+# generate
+output_ids = model.generate(
+    input_ids,
+    images=image_tensor,
+    max_new_tokens=100,
+    use_cache=True)[0]
+
+print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True).strip())
+```
+
+# License
+This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses.
+The content of this project itself is licensed under the Apache license 2.0.
+
+
+