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README.md ADDED
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+ ---
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+ language:
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+ - en
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+ datasets:
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+ - liuhaotian/LLaVA-Instruct-150K
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+ pipeline_tag: image-text-to-text
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+ inference: false
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+ arxiv: 2304.08485
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+ license: llama2
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+ tags:
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+ - vision
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+ - image-text-to-text
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+ ---
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+ # LLaVA Model Card
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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/62441d1d9fdefb55a0b7d12c/FPshq08TKYD0e-qwPLDVO.png)
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+
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+ Below is the model card of Llava model 7b, which is copied from the original Llava model card that you can find [here](https://huggingface.co/liuhaotian/llava-v1.5-13b).
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+
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+ Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1qsl6cd2c8gGtEW1xV5io7S8NHh-Cp1TV?usp=sharing)
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+
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+ Or check out our Spaces demo! [![Open in Spaces](https://huggingface.co/datasets/huggingface/badges/resolve/main/open-in-hf-spaces-md-dark.svg)](https://huggingface.co/spaces/llava-hf/llava-4bit)
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+
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+
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+ ## Model details
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+
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+ **Model type:**
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+ LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
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+ It is an auto-regressive language model, based on the transformer architecture.
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+
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+ **Model date:**
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+ LLaVA-v1.5-7B was trained in September 2023.
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+
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+ **Paper or resources for more information:**
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+ https://llava-vl.github.io/
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+
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+ ## How to use the model
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+
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+ First, make sure to have `transformers >= 4.35.3`.
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+ The model supports multi-image and multi-prompt generation. Meaning that you can pass multiple images in your prompt. Make sure also to follow the correct prompt template (`USER: xxx\nASSISTANT:`) and add the token `<image>` to the location where you want to query images:
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+
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+ ### Using `pipeline`:
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+
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+ Below we used [`"llava-hf/llava-1.5-7b-hf"`](https://huggingface.co/llava-hf/llava-1.5-7b-hf) checkpoint.
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+
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+ ```python
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+ from transformers import pipeline
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+ from PIL import Image
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+ import requests
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+
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+ model_id = "llava-hf/llava-1.5-7b-hf"
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+ pipe = pipeline("image-to-text", model=model_id)
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+ url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
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+ image = Image.open(requests.get(url, stream=True).raw)
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+
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+ # Define a chat history and use `apply_chat_template` to get correctly formatted prompt
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+ # Each value in "content" has to be a list of dicts with types ("text", "image")
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+ conversation = [
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+ {
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+
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+ "role": "user",
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+ "content": [
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+ {"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"},
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+ {"type": "image"},
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+ ],
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+ },
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+ ]
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+ prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
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+
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+ outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
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+ print(outputs)
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+ >>> {"generated_text": "\nUSER: What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nASSISTANT: Lava"}
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+ ```
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+
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+ ### Using pure `transformers`:
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+
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+ Below is an example script to run generation in `float16` precision on a GPU device:
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+
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+ ```python
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+ import requests
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+ from PIL import Image
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+
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+ import torch
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+ from transformers import AutoProcessor, LlavaForConditionalGeneration
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+
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+ model_id = "llava-hf/llava-1.5-7b-hf"
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+ model = LlavaForConditionalGeneration.from_pretrained(
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+ model_id,
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+ torch_dtype=torch.float16,
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+ low_cpu_mem_usage=True,
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+ ).to(0)
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+
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+ processor = AutoProcessor.from_pretrained(model_id)
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+
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+ # Define a chat histiry and use `apply_chat_template` to get correctly formatted prompt
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+ # Each value in "content" has to be a list of dicts with types ("text", "image")
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+ conversation = [
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+ {
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+
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+ "role": "user",
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+ "content": [
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+ {"type": "text", "text": "What are these?"},
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+ {"type": "image"},
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+ ],
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+ },
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+ ]
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+ prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
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+
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+ image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
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+ raw_image = Image.open(requests.get(image_file, stream=True).raw)
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+ inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(0, torch.float16)
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+
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+ output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
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+ print(processor.decode(output[0][2:], skip_special_tokens=True))
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+ ```
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+
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+ ### Model optimization
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+
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+ #### 4-bit quantization through `bitsandbytes` library
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+
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+ 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:
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+
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+ ```diff
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+ model = LlavaForConditionalGeneration.from_pretrained(
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+ model_id,
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+ torch_dtype=torch.float16,
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+ low_cpu_mem_usage=True,
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+ + load_in_4bit=True
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+ )
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+ ```
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+
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+ #### Use Flash-Attention 2 to further speed-up generation
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+
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+ 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:
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+
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+ ```diff
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+ model = LlavaForConditionalGeneration.from_pretrained(
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+ model_id,
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+ torch_dtype=torch.float16,
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+ low_cpu_mem_usage=True,
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+ + use_flash_attention_2=True
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+ ).to(0)
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+ ```
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+
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+ ## License
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+ Llama 2 is licensed under the LLAMA 2 Community License,
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+ Copyright (c) Meta Platforms, Inc. All Rights Reserved.
added_tokens.json ADDED
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+ {
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+ "<image>": 32000,
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+ "<pad>": 32001
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+ }
chat_template.json ADDED
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+ {
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+ "chat_template": "{% for message in messages %}{% if message['role'] != 'system' %}{{ message['role'].upper() + ': '}}{% endif %}{# Render all images first #}{% for content in message['content'] | selectattr('type', 'equalto', 'image') %}{{ '<image>\n' }}{% endfor %}{# Render all text next #}{% if message['role'] != 'assistant' %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{{ content['text'] + ' '}}{% endfor %}{% else %}{% for content in message['content'] | selectattr('type', 'equalto', 'text') %}{% generation %}{{ content['text'] + ' '}}{% endgeneration %}{% endfor %}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ 'ASSISTANT:' }}{% endif %}"
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+ }
config.json ADDED
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+ "pad_token_id": 32001,
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+ "projector_hidden_act": "gelu",
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+ "text_config": {
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+ "_name_or_path": "lmsys/vicuna-7b-v1.5",
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+ "architectures": [
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+ "LlamaForCausalLM"
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+ "max_position_embeddings": 4096,
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+ "rms_norm_eps": 1e-05,
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+ "image_size": 336,
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+ "model_type": "clip_vision_model",
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+ "num_attention_heads": 16,
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+ "num_hidden_layers": 24,
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+ "patch_size": 14,
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+ "projection_dim": 768,
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+ "vision_feature_layer": -2,
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+ "vision_feature_select_strategy": "default",
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