language:
- en
- zh
license: apache-2.0
tags:
- vision
- image-text-to-text
- transformers.js
datasets:
- lmms-lab/LLaVA-OneVision-Data
pipeline_tag: image-text-to-text
inference: false
arxiv: 2408.03326
library_name: transformers
LLaVA-Onevision Model Card
Check out also the Google Colab demo to run Llava on a free-tier Google Colab instance: 
Below is the model card of 0.5B LLaVA-Onevision model which is copied from the original LLaVA-Onevision model card that you can find here.
Model details
Model type: LLaVA-Onevision is an open-source multimodal LLM trained by fine-tuning Qwen2 on GPT-generated multimodal instruction-following data. LLaVA-OneVision is the first single model that can simultaneously push the performance boundaries of open LMMs in three important computer vision scenarios: single-image, multi-image, and video scenarios. Importantly, the design of LLaVA-OneVision allows strong transfer learning across different modalities/scenarios, yielding new emerging capabilities. In particular, strong video understanding and cross-scenario capabilities are demonstrated through task transfer from images to videos.
Model date: LLaVA-Onevision-0.5-ov was added in August 2024.
Paper or resources for more information: https://llava-vl.github.io/
- Architecture: SO400M + Qwen2
- Pretraining Stage: LCS-558K, 1 epoch, projector
- Mid Stage: A mixture of 4.7M high-quality synthetic data, 1 epoch, full model
- Final-Image Stage: A mixture of 3.6M single-image data, 1 epoch, full model
- OneVision Stage: A mixture of 1.6M single-image/multi-image/video data, 1 epoch, full model
- Precision: bfloat16
How to use the model
First, make sure to have transformers installed from branch or transformers >= 4.45.0.
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 by applying chat template:
Using pipeline:
Below we used "llava-hf/llava-onevision-qwen2-0.5b-ov-hf" checkpoint.
from transformers import pipeline
from PIL import Image
import requests
from transformers import AutoProcessor
model_id = "llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline("image-to-text", model=model_id)
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
# Each value in "content" has to be a list of dicts with types ("text", "image")
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"},
{"type": "image"},
],
},
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
print(outputs)
>>> {"generated_text": "user\n\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud\nassistant\nLava"}
Using pure transformers:
Below is an example script to run generation in float16 precision on a GPU device:
import requests
from PIL import Image
import torch
from transformers import AutoProcessor, LlavaOnevisionForConditionalGeneration
model_id = "llava-hf/llava-onevision-qwen2-0.5b-ov-hf"
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
).to(0)
processor = AutoProcessor.from_pretrained(model_id)
# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
# Each value in "content" has to be a list of dicts with types ("text", "image")
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "What are these?"},
{"type": "image"},
],
},
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(images=raw_image, text=prompt, return_tensors='pt').to(0, torch.float16)
output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
print(processor.decode(output[0][2:], skip_special_tokens=True))
Model optimization
4-bit quantization through bitsandbytes library
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:
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ load_in_4bit=True
)
Use Flash-Attention 2 to further speed-up generation
First make sure to install flash-attn. Refer to the original repository of Flash Attention regarding that package installation. Simply change the snippet above with:
model = LlavaOnevisionForConditionalGeneration.from_pretrained(
model_id,
torch_dtype=torch.float16,
low_cpu_mem_usage=True,
+ use_flash_attention_2=True
).to(0)
Usage w/ Transformers.js
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformers
Example: Multi-round conversations w/ PKV caching
import { AutoProcessor, AutoTokenizer, LlavaOnevisionForConditionalGeneration, RawImage } from '@huggingface/transformers';
// Load tokenizer, processor and model
const model_id = 'llava-hf/llava-onevision-qwen2-0.5b-ov-hf';
const tokenizer = await AutoTokenizer.from_pretrained(model_id);
const processor = await AutoProcessor.from_pretrained(model_id);
const model = await LlavaOnevisionForConditionalGeneration.from_pretrained(model_id, {
dtype: {
embed_tokens: 'fp16', // or 'fp32' or 'q8'
vision_encoder: 'fp16', // or 'fp32' or 'q8'
decoder_model_merged: 'q4', // or 'q8'
},
// device: 'webgpu',
});
// Prepare text inputs
const prompt = 'What does the text say?';
const messages = [
{ role: 'system', content: 'Answer the question.' },
{ role: 'user', content: `<image>\n${prompt}` }
]
const text = tokenizer.apply_chat_template(messages, { tokenize: false, add_generation_prompt: true });
const text_inputs = tokenizer(text);
// Prepare vision inputs
const url = 'https://huggingface.co/qnguyen3/nanoLLaVA/resolve/main/example_1.png';
const image = await RawImage.fromURL(url);
const vision_inputs = await processor(image);
// Generate response
const { past_key_values, sequences } = await model.generate({
...text_inputs,
...vision_inputs,
do_sample: false,
max_new_tokens: 64,
return_dict_in_generate: true,
});
// Decode output
const answer = tokenizer.decode(
sequences.slice(0, [text_inputs.input_ids.dims[1], null]),
{ skip_special_tokens: true },
);
console.log(answer);
// The text says "small but mighty" in a playful font.
const new_messages = [
...messages,
{ role: 'assistant', content: answer },
{ role: 'user', content: 'How does the text correlate to the context of the image?' }
]
const new_text = tokenizer.apply_chat_template(new_messages, { tokenize: false, add_generation_prompt: true });
const new_text_inputs = tokenizer(new_text);
// Generate another response
const output = await model.generate({
...new_text_inputs,
past_key_values,
do_sample: false,
max_new_tokens: 256,
});
const new_answer = tokenizer.decode(
output.slice(0, [new_text_inputs.input_ids.dims[1], null]),
{ skip_special_tokens: true },
);
console.log(new_answer);
// The text "small but mighty" is likely a playful or humorous reference to the image of the blue mouse with the orange dumbbell. It could be used as a motivational phrase or a playful way to express the idea that even small things can be impressive or powerful.
Citation
@misc{li2024llavaonevisioneasyvisualtask,
title={LLaVA-OneVision: Easy Visual Task Transfer},
author={Bo Li and Yuanhan Zhang and Dong Guo and Renrui Zhang and Feng Li and Hao Zhang and Kaichen Zhang and Yanwei Li and Ziwei Liu and Chunyuan Li},
year={2024},
eprint={2408.03326},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2408.03326},
}