llava-onevision-qwen2-0.5b-si#

README(From Huggingface)#

datasets:

lmms-lab/LLaVA-OneVision-Data language:
en
zh library_name: transformers license: apache-2.0 metrics:
accuracy tags:
multimodal model-index:
name: llava-onevision-qwen-0.5b-si results:
- task: type: multimodal dataset: name: AI2D type: ai2d metrics:
  - type: accuracy value: 54.2 name: accuracy verified: true
- task: type: multimodal dataset: name: ChartQA type: chartqa metrics:
  - type: accuracy value: 61.0 name: accuracy verified: true
- task: type: multimodal dataset: name: DocVQA type: docvqa metrics:
  - type: accuracy value: 75.0 name: accuracy verified: true
- task: type: multimodal dataset: name: InfoVQA type: infovqa metrics:
  - type: accuracy value: 44.8 name: accuracy verified: true
- task: type: multimodal dataset: name: MathVerse type: mathverse metrics:
  - type: accuracy value: 17.3 name: accuracy verified: true
- task: type: multimodal dataset: name: MathVista type: mathvista metrics:
  - type: accuracy value: 34.6 name: accuracy verified: true
- task: type: multimodal dataset: name: MMBench type: mmbench metrics:
  - type: accuracy value: 43.8 name: accuracy verified: true
- task: type: multimodal dataset: name: MME-Perception type: mme-perception metrics:
  - type: score value: 1217 name: score verified: true
- task: type: multimodal dataset: name: MME-Cognition type: mme-cognition metrics:
  - type: score value: 272 name: score verified: true
- task: type: multimodal dataset: name: MMMU type: mmmu metrics:
  - type: accuracy value: 31.2 name: accuracy verified: true
- task: type: multimodal dataset: name: MMVet type: mmvet metrics:
  - type: accuracy value: 26.9 name: accuracy verified: true
- task: type: multimodal dataset: name: MMStar type: mmstar metrics:
  - type: accuracy value: 36.3 name: accuracy verified: true
- task: type: multimodal dataset: name: Seed-Bench type: seed-bench metrics:
  - type: accuracy value: 63.4 name: accuracy verified: true
- task: type: multimodal dataset: name: Science-QA type: science-qa metrics:
  - type: accuracy value: 67.8 name: accuracy verified: true
- task: type: multimodal dataset: name: ImageDC type: imagedc metrics:
  - type: accuracy value: 83.0 name: accuracy verified: true
- task: type: multimodal dataset: name: MMLBench type: mmlbench metrics:
  - type: accuracy value: 43.2 name: accuracy verified: true
- task: type: multimodal dataset: name: RealWorldQA type: realworldqa metrics:
  - type: accuracy value: 53.7 name: accuracy verified: true
- task: type: multimodal dataset: name: Vibe-Eval type: vibe-eval metrics:
  - type: accuracy value: 34.9 name: accuracy verified: true
- task: type: multimodal dataset: name: LLaVA-W type: llava-w metrics:
  - type: accuracy value: 71.2 name: accuracy verified: true
- task: type: multimodal dataset: name: L-Wilder type: l-wilder metrics:
  - type: accuracy value: 51.5 name: accuracy verified: true

LLaVA-OneVision#

![ banner

Play with the model on the LLaVA OneVision Chat.

Model Summary#

The LLaVA-OneVision models are 0.5/7/72B parameter models trained on LLaVA-OneVision, based on Qwen2 language model with a context window of 32K tokens.

Repository: LLaVA-VL/LLaVA-NeXT
Project Website: llava-onevision.lmms-lab.com
Paper: LLaVA-OneVision
Point of Contact: Bo Li
Languages: English, Chinese

Use#

Intended use#

The model was trained on LLaVA-OneVision Dataset and have the ability to interact with images, multi-image and videos.

Feel free to share your generations in the Community tab!

Generation#

We provide the simple generation process for using our model. For more details, you could refer to Github.

# pip install git+https://github.com/LLaVA-VL/LLaVA-NeXT.git
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, IGNORE_INDEX
from llava.conversation import conv_templates, SeparatorStyle

from PIL import Image
import requests
import copy
import paddle

import sys
import warnings

warnings.filterwarnings("ignore")
pretrained = "lmms-lab/llava-onevision-qwen2-0.5b-si"
model_name = "llava_qwen"
device = "cuda"
device_map = "auto"
tokenizer, model, image_processor, max_length = load_pretrained_model(pretrained, None, model_name, device_map=device_map)  # Add any other thing you want to pass in llava_model_args

model.eval()

url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
image = Image.open(requests.get(url, stream=True).raw)
image_tensor = process_images([image], image_processor, model.config)
image_tensor = [_image.to(dtype=paddle.float16, device=device) for _image in image_tensor]

conv_template = "qwen_1_5"  # Make sure you use correct chat template for different models
question = DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image?"
conv = copy.deepcopy(conv_templates[conv_template])
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt_question = conv.get_prompt()

input_ids = tokenizer_image_token(prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pd").unsqueeze(0)
image_sizes = [image.size]


cont = model.generate(
    input_ids,
    images=image_tensor,
    image_sizes=image_sizes,
    do_sample=False,
    temperature=0,
    max_new_tokens=4096,
)[0]
text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
print(text_outputs)

Training#

Model#

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

Hardware & Software#

GPUs: 256 * Nvidia Tesla A100 (for whole model series training)
Orchestration: Huggingface Trainer
Neural networks: PyTorch

Citation#

@article{li2024llavaonevision,
      title={LLaVA-OneVision}, 
}

Model Files#

README.md (7.7 KB)
added_tokens.json (101.0 B)
config.json (2.9 KB)
generation_config.json (207.0 B)
merges.txt (1.6 MB)
model.safetensors (1.7 GB)
preprocessor_config.json (393.0 B)
special_tokens_map.json (367.0 B)
tokenizer_config.json (1.5 KB)
trainer_state.json (1.9 MB)
training_args.bin (7.7 KB)
vocab.json (2.6 MB)

Back to Main

llava-onevision-qwen2-0.5b-si

目录