Edit model card

Model description

We are excited to announce the continuation and rebranding of our BLIP series into XGen-MM, to be better aligned with Salesforce's unified XGen initiative for large foundation models! This rebranding marks a significant step in our ongoing development of cutting-edge multimodal technologies.

XGen-MM is a series of the latest foundational Large Multimodal Models (LMMs) developed by Salesforce AI Research. This series advances upon the successful designs of the BLIP series, incorporating fundamental enhancements that ensure a more robust and superior foundation.
These models have been trained at scale on high-quality image caption datasets and interleaved image-text data. XGen-MM highlights a few features below,

  • The pretrained foundation model, xgen-mm-phi3-mini-base-r-v1, achieves state-of-the-art performance under 5b parameters and demonstrates strong in-context learning capabilities.
  • The instruct fine-tuned model, xgen-mm-phi3-mini-instruct-r-v1, achieves state-of-the-art performance among open-source and closed-source VLMs under 5b parameters.
  • xgen-mm-phi3-mini-instruct-r-v1 supports flexible high-resolution image encoding with efficient visual token sampling.

More technical details will come with a technical report soon.

Datasets

Dataset Type Dataset(s) Used
Pretrain caption data: (datacomp, cc12m, cc3m, SBU, vg) && interleaved data: obelics
Instruction Tuning LLaVA-Instruct-150K, ShareGPT4V captions, a mixture of academic VQA data including OCR/Document/Chart-focused tasks, publicly available text-only instruction data

Results

Pretrain (base model without instruction tuning)

Model Shot COCO (val) NoCaps (val) TextCaps (val) OKVQA (val) TextVQA (val) VizWiz (testdev) VQAv2 (testdev)
Flamingo-3B 4 85.0 - - 43.3 32.7 34 53.2
8 90.6 - - 44.6 32.4 38.4 55.4
MM1-3B 0 73.5 55.6 63.3 26.1 29.4 15.6 46.2
4 112.3 99.7 84.1 48.6 45.3 38.0 57.9
8 114.6 104.7 88.8 48.4 44.6 46.4 63.6
xgen-mm-phi3-mini-base-r-v1 (Ours) 0 81.7 80.2 60.7 26.5 36.0 21.2 48.1
4 110.5 101.7 84.6 49.2 46.1 38.4 63.9
8 112.1 104.4 87.7 49.1 46.4 44.3 63.8

Instruct (after instruction tuning)

Model SEED-IMG MMBench(dev) MME-total MME-P MME-C MMStar MMMU (val) MMVet MathVista (mini) ScienceQA (test) POPE AI2D
MM1-3B-Chat 68.8 67.8 1761 1482 279 - 33.9 43.7 - - 87.4 -
openbmb/MiniCPM-V-2 67.1 69.6 1808 - - - 38.2 - 38.7 - - -
VILA1.5-3B 67.9 63.4 - 1442 - - 33.3 35.4 - 69.0 85.9 -
xtuner/llava-phi-3-mini-hf 70.0 69.2 1790 1477 313 43.7 41.4 - - 73.7 87.3 69.3
xgen-mm-phi3-mini-instruct-r-v1 (Ours) 72.1 74.1 1827 1467 360 44.6 39.8 45.1 39.3 74.2 87.2 75.8

How to use

We require the use of the development version ("4.41.0.dev0") of the transformers library. To get it, as of 05/07/2024, one can use pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers.

from transformers import AutoModelForVision2Seq, AutoTokenizer, AutoImageProcessor, StoppingCriteria
import torch
import requests
from PIL import Image

# define the prompt template
def apply_prompt_template(prompt):
    s = (
            '<|system|>\nA chat between a curious user and an artificial intelligence assistant. '
            "The assistant gives helpful, detailed, and polite answers to the user's questions.<|end|>\n"
            f'<|user|>\n<image>\n{prompt}<|end|>\n<|assistant|>\n'
        )
    return s 
class EosListStoppingCriteria(StoppingCriteria):
    def __init__(self, eos_sequence = [32007]):
        self.eos_sequence = eos_sequence

    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
        last_ids = input_ids[:,-len(self.eos_sequence):].tolist()
        return self.eos_sequence in last_ids      

# load models
model_name_or_path = "Salesforce/xgen-mm-phi3-mini-instruct-r-v1"
model = AutoModelForVision2Seq.from_pretrained(model_name_or_path, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, trust_remote_code=True, use_fast=False, legacy=False)
image_processor = AutoImageProcessor.from_pretrained(model_name_or_path, trust_remote_code=True)
tokenizer = model.update_special_tokens(tokenizer)

# craft a test sample
img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg'
raw_image = Image.open(requests.get(img_url, stream=True).raw).convert('RGB')
query = "how many dogs are in the picture?"

model = model.cuda()
inputs = image_processor([raw_image], return_tensors="pt", image_aspect_ratio='anyres')
prompt = apply_prompt_template(query)
language_inputs = tokenizer([prompt], return_tensors="pt")
inputs.update(language_inputs)
inputs = {name: tensor.cuda() for name, tensor in inputs.items()}
generated_text = model.generate(**inputs, image_size=[raw_image.size],
                                pad_token_id=tokenizer.pad_token_id,
                                do_sample=False, max_new_tokens=768, top_p=None, num_beams=1,
                                stopping_criteria = [EosListStoppingCriteria()],
                                )
prediction = tokenizer.decode(generated_text[0], skip_special_tokens=True).split("<|end|>")[0]
print("==> prediction: ", prediction)
# output: ==> prediction: There is one dog in the picture.

More comprehensive examples can be found in the notebook.

Reproducibility:

Our SFT evaluation is based on the VLMEvalKit, in which we fixed some inconsistencies with the official benchmarks (e.g., LLM judge API). During our development, we noticed that the raw resolution of the input image would noticeably affect the model output in some cases.

Bias, Risks, Limitations, and Ethical Considerations

The main data sources are from the internet, including webpages, image stock sites, and curated datasets released by the research community. We have excluded certain data, such as LAION, due to known CSAM concerns. The model may be subject to bias from the original data source, as well as bias from LLMs and commercial APIs. We strongly recommend users assess safety and fairness before applying to downstream applications.

License

Our code and weights are released under the Creative Commons Attribution Non Commercial 4.0 LICENSE. Please fill out a form at here to consult the commercial use of model weights.

Code acknowledgement

LAVIS
openflamingo
VLMEvalKit

Citation

@misc{xgen_mm_phi3_mini,
    title={xgen-mm-phi3-mini-instruct Model Card},
    url={https://huggingface.co/Salesforce/xgen-mm-phi3-mini-instruct-r-v1},
    author={Salesforce AI Research},
    month={May},
    year={2024}
}

Troubleshoot

  1. If you missed any packages, please consider the following
pip install torch==2.2.1 torchvision==0.17.1 torchaudio==2.2.1 --index-url https://download.pytorch.org/whl/cu121
pip install open_clip_torch==2.24.0
pip install einops
pip install einops-exts
Downloads last month
2,718
Safetensors
Model size
4.59B params
Tensor type
F32
·
Inference API (serverless) does not yet support model repos that contain custom code.

Collection including Salesforce/xgen-mm-phi3-mini-instruct-r-v1