Hugging Face's logo

xtuner
/
llava-phi-3-mini-hf

Image-to-Text
Transformers
Safetensors
llava
image-text-to-text
Inference Endpoints
Model card Files Community
llava-phi-3-mini-hf / README.md
LZHgrla's picture
LZHgrla
Update README.md
218fa56 verified
preview code
|
raw
history blame contribute delete
5.74 kB
metadata
datasets:
  - Lin-Chen/ShareGPT4V
pipeline_tag: image-to-text

Generic badge

Model

llava-phi-3-mini is a LLaVA model fine-tuned from microsoft/Phi-3-mini-4k-instruct and CLIP-ViT-Large-patch14-336 with ShareGPT4V-PT and InternVL-SFT by XTuner.

Note: This model is in HuggingFace LLaVA format.

Resources:

Details

Model Visual Encoder Projector Resolution Pretraining Strategy Fine-tuning Strategy Pretrain Dataset Fine-tune Dataset Pretrain Epoch Fine-tune Epoch
LLaVA-v1.5-7B CLIP-L MLP 336 Frozen LLM, Frozen ViT Full LLM, Frozen ViT LLaVA-PT (558K) LLaVA-Mix (665K) 1 1
LLaVA-Llama-3-8B CLIP-L MLP 336 Frozen LLM, Frozen ViT Full LLM, LoRA ViT LLaVA-PT (558K) LLaVA-Mix (665K) 1 1
LLaVA-Llama-3-8B-v1.1 CLIP-L MLP 336 Frozen LLM, Frozen ViT Full LLM, LoRA ViT ShareGPT4V-PT (1246K) InternVL-SFT (1268K) 1 1
LLaVA-Phi-3-mini CLIP-L MLP 336 Frozen LLM, Frozen ViT Full LLM, Full ViT ShareGPT4V-PT (1246K) InternVL-SFT (1268K) 1 2

Results

Image
Model MMBench Test (EN) MMMU Val SEED-IMG AI2D Test ScienceQA Test HallusionBench aAcc POPE GQA TextVQA MME MMStar
LLaVA-v1.5-7B 66.5 35.3 60.5 54.8 70.4 44.9 85.9 62.0 58.2 1511/348 30.3
LLaVA-Llama-3-8B 68.9 36.8 69.8 60.9 73.3 47.3 87.2 63.5 58.0 1506/295 38.2
LLaVA-Llama-3-8B-v1.1 72.3 37.1 70.1 70.0 72.9 47.7 86.4 62.6 59.0 1469/349 45.1
LLaVA-Phi-3-mini 69.2 41.4 70.0 69.3 73.7 49.8 87.3 61.5 57.8 1477/313 43.7

Quickstart

Chat by pipeline 

from transformers import pipeline
from PIL import Image    
import requests

model_id = "xtuner/llava-phi-3-mini-hf"
pipe = pipeline("image-to-text", model=model_id, device=0)
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"

image = Image.open(requests.get(url, stream=True).raw)
prompt = "<|user|>\n<image>\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud<|end|>\n<|assistant|>\n"

outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
print(outputs)
>>> [{'generated_text': '\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud (1) lava'}]

Chat by pure transformers 

import requests
from PIL import Image

import torch
from transformers import AutoProcessor, LlavaForConditionalGeneration

model_id = "xtuner/llava-phi-3-mini-hf"

prompt = "<|user|>\n<image>\nWhat are these?<|end|>\n<|assistant|>\n"
image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"

model = LlavaForConditionalGeneration.from_pretrained(
    model_id, 
    torch_dtype=torch.float16, 
    low_cpu_mem_usage=True, 
).to(0)

processor = AutoProcessor.from_pretrained(model_id)


raw_image = Image.open(requests.get(image_file, stream=True).raw)
inputs = processor(prompt, raw_image, 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))
>>> What are these? These are two cats sleeping on a pink couch.

Reproduce

Please refer to docs.

Citation 

@misc{2023xtuner,
    title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
    author={XTuner Contributors},
    howpublished = {\url{https://github.com/InternLM/xtuner}},
    year={2023}
}