metadata
datasets:
- Lin-Chen/ShareGPT4V
pipeline_tag: image-text-to-text
library_name: xtuner
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 official LLaVA format.
Resources:
- GitHub: xtuner
- HuggingFace LLaVA format model: xtuner/llava-phi-3-mini-hf
- GGUF LLaVA model: xtuner/llava-phi-3-mini-gguf
- XTuner LLaVA format model: xtuner/llava-phi-3-mini-xtuner
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
| 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 LLaVA official library
- Install official LLaVA library
pip install git+https://github.com/haotian-liu/LLaVA.git
- Chat by below script
cli.py
import argparse
from io import BytesIO
import requests
import torch
from llava.constants import DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX
from llava.conversation import Conversation, SeparatorStyle
from llava.mm_utils import process_images, tokenizer_image_token
from llava.model import LlavaLlamaForCausalLM
from PIL import Image
from transformers import (AutoTokenizer, BitsAndBytesConfig, StoppingCriteria,
StoppingCriteriaList, TextStreamer)
def load_image(image_file):
if image_file.startswith('http://') or image_file.startswith('https://'):
response = requests.get(image_file)
image = Image.open(BytesIO(response.content)).convert('RGB')
else:
image = Image.open(image_file).convert('RGB')
return image
class StopWordStoppingCriteria(StoppingCriteria):
"""StopWord stopping criteria."""
def __init__(self, tokenizer, stop_word):
self.tokenizer = tokenizer
self.stop_word = stop_word
self.length = len(self.stop_word)
def __call__(self, input_ids, *args, **kwargs) -> bool:
cur_text = self.tokenizer.decode(input_ids[0])
cur_text = cur_text.replace('\r', '').replace('\n', '')
return cur_text[-self.length:] == self.stop_word
def get_stop_criteria(tokenizer, stop_words=[]):
stop_criteria = StoppingCriteriaList()
for word in stop_words:
stop_criteria.append(StopWordStoppingCriteria(tokenizer, word))
return stop_criteria
def main(args):
kwargs = {'device_map': args.device}
if args.load_8bit:
kwargs['load_in_8bit'] = True
elif args.load_4bit:
kwargs['load_in_4bit'] = True
kwargs['quantization_config'] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4')
else:
kwargs['torch_dtype'] = torch.float16
tokenizer = AutoTokenizer.from_pretrained(args.model_path)
model = LlavaLlamaForCausalLM.from_pretrained(
args.model_path, low_cpu_mem_usage=True, **kwargs)
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model(device_map=args.device)
image_processor = vision_tower.image_processor
conv = Conversation(
system=system='<|system|>\nAnswer the questions.',
roles=('<|user|>\n', '<|assistant|>\n'),
messages=[],
offset=0,
sep_style=SeparatorStyle.MPT,
sep='<|end|>',
)
roles = conv.roles
image = load_image(args.image_file)
image_size = image.size
image_tensor = process_images([image], image_processor, model.config)
if type(image_tensor) is list:
image_tensor = [
image.to(model.device, dtype=torch.float16)
for image in image_tensor
]
else:
image_tensor = image_tensor.to(model.device, dtype=torch.float16)
while True:
try:
inp = input(f'{roles[0]}: ')
except EOFError:
inp = ''
if not inp:
print('exit...')
break
print(f'{roles[1]}: ', end='')
if image is not None:
inp = DEFAULT_IMAGE_TOKEN + '\n' + inp
image = None
conv.append_message(conv.roles[0], inp)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer_image_token(
prompt, tokenizer, IMAGE_TOKEN_INDEX,
return_tensors='pt').unsqueeze(0).to(model.device)
stop_criteria = get_stop_criteria(
tokenizer=tokenizer, stop_words=[conv.sep])
streamer = TextStreamer(
tokenizer, skip_prompt=True, skip_special_tokens=True)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor,
image_sizes=[image_size],
do_sample=True if args.temperature > 0 else False,
temperature=args.temperature,
max_new_tokens=args.max_new_tokens,
streamer=streamer,
stopping_criteria=stop_criteria,
use_cache=True)
outputs = tokenizer.decode(output_ids[0]).strip()
conv.messages[-1][-1] = outputs
if args.debug:
print('\n', {'prompt': prompt, 'outputs': outputs}, '\n')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'--model-path', type=str, default='xtuner/llava-llama-3-8b-v1_1-hf')
parser.add_argument('--image-file', type=str, required=True)
parser.add_argument('--device', type=str, default='auto')
parser.add_argument('--temperature', type=float, default=0.2)
parser.add_argument('--max-new-tokens', type=int, default=512)
parser.add_argument('--load-8bit', action='store_true')
parser.add_argument('--load-4bit', action='store_true')
parser.add_argument('--debug', action='store_true')
args = parser.parse_args()
main(args)
python ./cli.py --model-path xtuner/llava-phi-3-mini --image-file https://raw.githubusercontent.com/open-mmlab/mmdeploy/main/tests/data/tiger.jpeg --load-4bit
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}
}