language:
- multilingual
license: apache-2.0
library_name: transformers
datasets:
- lamm-mit/Cephalo-Bioinspired-Mechanics-Materials
- lamm-mit/Cephalo-Wikipedia-Materials
tags:
- nlp
- code
- vision
- chemistry
- engineering
- biology
- bio-inspired
- text-generation-inference
- materials science
pipeline_tag: image-text-to-text
inference:
parameters:
temperature: 0.3
widget:
- messages:
- role: user
content: <|image_1|>Can you describe what you see in the image?
Model Summary
Cephalo is a series of multimodal materials science focused vision large language models (V-LLMs) designed to integrate visual and linguistic data for advanced understanding and interaction in human-AI or multi-agent AI frameworks.
A novel aspect of Cephalo's development is the innovative dataset generation method. The extraction process employs advanced algorithms to accurately detect and separate images and their corresponding textual descriptions from complex PDF documents. It involves extracting images and captions from PDFs to create well-reasoned image-text pairs, utilizing large language models (LLMs) for natural language processing. These image-text pairs are then refined and validated through LLM-based NLP processing, ensuring high-quality and contextually relevant data for training.
Cephalo can interpret complex visual scenes and generating contextually accurate language descriptions and answer queries.
The model is developed to process diverse inputs, including images and text, facilitating a broad range of applications such as image captioning, visual question answering, and multimodal content generation. The architecture combines a vision encoder model and an autoregressive transformer to process complex natural language understanding.
Cephalo provides a robust framework for multimodal interaction and understanding, including the development of complex generative pipelines to create 2D and 3D renderings of material microstructures as input for additive manufacturing methods.
This version of Cephalo, lamm-mit/Cephalo-Phi-3-vision-128k-4b-beta, is based on the Phi-3-Vision-128K-Instruct model. The model was trained on a combination of scientific text-image and text-only data. The model has a context length of 128,000 tokens. Further details, see: https://huggingface.co/microsoft/Phi-3-vision-128k-instruct.
Chat Format
Given the nature of the training data, the Cephalo-Phi-3-vision-128k-4b-beta model is best suited for a single image input wih prompts using the chat format as follows. You can provide the prompt as a single image with a generic template as follow:
<|user|>\n<|image_1|>\n{prompt}<|end|>\n<|assistant|>\n
where the model generates the text after <|assistant|> . For multi-turn conversations, the prompt should be formatted as follows:
<|user|>\n<|image_1|>\n{prompt_1}<|end|>\n<|assistant|>\n{response_1}<|end|>\n<|user|>\n{prompt_2}<|end|>\n<|assistant|>\n
Sample inference code
This code snippets show how to get quickly started on a GPU:
from PIL import Image
import requests
from transformers import AutoModelForCausalLM
from transformers import AutoProcessor
model_id = "lamm-mit/Cephalo-Phi-3-vision-128k-4b-beta"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto")
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
question = "What is shown in this image, and what is the relevance for materials design? Include a discussion of multi-agent AI."
messages = [
{"role": "user", "content": f"<|image_1|>\n{question}"},
]
url = "https://d2r55xnwy6nx47.cloudfront.net/uploads/2018/02/Ants_Lede1300.jpg"
image = Image.open(requests.get(url, stream=True).raw)
prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = processor(prompt, [image], return_tensors="pt").to("cuda:0")
generation_args = {
"max_new_tokens": 512,
"temperature": 0.1,
"do_sample": True,
"stop_strings": ['<|end|>',
'<|endoftext|>'],
"tokenizer": processor.tokenizer,
}
generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args)
# remove input tokens
generate_ids = generate_ids[:, inputs['input_ids'].shape[1]:]
response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
print(response)
Sample output:
Image by Vaishakh Manohar
The image shows a group of red ants (Solenopsis invicta) climbing over a vertical wooden post. The ants are using their long legs and antennae to navigate the rough surface of the wood, demonstrating their ability to adapt to different materials and environments. This behavior is relevant for materials design because it highlights the importance of considering the interactions between materials and living organisms, such as ants, when designing new materials. Multi-agent AI (Artificial Intelligence) is a field of study that focuses on the development of AI systems that can work together with other AI systems to achieve a common goal. In the context of this image, multi-agent AI could be used to design materials that are more compatible with the natural behaviors of living organisms, such as ants, and that can adapt to different environments and conditions. By studying the behavior of ants and other living organisms, researchers can gain insights into how materials can be designed to better interact with these organisms and to better mimic their natural behaviors. This can lead to the development of new materials that are more sustainable, efficient, and effective in a variety of applications. In summary, the image of red ants climbing over a wooden post highlights the importance of considering the interactions between materials and living organisms when designing new materials, and the potential of multi-agent AI to help achieve this goal.
Dataset generation
The schematic below shows a visualization of the approach to generate datasets for training the vision model. The extraction process employs advanced algorithms to accurately detect and separate images and their corresponding textual descriptions from complex PDF documents. It involves extracting images and captions from PDFs to create well-reasoned image-text pairs, utilizing large language models (LLMs) for natural language processing. These image-text pairs are then refined and validated through LLM-based NLP processing, ensuring high-quality and contextually relevant data for training.
The image below shows reproductions of two representative pages of the scientific article (here, Spivak, Buehler, et al., 2011), and how they are used to extract visual scientific data for training the Cephalo model.
Example applications
The paper provides detailed examples and use cases. Here is a visual of a pipeline that consists of 1) analysis of an image provided to Cephalo-Phi-3-vision-128k-4b-beta, 2) generation of an image generation fromt, and 3) generation of a new image using Stable Diffusion XL Turbo.
A similar mechanism can be employed to generate 3D models:
Fine-tuning
Load base model
model_id = "microsoft/Phi-3-vision-128k-instruct"
model = AutoModelForCausalLM.from_pretrained(model_id, device_map="cuda", trust_remote_code=True, torch_dtype="auto")
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
Define FT_repo_id to push on HF hub/save model:
FT_repo_id='xxxxx/' #<repo_ID>
from datasets import load_dataset
train_dataset = load_dataset("lamm-mit/Cephalo-Wikipedia-Materials", split="train")
import random
class MyDataCollator:
def __init__(self, processor):
self.processor = processor
def __call__(self, examples):
texts = []
images = []
for example in examples:
image = example["image"]
question = example["query"]
answer = example["answer"]
messages = [ {
"role": "user", "content": '<|image_1|>\n'+question},
{"role": "assistant", "content": f"{answer}"}, ]
text = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=False)
images.append(image)
batch = processor(text=text, images=[image], return_tensors="pt", padding=True
labels = batch["input_ids"].clone()
labels[labels <0] = -100
batch["labels"] = labels
return batch
data_collator = MyDataCollator(processor)
Then set up trainer, and train:
from transformers import TrainingArguments, Trainer
optim = "paged_adamw_8bit"
training_args = TrainingArguments(
num_train_epochs=2,
per_device_train_batch_size=1,
#per_device_eval_batch_size=4,
gradient_accumulation_steps=4,
warmup_steps=250,
learning_rate=1e-5,
weight_decay=0.01,
logging_steps=25,
output_dir="output_training",
optim=optim,
save_strategy="steps",
save_steps=1000,
save_total_limit=16,
#fp16=True,
bf16=True,
push_to_hub_model_id=FT_repo_id,
remove_unused_columns=False,
report_to="none",
)
trainer = Trainer(
model=model,
args=training_args,
data_collator=data_collator,
train_dataset=train_dataset,
)
trainer.train()
Citation
Please cite as:
@article{Buehler_Cephalo_2024,
title={Cephalo: Multi-Modal Vision-Language Models for Bio-Inspired Materials Analysis and Design},
author={Markus J. Buehler},
journal={arXiv preprint arXiv:2405.19076},
year={2024}
}