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Crystal-based-MLLM-7B

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llava_crystal
nlp
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mllm
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metadata
license: apache-2.0
language:
  - en
pipeline_tag: text-generation
library_name: transformers
tags:
  - nlp
  - llm
  - mllm

CrystalChat-7B-Web2Code: a fully-reproducible vision large language model based on CrystalChat-7B LLM for webpage code generation

Model Description

CrystalChat-7B based multi-modal large language model (MLLM) mimics the training recipe used for Vicuna-7B based LLaVa-v1.5. CrystalChat-7B based MLLMs models are entirely transparent, having open-sourced all materials, including code, data, model checkpoint, intermediate results, and more at TODO: Add paper link. CrystalChat-7B-Web2Code MLLM is specialized in webpage images-to-html code generation.

About CrystalChat-7B-Web2Code:

  • 7 billion parameter LLM
  • CLIP ViT-L/14-336px vision encoder
  • Languages: English
  • Models Released: CrystalChat-7B-Web2Code
  • Trained in 2 stages
  • License: ?

Crystal-based models were developed as a collaboration between MBZUAI, Petuum, and LLM360????.

Evaluation

General Evaluation Metrics for MLLMs. MME serves as an extensive evaluative benchmark, aiming to assess perceptual and cognitive capability of MLLMs within 14 sub-tasks. Additionally, we also evaluate the performance of our models on text-oriented visual question answering tasks employing a diverse set of benchmark datasets including ScienceQA and TextVQA. Furthermore, we assess our models’ ability toward anti-hallucination through POPE.

LLM Backbone MME-P MME-C POPE SciQA TextVQA
CrystalCoder-7B 1359.83 238.92 86.182 64.15 50.39
CrystalChat-7B 1456.53 308.21 86.96 67.77 57.84
Vicuna-7B 1481.12 302.85 87.174 67.97 56.49

Table 1: Comparison of different LLM backbones on visual language understanding benchmarks. All models are instruction-tuned on the general domain data (i.e. LLaVA)

TODO: Add general and code evaluations once jason confirms

Data and Training Details

Pretrain Data

LLaVA Visual Instruct Pretrain LCS-558K is a filtered subset of the LAION, CC, and SBU datasets, featuring a more balanced distribution of concept coverage. The file includes multimodal synthesized conversations generated from image-caption pairs by incorporating randomly selected instructions such as "Describe this image." It is used for pretraining in LLaVA, with the raw CC-3M caption serving as the default answer.

Finetune Data

The finetuning data contains the following:

LLaVa Finetuning Data

The dataset chosen was created by LLaVA with academic-task-oriented VQA data mixture and data from ShareGPT. LLaVA Visual Instruct 150K is a dataset of GPT-generated multimodal instruction-following data. It is designed for visual instruction tuning and aims to develop large multimodal models with capabilities akin to GPT-4 in both vision and language.

Data Size Response formatting prompts
LLaVA [36] 158K –
ShareGPT [46] 40K –
VQAv2 [19] 83K Answer the question using a single word or phrase.
GQA [21] 72K Answer the question using a single word or phrase.
OKVQA [41] 9K Answer the question using a single word or phrase.
OCRVQA [42] 80K Answer the question using a single word or phrase.
A-OKVQA [45] 66K Answer with the option’s letter from the given choices directly.
TextCaps [47] 22K Provide a one-sentence caption for the provided image.
RefCOCO [24, 40] 48K Note: randomly choose between the two formats. Provide a short description for this region.
VG [25] 86K Provide the bounding box coordinate of the region this sentence describes.
Total 665K

Table 2. Instruction-following Data Mixture of LLaVA-1.5.

Web2Code Data

The Web2Code instruction tuning dataset was released in [Web2Code: A Large-scale Webpage-to-Code Dataset and Evaluation Framework for Multimodal LLMs](TODO: Add link). The dataset construction and instruction generation process involves four key components:

DWCG: We created new webpage image-code pair data DWCG by generating high-quality HTML webpage-code pairs following the CodeAlpaca prompt using GPT-3.5 and converting them into instruction-following data.

DWCGR: We refined existing webpage code generation data by transforming existing datasets, including WebSight and Pix2Code, into an instruction-following data format similar to LLaVA data, so they can be used as instruction-following data to train MLLMs.

DWU: We created new text question-answer pair data by generating a new question-answer pair dataset utilizing our new GPT-3.5 generated data for webpage understanding.

DWUR: We refined the WebSRC question-answer data to improve its quality using GPT-4.

Code Datasets

Dataset DWCG (ours) DWCGR (ours)
Instruction ✓ ✓
Source Synthetic Synthetic
Size 60K 824.7K
Avg Length (tokens) 471.8±162.3 652.85±157.0
Avg Tag Count 28.1±10.6 35.3±9.0
Avg DOM Depth 5.3±1.0 6.5±1.0
Avg Unique Tags 13.6±2.7 13.5±2.5

Table 3. DWCG is a newly generated GPT-3.5-based dataset, while DWCGR is the refined dataset that utilizes WebSight and Pix2Code datasets

Webpage Understanding Datasets

Dataset DWU DWUR
Instruction ✓ ✓
Size 243.5K 51.5K

Table 4. Distribution of DWU and DWUR datasets. Both datasets include high-quality question-answer pairs for webpage understanding.

#TODO: check if this is needed, if yes, replace with corresponding for code model

Stage 2 - Finetuning

Checkpoints
CrystalChat
CrystalCoder

Stage 1 - Pretraining

Checkpoints
CrystalChat
CrystalCoder

[to find all branches: git branch -a]

Examples

TODO: Add image as sample example

Example 1:

Original Input image
*Image 1. Original Input Image.*
CrsytalChat-7B model generated output
*Image 2. CrystalChat-7B-Web2Code model output.*

Example 2:

CrsytalChat-7B model generated output
*Image 3. Hand-drawn webpage input to CrystalChat-7B-Web2Code generated output.*

Loading Crystal 

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained(
                    "LLM360/CrystalChat-7B-MLLM",
                    padding_side="right",
                    trust_remote_code=True)

model = AutoModelForCausalLM.from_pretrained(
    "LLM360/CrystalChat-7B-MLLM",
    trust_remote_code=True,
    torch_dtype=torch.float16,
    device_map='auto',
    low_cpu_mem_usage=True
)

LLM-360

LLM-360 is an open research lab enabling community-owned AGI through open-source large model research and development.

Crystal-based Models enables community-owned AGI by creating standards and tools to advance the bleeding edge of LLM capability and empower knowledge transfer, research, and development.

We believe in a future where artificial general intelligence (AGI) is created by the community, for the community. Through an open ecosystem of equitable computational resources, high-quality data, and flowing technical knowledge, we can ensure ethical AGI development and universal access for all innovators.

Visit us

Citation

BibTeX:

@article{
      title={}, 
      author={},
      year={},
}