--- license: mit language: - en pipeline_tag: text-generation library_name: transformers tags: - nlp - llm - mllm --- # CrystalChat-7B-MLLM: a fully-reproducible vision language model based on CrystalChat-7B ## Model Description CrystalChat-7B based multi-modal large language model (MLLM) mimics the training recipe used for Vicuna-7B based [LLaVa-v1.5](https://huggingface.co./docs/transformers/main/model_doc/llava). CrystalChat-7B-MLLM models are entirely transparent, having open-sourced all materials, including code, data, model checkpoint, intermediate results, and more at [TODO: Add paper link](). ### About CrystalChat-7B-MLLM: * 7 billion parameter LLM * CLIP ViT-L/14-336px vision encoder * Languages: English * Models Released: CrystalChat-7B-MLLM * Trained in 2 stages * License: MIT Crystal-based models were developed as a collaboration between [MBZUAI](https://mbzuai.ac.ae/institute-of-foundation-models/), [Petuum](https://www.petuum.com/), and [LLM360](https://www.llm360.ai/) TODO- check????. ## 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 | 86.18 | 64.15 | 50.39 | | CrystalChat-7B | 1456.53 | **308.21** | 86.96 | 67.77 | **57.84** | | Vicuna-7B | **1481.12** | 302.85 | **87.17** | **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)* ## 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 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.* TODO: Check if we need to publish these 2 ## Stage 2 - Finetuning | Checkpoints | | | ----------- | ----------- | | [CrystalChat](https://huggingface.co./qazimbhat1/my-model-repo3/tree/main) | | [CrystalCoder](https://huggingface.co./qazimbhat1/Crystal-based-MLLM-7B/tree/Crystal-coder-7B) | ## Stage 1 - Pretraining | Checkpoints | | | ----------- | ----------- | | [CrystalChat](https://huggingface.co./qazimbhat1/Crystal-based-MLLM-7B/tree/Crystal-based-MLLM-7B-pretrain) | | [CrystalCoder](https://huggingface.co./qazimbhat1/Crystal-based-MLLM-7B/tree/Crystal-coder-7B-pretrain) | [to find all branches: git branch -a] ## Examples TODO: Add image as sample example
k2 big eval table
## Loading Crystal ```python 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](https://www.llm360.ai/) ## Citation **BibTeX:** ```bibtex @article{ title={}, author={}, year={}, } ```