--- license: apache-2.0 datasets: - kaist-ai/Perception-Collection - kaist-ai/Perception-Bench language: - en metrics: - pearsonr - spearmanr library_name: transformers pipeline_tag: image-to-text tags: - Image-to-Text - Visual Question Answering - Text2Text Generation --- ## Links for Reference - **Homepage: https://kaistai.github.io/prometheus-vision/** - **Repository: https://github.com/kaistAI/prometheus-vision** - **Paper: https://arxiv.org/abs/2401.06591** - **Point of Contact: seongyun@kaist.ac.kr** # TL;DR Prometheus-Vision is the first open-source VLM specialized for evaluation purposes. Prometheus-Vision shows a high correlation with both GPT-4V and human evaluators, indicating its potential to be used as a cheap alternative for GPT-4V evaluation. ![image/png](./prometheus_vision.png) Prometheus-Vision have five input components (image, instruction, response to evaluate, customized score rubric, reference answer) and two output components (language feedback and score decision). ![image/png](./perception_collection.png) # Model Details ## Model Description - **Model type:** Vision-Language Model - **Language(s) (NLP):** English - **License:** Apache 2.0 - **Related Models:** [All Prometheus Checkpoints](https://huggingface.co./models?search=kaist-ai/Prometheus-Vision) - **Resources for more information:** - [Research paper](https://arxiv.org/abs/2401.06591) - [GitHub Repo](https://github.com/kaistAI/prometheus-vision) Prometheu-Vision is trained with two different sizes (7B and 13B). You could check the 7B sized VLM on [this page](https://huggingface.co./kaist-ai/prometheus-vision-7b-v1.0). Also, check out our dataset as well on [this page](https://huggingface.co./datasets/kaist-ai/Perception-Collection). ## Prompt Format Prometheus-Vision requires 5 components in the input: An image, an instruction, a response to evaluate, a score rubric, and a reference answer. You could refer to the prompt format below. You should fill in the instruction, response, reference answer, criteria description, and score description for score in range of 1 to 5. ``` ###Task Description: An instruction (might include an Input inside it), a response to evaluate, a reference answer that gets a score of 5, an image and a score rubric representing an evaluation criterion is given. 1. Write a detailed feedback that assess the quality of the response strictly based on the given score rubric, not evaluating in general. 2. After writing a feedback, write a score that is an integer between 1 and 5. You should refer to the score rubric. 3. The output format should look as follows: \"Feedback: (write a feedback for criteria) [RESULT] (an integer number between 1 and 5)\" 4. Please do not generate any other opening, closing, and explanations. ###The instruction to evaluate: {instruction} ###Response to evaluate: {response} ###Reference Answer (Score 5): {reference_answer} ###Score Rubrics: [{criteria_description}] Score 1: {score1_description} Score 2: {score2_description} Score 3: {score3_description} Score 4: {score4_description} Score 5: {score5_description} ###Feedback: ``` ## License Perception Collection and Prometheus-Vision are subject to OpenAI's Terms of Use for the generated data. If you suspect any violations, please reach out to us. # Usage Find below some example scripts on how to use the model in `transformers`: ## Using the Pytorch model ### Running the model on a GPU
Click to expand ```python import argparse import torch import os import json from tqdm import tqdm import shortuuid from llava.constants import IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN from llava.conversation import conv_templates, SeparatorStyle from llava.model.builder import load_pretrained_model from llava.utils import disable_torch_init from llava.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria from PIL import Image import math def split_list(lst, n): """Split a list into n (roughly) equal-sized chunks""" chunk_size = math.ceil(len(lst) / n) # integer division return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)] def get_chunk(lst, n, k): chunks = split_list(lst, n) return chunks[k] def eval_model(args): # Model disable_torch_init() model_path = 'kaist-ai/prometheus-vision-13b-v1.0' model_name = 'llava-v1.5' tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, args.model_base, model_name) questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")] questions = get_chunk(questions, args.num_chunks, args.chunk_idx) answers_file = os.path.expanduser(args.answers_file) os.makedirs(os.path.dirname(answers_file), exist_ok=True) ans_file = open(answers_file, "w") for line in tqdm(questions): idx = line["question_id"] image_file = line["image"] qs = line["text"] cur_prompt = qs if model.config.mm_use_im_start_end: qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + '\n' + qs else: qs = DEFAULT_IMAGE_TOKEN + '\n' + qs conv = conv_templates[args.conv_mode].copy() conv.append_message(conv.roles[0], qs) 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).cuda() image = Image.open(os.path.join(args.image_folder, image_file)) image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0] stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids) with torch.inference_mode(): output_ids = model.generate( input_ids, images=image_tensor.unsqueeze(0).half().cuda(), do_sample=True if args.temperature > 0 else False, temperature=args.temperature, top_p=args.top_p, num_beams=args.num_beams, # no_repeat_ngram_size=3, max_new_tokens=1024, use_cache=True) input_token_len = input_ids.shape[1] n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item() if n_diff_input_output > 0: print(f'[Warning] {n_diff_input_output} output_ids are not the same as the input_ids') outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0] outputs = outputs.strip() if outputs.endswith(stop_str): outputs = outputs[:-len(stop_str)] outputs = outputs.strip() ans_id = shortuuid.uuid() ans_file.write(json.dumps({"question_id": idx, "prompt": cur_prompt, "text": outputs, "answer_id": ans_id, "model_id": model_name, "metadata": {}}) + "\n") ans_file.flush() ans_file.close() if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--model-path", type=str, default="facebook/opt-350m") parser.add_argument("--model-base", type=str, default=None) parser.add_argument("--image-folder", type=str, default="") parser.add_argument("--question-file", type=str, default="tables/question.jsonl") parser.add_argument("--answers-file", type=str, default="answer.jsonl") parser.add_argument("--conv-mode", type=str, default="llava_v1") parser.add_argument("--num-chunks", type=int, default=1) parser.add_argument("--chunk-idx", type=int, default=0) parser.add_argument("--temperature", type=float, default=0.2) parser.add_argument("--top_p", type=float, default=None) parser.add_argument("--num_beams", type=int, default=1) args = parser.parse_args() eval_model(args) ```
# Citation If you find the following model helpful, please consider citing our paper! **BibTeX:** ```bibtex @misc{lee2024prometheusvision, title={Prometheus-Vision: Vision-Language Model as a Judge for Fine-Grained Evaluation}, author={Seongyun Lee and Seungone Kim and Sue Hyun Park and Geewook Kim and Minjoon Seo}, year={2024}, eprint={2401.06591}, archivePrefix={arXiv}, primaryClass={cs.CL} } ```