Create README.md

README.md

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+---
+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:** 
+- **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)
+# 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 13B sized VLM on [this page](https://huggingface.co/kaist-ai/prometheus-vision-13b-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: 
+```
+Also, we use the following output format. During inference, you could parse the score by splitting the number that is generated next to the [RESULT] phrase.
+```
+{orig_feedback}
+[RESULT] {orig_score}
+```
+## 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
+<details>
+<summary> Click to expand </summary>
+  
+```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-7b-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)
+
+```
+</details>
+
+# 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}
+}
+```