---
license: mit
metrics:
- f1
- exact_match
base_model:
- Qwen/Qwen2.5-7B-Instruct
pipeline_tag: question-answering
library_name: transformers
---
# EviOmni-nq_train-7B

## Introduction

EviOmni is a rational evidence extraction model. Compared to vanilla evidence extraction models, EviOmni demonstrates the superiority in terms of performance, generalization, efficiency, and robustness.

## Requirements

The code of EviOmni has been in the latest Huggingface `transformers` and we advise you to use the latest version of `transformers`.

With `transformers<4.37.0`, you will encounter the following error:

    KeyError: 'qwen2'

## Quickstart

    from transformers import AutoModelForCausalLM, AutoTokenizer
    from transformers import StoppingCriteria, StoppingCriteriaList
    import re
    
    class MultiTokenStoppingCriteria(StoppingCriteria):
        def __init__(self, stop_ids, device):
            self.stop_ids = stop_ids
            self.stop_len = len(stop_ids)
    
        def __call__(self, input_ids, scores, **kwargs):
            if len(input_ids[0]) >= self.stop_len:
                last_tokens = input_ids[0][-self.stop_len:].tolist()
                return last_tokens == self.stop_ids
            return False
    
    model_name = "HIT-TMG/EviOmni-nq_train-7B"
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        torch_dtype="auto",
        device_map="auto"
    )
    tokenizer = AutoTokenizer.from_pretrained(model_name)
    
    prompt = open("eviomni_prompt", "r").read()
    question = "..."
    passages = "..."
    instruction = prompt.format(question=question, passages=passages)
    
    messages = [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": instruction}
    ]
    
    stop_token = "</extract>\n\n"
    stop_ids = tokenizer.encode(stop_token, add_special_tokens=False)
    
    stopping_criteria = StoppingCriteriaList([
        MultiTokenStoppingCriteria(stop_ids, model.device)
    ])
    
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=True
    )
    model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
    
    generated_ids = model.generate(
        **model_inputs,
        max_new_tokens=512,
        stopping_criteria=stopping_criteria
    )
    generated_ids = [
        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
    ]
    
    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
    match = re.search(r"<extract>(.*?)</extract>", response, re.DOTALL)
    evidence = match.group(1).strip()

## Performance
Main results.
![main](./img/main_results.jpg)


## Citation

If you find our work helpful, feel free to give us a cite.

    @misc{EviOmni,
          title={Learning to Extract Rational Evidence via Reinforcement Learning for Retrieval-Augmented Generation}, 
          author={Xinping Zhao and Shouzheng Huang and Yan Zhong and Xinshuo Hu and Meishan Zhang and Baotian Hu and Min Zhang},
          year={2025},
          eprint={2507.15586},
          archivePrefix={arXiv},
          primaryClass={cs.CL},
          url={https://arxiv.org/abs/2507.15586}, 
    }