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---
license: mit
metrics:
- f1
- exact_match
base_model:
- Qwen/Qwen2.5-1.5B-Instruct
pipeline_tag: question-answering
library_name: transformers
---
# EviOmni-nq_train-1.5B
## 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-1.5B"
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.
## 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},
} |