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Model: HIT-TMG/EviOmni-nq_train-qwen2.5-1.5B
Source: Original Platform

README.md

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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.
+![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}, 
+    }