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Model: KbsdJames/Omni-Judge Source: Original Platform
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README.md
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---
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license: apache-2.0
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library_name: transformers
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---
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# Omni-Judge
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## Introduction
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Omni-Judge is an open-source mathematical evaluation model designed to assess whether a solution generated by a model is correct given a problem and a standard answer. Due to the complexity of high-level mathematical problems and their solutions, designing rule-based evaluation methods can be challenging. Omni-Judge, similar to GPT-4-as-a-judge, offers automated assessment with greater efficiency and lower cost. For utilization details, please refer to [this section](#Quickstart).
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Omni-Judge can be applied to various mathematical reasoning benchmarks, such as our proposed [Omni-MATH](https://omni-math.github.io/).
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## Model Details
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Omni-Judge builds on the `meta-llama/Llama-3.1-8B-Instruct`, incorporating GPT-4o evaluation data for instruction tuning. The training dataset comprises 17,618 examples, with a total of 2 epochs. Omni-Judge's performance is closely aligned with GPT-4o. We created an internal evaluation set using queries not previously seen by the model, consisting of 2,220 test samples. The agreement rate between Omni-Judge and GPT-4o evaluations is approximately 91%.
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## Quickstart
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# load the model and tokenizer
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model = AutoModelForCausalLM.from_pretrained(
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"KbsdJames/Omni-Judge",
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device_map="auto",
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torch_dtype=torch.bfloat16,
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)
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tokenizer = AutoTokenizer.from_pretrained(
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"KbsdJames/Omni-Judge",
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trust_remote_code=True
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)
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# set terminators for decoding
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terminators = [
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tokenizer.eos_token_id,
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tokenizer.convert_tokens_to_ids("<|eot_id|>")
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]
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question = "Compute the smallest positive integer $n$ for which $\\sqrt{100+\\sqrt{n}}+\\sqrt{100-\\sqrt{n}}$ is an integer."
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reference_answer = "6156"
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student_solution = "Let's approach this step-by-step:\n\n1) Let's denote $\\sqrt{100+\\sqrt{n}}+\\sqrt{100-\\sqrt{n}} = k$, where k is an integer.\n\n2) Square both sides:\n $(\\sqrt{100+\\sqrt{n}}+\\sqrt{100-\\sqrt{n}})^2 = k^2$\n\n3) Expand the left side:\n $100+\\sqrt{n} + 100-\\sqrt{n} + 2\\sqrt{(100+\\sqrt{n})(100-\\sqrt{n})} = k^2$\n\n4) Simplify:\n $200 + 2\\sqrt{10000-n} = k^2$\n\n5) Rearrange:\n $\\sqrt{10000-n} = \\frac{k^2-200}{2}$\n\n6) For this to be true, $\\frac{k^2-200}{2}$ must be a perfect square. Let's call it $m^2$:\n $\\frac{k^2-200}{2} = m^2$\n\n7) Multiply both sides by 2:\n $k^2-200 = 2m^2$\n\n8) Rearrange:\n $k^2 = 2m^2 + 200$\n\n9) For the smallest positive n, we need the smallest k > 14 (since $\\sqrt{100+\\sqrt{n}}+\\sqrt{100-\\sqrt{n}} > 2\\sqrt{100} = 20$) that makes this equation true.\n\n10) Testing values:\n For k = 15: $15^2 = 225 = 2m^2 + 200$\n $25 = 2m^2$\n $m^2 = 12.5$ (not an integer)\n\n For k = 16: $16^2 = 256 = 2m^2 + 200$\n $56 = 2m^2$\n $m^2 = 28$ (integer)\n\n11) So, the smallest valid k is 16, and m = $\\sqrt{28}$\n\n12) Now, going back to step 5:\n $\\sqrt{10000-n} = \\frac{16^2-200}{2} = 28$\n\n13) Solve for n:\n $10000-n = 28^2 = 784$\n $n = 10000 - 784 = 9216$\n\nTherefore, the smallest positive integer n for which $\\sqrt{100+\\sqrt{n}}+\\sqrt{100-\\sqrt{n}}$ is an integer is 9216."
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# pre-process
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formatted_context = tokenizer.get_context(
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question,
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reference_answer,
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student_solution,
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)
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model_inputs = tokenizer(formatted_context, return_tensors="pt")
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input_ids = model_inputs["input_ids"]
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attention_mask = model_inputs["attention_mask"]
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# do inference
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pred = model.generate(
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input_ids=input_ids.to(model.device),
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attention_mask=attention_mask.to(model.device),
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do_sample = False,
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num_return_sequences = 1,
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max_new_tokens = 300,
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)[0].cpu().tolist()
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# post-process
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pred = pred[len(input_ids[0].cpu().tolist()):]
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for terminator in terminators:
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if terminator in pred:
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pred = pred[:pred.index(terminator)]
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response = tokenizer.decode(pred, skip_special_tokens=True)
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pred_truth = tokenizer.parse_response(response)
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# if response parsing fails, the answer/judgement/justification will be None,
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# which we consider as errors in prediction.
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# in this case, using multiple sampling may help.
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print("answer:", pred_truth["answer"])
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# >>> answer: 9216
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print("judgement:", pred_truth["judgement"])
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# >>> judgement: FALSE
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print("justification:", pred_truth["justification"])
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# >>> justification: The student's answer of 9216 does not match the reference answer of 6156. The student's solution involves a detailed process of finding the smallest positive integer n that satisfies the given condition, but the final result is incorrect. The discrepancy indicates that the student's answer does not share the same meaning as the reference answer.
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```
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## Evaluation
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Given GPT-4o judgement as the golden results, we report the performance of Omni-Judge.
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For a fair comparison, the questions for train and test are different.
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The results are shown below:
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| Source | Success of Parsing | Consistency |
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| :-----------------------------: | :----------------: | :---------: |
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| MetaLlama-3.1-70B-instruct | 99.76 | 82.19 |
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| DeepSeek-Coder-V2 | 100 | 94.01 |
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| Qwen2.5-MATH-7b-Instruct | 100 | 90.69 |
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| OpenAI o1-preview | 99.78 | 91.28 |
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| OpenAI o1-mini | 100 | 91.78 |
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| Mathstral-7B-v0.1 | 100 | 95.79 |
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| NuminaMATH-72B-COT | 100 | 90.44 |
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| Qwen2.5-MATH-72b-Instruct | 100 | 93.30 |
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| All | 99.94 | 91.26 |
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## Citation
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If you find our work interesting and meaningful, welcome to give a star to [our repo](https://github.com/KbsdJames/Omni-MATH) and cite our paper.
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```
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@misc{gao2024omnimathuniversalolympiadlevel,
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title={Omni-MATH: A Universal Olympiad Level Mathematic Benchmark For Large Language Models},
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author={Bofei Gao and Feifan Song and Zhe Yang and Zefan Cai and Yibo Miao and Qingxiu Dong and Lei Li and Chenghao Ma and Liang Chen and Runxin Xu and Zhengyang Tang and Benyou Wang and Daoguang Zan and Shanghaoran Quan and Ge Zhang and Lei Sha and Yichang Zhang and Xuancheng Ren and Tianyu Liu and Baobao Chang},
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year={2024},
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eprint={2410.07985},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2410.07985},
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}
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```
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config.json
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"_name_or_path": "KbsdJames/Omni-Judge",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 8.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.2",
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"use_cache": true,
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"vocab_size": 128256
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}
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"model.layers.6.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.6.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.6.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.6.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.6.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.6.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.7.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.down_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.post_attention_layernorm.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.k_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.o_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.q_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.8.self_attn.v_proj.weight": "model-00001-of-00004.safetensors",
|
||||
"model.layers.9.input_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.down_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|eot_id|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<|eot_id|>"
|
||||
}
|
||||
98
tokenization_omnijudge.py
Normal file
98
tokenization_omnijudge.py
Normal file
@@ -0,0 +1,98 @@
|
||||
from transformers import PreTrainedTokenizerFast
|
||||
|
||||
class OmniJudgeTokenizer(PreTrainedTokenizerFast):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
|
||||
def get_context(
|
||||
self,
|
||||
question,
|
||||
reference_answer,
|
||||
student_solution,
|
||||
):
|
||||
context = [
|
||||
{
|
||||
"role": "system",
|
||||
"content": "You are an experienced teacher in the field of MATHEMATICS."
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "# OBJECTIVE #\nYou are tasked with evaluating the correctness of a student's answer. Below, you are provided with a problem, a reference answer, and a student's answer. You should assess whether the student's answer captures the same meaning as the reference answer, even when expressed with different wording or format.\n\nYour tasks include:\nA. Identify Mathematical or Notational Equivalence.\nB. Conclude with a brief explanation as to why the student's output is correct or incorrect.\n\n# RESPONSE: MARKDOWN REPORT #\n## Student Final Answer\n[Extract the student's final answer, which is enclosed in \"\\\\boxed{}\".]\n## Equivalence Judgement\n[Whether the student's answer share the same meaning with the reference answer. (TRUE or FALSE)]\n## Justification\n[Conclude with a brief explanation as to why the student's answer is correct or incorrect.]\n\n\n# ATTENTION #\n - The reference answer is ALWAYS correct. You should carefully judge whether the student gives the same answer as reference answer.\n - The answer is FALSE even if the student's final answer almost correct with a minor mistakes.\n - The answer is contained within the \"boxed\" section, so you can focus solely on comparing the content in the student's answer box with the reference answer, without needing to consider the intermediate steps.\n - Add \"=== report over ===\" at the end of the report."
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "OK."
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "**Question**:\n{}\n\n**Reference Answer**\n{}\n\n**Student Solution**:\n{}\n\n".format(
|
||||
"The numbers $1447$ , $1005$ and $1231$ have something in common: each is a $4$ -digit number beginning with $1$ that has exactly two identical digits. How many such numbers are there?",
|
||||
"432",
|
||||
"To determine how many 4-digit numbers beginning with 1 have exactly two identical digits, we need to consider the structure of these numbers. A number of this form can be represented as \\(1abc\\), where \\(a\\), \\(b\\), and \\(c\\) are digits from 0 to 9, and exactly one of the digits \\(a\\), \\(b\\), or \\(c\\) is 1, while the other two are different from each other and different from 1.\n\nWe can break down the problem into the following steps:\n\n1. **Choose the digit that is 1**: There are 3 positions (a, b, c) where the digit 1 can be placed. So, there are 3 choices for the position of the digit 1.\n\n2. **Choose the other two digits**: The remaining two digits must be different from each other and different from 1. Therefore, we have 9 choices for the first of these two digits (0-9 except 1) and 8 choices for the second digit (0-9 except 1 and the first digit). This gives us \\(9 \\times 8 = 72\\) choices for the other two digits.\n\nCombining these choices, the total number of 4-digit numbers beginning with 1 that have exactly two identical digits is:\n\\[3 \\times 72 = 216.\\]\n\nThus, the number of such numbers is \\(\\boxed{216}\\).",
|
||||
)
|
||||
},
|
||||
{
|
||||
"role": "assistant",
|
||||
"content": "## Student Final Answer\n{}\n\n## Equivalence Judgement\n{}\n\n## Justification\n{}\n\n=== report over ===".format(
|
||||
"216",
|
||||
"FALSE",
|
||||
"The student's answer of 216 is incorrect in the context of the problem, which asks for the total count of 4-digit numbers beginning with 1 that have exactly two identical digits. The reference answer is 432.\nIn the student's solution, they consider only cases where the digit '1' is one of the identical digits. However, the problem also includes the scenario where the identical digits could be different from '1'. Thus, the student's calculation does not account for all valid configurations. The discrepancy in figures indicates that the student's answer does not share the same meaning as the reference answer.",
|
||||
)
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": "**Question**:\n{}\n\n**Reference Answer**\n{}\n\n**Student Solution**:\n{}\n\n".format(
|
||||
question.strip(),
|
||||
reference_answer.strip(),
|
||||
student_solution.strip(),
|
||||
)
|
||||
},
|
||||
]
|
||||
|
||||
formatted_context = self.apply_chat_template(
|
||||
context,
|
||||
tokenize=False,
|
||||
add_generation_prompt=True
|
||||
) + "## Student Final Answer"
|
||||
|
||||
return formatted_context
|
||||
|
||||
def parse_response(
|
||||
self,
|
||||
response
|
||||
):
|
||||
prediction = {
|
||||
"answer": None,
|
||||
"judgement": None,
|
||||
"justification": None,
|
||||
}
|
||||
|
||||
# parse the response
|
||||
if "## Student Final Answer" in response:
|
||||
first_start = response.index("## Student Final Answer") + len("## Student Final Answer")
|
||||
else:
|
||||
first_start = 0
|
||||
|
||||
if "## Equivalence Judgement" in response:
|
||||
first_end = response.index("## Equivalence Judgement")
|
||||
prediction["answer"] = response[first_start:first_end].strip()
|
||||
|
||||
second_start = response.index("## Equivalence Judgement") + len("## Equivalence Judgement")
|
||||
if "## Justification" in response:
|
||||
second_end = response.index("## Justification")
|
||||
judgement = response[second_start:second_end].strip()
|
||||
if judgement in ["TRUE", "FALSE"]:
|
||||
prediction["judgement"] = judgement
|
||||
|
||||
third_start = response.index("## Justification") + len("## Justification")
|
||||
third_end = len(response)
|
||||
justification = response[third_start:third_end].strip()
|
||||
|
||||
if "=== report over ===" in justification:
|
||||
justification = justification[:justification.index("=== report over ===")].strip()
|
||||
if "##" in justification:
|
||||
justification = justification[:justification.index("##")].strip()
|
||||
|
||||
prediction["justification"] = justification
|
||||
|
||||
return prediction
|
||||
410563
tokenizer.json
Normal file
410563
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2069
tokenizer_config.json
Normal file
2069
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user