53 lines
1.6 KiB
Markdown
53 lines
1.6 KiB
Markdown
|
---
|
||
|
|
license: apache-2.0
|
||
|
|
language:
|
||
|
|
- en
|
||
|
|
base_model:
|
||
|
|
- Qwen/Qwen2.5-Math-1.5B
|
||
|
|
pipeline_tag: question-answering
|
||
|
|
library_name: transformers
|
||
|
|
tags:
|
||
|
|
- verifier
|
||
|
|
---
|
||
|
|
|
||
|
|
This is the verifier we used in [General Reasoner](https://github.com/TIGER-AI-Lab/General-Reasoner).
|
||
|
|
|
||
|
|
## Usage
|
||
|
|
```python
|
||
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM
|
||
|
|
import torch
|
||
|
|
|
||
|
|
# Replace with your model path
|
||
|
|
model_path = "TIGER-Lab/general-verifier"
|
||
|
|
|
||
|
|
# Load tokenizer and model
|
||
|
|
tokenizer = AutoTokenizer.from_pretrained(model_path)
|
||
|
|
model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16).cuda()
|
||
|
|
|
||
|
|
# Example inputs
|
||
|
|
question = "Factor the following quadratic: $3 x^3+\frac{69 x^2}{2}-36 x-810$"
|
||
|
|
ground_truth = "\\frac{3(2x-9)(x+6)(x+10)}{2}"
|
||
|
|
student_answer = "\\frac{3}{2}(x+6)(2x-9)(x+10)"
|
||
|
|
|
||
|
|
# Create prompt
|
||
|
|
prompt = (
|
||
|
|
f"User: ### Question: {question}\n\n"
|
||
|
|
f"### Ground Truth Answer: {ground_truth}\n\n"
|
||
|
|
f"### Student Answer: {student_answer}\n\n"
|
||
|
|
"For the above question, please verify if the student's answer is equivalent to the ground truth answer.\n"
|
||
|
|
"Do not solve the question by yourself; just check if the student's answer is equivalent to the ground truth answer.\n"
|
||
|
|
"If the student's answer is correct, output \"Final Decision: Yes\". If the student's answer is incorrect, output \"Final Decision: No\". Assistant:"
|
||
|
|
)
|
||
|
|
|
||
|
|
# Tokenize and generate
|
||
|
|
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
|
||
|
|
outputs = model.generate(
|
||
|
|
**inputs,
|
||
|
|
max_new_tokens=1024,
|
||
|
|
temperature=0.0,
|
||
|
|
do_sample=False
|
||
|
|
)
|
||
|
|
|
||
|
|
# Decode and print output
|
||
|
|
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
||
|
|
```
|