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Model: TIGER-Lab/general-verifier
Source: Original Platform

README.md

@@ -0,0 +1,53 @@
+---
+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))
+```