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

.gitattributes

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README.md

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+---
+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))
+```

added_tokens.json

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config.json

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+{
+  "_name_or_path": "Qwen/Qwen2.5-Math-1.5B",
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151643,
+  "hidden_act": "silu",
+  "hidden_size": 1536,
+  "initializer_range": 0.02,
+  "intermediate_size": 8960,
+  "max_position_embeddings": 4096,
+  "max_window_layers": 21,
+  "model_type": "qwen2",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 28,
+  "num_key_value_heads": 2,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.48.2",
+  "use_cache": false,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

configuration.json

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+{"framework": "pytorch", "task": "question-answering", "allow_remote": true}

generation_config.json

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+  "eos_token_id": 151643,
+  "max_new_tokens": 2048,
+  "transformers_version": "4.48.2"
+}

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special_tokens_map.json

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+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

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tokenizer_config.json

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+}