Create README.md

README.md

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+Usage:
+
+```
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "ScalableMath/llemma-7b-prm-metamath-level-1to3-hf"
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+
+tokenizer = AutoTokenizer.from_pretrained("EleutherAI/llemma_7b")
+
+qa_example = """# Question
+
+Convert the point $(0,3)$ in rectangular coordinates to polar coordinates.  Enter your answer in the form $(r,\theta),$ where $r > 0$ and $0 \le \theta < 2 \pi.$
+
+# Solution
+
+To convert from rectangular coordinates to polar coordinates, we use the formulas $r = \sqrt{x^2 + y^2}$ and $\theta = \arctan\left(\frac{y}{x}\right)$.
+
+In this case, $x = 0$ and $y = 3$, so $r = \sqrt{0^2 + 3^2} = 3$ and $\theta = \arctan\left(\frac{3}{0}\right)$.
+
+Since $\frac{3}{0}$ is undefined, we can say that $\theta$ is undefined.
+However, we know that $\theta$ is an angle, and since $r > 0$, we can say that $\theta$ is any angle that satisfies $0 \le \theta < 2 \pi$.
+
+Therefore, the polar coordinates of the point $(0,3)$ are $\boxed{(3,\theta)}$, where $0 \le \theta < 2 \pi$.
+
+# Answer
+
+(3,\theta)"""
+
+begin_solution_tokens = tokenizer.encode("\n\n# Solution", add_special_tokens=False)[1:]
+scoring_tokens = tokenizer.encode("\n\n", add_special_tokens=False)[1:]
+eos_token = tokenizer.eos_token_id
+
+input_ids = tokenizer.encode(qa_example)
+
+begin_solution_flag = False
+
+candidate_positions = []
+
+for start_idx in range(len(input_ids)):
+    if tuple(input_ids[start_idx:start_idx+len(begin_solution_tokens)]) == tuple(begin_solution_tokens):
+        begin_solution_flag = True
+
+    if begin_solution_flag and tuple(input_ids[start_idx:start_idx+len(scoring_tokens)]) == tuple(scoring_tokens):
+        candidate_positions.append(start_idx)
+
+    if input_ids[start_idx] == eos_token:
+        candidate_positions.append(start_idx)
+        break
+
+# maybe delete the first and the second to last candidate_positions
+# because they are "\n\n" after "# Solution" and after "# Answer"
+del candidate_positions[0]
+del candidate_positions[-2]
+
+input_tensor = torch.tensor([input_ids])
+candidate_positions = torch.tensor(candidate_positions)
+
+with torch.no_grad():
+    logits = model(input_tensor).logits
+    scores =logits.mean(dim=-1)
+    step_scores = scores[0][candidate_positions]
+    step_probs = torch.sigmoid(step_scores)
+
+print(step_probs)
+
+# tensor([0.7264, 0.8152, 0.7827, 0.4709, 0.5181])
+```