Math Shepherd Mistral 7b Prm

🚀 Process Reward Model (mistral - 7b)

This is a process reward model (mistral - 7b) used in Math - Shepherd. It evaluates the step - by - step solutions of math problems and outputs logits, which can be post - processed to get scores for each step.

🚀 Quick Start

Input

The input consists of a question and step - by - step solutions with a special step tag ки. For example:

Janet’s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes .... ? Step 1: Janet's ducks lay 16 eggs per day. ки
Step 2: She eats three for breakfast every morning, so she has 16 - 3 = 13 eggs left. ки
Step 3: She bakes muffins for her friends every day with four eggs, so she has 13 - 4 = 9 eggs left. ки
Step 4: She sells the remainder at the farmers' market daily for $2 per fresh duck egg, so she makes 9 * $2 = $18 every day at the farmers' market. The answer is: 18 ки

Output

The output is the logits. You need to post - process it to achieve the score of each step.

💻 Usage Examples

Basic Usage

from transformers import AutoTokenizer
from transformers import AutoModelForCausalLM
import torch

good_token = '+'
bad_token = '-'
step_tag = 'ки'

tokenizer = AutoTokenizer.from_pretrained('peiyi9979/math-shepherd-mistral-7b-prm')
candidate_tokens = tokenizer.encode(f"{good_token} {bad_token}")[1:] # [648, 387]
step_tag_id = tokenizer.encode(f"{step_tag}")[-1] # 12902
model = AutoModelForCausalLM.from_pretrained('peiyi9979/math-shepherd-mistral-7b-prm').eval()

question = """Janet’s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?"""
output1 = """Step 1: Janet's ducks lay 16 eggs per day. ки
Step 2: She eats three for breakfast every morning, so she has 16 - 3 = 13 eggs left. ки
Step 3: She bakes muffins for her friends every day with four eggs, so she has 13 - 4 = 9 eggs left. ки
Step 4: She sells the remainder at the farmers' market daily for $2 per fresh duck egg, so she makes 9 * $2 = $18 every day at the farmers' market. The answer is: 18 ки""" # 18 is right
output2 = """Step 1: Janet's ducks lay 16 eggs per day. ки
Step 2: She eats three for breakfast every morning, so she has 16 - 3 = 13 eggs left. ки
Step 3: She bakes muffins for her friends every day with four eggs, so she has 13 - 4 = 9 eggs left. ки
Step 4: She sells the remainder at the farmers' market daily for $2 per fresh duck egg, so she makes 9 * $2 = $17 every day at the farmers' market. The answer is: 17 ки""" # 17 is wrong

for output in [output1, output2]:
    input_for_prm = f"{question} {output}"
    input_id = torch.tensor([tokenizer.encode(input_for_prm)])

    with torch.no_grad():
        logits = model(input_id).logits[:,:,candidate_tokens]
        scores = logits.softmax(dim=-1)[:,:,0] 
        step_scores = scores[input_id == step_tag_id]
        print(step_scores)
        
# tensor([0.9955, 0.9958, 0.9983, 0.9957])
# tensor([0.9955, 0.9958, 0.9983, 0.0240])