Instructions to use peiyi9979/math-shepherd-mistral-7b-prm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use peiyi9979/math-shepherd-mistral-7b-prm with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="peiyi9979/math-shepherd-mistral-7b-prm")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("peiyi9979/math-shepherd-mistral-7b-prm")
model = AutoModelForCausalLM.from_pretrained("peiyi9979/math-shepherd-mistral-7b-prm")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use peiyi9979/math-shepherd-mistral-7b-prm with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "peiyi9979/math-shepherd-mistral-7b-prm"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "peiyi9979/math-shepherd-mistral-7b-prm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/peiyi9979/math-shepherd-mistral-7b-prm

SGLang

How to use peiyi9979/math-shepherd-mistral-7b-prm with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "peiyi9979/math-shepherd-mistral-7b-prm" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "peiyi9979/math-shepherd-mistral-7b-prm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "peiyi9979/math-shepherd-mistral-7b-prm" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "peiyi9979/math-shepherd-mistral-7b-prm",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use peiyi9979/math-shepherd-mistral-7b-prm with Docker Model Runner:
```
docker model run hf.co/peiyi9979/math-shepherd-mistral-7b-prm
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Process reward model (mistral-7b) used in Math-Shepherd.

Input: question + step-by-step solutions with a special step tag ки, e.g.,

Janet\u2019s 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\nStep 2: She eats three for breakfast every morning, so she has 16 - 3 = 13 eggs left. ки\nStep 3: She bakes muffins for her friends every day with four eggs, so she has 13 - 4 = 9 eggs left. ки\nStep 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 logits. You need to post-process it to achieve the score of each step.

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\u2019s 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\nStep 2: She eats three for breakfast every morning, so she has 16 - 3 = 13 eggs left. ки\nStep 3: She bakes muffins for her friends every day with four eggs, so she has 13 - 4 = 9 eggs left. ки\nStep 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\nStep 2: She eats three for breakfast every morning, so she has 16 - 3 = 13 eggs left. ки\nStep 3: She bakes muffins for her friends every day with four eggs, so she has 13 - 4 = 9 eggs left. ки\nStep 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])

Downloads last month: 2,333

Model tree for peiyi9979/math-shepherd-mistral-7b-prm

Adapters

26 models

Finetunes

2 models

Spaces using peiyi9979/math-shepherd-mistral-7b-prm 2

Paper for peiyi9979/math-shepherd-mistral-7b-prm

Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations

Paper • 2312.08935 • Published Dec 14, 2023 • 4