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

Libraries

How to use QuantFactory/math-shepherd-mistral-7b-rl-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/math-shepherd-mistral-7b-rl-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/math-shepherd-mistral-7b-rl-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/math-shepherd-mistral-7b-rl-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/math-shepherd-mistral-7b-rl-GGUF",
	filename="math-shepherd-mistral-7b-rl.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/math-shepherd-mistral-7b-rl-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/math-shepherd-mistral-7b-rl-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/math-shepherd-mistral-7b-rl-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/math-shepherd-mistral-7b-rl-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/math-shepherd-mistral-7b-rl-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/math-shepherd-mistral-7b-rl-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/math-shepherd-mistral-7b-rl-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/math-shepherd-mistral-7b-rl-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/math-shepherd-mistral-7b-rl-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/math-shepherd-mistral-7b-rl-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/math-shepherd-mistral-7b-rl-GGUF with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/QuantFactory/math-shepherd-mistral-7b-rl-GGUF:Q4_K_M

SGLang

How to use QuantFactory/math-shepherd-mistral-7b-rl-GGUF 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 "QuantFactory/math-shepherd-mistral-7b-rl-GGUF" \
    --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": "QuantFactory/math-shepherd-mistral-7b-rl-GGUF",
		"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 "QuantFactory/math-shepherd-mistral-7b-rl-GGUF" \
        --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": "QuantFactory/math-shepherd-mistral-7b-rl-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use QuantFactory/math-shepherd-mistral-7b-rl-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/math-shepherd-mistral-7b-rl-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/math-shepherd-mistral-7b-rl-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/math-shepherd-mistral-7b-rl-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/math-shepherd-mistral-7b-rl-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/math-shepherd-mistral-7b-rl-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/math-shepherd-mistral-7b-rl-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/math-shepherd-mistral-7b-rl-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/math-shepherd-mistral-7b-rl-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/math-shepherd-mistral-7b-rl-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.math-shepherd-mistral-7b-rl-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/math-shepherd-mistral-7b-rl-GGUF

This is quantized version of peiyi9979/math-shepherd-mistral-7b-rl created using llama.cpp

Original Model Card

Mistral-7b-MetaMATH with step-by-step PPO in Math-Shepherd.

Base model: mistral-7b-sft.

PRM: math-shepherd-mistral-7b-prm.

PPO training set: questions in MetaMATH [1].

Pass@1:

GSM8K: 84.1
MATH: 33.0

Input: only the math problem, without any system prompt, e.g.,

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?

Output: Step-by-step solutions with a special step tag ки, e.g.,

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

[1] MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models.

Downloads last month: 107

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Paper for QuantFactory/math-shepherd-mistral-7b-rl-GGUF

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

Paper • 2312.08935 • Published Dec 14, 2023 • 4