Instructions to use QuantFactory/Neumind-Math-7B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Neumind-Math-7B-Instruct-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="QuantFactory/Neumind-Math-7B-Instruct-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Neumind-Math-7B-Instruct-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Neumind-Math-7B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Neumind-Math-7B-Instruct-GGUF",
	filename="Neumind-Math-7B-Instruct.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Neumind-Math-7B-Instruct-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/Neumind-Math-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Neumind-Math-7B-Instruct-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/Neumind-Math-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Neumind-Math-7B-Instruct-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/Neumind-Math-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Neumind-Math-7B-Instruct-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/Neumind-Math-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Neumind-Math-7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Neumind-Math-7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Neumind-Math-7B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Neumind-Math-7B-Instruct-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Neumind-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Neumind-Math-7B-Instruct-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Neumind-Math-7B-Instruct-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/Neumind-Math-7B-Instruct-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Neumind-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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/Neumind-Math-7B-Instruct-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Neumind-Math-7B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Neumind-Math-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Neumind-Math-7B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Neumind-Math-7B-Instruct-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/Neumind-Math-7B-Instruct-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/Neumind-Math-7B-Instruct-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/Neumind-Math-7B-Instruct-GGUF to start chatting

Pi new

How to use QuantFactory/Neumind-Math-7B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Neumind-Math-7B-Instruct-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "QuantFactory/Neumind-Math-7B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use QuantFactory/Neumind-Math-7B-Instruct-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf QuantFactory/Neumind-Math-7B-Instruct-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default QuantFactory/Neumind-Math-7B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use QuantFactory/Neumind-Math-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Neumind-Math-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Neumind-Math-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Neumind-Math-7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Neumind-Math-7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Neumind-Math-7B-Instruct-GGUF

This is quantized version of prithivMLmods/Neumind-Math-7B-Instruct created using llama.cpp

Original Model Card

Neumind-Math-7B-Instruct Model Files

The Neumind-Math-7B-Instruct is a fine-tuned model based on Qwen2.5-7B-Instruct, optimized for mathematical reasoning, step-by-step problem-solving, and instruction-based tasks in the mathematics domain. The model is designed for applications requiring structured reasoning, numerical computations, and mathematical proof generation.

File Name	Size	Description	Upload Status
`.gitattributes`	1.57 kB	Git attributes configuration file	Uploaded
`README.md`	265 Bytes	ReadMe file with basic information	Updated
`added_tokens.json`	657 Bytes	Additional token definitions	Uploaded
`config.json`	860 Bytes	Model configuration settings	Uploaded
`generation_config.json`	281 Bytes	Generation settings	Uploaded
`merges.txt`	1.82 MB	Tokenizer merge rules	Uploaded
`pytorch_model-00001-of-00004.bin`	4.88 GB	Model shard 1 of 4	Uploaded (LFS)
`pytorch_model-00002-of-00004.bin`	4.93 GB	Model shard 2 of 4	Uploaded (LFS)
`pytorch_model-00003-of-00004.bin`	4.33 GB	Model shard 3 of 4	Uploaded (LFS)
`pytorch_model-00004-of-00004.bin`	1.09 GB	Model shard 4 of 4	Uploaded (LFS)
`pytorch_model.bin.index.json`	28.1 kB	Model index JSON	Uploaded
`special_tokens_map.json`	644 Bytes	Mapping of special tokens	Uploaded
`tokenizer.json`	11.4 MB	Tokenizer configuration	Uploaded (LFS)
`tokenizer_config.json`	7.73 kB	Additional tokenizer settings	Uploaded
`vocab.json`	2.78 MB	Vocabulary for tokenization	Uploaded

Key Features:

Mathematical Reasoning:
Specifically fine-tuned for solving mathematical problems, including arithmetic, algebra, calculus, and geometry.
Step-by-Step Problem Solving:
Provides detailed, logical solutions for complex mathematical tasks and demonstrates problem-solving methodologies.
Instructional Applications:
Tailored for use in educational settings, such as tutoring systems, math content creation, and interactive learning tools.

Training Details:

Base Model: Qwen2.5-7B
Dataset: Trained on AI-MO/NuminaMath-CoT, a large dataset of mathematical problems and chain-of-thought (CoT) reasoning. The dataset contains 860k problems across various difficulty levels, enabling the model to tackle a wide spectrum of mathematical tasks.

Capabilities:

Complex Problem Solving:
Solves a wide range of mathematical problems, from basic arithmetic to advanced calculus and algebraic equations.
Chain-of-Thought Reasoning:
Excels in step-by-step logical reasoning, making it suitable for tasks requiring detailed explanations.
Instruction-Based Generation:
Ideal for generating educational content, such as worked examples, quizzes, and tutorials.

Usage Instructions:

Model Setup:
Download all model shards and the associated configuration files. Ensure the files are correctly placed for seamless loading.
Inference:
Load the model using frameworks like PyTorch and Hugging Face Transformers. Ensure the pytorch_model.bin.index.json file is in the same directory for shard-based loading.
Customization:
Adjust generation parameters using generation_config.json to optimize outputs for your specific application.

Applications:

Education:
Interactive math tutoring, content creation, and step-by-step problem-solving tools.
Research:
Automated theorem proving and symbolic mathematics.
General Use:
Solving everyday mathematical queries and generating numerical datasets.

Downloads last month: 116

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Neumind-Math-7B-Instruct-GGUF

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(310)

this model

QuantFactory
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Neumind-Math-7B-Instruct-GGUF