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", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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

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

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 serve -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 serve -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

Atomic Chat new

OpenClaw new

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

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "QuantFactory/Neumind-Math-7B-Instruct-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

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

Neumind-Math-7B-Instruct-GGUF / README.md

aashish1904

Upload README.md with huggingface_hub

8da45b6 verified over 1 year ago

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	---

	license: creativeml-openrail-m
	datasets:
	- AI-MO/NuminaMath-CoT
	language:
	- en
	base_model:
	- Qwen/Qwen2.5-7B-Instruct
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- Qwen2.5
	- Ollama
	- Neumind
	- Math
	- Instruct
	- safetensors
	- pytorch
	- trl

	---

	[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)


	# QuantFactory/Neumind-Math-7B-Instruct-GGUF
	This is quantized version of [prithivMLmods/Neumind-Math-7B-Instruct](https://huggingface.co/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:

	1. Mathematical Reasoning:
	Specifically fine-tuned for solving mathematical problems, including arithmetic, algebra, calculus, and geometry.

	2. Step-by-Step Problem Solving:
	Provides detailed, logical solutions for complex mathematical tasks and demonstrates problem-solving methodologies.

	3. 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](https://huggingface.co/Qwen/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:

	1. Model Setup:
	Download all model shards and the associated configuration files. Ensure the files are correctly placed for seamless loading.

	2. 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.

	3. 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.
	---