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

aashish1904 commited on Dec 13, 2024

Commit

8da45b6

verified ·

1 Parent(s): a5febc3

Upload README.md with huggingface_hub

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README.md +110 -0

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	@@ -0,0 +1,110 @@

+---
+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.
+---