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

Libraries

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

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Neumind-Math-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Neumind-Math-7B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

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

Install from pip and serve model

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

Use Docker

docker model run hf.co/prithivMLmods/Neumind-Math-7B-Instruct

SGLang

How to use prithivMLmods/Neumind-Math-7B-Instruct 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 "prithivMLmods/Neumind-Math-7B-Instruct" \
    --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": "prithivMLmods/Neumind-Math-7B-Instruct",
		"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 "prithivMLmods/Neumind-Math-7B-Instruct" \
        --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": "prithivMLmods/Neumind-Math-7B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

Neumind-Math-7B-Instruct

File size: 4,722 Bytes

---
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
---

### 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.
---