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 / README.md

prithivMLmods

Update README.md

b45d894 verified over 1 year ago

preview code

Raw

History Blame Contribute Delete

4.72 kB

	---
	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.
	---