Instructions to use madhu-sagar123/math-llama-problemMath-Llama3.5K with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use madhu-sagar123/math-llama-problemMath-Llama3.5K with Transformers:

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

pipe = pipeline("text-generation", model="madhu-sagar123/math-llama-problemMath-Llama3.5K")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("madhu-sagar123/math-llama-problemMath-Llama3.5K", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use madhu-sagar123/math-llama-problemMath-Llama3.5K 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 madhu-sagar123/math-llama-problemMath-Llama3.5K:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf madhu-sagar123/math-llama-problemMath-Llama3.5K:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf madhu-sagar123/math-llama-problemMath-Llama3.5K:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf madhu-sagar123/math-llama-problemMath-Llama3.5K:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf madhu-sagar123/math-llama-problemMath-Llama3.5K:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf madhu-sagar123/math-llama-problemMath-Llama3.5K:Q4_K_M

Use Docker

docker model run hf.co/madhu-sagar123/math-llama-problemMath-Llama3.5K:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use madhu-sagar123/math-llama-problemMath-Llama3.5K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "madhu-sagar123/math-llama-problemMath-Llama3.5K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "madhu-sagar123/math-llama-problemMath-Llama3.5K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/madhu-sagar123/math-llama-problemMath-Llama3.5K:Q4_K_M

SGLang

How to use madhu-sagar123/math-llama-problemMath-Llama3.5K 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 "madhu-sagar123/math-llama-problemMath-Llama3.5K" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "madhu-sagar123/math-llama-problemMath-Llama3.5K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "madhu-sagar123/math-llama-problemMath-Llama3.5K" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "madhu-sagar123/math-llama-problemMath-Llama3.5K",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use madhu-sagar123/math-llama-problemMath-Llama3.5K with Ollama:
```
ollama run hf.co/madhu-sagar123/math-llama-problemMath-Llama3.5K:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use madhu-sagar123/math-llama-problemMath-Llama3.5K with Docker Model Runner:
```
docker model run hf.co/madhu-sagar123/math-llama-problemMath-Llama3.5K:Q4_K_M
```

Lemonade

How to use madhu-sagar123/math-llama-problemMath-Llama3.5K with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull madhu-sagar123/math-llama-problemMath-Llama3.5K:Q4_K_M

Run and chat with the model

lemonade run user.math-llama-problemMath-Llama3.5K-Q4_K_M

List all available models

lemonade list

Atomic Chat

🧮 LLaMA 3.2 3B Instruct (Unsloth 4-bit) — Finetuned on Rimyy/problemMath-Llama3.5K (GGUF)

This model is a 4-bit GGUF variant of unsloth/llama-3.2-3b-instruct-bnb-4bit, fine-tuned on Rimyy/problemMath-Llama3.5K, a high-quality dataset of math reasoning and problem-solving questions. The model is tailored for math instruction, step-by-step reasoning, and educational applications.

🚨 Designed to reason, not just regurgitate. Small model, big brain.

🧠 Model Details

Feature	Value
Base	`unsloth/llama-3.2-3b-instruct-bnb-4bit`
Finetuned Dataset	`Rimyy/problemMath-Llama3.5K`
Quantization	4-bit GGUF (compatible with llama.cpp/text-generation-webui)
Format	GGUF
Language	English
Instruction Tuned	✅ Yes

📚 Dataset: `Rimyy/problemMath-Llama3.5K`

~3.5K math word problems and reasoning tasks
Emphasizes chain-of-thought (CoT) explanations
Covers arithmetic, algebra, and word problems
Aligns with OpenAI-style "question → step-by-step answer" format

🔧 Quick Usage Example (llama.cpp)

./main -m llama-3.2-3b-math.gguf   --prompt "### Question: What is the value of x if x + 3 = 7?
### Answer:"

Expected output:

To solve for x, subtract 3 from both sides of the equation:
x + 3 = 7
x = 7 - 3
x = 4

Answer: 4

🧪 Usage in Python

from llama_cpp import Llama

llm = Llama(
    model_path="llama-3.2-3b-instruct-math.q4_K.gguf",
    n_ctx=2048,
    n_gpu_layers=32,  # adjust based on your GPU
)

prompt = (
    "### Question: If a rectangle has length 10 and width 5, what is its area?
"
    "### Answer:"
)
response = llm(prompt)
print(response["choices"][0]["text"])

📦 Applications

🤖 Math tutoring agents
📚 AI-driven educational platforms
🧩 RAG pipelines for mathematical queries
📝 Automated solution generators

⚠️ Limitations

Occasional step hallucinations
Not optimized for LaTeX-heavy symbolic math
May struggle on very long multi-step problems

📊 Qualitative Benchmark

Task Type	Performance
Simple Arithmetic	✅ Excellent
One-Step Algebra	✅ Strong
Multi-Step CoT	⚠️ Good (some drift)
Logic Puzzles	⚠️ Mixed

📌 Quantitative benchmarks forthcoming.

🔗 Citation

If you use this model, please cite:

@misc{rimyy2025math,
  author = {Rimyy},
  title = {ProblemMath-Llama3.5K: A Dataset for Math Problem Solving},
  year = {2025},
  url = {https://huggingface.co/datasets/Rimyy/problemMath-Llama3.5K}
}

🙌 Acknowledgements

Meta for LLaMA 3.
Unsloth for the 4-bit instruct base.
Rimyy for an excellent math dataset.
llama.cpp & GGUF community for stellar tooling.

🔢 Small enough to run on your laptop, smart enough to teach algebra.

Downloads last month: 58

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

madhu-sagar123
/

math-llama-problemMath-Llama3.5K

🧮 LLaMA 3.2 3B Instruct (Unsloth 4-bit) — Finetuned on Rimyy/problemMath-Llama3.5K (GGUF)

🧠 Model Details

📚 Dataset: `Rimyy/problemMath-Llama3.5K`

🔧 Quick Usage Example (llama.cpp)

🧪 Usage in Python

📦 Applications

⚠️ Limitations

📊 Qualitative Benchmark

🔗 Citation

🙌 Acknowledgements

Dataset used to train madhu-sagar123/math-llama-problemMath-Llama3.5K

🧮 LLaMA 3.2 3B Instruct (Unsloth 4-bit) — Finetuned on Rimyy/problemMath-Llama3.5K (GGUF)

🧠 Model Details

📚 Dataset: Rimyy/problemMath-Llama3.5K

🔧 Quick Usage Example (llama.cpp)

🧪 Usage in Python

📦 Applications

⚠️ Limitations

📊 Qualitative Benchmark

🔗 Citation

🙌 Acknowledgements

Dataset used to train madhu-sagar123/math-llama-problemMath-Llama3.5K

📚 Dataset: `Rimyy/problemMath-Llama3.5K`