Instructions to use Denisijcu/vertex-math with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Denisijcu/vertex-math with Transformers:

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Denisijcu/vertex-math", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Denisijcu/vertex-math 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 Denisijcu/vertex-math:Q8_0
# Run inference directly in the terminal:
llama cli -hf Denisijcu/vertex-math:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Denisijcu/vertex-math:Q8_0
# Run inference directly in the terminal:
llama cli -hf Denisijcu/vertex-math:Q8_0

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 Denisijcu/vertex-math:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf Denisijcu/vertex-math:Q8_0

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 Denisijcu/vertex-math:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Denisijcu/vertex-math:Q8_0

Use Docker

docker model run hf.co/Denisijcu/vertex-math:Q8_0

LM Studio
Jan
Ollama
How to use Denisijcu/vertex-math with Ollama:
```
ollama run hf.co/Denisijcu/vertex-math:Q8_0
```
Unsloth Desktop

How to use Denisijcu/vertex-math with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Denisijcu/vertex-math:Q8_0

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Denisijcu/vertex-math:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Denisijcu/vertex-math with Docker Model Runner:
```
docker model run hf.co/Denisijcu/vertex-math:Q8_0
```

Lemonade

How to use Denisijcu/vertex-math with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Denisijcu/vertex-math:Q8_0

Run and chat with the model

lemonade run user.vertex-math-Q8_0

List all available models

lemonade list

Hermes Agent

How to use Denisijcu/vertex-math with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Denisijcu/vertex-math:Q8_0

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 Denisijcu/vertex-math:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Denisijcu/vertex-math with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Denisijcu/vertex-math:Q8_0

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 "Denisijcu/vertex-math:Q8_0" \
  --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"

Vertex-Math-0.5B (Q8_0)

Vertex-Math-0.5B is a compact mathematical reasoning language model fine-tuned from Qwen2.5-0.5B-Instruct using QLoRA. Optimized specifically for edge devices, offline mobile assistants, and resource-constrained environments, this model delivers rigorous step-by-step mathematical problem-solving while maintaining an ultra-lightweight footprint.

Model Details

Base Model: Qwen/Qwen2.5-0.5B-Instruct
Architecture: Qwen2 (0.5B parameters)
Training Dataset: HuggingFaceH4/MATH-500 (Filtered and token-length audited)
Quantization: Q8_0 (GGUF)
Developer: Denis Sanchez Leyva (Vertex Coders LLC)

Training Configuration & Hyperparameters

Method: QLoRA (Rank r=8, Alpha 16)
Target Modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
Hardware: Local training on NVIDIA GTX 1650 (4GB VRAM)
Max Sequence Length: 1024 tokens
Optimizer: Paged AdamW 8-bit
Loss Function: Fused Cross-Entropy Loss
Epochs: 2 (80 steps)

Prompt Format

The model is trained using the ChatML template with a specialized system prompt enforcing step-by-step deductive reasoning:

<|im_start|>system
You are a compact mathematical reasoning engine optimized for mobile edge devices. Solve problems step by step with precision and conclude with '#### <final_answer>'.<|im_end|>
<|im_start|>user
[Your math problem here]<|im_end|>
<|im_start|>assistant

Downloads last month: 6

GGUF

Model size

0.5B params

Architecture

qwen2

Hardware compatibility

8-bit

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Model tree for Denisijcu/vertex-math

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Qwen/Qwen2.5-0.5B

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Qwen/Qwen2.5-0.5B-Instruct

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