Instructions to use OscarFeMa/ExpertiaMath-Q4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use OscarFeMa/ExpertiaMath-Q4 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 OscarFeMa/ExpertiaMath-Q4:F16
# Run inference directly in the terminal:
llama cli -hf OscarFeMa/ExpertiaMath-Q4:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf OscarFeMa/ExpertiaMath-Q4:F16
# Run inference directly in the terminal:
llama cli -hf OscarFeMa/ExpertiaMath-Q4:F16

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 OscarFeMa/ExpertiaMath-Q4:F16
# Run inference directly in the terminal:
./llama-cli -hf OscarFeMa/ExpertiaMath-Q4:F16

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 OscarFeMa/ExpertiaMath-Q4:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OscarFeMa/ExpertiaMath-Q4:F16

Use Docker

docker model run hf.co/OscarFeMa/ExpertiaMath-Q4:F16

LM Studio
Jan
Ollama
How to use OscarFeMa/ExpertiaMath-Q4 with Ollama:
```
ollama run hf.co/OscarFeMa/ExpertiaMath-Q4:F16
```
Unsloth Desktop

How to use OscarFeMa/ExpertiaMath-Q4 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OscarFeMa/ExpertiaMath-Q4:F16

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": "OscarFeMa/ExpertiaMath-Q4:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use OscarFeMa/ExpertiaMath-Q4 with Docker Model Runner:
```
docker model run hf.co/OscarFeMa/ExpertiaMath-Q4:F16
```

Lemonade

How to use OscarFeMa/ExpertiaMath-Q4 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OscarFeMa/ExpertiaMath-Q4:F16

Run and chat with the model

lemonade run user.ExpertiaMath-Q4-F16

List all available models

lemonade list

Hermes Agent

How to use OscarFeMa/ExpertiaMath-Q4 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OscarFeMa/ExpertiaMath-Q4:F16

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 OscarFeMa/ExpertiaMath-Q4:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use OscarFeMa/ExpertiaMath-Q4 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf OscarFeMa/ExpertiaMath-Q4:F16

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 "OscarFeMa/ExpertiaMath-Q4:F16" \
  --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"

ExpertiaMath (Q4_K_M)

Spanish-first mathematics specialist: formal definitions + formulae, no web opinion. Fine-tuned from microsoft/Phi-4-mini-reasoning (MIT) with QLoRA r16 on 45k Wikidata-derived definition/formula pairs (+5k validation), 3 epochs on RTX 3070.

Evaluation (500 held-out samples)

	Perplexity
Base	827.1
+ adapter	63.8 (-92.3%)

Production: 0.89 quality, 100% cycle success, ~310 pkgs/cycle in the Expertia pipeline.

Variants in this repo

File	Size	Needs	Use
`expertia-math-q4_k_m.gguf`	2.5GB	6GB VRAM	Daily inference (Ollama)
`expertia-math-f16.gguf`	7.7GB	16GB VRAM	Max quality inference
`fp16/`	7.2GB	—	Base for further fine-tuning

Usage (Ollama)

ollama create ExpertiaMath -f Modelfile-ExpertiaMath-Q4

Note: GGUF uses gpt-2 pre-tokenizer (Ollama ≤0.33.3 does not know phi-3); set num_ctx 8192 (Qwen-family 256K default loads 43GB otherwise).

Limitations

Narrow domain specialist (mathematics). Not a general assistant. Adapter weights: CC-BY-NC-4.0. Base model: MIT (Microsoft).

Downloads last month: 29

GGUF

Model size

4B params

Architecture

phi3

Hardware compatibility

4-bit

16-bit

Inference Providers NEW

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Model tree for OscarFeMa/ExpertiaMath-Q4

Base model

microsoft/Phi-4-mini-reasoning

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