Instructions to use emperorofrome/OxCoder-9B-GMCE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use emperorofrome/OxCoder-9B-GMCE 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 emperorofrome/OxCoder-9B-GMCE:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf emperorofrome/OxCoder-9B-GMCE:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf emperorofrome/OxCoder-9B-GMCE:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf emperorofrome/OxCoder-9B-GMCE:Q4_K_M

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 emperorofrome/OxCoder-9B-GMCE:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf emperorofrome/OxCoder-9B-GMCE:Q4_K_M

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 emperorofrome/OxCoder-9B-GMCE:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf emperorofrome/OxCoder-9B-GMCE:Q4_K_M

Use Docker

docker model run hf.co/emperorofrome/OxCoder-9B-GMCE:Q4_K_M

LM Studio
Jan
Ollama
How to use emperorofrome/OxCoder-9B-GMCE with Ollama:
```
ollama run hf.co/emperorofrome/OxCoder-9B-GMCE:Q4_K_M
```
Unsloth Desktop

How to use emperorofrome/OxCoder-9B-GMCE with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf emperorofrome/OxCoder-9B-GMCE:Q4_K_M

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": "emperorofrome/OxCoder-9B-GMCE:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use emperorofrome/OxCoder-9B-GMCE with Docker Model Runner:
```
docker model run hf.co/emperorofrome/OxCoder-9B-GMCE:Q4_K_M
```

Lemonade

How to use emperorofrome/OxCoder-9B-GMCE with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull emperorofrome/OxCoder-9B-GMCE:Q4_K_M

Run and chat with the model

lemonade run user.OxCoder-9B-GMCE-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use emperorofrome/OxCoder-9B-GMCE with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf emperorofrome/OxCoder-9B-GMCE:Q4_K_M

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 emperorofrome/OxCoder-9B-GMCE:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use emperorofrome/OxCoder-9B-GMCE with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf emperorofrome/OxCoder-9B-GMCE:Q4_K_M

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 "emperorofrome/OxCoder-9B-GMCE:Q4_K_M" \
  --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"

OxCoder-9B-GMCE (GMCoder Efficiency, experimental)

OrionLLM/OxCoder-9B fine-tuned for GMCoder Efficiency. Files: GGUF (Q8_0, Q6_K, Q5_K_M, Q4_K_M, BF16) and BF16 safetensors. The original checkpoint has no multi-token-prediction head, so the GGUFs are built with --no-mtp.

What it does (measured)

Our own unseen "Form B" exams, 60k token cap, single run per setting, graded blind by one grader. Scores are approximate.

Setting	Historical	Everyday (/150)	Notes
OxCoder-9B, thinking on	looped to 60k, no answer	~118 at 6.9k tokens	Ultimate ran to the cap
OxCoder-9B, thinking off	~66 at 3.8k	6.2k tokens (not graded)
GMCE, thinking on	~62 at 5.5k	~105 at 5.9k	finishes Historical where the base looped; Ultimate ran to the cap
GMCE, thinking off	~52 at 2.0k	2.3k tokens (not graded)	all 3 exams in 5.7k tokens; Ultimate 11.5/25; invented citations

Caveats

Mixed result: GMCE stops OxCoder's runaway thinking on one exam, but costs about 13 points on the other. The base model with thinking off was a better cheap option in our tests.
Published as an experiment. Benchmarks are our own and small.