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 with the GMCoder efficiency recipe. 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.

Recipe

QLoRA (NF4), r=8, alpha=8, LR 5e-5, batch 1, 16 epochs, completion-only loss, 531 short examples rendered in thinking-off format. Same recipe as GMCoder 9B.

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.

Downloads last month: -

Safetensors

Model size

9B params

Tensor type

BF16

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Model tree for emperorofrome/OxCoder-9B-GMCE

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

OrionLLM/OxCoder-9B

Quantized

(14)

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