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

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use wepiqx/OxCoder-9B-MERNIK-GGUF 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 wepiqx/OxCoder-9B-MERNIK-GGUF
# Run inference directly in the terminal:
llama cli -hf wepiqx/OxCoder-9B-MERNIK-GGUF

Install from WinGet (Windows)

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

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 wepiqx/OxCoder-9B-MERNIK-GGUF
# Run inference directly in the terminal:
./llama-cli -hf wepiqx/OxCoder-9B-MERNIK-GGUF

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 wepiqx/OxCoder-9B-MERNIK-GGUF
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wepiqx/OxCoder-9B-MERNIK-GGUF

Use Docker

docker model run hf.co/wepiqx/OxCoder-9B-MERNIK-GGUF

LM Studio
Jan

vLLM

How to use wepiqx/OxCoder-9B-MERNIK-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "wepiqx/OxCoder-9B-MERNIK-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "wepiqx/OxCoder-9B-MERNIK-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/wepiqx/OxCoder-9B-MERNIK-GGUF

Ollama
How to use wepiqx/OxCoder-9B-MERNIK-GGUF with Ollama:
```
ollama run hf.co/wepiqx/OxCoder-9B-MERNIK-GGUF
```
Unsloth Desktop

How to use wepiqx/OxCoder-9B-MERNIK-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wepiqx/OxCoder-9B-MERNIK-GGUF

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": "wepiqx/OxCoder-9B-MERNIK-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use wepiqx/OxCoder-9B-MERNIK-GGUF with Docker Model Runner:
```
docker model run hf.co/wepiqx/OxCoder-9B-MERNIK-GGUF
```

Lemonade

How to use wepiqx/OxCoder-9B-MERNIK-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wepiqx/OxCoder-9B-MERNIK-GGUF

Run and chat with the model

lemonade run user.OxCoder-9B-MERNIK-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use wepiqx/OxCoder-9B-MERNIK-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wepiqx/OxCoder-9B-MERNIK-GGUF

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 wepiqx/OxCoder-9B-MERNIK-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use wepiqx/OxCoder-9B-MERNIK-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf wepiqx/OxCoder-9B-MERNIK-GGUF

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 "wepiqx/OxCoder-9B-MERNIK-GGUF" \
  --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 — MERNIK Quantization

MERNIK ("the one who measures") quants of OrionLLM/OxCoder-9B (Qwen3.5-9B agentic coding finetune, 262K context): imatrix-driven priority-queue allocation, judged by all three columns in synergy — PPL (canary), KLD-vs-ref (rank), scored tasks (verdict).

Results (all builds, all columns)

HE = HumanEval, 164 tasks, pass@1. llama-server --jinja, temp 1.0 / top_p 0.95 / top_k 20 / presence 0.0, max_tokens 2048, fixed seeds. PPL/KLD: llama-perplexity -ngl 99 -c 1024 -n 64 -b 512 --seed 7, wikitext-2-raw.

Build	Size	PPL	KLD vs Q8	HE pass@1
`OxCoder-9B-MERNIK-6500-Q8.gguf`	6.83 GB	7.5125	0.0386	90.24% (148/164) 👑 — beats NeoHorse-MSE by +4.3pp at same weight
`OxCoder-9B-MERNIK-6500-SMSE.gguf`	6.83 GB	7.5410	—	91.46% (150/164), HE+ 87.8 — thank-you build TAKES the Q8 crown by +1.2pp
`OxCoder-9B-MERNIK-5100-Q4.gguf`	5.36 GB	7.5670	0.0505	88.41% (145/164) — small beats NeoHorse big: distillate depth
Q6_K (stock)	7.36 GB	7.6758	0.0116	84.15% (138/164) — flat keeps up, allocation still wins

Finetune-vs-finetune duel: distillate leads on all weights.

⚡ Fast ring: GPQA-recognition (non-standard). Ox-MSE-6500 49.5%, Ox-SMAPE-5100 49.0% — below Neo despite winning HE: thinkers spread first-token mass. Recognition ≠ reasoning; details in wepiqx/MERNIK.

Method + protocol: wepiqx/MERNIK.