Instructions to use TheOneWhoWill/Coding-Monkey-Gemma-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use TheOneWhoWill/Coding-Monkey-Gemma-GGUF with PEFT:
```
Task type is invalid.
```

How to use TheOneWhoWill/Coding-Monkey-Gemma-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TheOneWhoWill/Coding-Monkey-Gemma-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("TheOneWhoWill/Coding-Monkey-Gemma-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use TheOneWhoWill/Coding-Monkey-Gemma-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 TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf TheOneWhoWill/Coding-Monkey-Gemma-GGUF: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 TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf TheOneWhoWill/Coding-Monkey-Gemma-GGUF: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 TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M

Use Docker

docker model run hf.co/TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use TheOneWhoWill/Coding-Monkey-Gemma-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TheOneWhoWill/Coding-Monkey-Gemma-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": "TheOneWhoWill/Coding-Monkey-Gemma-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M

SGLang

How to use TheOneWhoWill/Coding-Monkey-Gemma-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "TheOneWhoWill/Coding-Monkey-Gemma-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheOneWhoWill/Coding-Monkey-Gemma-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "TheOneWhoWill/Coding-Monkey-Gemma-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TheOneWhoWill/Coding-Monkey-Gemma-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use TheOneWhoWill/Coding-Monkey-Gemma-GGUF with Ollama:
```
ollama run hf.co/TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M
```

Unsloth Studio

How to use TheOneWhoWill/Coding-Monkey-Gemma-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for TheOneWhoWill/Coding-Monkey-Gemma-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for TheOneWhoWill/Coding-Monkey-Gemma-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TheOneWhoWill/Coding-Monkey-Gemma-GGUF to start chatting

How to use TheOneWhoWill/Coding-Monkey-Gemma-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use TheOneWhoWill/Coding-Monkey-Gemma-GGUF with Docker Model Runner:
```
docker model run hf.co/TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M
```

Lemonade

How to use TheOneWhoWill/Coding-Monkey-Gemma-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Coding-Monkey-Gemma-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use TheOneWhoWill/Coding-Monkey-Gemma-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 TheOneWhoWill/Coding-Monkey-Gemma-GGUF: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 TheOneWhoWill/Coding-Monkey-Gemma-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use TheOneWhoWill/Coding-Monkey-Gemma-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf TheOneWhoWill/Coding-Monkey-Gemma-GGUF: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 "TheOneWhoWill/Coding-Monkey-Gemma-GGUF: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"

Coding-Monkey-Gemma-GGUF / README.md

TheOneWhoWill

Update README.md

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	---
	license: apache-2.0
	base_model: google/gemma-4-12B-it
	library_name: peft
	pipeline_tag: text-generation
	tags:
	- lora
	- transformers
	- unsloth
	---

	# Coding Monkey Gemma

	Many people praise Gemma 4 26B for it's coding capabilities and for good reason. However the model is simply too large to run comfortable without heavy quantization compromises on a consumer GPU and so I turned to the 12B varient only to find that it fails at even basic coding tasks because it doesn't understand how to properly utilize the correct tools from the agent harness. So this model is a fine tune of Gemma 4 12B with reliable tool calling. The results speak for themselves showing a 71% reliability on exact tool calling on a 102-prompt held-out set which means correct ChatML syntax and parameter placement. This is a huge improvement over the base model which only had a 26.5% reliability on exact tool calling. I also saw a 16% increase in the number of tool calls actually emitted by the model which means that the model is more likely to actually use the tools it has access to which is huge for coding tasks. This model is great for anyone who wants to do local coding but is tight on VRAM.

	\| Metric \| Base `-it` \| Fine-tuned \| Δ \|
	\| --- \| ---: \| ---: \| ---: \|
	\| Format Validity \| 0.824 \| 0.980 \| +0.157 \|
	\| Exact Accuracy \| 0.265 \| 0.706 \| +0.441 \|
	\| Emitted Calls Fraction \| 0.824 \| 0.980 \| +0.157 \|

	_n = 102 held-out prompts, none ever seen during training. This set is harder than
	an earlier 67-item eval because it now includes multi-argument database,
	knowledge-graph and shell tasks that are matched exactly._

	## Training Details
	For the training of this model I used the QLoRA method with a 16GB 5070 Ti. It 3 hours and 48 minutes to complete (480 steps) however previous protyping totaled 30 hours aggregate. The training loss went from 1.695 to 0.120 and the held-out eval loss was 0.206. I could've trained for much longer but I ran into overfitting issues on previous attempts so I decided that training on a small concentrated dataset was the best option. The training dataset consisted of 5,211 high-quality agentic/tool-calling examples, every one formatted with Gemma 4's own chat template. The glaiveai was the bulk of the data but I also included AgentInstruct examples because they had real world bash and terminal actions. I converted those raw ReAct trajectories (bash, SQL, web, and knowledge-graph actions) into real structured Gemma tool calls instead of plain text. I split it 98/2 into 5,107 train / 104 held-out val, and the eval set was built only from the val split so none of the 102 scored prompts were ever seen during training preventing contamination.

	The breakdown by source:

	\| Source \| Examples \|
	\| --- \| ---: \|
	\| `glaiveai/glaive-function-calling-v2` \| 3,347 \|
	\| `zai-org/AgentInstruct` — alfworld \| 336 \|
	\| `zai-org/AgentInstruct` — webshop \| 351 \|
	\| `zai-org/AgentInstruct` — os \| 195 \|
	\| `zai-org/AgentInstruct` — mind2web \| 120 \|
	\| `zai-org/AgentInstruct` — db \| 538 \|
	\| `zai-org/AgentInstruct` — kg \| 324 \|
	\| Total \| 5,211 \|

	That's \~5,161 actual tool-calling examples (\~99% of the set) trained over 3 epochs with QLoRA `r=16/alpha=32`, LR 2e-4 cosine, and an effective batch size of 32.

	## Quantization
	This model has fp16, Q8_0, Q6_K, Q5_K_M, and Q4_K_M quantization available in the gguf format. You can find them in this [repository](https://huggingface.co/TheOneWhoWill/Coding-Monkey-Gemma-GGUF)

	## Usage
	### llama.cpp
	To use this model with llama.cpp you can use the ggufs like so
	```bash
	llama-server \
	-m gemma-coder-Q4_K_M.gguf
	-c 32768
	```

	### Ollama
	Ollama requires you to create a model file with each gguf so you could have something like this
	```
	FROM ./gemma-coder-Q4_K_M.gguf
	PARAMETER num_ctx 32768
	```
	then you can run it like so with the Modelfile in the same directory as the gguf
	```bash
	ollama create coding-monkey -f Modelfile
	```