Instructions to use ahpah/YokoCoder-4B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ahpah/YokoCoder-4B 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 ahpah/YokoCoder-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ahpah/YokoCoder-4B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf ahpah/YokoCoder-4B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ahpah/YokoCoder-4B:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf ahpah/YokoCoder-4B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf ahpah/YokoCoder-4B:Q4_K_M

Use Docker

docker model run hf.co/ahpah/YokoCoder-4B:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use ahpah/YokoCoder-4B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ahpah/YokoCoder-4B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ahpah/YokoCoder-4B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ahpah/YokoCoder-4B:Q4_K_M

Ollama
How to use ahpah/YokoCoder-4B with Ollama:
```
ollama run hf.co/ahpah/YokoCoder-4B:Q4_K_M
```
Unsloth Desktop

How to use ahpah/YokoCoder-4B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ahpah/YokoCoder-4B: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": "ahpah/YokoCoder-4B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ahpah/YokoCoder-4B with Docker Model Runner:
```
docker model run hf.co/ahpah/YokoCoder-4B:Q4_K_M
```

Lemonade

How to use ahpah/YokoCoder-4B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ahpah/YokoCoder-4B:Q4_K_M

Run and chat with the model

lemonade run user.YokoCoder-4B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ahpah/YokoCoder-4B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ahpah/YokoCoder-4B: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 ahpah/YokoCoder-4B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ahpah/YokoCoder-4B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ahpah/YokoCoder-4B: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 "ahpah/YokoCoder-4B: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"

YokoCoder-4B / README.md

ahpah

Document verified Ollama generation for the five-epoch release

63e6d5b verified 9 days ago

preview code

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2.44 kB

metadata

license: apache-2.0
base_model: wangzhang/Qwen3.5-4B-abliterated
pipeline_tag: text-generation
library_name: gguf
tags:
  - gguf
  - qwen3.5
  - code
  - coding
  - abliterated
  - ollama
language:
  - en

YokoCoder-4B

YokoCoder-4B is a compact, locally runnable coding assistant built on the abliterated Qwen3.5 4B model. It was fine-tuned for coding, further trained on Flappy Bird examples, and continued for five more epochs on a new coding dataset. It builds on the author's checkpoint that passed the Flappy Bird game-generation test. This five-epoch release was tested in Ollama and passed a three-case Python function smoke test, offering practical coding value for its size.

Model details

Format: GGUF, Q4_K_M
Parameters: 4B
Download size: about 2.7 GB
Base: wangzhang/Qwen3.5-4B-abliterated, derived from Qwen3.5-4B
Training: coding fine-tune, three additional epochs on Flappy Bird examples, then five additional epochs on 12 coding examples (28 training windows)
License: Apache 2.0

Run with Ollama

Download the files in this repository, then run the following from their directory:

ollama create yokocoder-4b -f Modelfile
ollama run yokocoder-4b

The included Modelfile configures a 16,384-token context and sets num_predict -1 for generation without a fixed output-token cap. The GGUF can also be used with other compatible Qwen3.5 GGUF runtimes.

Attribution

YokoCoder-4B is a fine-tuned and quantized derivative of Qwen3.5-4B-abliterated by Wangzhang Wu, based on Qwen3.5-4B. The original model card and notices are included in UPSTREAM_MODEL_CARD.md, and the license is in LICENSE.

Latest training update

The October 29, 2026 update continues the existing YokoCoder checkpoint for five epochs, completing 20 optimizer steps with training loss 1.0559. The Q4_K_M file contains the merged updated weights. The continued-training LoRA adapter and its training records are included under adapters/dataset4-5epochs/. Export hashes and provenance are in export_manifest_5epochs.json.

The local generation check and model identity are recorded in ollama_smoke_test_5epochs.json.