Instructions to use WithinUsAI/WithIn-Us-Coder-4B.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 WithinUsAI/WithIn-Us-Coder-4B.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 WithinUsAI/WithIn-Us-Coder-4B.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WithinUsAI/WithIn-Us-Coder-4B.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 WithinUsAI/WithIn-Us-Coder-4B.gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf WithinUsAI/WithIn-Us-Coder-4B.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 WithinUsAI/WithIn-Us-Coder-4B.gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf WithinUsAI/WithIn-Us-Coder-4B.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 WithinUsAI/WithIn-Us-Coder-4B.gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WithinUsAI/WithIn-Us-Coder-4B.gguf:Q4_K_M

Use Docker

docker model run hf.co/WithinUsAI/WithIn-Us-Coder-4B.gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use WithinUsAI/WithIn-Us-Coder-4B.gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WithinUsAI/WithIn-Us-Coder-4B.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": "WithinUsAI/WithIn-Us-Coder-4B.gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/WithinUsAI/WithIn-Us-Coder-4B.gguf:Q4_K_M

Ollama
How to use WithinUsAI/WithIn-Us-Coder-4B.gguf with Ollama:
```
ollama run hf.co/WithinUsAI/WithIn-Us-Coder-4B.gguf:Q4_K_M
```

Unsloth Studio

How to use WithinUsAI/WithIn-Us-Coder-4B.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 WithinUsAI/WithIn-Us-Coder-4B.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 WithinUsAI/WithIn-Us-Coder-4B.gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for WithinUsAI/WithIn-Us-Coder-4B.gguf to start chatting

How to use WithinUsAI/WithIn-Us-Coder-4B.gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WithinUsAI/WithIn-Us-Coder-4B.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": "WithinUsAI/WithIn-Us-Coder-4B.gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use WithinUsAI/WithIn-Us-Coder-4B.gguf with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf WithinUsAI/WithIn-Us-Coder-4B.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 "WithinUsAI/WithIn-Us-Coder-4B.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"

Docker Model Runner
How to use WithinUsAI/WithIn-Us-Coder-4B.gguf with Docker Model Runner:
```
docker model run hf.co/WithinUsAI/WithIn-Us-Coder-4B.gguf:Q4_K_M
```

Lemonade

How to use WithinUsAI/WithIn-Us-Coder-4B.gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WithinUsAI/WithIn-Us-Coder-4B.gguf:Q4_K_M

Run and chat with the model

lemonade run user.WithIn-Us-Coder-4B.gguf-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use WithinUsAI/WithIn-Us-Coder-4B.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 WithinUsAI/WithIn-Us-Coder-4B.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 WithinUsAI/WithIn-Us-Coder-4B.gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat

11-47 commited on Jun 19

Commit

960d07b

verified ·

1 Parent(s): e39ab73

Update README.md

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README.md +4 -13

README.md CHANGED Viewed

@@ -16,15 +16,7 @@ language:
 - en
 datasets:
 - WithinUsAI/Python_GOD_Coder_50k
-- reedmayhew/gemini-3.1-pro-2048-reasoning-1100x
 - m-a-p/Code-Feedback
-- crownelius/Opus-4.6-Reasoning-2100x-formatted
-- crownelius/Opus4.6-No-Reasoning-260x
-- crownelius/Creative_Writing_Multiturn_Enhanced
-- HuggingFaceH4/llava-instruct-mix-vsft
-- Roman1111111/gemini-3-pro-10000x-hard-high-reasoning
-- TeichAI/Pony-Alpha-15k
-- Crownelius/Pony-Alpha-3000x-formatted
 model_type: gguf
 inference: false
 ---
@@ -66,13 +58,12 @@ Credit and appreciation go to the original creators of the base LLM architecture
 The current repository metadata lists the following datasets as part of the model’s training / fine-tuning lineage:
 - `WithinUsAI/Python_GOD_Coder_50k`
-- `reedmayhew/gemini-3.1-pro-2048-reasoning-1100x`
 - `m-a-p/Code-Feedback`
-- `crownelius/Opus-4.6-Reasoning-2100x-formatted`
-- `crownelius/Opus4.6-No-Reasoning-260x`
-- `crownelius/Creative_Writing_Multiturn_Enhanced`
 - `HuggingFaceH4/llava-instruct-mix-vsft`
-- `Roman1111111/gemini-3-pro-10000x-hard-high-reasoning`
 **Attribution note:**
 WithIn Us AI does not claim ownership over third-party base models or third-party datasets. Full credit, thanks, and attribution belong to the original model and dataset creators.

 - en
 datasets:
 - WithinUsAI/Python_GOD_Coder_50k
 - m-a-p/Code-Feedback
 model_type: gguf
 inference: false
 ---
 The current repository metadata lists the following datasets as part of the model’s training / fine-tuning lineage:
 - `WithinUsAI/Python_GOD_Coder_50k`
+- `
 - `m-a-p/Code-Feedback`
+- `
+- `
 - `HuggingFaceH4/llava-instruct-mix-vsft`
+-
 **Attribution note:**
 WithIn Us AI does not claim ownership over third-party base models or third-party datasets. Full credit, thanks, and attribution belong to the original model and dataset creators.