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

WithIn-Us-Coder-4B.gguf

File size: 4,502 Bytes

---
license: other
base_model:
- Qwen/Qwen3.5-4B
library_name: llama.cpp
tags:
- gguf
- qwen
- qwen3.5
- code
- coder
- conversational
- text-generation
- withinusai
language:
- en
datasets:
- WithinUsAI/Python_GOD_Coder_50k
- m-a-p/Code-Feedback
model_type: gguf
inference: false
---

# WithIn-Us-Coder-4B.gguf

**WithIn-Us-Coder-4B.gguf** is a GGUF release from **WithIn Us AI**, built for local inference and coding-focused assistant use cases. It is based on **Qwen/Qwen3.5-4B** and distributed in quantized GGUF formats for efficient deployment in llama.cpp-compatible runtimes.

## Model Summary

This model is intended as a coding-oriented conversational assistant with emphasis on:

- code generation
- code reasoning
- implementation planning
- debugging assistance
- instruction following
- general assistant-style chat for development workflows

This repository currently provides the following GGUF variants:

- `WithIn-Us-Coder-4B.Q4_K_M.gguf`
- `WithIn-Us-Coder-4B.Q5_K_M.gguf`

## Creator

**WithIn Us AI** is the creator of this model release, including the model packaging, fine-tuning / merging concept, process, naming, and GGUF distribution.

## Base Model

This model is based on:

- **Qwen/Qwen3.5-4B**

Credit and appreciation go to the original creators of the base LLM architecture and weights.

## Training Data

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.

## Intended Use

This model is intended for:

- local coding assistants
- offline development help
- code explanation
- bug-fixing support
- prompt-based code generation
- experimentation in llama.cpp and GGUF-compatible environments

### Suggested Use Cases

- generating Python, JavaScript, C++, and other programming language snippets
- explaining code blocks
- rewriting or improving functions
- brainstorming implementation strategies
- creating scaffolding and prototypes
- assisting with debugging and refactoring

## Out-of-Scope Use

This model is not guaranteed to be reliable for:

- high-stakes legal advice
- medical advice
- financial decision-making
- autonomous execution without review
- security-critical production decisions without human verification

Users should always validate generated code before deployment.

## Quantization Formats

This repository currently includes:

- **Q4_K_M** for smaller memory footprint and faster local inference
- **Q5_K_M** for improved quality while remaining efficient

Choose the quant level based on your hardware budget and quality needs.

## Prompting Notes

As a coding-focused conversational model, best results usually come from prompts that are:

- specific
- structured
- explicit about language, framework, or goal
- clear about desired output format

Example prompt style:

> Write a Python function that parses a CSV file, removes duplicate rows by email, and saves the cleaned result. Include error handling and comments.

## Limitations

Like other language models, this model may:

- hallucinate APIs or library behavior
- generate insecure or inefficient code
- make reasoning mistakes
- produce outdated patterns
- require prompt iteration for best results

Human review is strongly recommended, especially for production code.

## License

This repository uses a **custom WithIn Us AI license approach**.

- The base model may be subject to its original upstream license and terms.
- Third-party datasets remain the property of their respective creators / licensors.
- WithIn Us AI claims authorship of the fine-tuning / merging concept, process, packaging, naming, and release structure for this model distribution.
- This repository does **not** claim ownership over third-party datasets or the underlying upstream base model.

You can include a `LICENSE` file in this repository with the exact custom terms you want enforced.

## Acknowledgments

Special thanks to:

- **Qwen** for the base model
- all third-party dataset creators listed above
- the open-source GGUF / llama.cpp ecosystem
- the broader Hugging Face community

## Files

Current repository files include:

- `WithIn-Us-Coder-4B.Q4_K_M.gguf`
- `WithIn-Us-Coder-4B.Q5_K_M.gguf`