Instructions to use WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF",
	filename="IBM-Agentic-Nvidia-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf WithinUsAI/Nvidia.Agentic.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-server -hf WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf WithinUsAI/Nvidia.Agentic.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/Nvidia.Agentic.Coder-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf WithinUsAI/Nvidia.Agentic.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/Nvidia.Agentic.Coder-4B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF with Ollama:
```
ollama run hf.co/WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use WithinUsAI/Nvidia.Agentic.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/Nvidia.Agentic.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/Nvidia.Agentic.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/Nvidia.Agentic.Coder-4B-GGUF to start chatting

Pi new

How to use WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf WithinUsAI/Nvidia.Agentic.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/Nvidia.Agentic.Coder-4B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use WithinUsAI/Nvidia.Agentic.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-server -hf WithinUsAI/Nvidia.Agentic.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/Nvidia.Agentic.Coder-4B-GGUF:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF with Docker Model Runner:
```
docker model run hf.co/WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF:Q4_K_M
```

Lemonade

How to use WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Nvidia.Agentic.Coder-4B-GGUF-Q4_K_M

List all available models

lemonade list

Nvidia.Agentic.Coder-4B-GGUF

📌 Model Overview

Model Name: WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF Organization: Within Us AI Model Type: Code LLM (Agentic, Instruction-Following) Parameter Size: 4B Format: GGUF (quantized for local inference) Primary Use: Agentic coding, tool-using workflows, software engineering reasoning

This model is part of the Within Us AI ecosystem focused on building agentic, reasoning-driven coding systems designed to think, act, and verify like real engineers.

⸻

🧬 Architecture & Lineage

Base Family: NVIDIA Nemotron-style 4B class models (inferred lineage from naming + ecosystem alignment)
Format Conversion: GGUF quantization for efficient local inference
Training Approach:
- Instruction-tuned for coding tasks
- Agentic workflow emphasis (multi-step reasoning, tool usage)
- Likely merged / fine-tuned using Within Us AI proprietary pipelines

Related ecosystem models include:

NVIDIA-Nemotron-3-Nano-4B
Other 4B agentic coders and merges in the same class

⸻

⚙️ Key Capabilities

🧑‍💻 Code Intelligence

Multi-language code generation
Bug fixing and refactoring
Structured output generation

🤖 Agentic Behavior

Step-by-step reasoning
Task decomposition
Tool-calling alignment (design goal)

🧠 Reasoning Focus

Instruction-following with logical chaining
Designed for evaluation-style datasets (tests-as-truth philosophy)

⸻

📦 GGUF Quantization

GGUF allows efficient local inference with tools like:

llama.cpp
LM Studio
Ollama (GGUF-compatible builds)

Typical quantizations for 4B GGUF models include:

Q2_K (~1.8GB)
Q3_K (~2.0–2.3GB)
Q4_K (~2.5GB, recommended balance)

⸻

🚀 Intended Use

✅ Ideal Use Cases

Local AI coding assistants
Autonomous coding agents
SWE-bench style evaluation
Tool-augmented workflows
Offline developer copilots

⚠️ Limitations

Smaller 4B parameter size limits deep reasoning vs larger models
Performance depends heavily on prompt structure
Tool-use requires external orchestration (not built-in runtime)

⸻

🛠️ Usage Example (llama.cpp)

./main -m Nvidia.Agentic.Coder-4B.Q4_K.gguf
-p "Write a Python function to parse JSON logs and extract errors."
-n 512

⸻

🧪 Training Philosophy (Within Us AI)

Within Us AI focuses on:

Agentic AI systems
Test-driven training (tests-as-truth)
Diff-first patching workflows
Secure and auditable code generation
Evaluation-first development pipelines

⸻

📊 Evaluation

No formal benchmark results published yet.

Expected strengths:

Strong instruction adherence
Lightweight agentic reasoning
Efficient local deployment

⸻

📚 Datasets & Training Sources

This model follows the Within Us AI methodology:

Proprietary datasets created by Within Us AI
May include third-party datasets for training (no ownership claimed)
Emphasis on:
- Code reasoning traces
- Agentic workflows
- Evaluation-driven samples

⸻

📜 License

License Type: Custom / Other (Within Us AI License)

Terms:

Within Us AI created the fine-tuning, merging, and training methodology
Base model architecture originates from third-party LLM ecosystems (e.g., NVIDIA / Nemotron class)
Third-party datasets may be used without claiming ownership
Full credit and acknowledgment belong to original dataset and base model creators

⸻

🙏 Acknowledgements

Special thanks to:

NVIDIA Nemotron ecosystem contributors
Open-source GGUF tooling community
Dataset creators across Hugging Face
The broader open-source AI research community

⸻

🔗 Links

Model: https://huggingface.co/WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF
Organization: https://huggingface.co/WithinUsAI

Downloads last month: 914

GGUF

Model size

4B params

Architecture

nemotron_h

Hardware compatibility

4-bit

Inference Providers NEW

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Datasets used to train WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF

Collection including WithinUsAI/Nvidia.Agentic.Coder-4B-GGUF

WithIn US AI (((GGUF MODELS)))

Collection

LLM MODELS TRAINED, FINE-TUNED, MERGED BY (WITHIN US AI) • 21 items • Updated 8 days ago • 5