Instructions to use srisree/nano_coder_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use srisree/nano_coder_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="srisree/nano_coder_GGUF",
	filename="nano_coder.Q8_0.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 srisree/nano_coder_GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf srisree/nano_coder_GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf srisree/nano_coder_GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf srisree/nano_coder_GGUF:Q8_0
# Run inference directly in the terminal:
llama-cli -hf srisree/nano_coder_GGUF:Q8_0

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 srisree/nano_coder_GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf srisree/nano_coder_GGUF:Q8_0

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 srisree/nano_coder_GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf srisree/nano_coder_GGUF:Q8_0

Use Docker

docker model run hf.co/srisree/nano_coder_GGUF:Q8_0

LM Studio
Jan
Ollama
How to use srisree/nano_coder_GGUF with Ollama:
```
ollama run hf.co/srisree/nano_coder_GGUF:Q8_0
```

Unsloth Studio new

How to use srisree/nano_coder_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 srisree/nano_coder_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 srisree/nano_coder_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for srisree/nano_coder_GGUF to start chatting

Pi new

How to use srisree/nano_coder_GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf srisree/nano_coder_GGUF:Q8_0

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": "srisree/nano_coder_GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use srisree/nano_coder_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 srisree/nano_coder_GGUF:Q8_0

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 srisree/nano_coder_GGUF:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use srisree/nano_coder_GGUF with Docker Model Runner:
```
docker model run hf.co/srisree/nano_coder_GGUF:Q8_0
```

Lemonade

How to use srisree/nano_coder_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull srisree/nano_coder_GGUF:Q8_0

Run and chat with the model

lemonade run user.nano_coder_GGUF-Q8_0

List all available models

lemonade list

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Check out the documentation for more information.

NanoCoder is a Fill-in-the-Middle (FIM) language model specifically designed for React frontend development and coding assistance. It helps users with intelligent code autocompletion and context-aware generation. The model was fine-tuned using Unsloth on the Qwen 3 0.6B base model, leveraging a high-quality FIM dataset curated from GitHub repositories to enhance coding capabilities and developer productivity.

🧠 Datasets

We trained NanoCoder using a high-quality Fill-in-the-Middle (FIM) dataset curated from GitHub repositories:
srisree/nextjs_typescript_fim_dataset on Hugging Face.

This dataset focuses on React/Next.js and TypeScript projects, providing rich, real-world coding examples that help the model understand frontend architecture, component composition, and React ecosystem patterns.

By leveraging this dataset, NanoCoder learns to:

Predict and fill missing code intelligently using FIM objectives.
Understand React component structures and TypeScript typing patterns.
Generate clean, production-grade frontend code snippets.

⚙️ FIM Training Colab Script

We’re preparing an interactive Google Colab notebook for reproducing the Fill-in-the-Middle (FIM) fine-tuning process used to train NanoCoder with Unsloth on the Qwen 3 0.6B base model.

The Colab script will include:

✅ Environment setup with Unsloth and Qwen 3 0.6B
✅ Loading and preprocessing the Next.js TypeScript FIM Dataset
✅ Training configuration (LoRA, batch size, sequence length, etc.)
✅ Evaluation and inference examples

🚀 Coming soon... Stay tuned for the full release!

⚙️ Setup and Run NanoCoder Locally with Ollama in VS Code

Step-by-step guide to install, configure, and use NanoCoder for intelligent React frontend code completion with the Continue VS Code extension.

🧠 Prerequisites

Before getting started, ensure you have the following installed:

VS Code
Ollama (latest version)
Continue extension
A system with at least 8GB RAM (recommended for 0.6B models)

🧩 Step 1: Install Ollama

If you haven’t already, download and install Ollama:

macOS / Linux / Windows: https://ollama.ai/download

Once installed, open your terminal and verify the installation.

💾 Step 2: Pull NanoCoder Model

ollama pull srisree/nanocoder

⚡ Step 3: Run NanoCoder with Ollama

Once downloaded, you can test NanoCoder directly in the terminal:

ollama run nanocoder