Instructions to use XformAI-india/Qwen3-0.6B-coders-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use XformAI-india/Qwen3-0.6B-coders-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="XformAI-india/Qwen3-0.6B-coders-gguf",
	filename="qwen3-0.6b-coder-f16.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 XformAI-india/Qwen3-0.6B-coders-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf XformAI-india/Qwen3-0.6B-coders-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf XformAI-india/Qwen3-0.6B-coders-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 XformAI-india/Qwen3-0.6B-coders-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf XformAI-india/Qwen3-0.6B-coders-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 XformAI-india/Qwen3-0.6B-coders-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf XformAI-india/Qwen3-0.6B-coders-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 XformAI-india/Qwen3-0.6B-coders-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf XformAI-india/Qwen3-0.6B-coders-gguf:Q4_K_M

Use Docker

docker model run hf.co/XformAI-india/Qwen3-0.6B-coders-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use XformAI-india/Qwen3-0.6B-coders-gguf with Ollama:
```
ollama run hf.co/XformAI-india/Qwen3-0.6B-coders-gguf:Q4_K_M
```

Unsloth Studio new

How to use XformAI-india/Qwen3-0.6B-coders-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 XformAI-india/Qwen3-0.6B-coders-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 XformAI-india/Qwen3-0.6B-coders-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for XformAI-india/Qwen3-0.6B-coders-gguf to start chatting

Pi new

How to use XformAI-india/Qwen3-0.6B-coders-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf XformAI-india/Qwen3-0.6B-coders-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": "XformAI-india/Qwen3-0.6B-coders-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use XformAI-india/Qwen3-0.6B-coders-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 XformAI-india/Qwen3-0.6B-coders-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 XformAI-india/Qwen3-0.6B-coders-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use XformAI-india/Qwen3-0.6B-coders-gguf with Docker Model Runner:
```
docker model run hf.co/XformAI-india/Qwen3-0.6B-coders-gguf:Q4_K_M
```

Lemonade

How to use XformAI-india/Qwen3-0.6B-coders-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull XformAI-india/Qwen3-0.6B-coders-gguf:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-0.6B-coders-gguf-Q4_K_M

List all available models

lemonade list

💻 Qwen3-0.6B Coder – GGUF (Local Use Version)

Model: XformAI-india/qwen3-0.6b-coder-gguf
Base Model: Qwen-0.6B Fine-Tuned On: Code generation tasks
Architecture: Transformer decoder (GPT-style)
Parameter Size: 0.6B (~600M)
Quantization: GGUF (e.g., Q4_K_M / Q6_K)
Converted By: XformAI
Date: May 2025
License: Apache 2.0 (inherited from base)

🚀 Overview

This is the GGUF-converted version of XformAI-india/qwen3-0.6b-coder, optimized for local inference, including:

🔧 llama.cpp
⚙️ LM Studio
💻 Ollama
🐍 KoboldCpp / text-generation-webui

It is trained for task-oriented code generation, covering Python, Bash, HTML, JavaScript, and small app scaffolding.

🧱 Model Details

Feature	Value
Model Format	GGUF (Q4, Q5, Q6, etc.)
Model Type	Decoder-only LLM
Base	Qwen 0.5B
Fine-Tune Method	LoRA (code-tasks)
File Sizes	Q4_K_M: ~460MB / Q6: ~800MB
Context Length	2048 tokens
Tokenizer	Compatible with Qwen tokenizer (`qwen.tiktoken`)

🛠 Use Cases

Lightweight local coding assistants
VSCode extensions
CLI & DevOps helpers
Edge AI programming bots
Offline developer tools

💻 How to Use (locally with `llama.cpp`)

# Clone llama.cpp if not already
git clone https://github.com/ggerganov/llama.cpp
cd llama.cpp
make

# Run the model
./main -m qwen-0.6b-coder.Q4_K_M.gguf -p "Write a Python script that creates a zip file from a directory."

Downloads last month: 308

GGUF

Model size

0.6B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

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