Instructions to use Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Pluto-AI-Labs/Atlas-Coder-2-0.5B-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 Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Pluto-AI-Labs/Atlas-Coder-2-0.5B-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 Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Pluto-AI-Labs/Atlas-Coder-2-0.5B-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 Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Pluto-AI-Labs/Atlas-Coder-2-0.5B-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 Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Pluto-AI-Labs/Atlas-Coder-2-0.5B-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": "Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF:Q4_K_M

SGLang

How to use Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF with Ollama:
```
ollama run hf.co/Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF with Docker Model Runner:
```
docker model run hf.co/Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF:Q4_K_M
```

Lemonade

How to use Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Atlas-Coder-2-0.5B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Pluto-AI-Labs/Atlas-Coder-2-0.5B-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 Pluto-AI-Labs/Atlas-Coder-2-0.5B-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 Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Pluto-AI-Labs/Atlas-Coder-2-0.5B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Pluto-AI-Labs/Atlas-Coder-2-0.5B-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 "Pluto-AI-Labs/Atlas-Coder-2-0.5B-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"

Atlas-Coder-2-0.5B-GGUF / README.md

Siddh07ETH

Create README.md

91658df verified about 1 month ago

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	---
	license: apache-2.0
	language:
	- en
	tags:
	- gguf
	- llama-cpp
	- quantized
	- atlas-coder
	- atlas-coder-2
	- qwen2.5-coder
	- sub-1b
	- coding
	- code-generation
	- ollama
	- lm-studio
	base_model: Siddh07ETH/Atlas-Coder-2-0.5B
	pipeline_tag: text-generation
	library_name: transformers
	---

	# ⚡ Atlas-Coder-2-0.5B GGUF

	> Quantized GGUF releases of Atlas-Coder-2-0.5B, optimized for llama.cpp, Ollama, and LM Studio.

	---

	## 🏆 EvalPlus Strict Benchmarks

	Atlas-Coder-2 is a 0.5B parameter coding model fine-tuned on 50,000 execution-verified Python programming samples.

	Despite its compact size, it achieves state-of-the-art performance among strictly sub-1B coding models on the EvalPlus benchmark suite.

	\| Benchmark \| Atlas-Coder-2 \| Qwen2.5-Coder-0.5B \| DeepSeek-Coder-1.3B \| Llama-3.2-1B \|
	\|------------\|--------------:\|-------------------:\|--------------------:\|-------------:\|
	\| HumanEval+ \| 36.6% 🥇 \| 34.1% \| 35.4% \| 12.2% \|
	\| MBPP+ \| 43.9% 🥇 \| 42.1% \| 39.8% \| 25.6% \|

	> Evaluation: EvalPlus (Pass@1, Greedy Decoding)

	---

	## 📦 Available Quantizations

	\| File \| Quantization \| Size \| Recommended For \|
	\|------\|--------------\|------\|-----------------\|
	\| Atlas-Coder-2-0.5B-F16.gguf \| F16 \| 948 MB \| Maximum accuracy & benchmarking \|
	\| Atlas-Coder-2-0.5B-Q8_0.gguf \| Q8_0 \| 506 MB \| Near-lossless inference \|
	\| Atlas-Coder-2-0.5B-Q6_K.gguf \| Q6_K \| 482 MB \| Best quality / size balance \|
	\| Atlas-Coder-2-0.5B-Q5_K_M.gguf \| Q5_K_M \| 401 MB \| Recommended for most users ⭐ \|
	\| Atlas-Coder-2-0.5B-Q4_K_M.gguf \| Q4_K_M \| 379 MB \| Low-memory devices \|

	---

	# 🚀 Quick Start

	## Ollama

	Create a file named Modelfile

	```text
	FROM ./Atlas-Coder-2-0.5B-Q5_K_M.gguf

	SYSTEM "You are Atlas-Coder, an elite AI coding assistant. You write clean, efficient, and well-documented Python code."

	PARAMETER temperature 0.2
	PARAMETER top_p 0.95
	PARAMETER repeat_penalty 1.1

	PARAMETER stop "<\|im_start\|>"
	PARAMETER stop "<\|im_end\|>"
	```

	Create the model:

	```bash
	ollama create atlas-coder-2 -f Modelfile
	```

	Run:

	```bash
	ollama run atlas-coder-2 "Write a Python function to check whether a string is a palindrome."
	```

	---

	## LM Studio

	1. Open LM Studio
	2. Search for Siddh07ETH/Atlas-Coder-2-0.5B-GGUF
	3. Download Q5_K_M or Q6_K
	4. Load the model and start coding.

	---

	## llama.cpp

	```bash
	./llama-cli \
	-m Atlas-Coder-2-0.5B-Q5_K_M.gguf \
	-p "<\|im_start\|>system\nYou are a helpful coding assistant.<\|im_end\|>\n<\|im_start\|>user\nWrite a Python binary search implementation.<\|im_end\|>\n<\|im_start\|>assistant\n" \
	-n 512 \
	--temp 0.2 \
	--top-p 0.95 \
	--repeat-penalty 1.1
	```

	---

	# 💬 Chat Template

	Atlas-Coder-2 uses the standard ChatML prompt format.

	```text
	<\|im_start\|>system
	{system_message}
	<\|im_end\|>

	<\|im_start\|>user
	{user_message}
	<\|im_end\|>

	<\|im_start\|>assistant
	```

	---

	# 📚 Open-Source Ecosystem

	Base Model

	- Siddh07ETH/Atlas-Coder-2-0.5B

	Training Dataset

	- Siddh07ETH/Atlas-Coder-50K-ChatML
	- 50,000 execution-verified Python instruction samples in ChatML format.

	---

	# 👨‍💻 Author

	Siddharth N.R.
	Pluto AI Research

	---

	# 📄 License

	This repository is released under the Apache 2.0 License.

	The base model (Qwen2.5-Coder-0.5B-Instruct) is also licensed under Apache 2.0.