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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metadata

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

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:

ollama create atlas-coder-2 -f Modelfile

Run:

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

LM Studio

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

llama.cpp

./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.

<|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.