Instructions to use Siddh07ETH/Atlas-Coder-2-0.5B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Siddh07ETH/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="Siddh07ETH/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("Siddh07ETH/Atlas-Coder-2-0.5B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use Siddh07ETH/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 Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Siddh07ETH/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 Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf Siddh07ETH/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 Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf Siddh07ETH/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 Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use Siddh07ETH/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 "Siddh07ETH/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": "Siddh07ETH/Atlas-Coder-2-0.5B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:Q4_K_M
- SGLang
How to use Siddh07ETH/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 "Siddh07ETH/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": "Siddh07ETH/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 "Siddh07ETH/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": "Siddh07ETH/Atlas-Coder-2-0.5B-GGUF", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use Siddh07ETH/Atlas-Coder-2-0.5B-GGUF with Ollama:
ollama run hf.co/Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:Q4_K_M
- Unsloth Studio
How to use Siddh07ETH/Atlas-Coder-2-0.5B-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 Siddh07ETH/Atlas-Coder-2-0.5B-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 Siddh07ETH/Atlas-Coder-2-0.5B-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for Siddh07ETH/Atlas-Coder-2-0.5B-GGUF to start chatting
- Pi
How to use Siddh07ETH/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 Siddh07ETH/Atlas-Coder-2-0.5B-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": "Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use Siddh07ETH/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 Siddh07ETH/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 Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use Siddh07ETH/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 Siddh07ETH/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 "Siddh07ETH/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"
- Docker Model Runner
How to use Siddh07ETH/Atlas-Coder-2-0.5B-GGUF with Docker Model Runner:
docker model run hf.co/Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:Q4_K_M
- Lemonade
How to use Siddh07ETH/Atlas-Coder-2-0.5B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull Siddh07ETH/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
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:# Run inference directly in the terminal:
llama cli -hf Siddh07ETH/Atlas-Coder-2-0.5B-GGUF: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 Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:# Run inference directly in the terminal:
./llama-cli -hf Siddh07ETH/Atlas-Coder-2-0.5B-GGUF: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 Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:# Run inference directly in the terminal:
./build/bin/llama-cli -hf Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:Use Docker
docker model run hf.co/Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:โก Atlas-Coder-2-0.5B GGUF
Quantized GGUF releases of Atlas-Coder-2-0.5B, optimized for llama.cpp, Ollama, and LM Studio.
๐ 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% ๐ฅ | 34.1% | 35.4% | 12.2% |
| MBPP+ | 43.9% ๐ฅ | 42.1% | 39.8% | 25.6% |
Evaluation: EvalPlus (Pass@1, Greedy Decoding)
๐ฆ 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 โญ |
| Atlas-Coder-2-0.5B-Q4_K_M.gguf | Q4_K_M | 379 MB | Low-memory devices |
๐ 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
๐ฌ 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
๐ 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.
๐จโ๐ป Author
Siddharth N.R.
Pluto AI Research
๐ 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.
- Downloads last month
- 271
4-bit
5-bit
6-bit
8-bit
16-bit
Model tree for Siddh07ETH/Atlas-Coder-2-0.5B-GGUF
Base model
Qwen/Qwen2.5-0.5B
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf Siddh07ETH/Atlas-Coder-2-0.5B-GGUF:# Run inference directly in the terminal: llama cli -hf Siddh07ETH/Atlas-Coder-2-0.5B-GGUF: