Instructions to use YunusTAS13/ATLAS-AI with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YunusTAS13/ATLAS-AI with Transformers:

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

pipe = pipeline("text-generation", model="YunusTAS13/ATLAS-AI")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("YunusTAS13/ATLAS-AI", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use YunusTAS13/ATLAS-AI 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 YunusTAS13/ATLAS-AI
# Run inference directly in the terminal:
llama cli -hf YunusTAS13/ATLAS-AI

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf YunusTAS13/ATLAS-AI
# Run inference directly in the terminal:
llama cli -hf YunusTAS13/ATLAS-AI

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf YunusTAS13/ATLAS-AI
# Run inference directly in the terminal:
./build/bin/llama cli -hf YunusTAS13/ATLAS-AI

Use Docker

docker model run hf.co/YunusTAS13/ATLAS-AI

Llama new
LM Studio
Jan

vLLM

How to use YunusTAS13/ATLAS-AI with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YunusTAS13/ATLAS-AI"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YunusTAS13/ATLAS-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/YunusTAS13/ATLAS-AI

SGLang

How to use YunusTAS13/ATLAS-AI 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 "YunusTAS13/ATLAS-AI" \
    --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": "YunusTAS13/ATLAS-AI",
		"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 "YunusTAS13/ATLAS-AI" \
        --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": "YunusTAS13/ATLAS-AI",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use YunusTAS13/ATLAS-AI with Ollama:
```
ollama run hf.co/YunusTAS13/ATLAS-AI
```
Unsloth Desktop

How to use YunusTAS13/ATLAS-AI with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YunusTAS13/ATLAS-AI

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": "YunusTAS13/ATLAS-AI"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use YunusTAS13/ATLAS-AI with Docker Model Runner:
```
docker model run hf.co/YunusTAS13/ATLAS-AI
```

Lemonade

How to use YunusTAS13/ATLAS-AI with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull YunusTAS13/ATLAS-AI

Run and chat with the model

lemonade run user.ATLAS-AI-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use YunusTAS13/ATLAS-AI with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YunusTAS13/ATLAS-AI

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 YunusTAS13/ATLAS-AI

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use YunusTAS13/ATLAS-AI with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf YunusTAS13/ATLAS-AI

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 "YunusTAS13/ATLAS-AI" \
  --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"

YunusTAS13 commited on Aug 19

Commit

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@@ -1,74 +0,0 @@
----
-license: gpl-3.0
-language:
-  - tr
-base_model: Qwen/Qwen3-8B
-pipeline_tag: text-generation
-tags:
-  - turkish
-  - qwen3
-  - qlora
-  - gguf
----
-# Atlas
-Atlas, [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) tabanlı, Türkçe odaklı, QLoRA ile ince ayar yapılmış bir dil modelidir.
-## Özellikler
-- **Türkçe odaklı** konuşma ve bilgi üretimi
-- **Atlas kimliği** — kendini Atlas olarak tanıtır
-- **Güvenli davranış** — zararlı istekleri reddeder
-- **Tool calling** — güvenli sandbox ortamında dosya/terminal işlemleri
-## Dosyalar
-| Dosya | Boyut | Açıklama |
-|---|---|---|
-| `atlas-q4km.gguf` | ~4.7 GB | Q4_K_M kuantize GGUF (Ollama uyumlu) |
-## Kurulum (Ollama)
-```bash
-ollama create atlas -f Modelfile
-```
-```dockerfile
-FROM atlas-q4km.gguf
-PARAMETER num_ctx 8192
-PARAMETER temperature 0.7
-PARAMETER top_p 0.9
-```
-veya doğrudan:
-```bash
-ollama run atlas-q4km "Merhaba"
-```
-## Eğitim Verisi
-| Kaynak | Örnek | Lisans |
-|---|---|---|
-| Elle yazılmış (kimlik + davranış + Türkçe) | 800 | Özel |
-| [turkish-distilled-5K](https://huggingface.co/datasets/afkfatih/turkish-distilled-5K) | 5.660 | MIT |
-| [turkish-knowledge-sft](https://huggingface.co/datasets/Uunan/turkish-knowledge-sft) | 3.000 | Apache-2.0 |
-| Tool-calling örnekleri | 270 | Özel |
-## Eğitim
-- Yöntem: QLoRA (r=8, alpha=16, dropout=0.05)
-- 4-bit NF4 kuantizasyon
-- 1.212 adım, 2 tur fine-tune
-- Optimizer: AdamW 8-bit
-- Donanım: NVIDIA RTX 4050 (6 GB VRAM)
-## Lisans
-Bu model **GPL-3.0** lisansıyla yayınlanmıştır.
-Taban model [Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) **Apache License 2.0** ile lisanslıdır ve Alibaba Cloud tarafından geliştirilmiştir. Apache-2.0 şartlarına uyulması zorunludur.
-Eğitim verisi olarak kullanılan açık kaynak veri setleri kendi lisanslarına (MIT, Apache-2.0) tabidir.