Instructions to use ArmGPT/ArmenianGPT-0.1-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use ArmGPT/ArmenianGPT-0.1-12B with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="ArmGPT/ArmenianGPT-0.1-12B", filename="ArmenianGPT-0.1-12B-Q5_0.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use ArmGPT/ArmenianGPT-0.1-12B with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ArmGPT/ArmenianGPT-0.1-12B:Q5_0 # Run inference directly in the terminal: llama-cli -hf ArmGPT/ArmenianGPT-0.1-12B:Q5_0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf ArmGPT/ArmenianGPT-0.1-12B:Q5_0 # Run inference directly in the terminal: llama-cli -hf ArmGPT/ArmenianGPT-0.1-12B:Q5_0
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 ArmGPT/ArmenianGPT-0.1-12B:Q5_0 # Run inference directly in the terminal: ./llama-cli -hf ArmGPT/ArmenianGPT-0.1-12B:Q5_0
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 ArmGPT/ArmenianGPT-0.1-12B:Q5_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf ArmGPT/ArmenianGPT-0.1-12B:Q5_0
Use Docker
docker model run hf.co/ArmGPT/ArmenianGPT-0.1-12B:Q5_0
- LM Studio
- Jan
- vLLM
How to use ArmGPT/ArmenianGPT-0.1-12B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ArmGPT/ArmenianGPT-0.1-12B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ArmGPT/ArmenianGPT-0.1-12B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ArmGPT/ArmenianGPT-0.1-12B:Q5_0
- Ollama
How to use ArmGPT/ArmenianGPT-0.1-12B with Ollama:
ollama run hf.co/ArmGPT/ArmenianGPT-0.1-12B:Q5_0
- Unsloth Studio
How to use ArmGPT/ArmenianGPT-0.1-12B 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 ArmGPT/ArmenianGPT-0.1-12B 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 ArmGPT/ArmenianGPT-0.1-12B to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for ArmGPT/ArmenianGPT-0.1-12B to start chatting
- Docker Model Runner
How to use ArmGPT/ArmenianGPT-0.1-12B with Docker Model Runner:
docker model run hf.co/ArmGPT/ArmenianGPT-0.1-12B:Q5_0
- Lemonade
How to use ArmGPT/ArmenianGPT-0.1-12B with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull ArmGPT/ArmenianGPT-0.1-12B:Q5_0
Run and chat with the model
lemonade run user.ArmenianGPT-0.1-12B-Q5_0
List all available models
lemonade list
ArmenianGPT v0.1 - The First Ever Armenian Reasoning Model
Developed by Aleksandr Baghramyan
Codenamed ArMaThink v0.1: Enhanced for Mathematical Reasoning
This first version (v0.1) of ArmenianGPT, the first-ever Armenian reasoning model (not only responding, but also thinking natively in Armenian), is currently in a progressive training phase, with more powerful models of various sizes trained on a broader range of disciplines expected soon; to contribute, please send a screenshot or text of your questions along with the model's answers to Baghramyan@pm.me for us to analyze and prevent such errors in future iterations of this model line.
This model represents a breakthrough in natural language processing for the Armenian language by enabling natural communication through its unique ability to process prompts typed with English characters - a popular, faster, and more convenient method for many users.
Configurations for Optimal Model Performance
System Prompt
You are an Armenian AI assistant who always thinks before providing the final response.
Temperature
| Desired Output | Recommended Temperature | Use Case Examples |
|---|---|---|
| High Accuracy & Factual Precision | 0.0 - 0.3 |
Factual question answering, code generation, summarization. |
| Creative & Diverse Responses | 0.3 - 1.0 |
Brainstorming ideas, creative writing, exploring solutions to complex problems. |
Running the Model on a Single / Multi GPU
# pip install -U transformers
# pip install accelerate
from transformers import AutoProcessor, Gemma3ForConditionalGeneration
from huggingface_hub import login
from PIL import Image
import requests
import torch
HUGGINGFACE_TOKEN = "YOUR HUGGINGFACE TOKEN GOES HERE"
login(token=HUGGINGFACE_TOKEN)
model_id = "ArmGPT/ArmenianGPT-0.1-12B"
model = Gemma3ForConditionalGeneration.from_pretrained(
model_id, device_map="auto"
).eval()
processor = AutoProcessor.from_pretrained(model_id)
messages = [
{
"role": "system",
"content": [{"type": "text", "text": "You are an Armenian AI assistant who always thinks before providing the final response."}]
},
{
"role": "user",
"content": [{"type": "text", "text": "YOUR QUESTION/PROBLEM IN ARMENIAN GOES HERE"}]
}
]
inputs = processor.apply_chat_template(
messages, add_generation_prompt=True, tokenize=True,
return_dict=True, return_tensors="pt"
).to(model.device, dtype=torch.bfloat16)
input_len = inputs["input_ids"].shape[-1]
with torch.inference_mode():
generation = model.generate(**inputs, max_new_tokens=129999, do_sample=False)
generation = generation[0][input_len:]
decoded = processor.decode(generation, skip_special_tokens=True)
print(decoded)
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