Instructions to use AlienTurk/Turkish-Asistant-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use AlienTurk/Turkish-Asistant-gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="AlienTurk/Turkish-Asistant-gguf", filename="phi-3.5-mini-it.tr.asistant.Q4_K_M.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use AlienTurk/Turkish-Asistant-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 AlienTurk/Turkish-Asistant-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf AlienTurk/Turkish-Asistant-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 AlienTurk/Turkish-Asistant-gguf:Q4_K_M # Run inference directly in the terminal: llama cli -hf AlienTurk/Turkish-Asistant-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 AlienTurk/Turkish-Asistant-gguf:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf AlienTurk/Turkish-Asistant-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 AlienTurk/Turkish-Asistant-gguf:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf AlienTurk/Turkish-Asistant-gguf:Q4_K_M
Use Docker
docker model run hf.co/AlienTurk/Turkish-Asistant-gguf:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use AlienTurk/Turkish-Asistant-gguf with Ollama:
ollama run hf.co/AlienTurk/Turkish-Asistant-gguf:Q4_K_M
- Unsloth Studio
How to use AlienTurk/Turkish-Asistant-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 AlienTurk/Turkish-Asistant-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 AlienTurk/Turkish-Asistant-gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for AlienTurk/Turkish-Asistant-gguf to start chatting
- Atomic Chat new
- Docker Model Runner
How to use AlienTurk/Turkish-Asistant-gguf with Docker Model Runner:
docker model run hf.co/AlienTurk/Turkish-Asistant-gguf:Q4_K_M
- Lemonade
How to use AlienTurk/Turkish-Asistant-gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull AlienTurk/Turkish-Asistant-gguf:Q4_K_M
Run and chat with the model
lemonade run user.Turkish-Asistant-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 AlienTurk/Turkish-Asistant-gguf:Q4_K_M# Run inference directly in the terminal:
llama cli -hf AlienTurk/Turkish-Asistant-gguf:Q4_K_MUse 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 AlienTurk/Turkish-Asistant-gguf:Q4_K_M# Run inference directly in the terminal:
./llama-cli -hf AlienTurk/Turkish-Asistant-gguf:Q4_K_MBuild 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 AlienTurk/Turkish-Asistant-gguf:Q4_K_M# Run inference directly in the terminal:
./build/bin/llama-cli -hf AlienTurk/Turkish-Asistant-gguf:Q4_K_MUse Docker
docker model run hf.co/AlienTurk/Turkish-Asistant-gguf:Q4_K_MMira Turkish Assistant v7N (Qwen2.5-3B-IT-TR) — Doğal Dil SFT
v7N = v7 Natural. v6N üzerine iyileştirilmiş NLP patternleri ve ek senaryolarla genişletilmiş sürüm. Doğal Türkçe yanıt üretir; yapısal veri (intent, datetime, task_id) Hybrid NLP Engine tarafından runtime'da paralel çıkarılır.
Qwen2.5-3B-Instruct baz modeli üzerine Unsloth + TRL ile doğal dil SFT (Supervised Fine-Tuning) eğitilmiş Türkçe kişisel asistan modelidir.
v7N vs v6N
| Özellik | v6N | v7N |
|---|---|---|
| Türkçe time parser | saat X:XX, akşam X |
Lokatif (14'te), ayın 14, 5te, yazılı sayı |
| Date extraction | Gün isimleri | parseCalendarDay + parseWeekday (daha robust) |
| Disambiguation | P33 engine fallback | AlertDialog UI + engine |
| Undo stack | best-effort skip | 4 undo tipi (create/cancel/update + orijinal snapshot) |
Mimari
User → LLM (Qwen2.5-3B v7N) ↔ NLPEngine (rule-based)
↓ ↓
Doğal yanıt Intent + taskTitle + datetime
↓ ↓
└────────→ ActionDispatcher → DB + notification
Kullanım
from llama_cpp import Llama
from datetime import datetime
llm = Llama(
model_path="qwen2.5-3b-tr-asistant-v7N.Q4_K_M.gguf",
n_threads=3,
n_ctx=2048,
)
today_str = datetime.now().strftime("%d %B %Y, %A")
SYSTEM_PROMPT = f"""Sen son derece yardımcı, nazik, empatik ve organize bir Türkçe kişisel asistansın.
BUGÜN: {today_str}.
- Kullanıcıyla doğal, samimi ve akıcı bir sohbet kur.
- Görevlerini, randevularını ve hatırlatıcılarını yönet.
- Detaylar (özellikle saat) eksikse kibarca sor. Netse, doğal dille onayla.
- Asla ham JSON, kod bloğu veya teknik terim kullanma."""
output = llm.create_chat_completion(
messages=[
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": "Yarın saat 14'te fatura ödemeyi unutma."},
],
max_tokens=256,
)
print(output["choices"][0]["message"]["content"])
Engine Guard'lar
- TimeFabricationGuard — LLM saat uydurduğunda saat sorma template
- NotAlOverrideGuard — "not al" semantiği → saatsiz görev
- MultiTurnConfirmationRecoveryGuard — Çok-turlu onay
- ChatUpdateCancelRecoveryGuard — Multi-turn update/cancel
- P33 Disambiguation — 2+ match kullanıcıya seçim listele
Reply Formatları
| Senaryo | Format |
|---|---|
| 1.1 (saat sorma) | "Anladım [Gün] [Aktivite] için hangi saatte hatırlatma istersiniz?" |
| 1.3 (offset) | "[Gün] saat [HH:MM] [Aktivite] için hatırlatıcın [Süre] önce kuruldu." |
| 1.5 / Senaryo 2 | "[Gün] için [Aktivite] görevi kuruldu." |
| 3 (iptal) | "Tamam, [Aktivite] görevi silindi." |
| 5 (UPDATE) | "[Gün] [Aktivite] [Saat] için [Süre] önce güncellendi." |
Donanım Doğrulamaları (Mobile)
| Cihaz | Backend | Threads | Context | Warm Response | RAM |
|---|---|---|---|---|---|
| POCO X4 GT (Dimensity 8100) | CPU | 2 | 768 | ~6s | ~3 GB |
| iPhone 17 Pro (M3 Max sim) | CPU | 6 | 2048 | <2s | ~2.5 GB |
Lisans
Apache 2.0 (Qwen2.5 base modeliyle uyumlu).
Atıf
@misc{mira-turkish-assistant-v7n-2026,
author = {Algoritma Turk},
title = {Mira Turkish Assistant v7N (Natural): Qwen2.5-3B Fine-tuned with Natural-Language SFT for Turkish Personal Assistant Tasks},
year = {2026},
publisher = {Hugging Face},
url = {https://huggingface.co/AlienTurk/Turkish-Asistant-gguf}
}
Bağlantılar
- Base model: https://huggingface.co/Qwen/Qwen2.5-3B-Instruct
- Quantized GGUF: https://huggingface.co/AlienTurk/Turkish-Asistant-gguf
- Fine-tune framework: https://github.com/unslothai/unsloth
- Downloads last month
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4-bit
Install (macOS, Linux)
# Start a local OpenAI-compatible server with a web UI: llama serve -hf AlienTurk/Turkish-Asistant-gguf:Q4_K_M# Run inference directly in the terminal: llama cli -hf AlienTurk/Turkish-Asistant-gguf:Q4_K_M