Instructions to use minihizme/Mini-AI-v1.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use minihizme/Mini-AI-v1.5 with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="minihizme/Mini-AI-v1.5",
	filename="mini_hizli.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use minihizme/Mini-AI-v1.5 with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf minihizme/Mini-AI-v1.5
# Run inference directly in the terminal:
llama-cli -hf minihizme/Mini-AI-v1.5

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf minihizme/Mini-AI-v1.5
# Run inference directly in the terminal:
llama-cli -hf minihizme/Mini-AI-v1.5

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 minihizme/Mini-AI-v1.5
# Run inference directly in the terminal:
./llama-cli -hf minihizme/Mini-AI-v1.5

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 minihizme/Mini-AI-v1.5
# Run inference directly in the terminal:
./build/bin/llama-cli -hf minihizme/Mini-AI-v1.5

Use Docker

docker model run hf.co/minihizme/Mini-AI-v1.5

LM Studio
Jan
Ollama
How to use minihizme/Mini-AI-v1.5 with Ollama:
```
ollama run hf.co/minihizme/Mini-AI-v1.5
```

Unsloth Studio

How to use minihizme/Mini-AI-v1.5 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 minihizme/Mini-AI-v1.5 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 minihizme/Mini-AI-v1.5 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for minihizme/Mini-AI-v1.5 to start chatting

Atomic Chat new
Docker Model Runner
How to use minihizme/Mini-AI-v1.5 with Docker Model Runner:
```
docker model run hf.co/minihizme/Mini-AI-v1.5
```

Lemonade

How to use minihizme/Mini-AI-v1.5 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull minihizme/Mini-AI-v1.5

Run and chat with the model

lemonade run user.Mini-AI-v1.5-{{QUANT_TAG}}

List all available models

lemonade list

🤖 Mini-AI v1.5

Bu depo, Ahmet tarafından özel olarak optimize edilen Mini yapay zeka serisini içerir. Mini, her boyutta cihazda yüksek performansla çalışmak üzere tasarlanmış, samimi ve yardımsever bir asistandır.

✨ Mini'nin Özellikleri

Adı: Mini
Yapımcı: Ahmet
Dil: %100 Türkçe 🇹🇷
Karakter: Yardımsever, samimi ve çözüm odaklı.

📦 Mevcut Versiyonlar

mini_hizli.gguf: En düşük kaynak tüketimi, en yüksek hız.
mini_smart.gguf: Gelişmiş mantık ve akıl yürütme becerisi.
mini_denge.gguf: Hız ve zekanın optimize edilmiş dengesi.
mini_teknik.gguf: Kodlama ve teknik analiz yeteneği.

🛠️ Temel Komut (System Prompt)

Bütün versiyonlar şu temel kimlik üzerine inşa edilmiştir:

"Senin adın Mini. Seni Ahmet yaptı. Ahmet'in yardımsever, samimi ve her zaman Türkçe konuşan yapay zekasısın."

Model Sürümü: mini-ultime-pro-3-offline Geliştirici: Ahmet

Downloads last month: -

GGUF

Model size

291 params

Architecture

qwen2

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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