Instructions to use Fantominsight/YAPPERTAR-ai-32B-1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Fantominsight/YAPPERTAR-ai-32B-1.0 with Adapters:

from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("fill-in-model-name")
model.load_adapter("Fantominsight/YAPPERTAR-ai-32B-1.0", set_active=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Fantominsight/YAPPERTAR-ai-32B-1.0 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 Fantominsight/YAPPERTAR-ai-32B-1.0:Q2_K
# Run inference directly in the terminal:
llama cli -hf Fantominsight/YAPPERTAR-ai-32B-1.0:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Fantominsight/YAPPERTAR-ai-32B-1.0:Q2_K
# Run inference directly in the terminal:
llama cli -hf Fantominsight/YAPPERTAR-ai-32B-1.0:Q2_K

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 Fantominsight/YAPPERTAR-ai-32B-1.0:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf Fantominsight/YAPPERTAR-ai-32B-1.0:Q2_K

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 Fantominsight/YAPPERTAR-ai-32B-1.0:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Fantominsight/YAPPERTAR-ai-32B-1.0:Q2_K

Use Docker

docker model run hf.co/Fantominsight/YAPPERTAR-ai-32B-1.0:Q2_K

LM Studio
Jan

vLLM

How to use Fantominsight/YAPPERTAR-ai-32B-1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Fantominsight/YAPPERTAR-ai-32B-1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Fantominsight/YAPPERTAR-ai-32B-1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Fantominsight/YAPPERTAR-ai-32B-1.0:Q2_K

Ollama
How to use Fantominsight/YAPPERTAR-ai-32B-1.0 with Ollama:
```
ollama run hf.co/Fantominsight/YAPPERTAR-ai-32B-1.0:Q2_K
```
Unsloth Desktop
Docker Model Runner
How to use Fantominsight/YAPPERTAR-ai-32B-1.0 with Docker Model Runner:
```
docker model run hf.co/Fantominsight/YAPPERTAR-ai-32B-1.0:Q2_K
```

Lemonade

How to use Fantominsight/YAPPERTAR-ai-32B-1.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Fantominsight/YAPPERTAR-ai-32B-1.0:Q2_K

Run and chat with the model

lemonade run user.YAPPERTAR-ai-32B-1.0-Q2_K

List all available models

lemonade list

Atomic Chat

Это более серъёзная модель для генерации текста.
Содержит 32 миллиарда параметров в формате GGUF.
На основе **Qwen2**, но права принадлежат DeepSeek-AI.
Эта модель на основе DeepSeek R1 Distill Qwen 32B.
Запускается в GPT4ALL, LMStudio, Ollama и д.р с поддержкой lamma.cpp GGUF.

Downloads last month: 2

GGUF

Model size

33B params

Architecture

qwen2

Hardware compatibility

2-bit

Model tree for Fantominsight/YAPPERTAR-ai-32B-1.0

Base model

deepseek-ai/DeepSeek-R1-Distill-Qwen-32B

Adapter

(224)

this model

Collection including Fantominsight/YAPPERTAR-ai-32B-1.0

YAPPERTAR-ai-1.0

Collection

Модели искусственного интелекта для генерации текста. Серия 1.0. • 5 items • Updated Apr 26 • 1