Instructions to use RuCoverLAB/YaNA-Q4_K_M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use RuCoverLAB/YaNA-Q4_K_M-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 RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf RuCoverLAB/YaNA-Q4_K_M-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 RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M
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 RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama cli -hf RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M
Use Docker
docker model run hf.co/RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M
- Llama new
- LM Studio
- Jan
- Ollama
How to use RuCoverLAB/YaNA-Q4_K_M-GGUF with Ollama:
ollama run hf.co/RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use RuCoverLAB/YaNA-Q4_K_M-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M
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": "RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use RuCoverLAB/YaNA-Q4_K_M-GGUF with Docker Model Runner:
docker model run hf.co/RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M
- Lemonade
How to use RuCoverLAB/YaNA-Q4_K_M-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.YaNA-Q4_K_M-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use RuCoverLAB/YaNA-Q4_K_M-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M
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 RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use RuCoverLAB/YaNA-Q4_K_M-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M
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 "RuCoverLAB/YaNA-Q4_K_M-GGUF:Q4_K_M" \ --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"
RuCoverLAB/YaNA-Q4_K_M-GGUF
This model was converted to GGUF format from RuCoverLAB/YaNA using llama.cpp via the ggml.ai's GGUF-my-repo space.
Refer to the original model card for more details on the model.
English-language instructions:
How can it be used?
Install Ollama or Llama.cpp (works on MacOS, Windows and Linux) and download this model! or Download directly via the Hugging Face web interface or using the Hugging Face CLI.
Instructions for Ollama:
CLI:
ollama run RuCoverLAB/YaNA
cURL:
curl http://localhost:11434/api/chat \
-d '{
"model": "RuCoverLAB/YaNA",
"messages": [{"role": "user", "content": "Hello!"}]
}'
Python:
from ollama import chat
response = chat(
model='RuCoverLAB/YaNA',
messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)
JavaScript:
import ollama from 'ollama'
const response = await ollama.chat({
model: 'RuCoverLAB/YaNA',
messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)
Instructions for Hugging Face CLI:
CLI:
hf download RuCoverLAB/YaNA --local-dir "%Replace %% with the path to the folder where the model weights will be downloaded.%"
P.S. Please subscribe to my Telegram channel! Here is the link: https://t.me/rucoverlab
Русско язычная инструкция:
Как я могу запустить и/или скачать модель?
Установи Ollama или Llama.cpp (работает на MacOS, Windows, Linux) и скачай эту модель! или Скачай модель с веб интерфейса Hugging Face или с помощью Hugging Face CLI.
Инструкции для Ollama
CLI:
ollama run RuCoverLAB/YaNA
cURL:
curl http://localhost:11434/api/chat \
-d '{
"model": "RuCoverLAB/YaNA",
"messages": [{"role": "user", "content": "Hello!"}]
}'
Python:
from ollama import chat
response = chat(
model='RuCoverLAB/YaNA',
messages=[{'role': 'user', 'content': 'Hello!'}],
)
print(response.message.content)
JavaScript:
import ollama from 'ollama'
const response = await ollama.chat({
model: 'RuCoverLAB/YaNA',
messages: [{role: 'user', content: 'Hello!'}],
})
console.log(response.message.content)
Инструкция для Hugging Face CLI:
CLI:
hf download RuCoverLAB/YaNA --local-dir "%Замените %% на путь к папке, в которую будут загружены веса модели.%"
P.S. Пожалуйста подпишитесь на мой Telegram канал! Вот ссылка: https://t.me/rucoverlab
- Downloads last month
- 58
4-bit