Instructions to use SLT-AI/SLT-1b-GoToSmart-Pilot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use SLT-AI/SLT-1b-GoToSmart-Pilot 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 SLT-AI/SLT-1b-GoToSmart-Pilot
# Run inference directly in the terminal:
llama cli -hf SLT-AI/SLT-1b-GoToSmart-Pilot

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SLT-AI/SLT-1b-GoToSmart-Pilot
# Run inference directly in the terminal:
llama cli -hf SLT-AI/SLT-1b-GoToSmart-Pilot

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 SLT-AI/SLT-1b-GoToSmart-Pilot
# Run inference directly in the terminal:
./llama-cli -hf SLT-AI/SLT-1b-GoToSmart-Pilot

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 SLT-AI/SLT-1b-GoToSmart-Pilot
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SLT-AI/SLT-1b-GoToSmart-Pilot

Use Docker

docker model run hf.co/SLT-AI/SLT-1b-GoToSmart-Pilot

LM Studio
Jan
Ollama
How to use SLT-AI/SLT-1b-GoToSmart-Pilot with Ollama:
```
ollama run hf.co/SLT-AI/SLT-1b-GoToSmart-Pilot
```

Unsloth Studio

How to use SLT-AI/SLT-1b-GoToSmart-Pilot 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 SLT-AI/SLT-1b-GoToSmart-Pilot 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 SLT-AI/SLT-1b-GoToSmart-Pilot to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SLT-AI/SLT-1b-GoToSmart-Pilot to start chatting

How to use SLT-AI/SLT-1b-GoToSmart-Pilot with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SLT-AI/SLT-1b-GoToSmart-Pilot

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SLT-AI/SLT-1b-GoToSmart-Pilot"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use SLT-AI/SLT-1b-GoToSmart-Pilot with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SLT-AI/SLT-1b-GoToSmart-Pilot

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 "SLT-AI/SLT-1b-GoToSmart-Pilot" \
  --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"

Docker Model Runner
How to use SLT-AI/SLT-1b-GoToSmart-Pilot with Docker Model Runner:
```
docker model run hf.co/SLT-AI/SLT-1b-GoToSmart-Pilot
```

Lemonade

How to use SLT-AI/SLT-1b-GoToSmart-Pilot with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SLT-AI/SLT-1b-GoToSmart-Pilot

Run and chat with the model

lemonade run user.SLT-1b-GoToSmart-Pilot-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use SLT-AI/SLT-1b-GoToSmart-Pilot with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SLT-AI/SLT-1b-GoToSmart-Pilot

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 SLT-AI/SLT-1b-GoToSmart-Pilot

Run Hermes

hermes

Atomic Chat

SLT-1b-GoToSmart-Pilot

File size: 1,965 Bytes

---
license: apache-2.0
language:
- ru
- en
base_model:
- meta-llama/Llama-3.2-1B
tags:
- gguf
- q4km
- q4
- smart
- speakru
- en
- english
- russian
- llama
- pilot
- 1b
- small
- base
- mini
- ai
- model
---
Model Card: SLT-1b-GoToSmart-Pilot (English)
Model Details

Developer: slt ai

Model Type: Causal Language Model

Base Model: Llama 3.2 1B Instruct

Language(s): English, Russian

Version: Pilot

Model Description
SLT-1b-GoToSmart-Pilot is a pilot version of a conversational language model from the GoTo series. Built upon the Llama 3.2 1B Instruct architecture, this model has been specifically fine-tuned to understand and generate high-quality text in both English and Russian.

Training Details

Training Data: The model was fine-tuned on a custom, curated dataset consisting of exactly 13,500 examples.

Карточка модели: SLT-1b-GoToSmart-Pilot (Русский)
Сведения о модели

Разработчик: slt ai

Тип модели: Авторегрессионная языковая модель

Базовая модель: Llama 3.2 1B Instruct

Поддерживаемые языки: Английский, Русский

Версия: Пилотная (Pilot)

Описание модели
SLT-1b-GoToSmart-Pilot — это пилотная версия диалоговой языковой модели из линейки GoTo. Базируясь на архитектуре Llama 3.2 1B Instruct, модель была целенаправленно дообучена для качественного понимания и генерации текста как на английском, так и на русском языках.

Детали обучения

Обучающие данные: Модель прошла процесс дообучения (fine-tuning) на специально подготовленном наборе данных, состоящем из 13 500 примеров.