Instructions to use SLT-AI/SLT-1.5B-GoToSmart with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SLT-AI/SLT-1.5B-GoToSmart with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SLT-AI/SLT-1.5B-GoToSmart",
	filename="slt_Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SLT-AI/SLT-1.5B-GoToSmart 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-1.5B-GoToSmart:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SLT-AI/SLT-1.5B-GoToSmart:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SLT-AI/SLT-1.5B-GoToSmart:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf SLT-AI/SLT-1.5B-GoToSmart: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 SLT-AI/SLT-1.5B-GoToSmart:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf SLT-AI/SLT-1.5B-GoToSmart: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 SLT-AI/SLT-1.5B-GoToSmart:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SLT-AI/SLT-1.5B-GoToSmart:Q4_K_M

Use Docker

docker model run hf.co/SLT-AI/SLT-1.5B-GoToSmart:Q4_K_M

LM Studio
Jan

vLLM

How to use SLT-AI/SLT-1.5B-GoToSmart with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SLT-AI/SLT-1.5B-GoToSmart"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SLT-AI/SLT-1.5B-GoToSmart",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SLT-AI/SLT-1.5B-GoToSmart:Q4_K_M

Ollama
How to use SLT-AI/SLT-1.5B-GoToSmart with Ollama:
```
ollama run hf.co/SLT-AI/SLT-1.5B-GoToSmart:Q4_K_M
```

Unsloth Studio

How to use SLT-AI/SLT-1.5B-GoToSmart 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-1.5B-GoToSmart 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-1.5B-GoToSmart 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-1.5B-GoToSmart to start chatting

How to use SLT-AI/SLT-1.5B-GoToSmart 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-1.5B-GoToSmart:Q4_K_M

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-1.5B-GoToSmart:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SLT-AI/SLT-1.5B-GoToSmart 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-1.5B-GoToSmart: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 SLT-AI/SLT-1.5B-GoToSmart:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use SLT-AI/SLT-1.5B-GoToSmart with Docker Model Runner:
```
docker model run hf.co/SLT-AI/SLT-1.5B-GoToSmart:Q4_K_M
```

Lemonade

How to use SLT-AI/SLT-1.5B-GoToSmart with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SLT-AI/SLT-1.5B-GoToSmart:Q4_K_M

Run and chat with the model

lemonade run user.SLT-1.5B-GoToSmart-Q4_K_M

List all available models

lemonade list

SLT-1.5B-GoToSmart / README.md

Salatec123

Update README.md

2e22fcf verified 6 days ago

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metadata

license: apache-2.0
language:
  - ru
  - en
  - pl
base_model:
  - Qwen/Qwen2.5-Coder-1.5B-Instruct
pipeline_tag: text-generation
tags:
  - Smart
  - Code
  - Speak
  - Russian
  - Ru
  - En
  - English
  - Pl
  - Polish
  - Helpful
  - Ai
  - Slt
  - Mini
  - 1.5b
  - gguf
  - Transformers
  - Llama.cpp
  - Coder
  - Helper

SLT-1.5B-GoToSmart

A 1.5B parameter conversational model based on Qwen2.5-1.5B.

Training Dataset

The model was fine-tuned on 15,000 high-quality examples.

The dataset includes:

Natural conversations in Russian, English and Polish
Up-to-date general knowledge (as of 2025-2026)
Python coding tasks
Mathematics with step-by-step explanations
Instruction-following dialogues

How to Use

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_name = "SLT-AI/SLT-1.5B-GoToSmart"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name, 
    torch_dtype=torch.bfloat16, 
    device_map="auto"
)

messages = [{"role": "user", "content": "Hello! How are you?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

outputs = model.generate(
    **inputs, 
    max_new_tokens=512, 
    temperature=0.7, 
    top_p=0.9
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))