Instructions to use Slaxikov/VokiLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Slaxikov/VokiLLM 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 Slaxikov/VokiLLM:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Slaxikov/VokiLLM:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/Slaxikov/VokiLLM:Q4_K_M

LM Studio
Jan
Ollama
How to use Slaxikov/VokiLLM with Ollama:
```
ollama run hf.co/Slaxikov/VokiLLM:Q4_K_M
```
Unsloth Desktop

How to use Slaxikov/VokiLLM with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Slaxikov/VokiLLM: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": "Slaxikov/VokiLLM:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Slaxikov/VokiLLM with Docker Model Runner:
```
docker model run hf.co/Slaxikov/VokiLLM:Q4_K_M
```

Lemonade

How to use Slaxikov/VokiLLM with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Slaxikov/VokiLLM:Q4_K_M

Run and chat with the model

lemonade run user.VokiLLM-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Slaxikov/VokiLLM with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Slaxikov/VokiLLM: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 Slaxikov/VokiLLM:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Slaxikov/VokiLLM with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Slaxikov/VokiLLM: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 "Slaxikov/VokiLLM: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"

VokiLLM / README.md

Slaxikov

Update README.md

191af07 verified 23 days ago

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---
license: apache-2.0
language:
- ru
tags:
- gguf
- instruct
---
!!! ПРОЧИТАЙТЕ ВСË !!! 

# VokiLLM-0.5B-Instruct (GGUF)

Готовые файлы модели **VokiLLM-0.5B-Instruct** в формате **GGUF**: FP16 и квантовки для `llama.cpp`/LM Studio и других совместимых рантаймов.

## Кратко

- **Архитектура**: `qwen2`
- **Размер**: ~0.5B (лейбл в GGUF: `630M`)
- **Контекст**: 8192
- **Назначение**: Instruct / чат

## Файлы

Файлы лежат **в корне репозитория**. В таблице ниже названия — это **ссылки на скачивание**.

| Файл | Квантование | Размер (bytes) |
|---|---:|---:|
| [`vokillm-0.5b-instruct-fp16.gguf`](vokillm-0.5b-instruct-fp16.gguf?download=true) | FP16 | 1266425856 |
| [`vokillm-0.5b-instruct-q8_0.gguf`](vokillm-0.5b-instruct-q8_0.gguf?download=true) | Q8_0 | 675710976 |
| [`vokillm-0.5b-instruct-q6_k.gguf`](vokillm-0.5b-instruct-q6_k.gguf?download=true) | Q6_K | 650379296 |
| [`vokillm-0.5b-instruct-q5_k_m.gguf`](vokillm-0.5b-instruct-q5_k_m.gguf?download=true) | Q5_K_M | 522186752 |
| [`vokillm-0.5b-instruct-q5_1.gguf`](vokillm-0.5b-instruct-q5_1.gguf?download=true) | Q5_1 | 521348096 |
| [`vokillm-0.5b-instruct-q5_k_s.gguf`](vokillm-0.5b-instruct-q5_k_s.gguf?download=true) | Q5_K_S | 514810880 |
| [`vokillm-0.5b-instruct-q5_0.gguf`](vokillm-0.5b-instruct-q5_0.gguf?download=true) | Q5_0 | 490475520 |
| [`vokillm-0.5b-instruct-q4_k_m.gguf`](vokillm-0.5b-instruct-q4_k_m.gguf?download=true) | Q4_K_M | 491400192 |
| [`vokillm-0.5b-instruct-q4_k_s.gguf`](vokillm-0.5b-instruct-q4_k_s.gguf?download=true) | Q4_K_S | 479064064 |
| [`vokillm-0.5b-instruct-q4_1.gguf`](vokillm-0.5b-instruct-q4_1.gguf?download=true) | Q4_1 | 459602944 |
| [`vokillm-0.5b-instruct-q3_k_l.gguf`](vokillm-0.5b-instruct-q3_k_l.gguf?download=true) | Q3_K_L | 445933568 |
| [`vokillm-0.5b-instruct-iq4_nl.gguf`](vokillm-0.5b-instruct-iq4_nl.gguf?download=true) | IQ4_NL | 430880768 |
| [`vokillm-0.5b-instruct-q4_0.gguf`](vokillm-0.5b-instruct-q4_0.gguf?download=true) | Q4_0 | 428730368 |
| [`vokillm-0.5b-instruct-iq4_xs.gguf`](vokillm-0.5b-instruct-iq4_xs.gguf?download=true) | IQ4_XS | 428020736 |
| [`vokillm-0.5b-instruct-q3_k_m.gguf`](vokillm-0.5b-instruct-q3_k_m.gguf?download=true) | Q3_K_M | 432041984 |
| [`vokillm-0.5b-instruct-iq3_xxs.gguf`](vokillm-0.5b-instruct-iq3_xxs.gguf?download=true) | IQ3_XXS | 416919296 |
| [`vokillm-0.5b-instruct-iq3_s.gguf`](vokillm-0.5b-instruct-iq3_s.gguf?download=true) | IQ3_S | 415182848 |
| [`vokillm-0.5b-instruct-q2_k.gguf`](vokillm-0.5b-instruct-q2_k.gguf?download=true) | Q2_K | 415182848 |
| [`vokillm-0.5b-instruct-q3_k_s.gguf`](vokillm-0.5b-instruct-q3_k_s.gguf?download=true) | Q3_K_S | 414838784 |
| [`vokillm-0.5b-instruct-iq2_s.gguf`](vokillm-0.5b-instruct-iq2_s.gguf?download=true) | IQ2_S | 402312928 |
| [`vokillm-0.5b-instruct-iq2_xs.gguf`](vokillm-0.5b-instruct-iq2_xs.gguf?download=true) | IQ2_XS | 400985056 |
| [`vokillm-0.5b-instruct-iq2_xxs.gguf`](vokillm-0.5b-instruct-iq2_xxs.gguf?download=true) | IQ2_XXS | 398125024 |
| [`vokillm-0.5b-instruct-iq1_s.gguf`](vokillm-0.5b-instruct-iq1_s.gguf?download=true) | IQ1_S | 392404960 |
## Как запустить
### llama.cpp (CLI)
Пример (Windows):
```bash
llama-cli.exe -m "vokillm-0.5b-instruct-q4_k_m.gguf" -p "Привет! Коротко объясни, что такое GGUF."
```
### LM Studio
- Открой LM Studio → **Models** → **Add model** → выбери нужный `*.gguf`
- Рекомендуемый стартовый вариант: `q4_k_m` (баланс скорость/качество)
## Про квантовки IQ2/IQ1
Квантовки `IQ2_*` и `IQ1_S` сделаны с **importance matrix (imatrix)** (это повышает качество для “экстремальных” квантовок).
## Крипта
- Я создал крипту
- Если хотите можете купить
## Пожалуйста, хугинг фейс, не баньте
https://t.me/blum/app?startapp=memepadjetton_VT_ceNrH-ref_OvadS4OvlT
## Наш канал
https://max.ru/join/24APyr7y4WrcH0FT3gbRr_jE1nkFckNtRr2r_vuRppk

	---
	license: apache-2.0
	language:
	- ru
	tags:
	- gguf
	- instruct
	---

	!!! ПРОЧИТАЙТЕ ВСË !!!

	# VokiLLM-0.5B-Instruct (GGUF)

	Готовые файлы модели VokiLLM-0.5B-Instruct в формате GGUF: FP16 и квантовки для `llama.cpp`/LM Studio и других совместимых рантаймов.

	## Кратко

	- Архитектура: `qwen2`
	- Размер: ~0.5B (лейбл в GGUF: `630M`)
	- Контекст: 8192
	- Назначение: Instruct / чат

	## Файлы

	Файлы лежат в корне репозитория. В таблице ниже названия — это ссылки на скачивание.

	\| Файл \| Квантование \| Размер (bytes) \|
	\|---\|---:\|---:\|
	\| [`vokillm-0.5b-instruct-fp16.gguf`](vokillm-0.5b-instruct-fp16.gguf?download=true) \| FP16 \| 1266425856 \|
	\| [`vokillm-0.5b-instruct-q8_0.gguf`](vokillm-0.5b-instruct-q8_0.gguf?download=true) \| Q8_0 \| 675710976 \|
	\| [`vokillm-0.5b-instruct-q6_k.gguf`](vokillm-0.5b-instruct-q6_k.gguf?download=true) \| Q6_K \| 650379296 \|
	\| [`vokillm-0.5b-instruct-q5_k_m.gguf`](vokillm-0.5b-instruct-q5_k_m.gguf?download=true) \| Q5_K_M \| 522186752 \|
	\| [`vokillm-0.5b-instruct-q5_1.gguf`](vokillm-0.5b-instruct-q5_1.gguf?download=true) \| Q5_1 \| 521348096 \|
	\| [`vokillm-0.5b-instruct-q5_k_s.gguf`](vokillm-0.5b-instruct-q5_k_s.gguf?download=true) \| Q5_K_S \| 514810880 \|
	\| [`vokillm-0.5b-instruct-q5_0.gguf`](vokillm-0.5b-instruct-q5_0.gguf?download=true) \| Q5_0 \| 490475520 \|
	\| [`vokillm-0.5b-instruct-q4_k_m.gguf`](vokillm-0.5b-instruct-q4_k_m.gguf?download=true) \| Q4_K_M \| 491400192 \|
	\| [`vokillm-0.5b-instruct-q4_k_s.gguf`](vokillm-0.5b-instruct-q4_k_s.gguf?download=true) \| Q4_K_S \| 479064064 \|
	\| [`vokillm-0.5b-instruct-q4_1.gguf`](vokillm-0.5b-instruct-q4_1.gguf?download=true) \| Q4_1 \| 459602944 \|
	\| [`vokillm-0.5b-instruct-q3_k_l.gguf`](vokillm-0.5b-instruct-q3_k_l.gguf?download=true) \| Q3_K_L \| 445933568 \|
	\| [`vokillm-0.5b-instruct-iq4_nl.gguf`](vokillm-0.5b-instruct-iq4_nl.gguf?download=true) \| IQ4_NL \| 430880768 \|
	\| [`vokillm-0.5b-instruct-q4_0.gguf`](vokillm-0.5b-instruct-q4_0.gguf?download=true) \| Q4_0 \| 428730368 \|
	\| [`vokillm-0.5b-instruct-iq4_xs.gguf`](vokillm-0.5b-instruct-iq4_xs.gguf?download=true) \| IQ4_XS \| 428020736 \|
	\| [`vokillm-0.5b-instruct-q3_k_m.gguf`](vokillm-0.5b-instruct-q3_k_m.gguf?download=true) \| Q3_K_M \| 432041984 \|
	\| [`vokillm-0.5b-instruct-iq3_xxs.gguf`](vokillm-0.5b-instruct-iq3_xxs.gguf?download=true) \| IQ3_XXS \| 416919296 \|
	\| [`vokillm-0.5b-instruct-iq3_s.gguf`](vokillm-0.5b-instruct-iq3_s.gguf?download=true) \| IQ3_S \| 415182848 \|
	\| [`vokillm-0.5b-instruct-q2_k.gguf`](vokillm-0.5b-instruct-q2_k.gguf?download=true) \| Q2_K \| 415182848 \|
	\| [`vokillm-0.5b-instruct-q3_k_s.gguf`](vokillm-0.5b-instruct-q3_k_s.gguf?download=true) \| Q3_K_S \| 414838784 \|
	\| [`vokillm-0.5b-instruct-iq2_s.gguf`](vokillm-0.5b-instruct-iq2_s.gguf?download=true) \| IQ2_S \| 402312928 \|
	\| [`vokillm-0.5b-instruct-iq2_xs.gguf`](vokillm-0.5b-instruct-iq2_xs.gguf?download=true) \| IQ2_XS \| 400985056 \|
	\| [`vokillm-0.5b-instruct-iq2_xxs.gguf`](vokillm-0.5b-instruct-iq2_xxs.gguf?download=true) \| IQ2_XXS \| 398125024 \|
	\| [`vokillm-0.5b-instruct-iq1_s.gguf`](vokillm-0.5b-instruct-iq1_s.gguf?download=true) \| IQ1_S \| 392404960 \|

	## Как запустить

	### llama.cpp (CLI)

	Пример (Windows):

	```bash
	llama-cli.exe -m "vokillm-0.5b-instruct-q4_k_m.gguf" -p "Привет! Коротко объясни, что такое GGUF."
	```

	### LM Studio

	- Открой LM Studio → Models → Add model → выбери нужный `*.gguf`
	- Рекомендуемый стартовый вариант: `q4_k_m` (баланс скорость/качество)

	## Про квантовки IQ2/IQ1

	Квантовки `IQ2_` и `IQ1_S` сделаны с importance matrix (imatrix)* (это повышает качество для “экстремальных” квантовок).

	## Крипта

	- Я создал крипту
	- Если хотите можете купить

	## Пожалуйста, хугинг фейс, не баньте

	https://t.me/blum/app?startapp=memepadjetton_VT_ceNrH-ref_OvadS4OvlT

	## Наш канал

	https://max.ru/join/24APyr7y4WrcH0FT3gbRr_jE1nkFckNtRr2r_vuRppk