Instructions to use Rahspide/MNN-Pocket-0.3B-RU-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Rahspide/MNN-Pocket-0.3B-RU-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 Rahspide/MNN-Pocket-0.3B-RU-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Rahspide/MNN-Pocket-0.3B-RU-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 Rahspide/MNN-Pocket-0.3B-RU-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Rahspide/MNN-Pocket-0.3B-RU-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 Rahspide/MNN-Pocket-0.3B-RU-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Rahspide/MNN-Pocket-0.3B-RU-GGUF:Q4_K_M

Use Docker

docker model run hf.co/Rahspide/MNN-Pocket-0.3B-RU-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Rahspide/MNN-Pocket-0.3B-RU-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Rahspide/MNN-Pocket-0.3B-RU-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rahspide/MNN-Pocket-0.3B-RU-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Rahspide/MNN-Pocket-0.3B-RU-GGUF:Q4_K_M

Ollama
How to use Rahspide/MNN-Pocket-0.3B-RU-GGUF with Ollama:
```
ollama run hf.co/Rahspide/MNN-Pocket-0.3B-RU-GGUF:Q4_K_M
```
Unsloth Desktop

How to use Rahspide/MNN-Pocket-0.3B-RU-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Rahspide/MNN-Pocket-0.3B-RU-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": "Rahspide/MNN-Pocket-0.3B-RU-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Rahspide/MNN-Pocket-0.3B-RU-GGUF with Docker Model Runner:
```
docker model run hf.co/Rahspide/MNN-Pocket-0.3B-RU-GGUF:Q4_K_M
```

Lemonade

How to use Rahspide/MNN-Pocket-0.3B-RU-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Rahspide/MNN-Pocket-0.3B-RU-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.MNN-Pocket-0.3B-RU-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Rahspide/MNN-Pocket-0.3B-RU-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 Rahspide/MNN-Pocket-0.3B-RU-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 Rahspide/MNN-Pocket-0.3B-RU-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Rahspide/MNN-Pocket-0.3B-RU-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Rahspide/MNN-Pocket-0.3B-RU-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 "Rahspide/MNN-Pocket-0.3B-RU-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"

MNN-Pocket-0.3B-RU GGUF

A Russian text-continuation model in 0.36 B parameters, converted to GGUF for llama.cpp. It continues a picked prefix instead of answering it, so it suits keyboard suggestions and autocomplete, not chat.

Trained by continued pretraining on 1.2 B tokens (0.9 B ru + 0.204 B en + 0.096 B zh) on top of taobao-mnn/MNN-Pocket-0.3B-Base. Weights, training recipe and measurements live in Rahspide/MNN-Pocket-0.3B-RU.

Run

llama-cli -hf Rahspide/MNN-Pocket-0.3B-RU-GGUF:Q4_K_M -p "Сегодня утром я" -n 40

Quants

File	Size	Note
F16	581 MB	reference, not for shipping
Q8_0	310 MB	near-lossless, desktop
Q4_K_M	223 MB	the size to ship on a phone

Measured on a 16-thread x86 CPU: Q4_K_M ~123–130 tok/s generation, Q8_0 ~99–103 tok/s. Phone numbers are not measured here.

The tokenizer is byte-level, so a token can end in the middle of a Cyrillic character. llama.cpp buffers the partial UTF-8 sequence itself; a hand-rolled decoder has to do the same.

Downloads last month: -

GGUF

Model size

0.3B params

Architecture

qwen3

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for Rahspide/MNN-Pocket-0.3B-RU-GGUF

Base model

taobao-mnn/MNN-Pocket-0.3B-Base

Quantized

Rahspide/MNN-Pocket-0.3B-RU

Quantized

(2)

this model