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

Libraries

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

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Rahspide/MNN-Pocket-0.3B-RU")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Rahspide/MNN-Pocket-0.3B-RU")
model = AutoModelForCausalLM.from_pretrained("Rahspide/MNN-Pocket-0.3B-RU", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Use Docker

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

Llama new
LM Studio
Jan

vLLM

How to use Rahspide/MNN-Pocket-0.3B-RU 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"
# 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",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

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

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Rahspide/MNN-Pocket-0.3B-RU" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rahspide/MNN-Pocket-0.3B-RU",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Rahspide/MNN-Pocket-0.3B-RU" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Rahspide/MNN-Pocket-0.3B-RU",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

How to use Rahspide/MNN-Pocket-0.3B-RU 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: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:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

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

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Rahspide/MNN-Pocket-0.3B-RU 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: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: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 / bench /bench_cpu.sh

Rahspide

docs: designed card — cover, charts, tests, releases

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#!/usr/bin/env bash
# Пропускная способность GGUF на CPU: prompt processing и генерация.
#   bash bench/bench_cpu.sh gguf/MNN-Pocket-0.3B-RU-Q4_K_M.gguf [--chat]
# Нужен llama.cpp: LLAMA_CLI (по умолчанию llama-cli из PATH).
set -euo pipefail

MODEL="${1:?usage: bench_cpu.sh <model.gguf> [--chat]}"
shift || true
CHAT=0
for a in "$@"; do [ "$a" = "--chat" ] && CHAT=1; done

LLAMA_CLI="${LLAMA_CLI:-llama-cli}"
PROMPT="Объясни в двух предложениях, что такое инфляция."
EXTRA=()
if [ "$CHAT" = 1 ]; then EXTRA+=(--jinja); fi

raw="$("$LLAMA_CLI" -m "$MODEL" -p "$PROMPT" -n 64 --temp 0 -st --no-display-prompt \
        "${EXTRA[@]}" 2>&1 || true)"

# llama.cpp печатает тайминги в своей локали: "237,7 t/s"
echo "$raw" | tr ',' '.' | sed -n 's/.*Prompt: *\([0-9.]*\) *t\/s.*Generation: *\([0-9.]*\) *t\/s.*/prompt      \1 tok\/s\ngeneration  \2 tok\/s/p' | tail -2

# ответ — строки после эха промпта ("> ...") и до строки с таймингами
answer="$(echo "$raw" | awk '/^> /{p=1; next} /Prompt:/{p=0} p' | sed '/^\[/d' | sed '/^$/d')"
if [ -z "$answer" ]; then
  echo "FAIL: empty answer" >&2
  exit 1
fi
echo "--- sample ---"
echo "$answer" | head -4

	#!/usr/bin/env bash
	# Пропускная способность GGUF на CPU: prompt processing и генерация.
	# bash bench/bench_cpu.sh gguf/MNN-Pocket-0.3B-RU-Q4_K_M.gguf [--chat]
	# Нужен llama.cpp: LLAMA_CLI (по умолчанию llama-cli из PATH).
	set -euo pipefail

	MODEL="${1:?usage: bench_cpu.sh <model.gguf> [--chat]}"
	shift \|\| true
	CHAT=0
	for a in "$@"; do [ "$a" = "--chat" ] && CHAT=1; done

	LLAMA_CLI="${LLAMA_CLI:-llama-cli}"
	PROMPT="Объясни в двух предложениях, что такое инфляция."
	EXTRA=()
	if [ "$CHAT" = 1 ]; then EXTRA+=(--jinja); fi

	raw="$("$LLAMA_CLI" -m "$MODEL" -p "$PROMPT" -n 64 --temp 0 -st --no-display-prompt \
	"${EXTRA[@]}" 2>&1 \|\| true)"

	# llama.cpp печатает тайминги в своей локали: "237,7 t/s"
	echo "$raw" \| tr ',' '.' \| sed -n 's/.Prompt: \([0-9.]\) t\/s.Generation: \([0-9.]\) t\/s.*/prompt \1 tok\/s\ngeneration \2 tok\/s/p' \| tail -2

	# ответ — строки после эха промпта ("> ...") и до строки с таймингами
	answer="$(echo "$raw" \| awk '/^> /{p=1; next} /Prompt:/{p=0} p' \| sed '/^\[/d' \| sed '/^$/d')"
	if [ -z "$answer" ]; then
	echo "FAIL: empty answer" >&2
	exit 1
	fi
	echo "--- sample ---"
	echo "$answer" \| head -4