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 /README.md

Rahspide

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

b8f7f82 verified 1 day ago

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# bench

Three checks behind the numbers on the model card. All of them run on CPU.

| Script | What it proves |
|---|---|
| `eval_bpc.py` | bits/char, perplexity and top-k against the base model; exits 1 on regression |
| `bench_cpu.sh` | prompt and generation tok/s of a GGUF build, plus a sample answer |
| `chat_smoke.py` | the chat checkpoint answers instead of continuing the prompt |

```bash
pip install torch transformers datasets
# continuation model: 20k tokens of Russian web text against the base
python bench/eval_bpc.py --model Rahspide/MNN-Pocket-0.3B-RU \
    --base taobao-mnn/MNN-Pocket-0.3B-Base --dataset HuggingFaceFW/fineweb-2 --config rus_Cyrl
# quantised build on your own llama.cpp
LLAMA_CLI=/path/to/llama-cli bash bench/bench_cpu.sh gguf/MNN-Pocket-0.3B-RU-Q4_K_M.gguf
# assistant: template in place, answers not echoed
python bench/chat_smoke.py --model Rahspide/MNN-Pocket-0.3B-RU-Chat --cpu
```

Notes:

- `eval_bpc.py` uses bits/char, not perplexity, as its comparison unit: perplexity is not comparable between
  models with different tokenizers, bits per character is.
- fp32 is the default so the numbers match the card. `--bf16` is roughly 15% faster on AVX-512 machines and
  moves the third decimal.
- `bench_cpu.sh` parses llama.cpp's own timing line, so it measures the binary you actually ship. It also
  accepts a decimal comma, which some locales print.

	# bench

	Three checks behind the numbers on the model card. All of them run on CPU.

	\| Script \| What it proves \|
	\|---\|---\|
	\| `eval_bpc.py` \| bits/char, perplexity and top-k against the base model; exits 1 on regression \|
	\| `bench_cpu.sh` \| prompt and generation tok/s of a GGUF build, plus a sample answer \|
	\| `chat_smoke.py` \| the chat checkpoint answers instead of continuing the prompt \|

	```bash
	pip install torch transformers datasets

	# continuation model: 20k tokens of Russian web text against the base
	python bench/eval_bpc.py --model Rahspide/MNN-Pocket-0.3B-RU \
	--base taobao-mnn/MNN-Pocket-0.3B-Base --dataset HuggingFaceFW/fineweb-2 --config rus_Cyrl

	# quantised build on your own llama.cpp
	LLAMA_CLI=/path/to/llama-cli bash bench/bench_cpu.sh gguf/MNN-Pocket-0.3B-RU-Q4_K_M.gguf

	# assistant: template in place, answers not echoed
	python bench/chat_smoke.py --model Rahspide/MNN-Pocket-0.3B-RU-Chat --cpu
	```

	Notes:

	- `eval_bpc.py` uses bits/char, not perplexity, as its comparison unit: perplexity is not comparable between
	models with different tokenizers, bits per character is.
	- fp32 is the default so the numbers match the card. `--bf16` is roughly 15% faster on AVX-512 machines and
	moves the third decimal.
	- `bench_cpu.sh` parses llama.cpp's own timing line, so it measures the binary you actually ship. It also
	accepts a decimal comma, which some locales print.