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

Libraries

How to use Rahspide/MNN-Pocket-0.3B-RU-Chat 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-Chat")
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-Chat")
model = AutoModelForCausalLM.from_pretrained("Rahspide/MNN-Pocket-0.3B-RU-Chat", 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-Chat 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-Chat:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Rahspide/MNN-Pocket-0.3B-RU-Chat: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-Chat:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Rahspide/MNN-Pocket-0.3B-RU-Chat: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-Chat:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Rahspide/MNN-Pocket-0.3B-RU-Chat:Q4_K_M

Use Docker

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

Llama new
LM Studio
Jan

vLLM

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

Use Docker

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

SGLang

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

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

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

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

Hermes Agent

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

Run Hermes

hermes

Atomic Chat

OpenClaw

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

Rahspide commited on about 14 hours ago

Commit

b9bc2eb

verified ·

1 Parent(s): afad59a

docs: drop forward-looking notes, keep released facts only

Browse files

Files changed (1) hide show

README.md +1 -6

README.md CHANGED Viewed

@@ -58,9 +58,6 @@ The base `MNN-Pocket-0.3B` is bilingual zh/en and has essentially no Russian; a
 instruct model would fight both problems at once (new language and new format). Starting from the continuation
 model means Russian is already paid for, and SFT only has to teach the dialogue shape.
-A control run on the official `taobao-mnn/MNN-Pocket-0.3B` with the same SFT data is still planned, so that
-"we lost the assistant in pretraining" can be told apart from "there was no assistant to lose".
 ## Recipe (as run)
 - Start point: `Rahspide/MNN-Pocket-0.3B-RU` (step 36,621).
@@ -154,7 +151,6 @@ table above.
 The sheet with all 60 prompts and answers is in
 [`review/blind_sheet.md`](https://huggingface.co/Rahspide/MNN-Pocket-0.3B-RU-Chat/blob/main/review/blind_sheet.md).
-Human rating on instruction following, naturalness, refusal and 2–3 turn coherence is still open.
 A comparison against `taobao-mnn/MNN-Pocket-0.3B` was attempted on the same prompts and dropped. That checkpoint
 degenerates on Russian input, repeating the prompt in bold markdown fragments:
@@ -164,8 +160,7 @@ Q: Как стать популярным в яндекс дзен?
 A: Конечный откорт: **"Популярный в яндекс дзен"** — это **не** в яндекс (в яндекс) — **в яндекс** — **в яндекс** — …
 ```
-So it is not a baseline. A fair comparison needs the official checkpoint SFT-tuned on the same data, which is
-still open.
 ## Limitations

 instruct model would fight both problems at once (new language and new format). Starting from the continuation
 model means Russian is already paid for, and SFT only has to teach the dialogue shape.
 ## Recipe (as run)
 - Start point: `Rahspide/MNN-Pocket-0.3B-RU` (step 36,621).
 The sheet with all 60 prompts and answers is in
 [`review/blind_sheet.md`](https://huggingface.co/Rahspide/MNN-Pocket-0.3B-RU-Chat/blob/main/review/blind_sheet.md).
 A comparison against `taobao-mnn/MNN-Pocket-0.3B` was attempted on the same prompts and dropped. That checkpoint
 degenerates on Russian input, repeating the prompt in bold markdown fragments:
 A: Конечный откорт: **"Популярный в яндекс дзен"** — это **не** в яндекс (в яндекс) — **в яндекс** — **в яндекс** — …
 ```
+So it is not a baseline.
 ## Limitations