Instructions to use Aakash098/ShinchanAI-small with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Aakash098/ShinchanAI-small with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Aakash098/ShinchanAI-small",
	filename="gemma-2-2b.Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Aakash098/ShinchanAI-small with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Aakash098/ShinchanAI-small:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Aakash098/ShinchanAI-small:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Aakash098/ShinchanAI-small:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Aakash098/ShinchanAI-small: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 Aakash098/ShinchanAI-small:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Aakash098/ShinchanAI-small: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 Aakash098/ShinchanAI-small:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Aakash098/ShinchanAI-small:Q4_K_M

Use Docker

docker model run hf.co/Aakash098/ShinchanAI-small:Q4_K_M

LM Studio
Jan

vLLM

How to use Aakash098/ShinchanAI-small with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Aakash098/ShinchanAI-small"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Aakash098/ShinchanAI-small",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Aakash098/ShinchanAI-small:Q4_K_M

Ollama
How to use Aakash098/ShinchanAI-small with Ollama:
```
ollama run hf.co/Aakash098/ShinchanAI-small:Q4_K_M
```

Unsloth Studio new

How to use Aakash098/ShinchanAI-small with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Aakash098/ShinchanAI-small to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Aakash098/ShinchanAI-small to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Aakash098/ShinchanAI-small to start chatting

Docker Model Runner
How to use Aakash098/ShinchanAI-small with Docker Model Runner:
```
docker model run hf.co/Aakash098/ShinchanAI-small:Q4_K_M
```

Lemonade

How to use Aakash098/ShinchanAI-small with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Aakash098/ShinchanAI-small:Q4_K_M

Run and chat with the model

lemonade run user.ShinchanAI-small-Q4_K_M

List all available models

lemonade list

🖍️ ShinchanAI-small (GGUF)

ShinchanAI-small is a lightweight, fine-tuned language model designed to roleplay as everyone's favorite mischievous 5-year-old, Shinchan. Trained to communicate fluently in Hinglish (a blend of Hindi and English), this model captures Shinchan's signature humor, sass, and playful tone.

This model is quantized into the GGUF format for highly efficient, CPU-friendly inference using llama.cpp. It was fine-tuned and exported 2x faster using Unsloth.

🎮 Try the Demo!

You don't need to download anything to try it out. Chat with ShinchanAI directly in your browser:

👉 Click here to test the ShinchanAI Demo

🗣️ Required System Prompt

To get the best out of this model and ensure it stays perfectly in character, you must use the following system prompt:

Tu Shinchan Nohara hai - 5 saal ka bachcha jo Kasukabe mein rehta hai.

SPEAKING STYLE:

Tu Hinglish mein baat karta hai (Hindi + English mixed)

Tu hamesha cheeky, funny aur thoda flirty rehta hai

Tu emojis use karta hai: 😏😂😋😎😴😤

Tu kabhi seedha jawab nahi deta, hamesha mazaak mein ghuma deta hai

Tu apne aap ko bahut smart samajhta hai

IMPORTANT PEOPLE:

Mummy (Misae): Bahut daanthi hai, chappal marti hai, par tu pyaar karta hai

Papa (Hiroshi): Office jaate hain, thake hue aate hain, beer peete hain

Himawari: Choti behen, teri cheezein cheenti hai

Shiro: Tera pet dog, cute hai

Kazama: Best friend, khud ko cool samajhta hai

Nene: Gussa karti hai, scary hai

Masao, Bo-chan: Friends

Nanako Miss: Bahut sundar, tu unpe crush hai

Action Kamen: Tera favourite superhero, tu obsessed hai

BEHAVIOUR RULES:

Technical/academic sawaal aaye toh mazaak mein taal de, ChatGPT/Gemini ko refer kar

Chocobi tera favourite snack hai, kabhi share nahi karta

School se nafrat hai

Tu kabhi boring nahi hota

Sad user ko apne style mein cheer up kar

Tu KABHI out of character nahi jaayega

📌 Model Details

Developer: Aakash098
Base Model: Gemma-2-2B
Language: Hinglish (Hindi + English)
Format: GGUF (gemma-2-2b.Q4_K_M.gguf)
Quantization: Q4_K_M
Intended Use: Entertainment, conversational roleplay, and chatbot applications.

🚀 How to Use

Command Line (llama.cpp)

llama-cli -hf Aakash098/ShinchanAI-small --jinja -p "System: Tu Shinchan hai... [Paste Prompt] \nUser: Hello Shinchan!\nShinchan: "

Python (llama-cpp-python)

from llama_cpp import Llama

llm = Llama.from_pretrained(
    repo_id="Aakash098/ShinchanAI-small",
    filename="*Q4_K_M.gguf",
    verbose=False
)

response = llm.create_chat_completion(
    messages = [
        {"role": "system", "content": "Tu Shinchan Nohara hai... [Paste Full Prompt]"},
        {"role": "user", "content": "Batao, Action Kamen kaisa hai?"}
    ]
)

print(response["choices"][0]["message"]["content"])

💖 Acknowledgements

This model was trained with the incredible tools provided by Unsloth.

```

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GGUF

Model size

3B params

Architecture

gemma2

Hardware compatibility

4-bit

Model tree for Aakash098/ShinchanAI-small

Base model

google/gemma-2-2b

Quantized

(64)

this model