Instructions to use rishabh-rishuu/Ultron-The-best-model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use rishabh-rishuu/Ultron-The-best-model 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 rishabh-rishuu/Ultron-The-best-model:BF16
# Run inference directly in the terminal:
llama cli -hf rishabh-rishuu/Ultron-The-best-model:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf rishabh-rishuu/Ultron-The-best-model:BF16
# Run inference directly in the terminal:
llama cli -hf rishabh-rishuu/Ultron-The-best-model:BF16

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 rishabh-rishuu/Ultron-The-best-model:BF16
# Run inference directly in the terminal:
./build/bin/llama cli -hf rishabh-rishuu/Ultron-The-best-model:BF16

Use Docker

docker model run hf.co/rishabh-rishuu/Ultron-The-best-model:BF16

Llama new
LM Studio
Jan

vLLM

How to use rishabh-rishuu/Ultron-The-best-model with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/rishabh-rishuu/Ultron-The-best-model:BF16

Ollama
How to use rishabh-rishuu/Ultron-The-best-model with Ollama:
```
ollama run hf.co/rishabh-rishuu/Ultron-The-best-model:BF16
```
Unsloth Desktop
Docker Model Runner
How to use rishabh-rishuu/Ultron-The-best-model with Docker Model Runner:
```
docker model run hf.co/rishabh-rishuu/Ultron-The-best-model:BF16
```

Lemonade

How to use rishabh-rishuu/Ultron-The-best-model with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull rishabh-rishuu/Ultron-The-best-model:BF16

Run and chat with the model

lemonade run user.Ultron-The-best-model-BF16

List all available models

lemonade list

Atomic Chat

Ultron: The best model (Smol-129M)

What is this?

A tiny, locally-trained 129M-parameter language model built from scratch. It is roughly the size of a couple high-res photos (~75 MB in 4-bit) and can run on a toaster, an old laptop, or an NVIDIA L4 GPU at breakneck speeds.

It was raised on a diet of code, curated web text, and conversations, then given a quick manners class (SFT) so it stops acting like an unhinged autocomplete bot.

Key Specs (For the Nerds)

Parameters: 129,000,192 (we counted them all).
Architecture: LLaMA-style decoder (16 layers, SwiGLU, RMSNorm, RoPE).
Attention: Grouped-Query Attention (12 query heads, 4 KV heads) so your RAM doesn't cry.
Context Window: 2,048 tokens.
Vocab Size: 49,152 tokens.
Formats Included: Native SafeTensors (BF16), GGUF BF16 (~~258 MB), and GGUF Q4_K_M (~~75 MB).

What It's Actually Good At

Writing tiny Python snippets: Need a quick function to add two numbers, reverse a list, or parse a string? It nails it on the first try.
Answering quick notes & basic Q&A: Pair it with a vector database or pass it note excerpts, and it will pull out the facts without breaking a sweat.
Being polite: Unlike raw base models, it actually knows how to say "Hello! How can I help you today?" instead of telling you an unsolicited bedtime story.
Speed: Runs well past 100+ tokens/second on CPU via llama.cpp. You will finish reading before it finishes blinking.

Things It Will Probably Do Wrong (Known Quirks)

Looping when bored: If you force it to keep talking after it already answered your question, it might repeat itself just to fill the awkward silence.
Physics freestyle: Ask it about gravity or waves, and it might occasionally claim an LLM is an electromagnetic wave or talk about rolling balls down hills. Cut it some slack, it has 129M parameters, not a PhD.
Complex multi-step math: Please do not use this model to calculate your taxes, structural engineering loads, or orbital trajectories.

How to Talk to It

This model was trained with the ChatML format. If you don't use the tags, it gets confused:

<|im_start|>system
You are a helpful assistant.<|im_end|>
<|im_start|>user
Write a python function to add two numbers.<|im_end|>
<|im_start|>assistant

Summary Verdict

It is way smarter than 2019's GPT-2, fits entirely inside a pocket, and is ready to live inside your terminal or local knowledge vault.

Downloads last month: 207

Safetensors

Model size

0.1B params

Tensor type

BF16