Instructions to use SmallAICreator/Minigpt-chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SmallAICreator/Minigpt-chat with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SmallAICreator/Minigpt-chat",
	filename="minigpt-rp-sft-f32.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SmallAICreator/Minigpt-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 SmallAICreator/Minigpt-chat:F32
# Run inference directly in the terminal:
llama cli -hf SmallAICreator/Minigpt-chat:F32

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SmallAICreator/Minigpt-chat:F32
# Run inference directly in the terminal:
llama cli -hf SmallAICreator/Minigpt-chat:F32

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 SmallAICreator/Minigpt-chat:F32
# Run inference directly in the terminal:
./llama-cli -hf SmallAICreator/Minigpt-chat:F32

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 SmallAICreator/Minigpt-chat:F32
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SmallAICreator/Minigpt-chat:F32

Use Docker

docker model run hf.co/SmallAICreator/Minigpt-chat:F32

LM Studio
Jan
Ollama
How to use SmallAICreator/Minigpt-chat with Ollama:
```
ollama run hf.co/SmallAICreator/Minigpt-chat:F32
```

Unsloth Studio

How to use SmallAICreator/Minigpt-chat 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 SmallAICreator/Minigpt-chat 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 SmallAICreator/Minigpt-chat to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SmallAICreator/Minigpt-chat to start chatting

Atomic Chat new
Docker Model Runner
How to use SmallAICreator/Minigpt-chat with Docker Model Runner:
```
docker model run hf.co/SmallAICreator/Minigpt-chat:F32
```

Lemonade

How to use SmallAICreator/Minigpt-chat with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SmallAICreator/Minigpt-chat:F32

Run and chat with the model

lemonade run user.Minigpt-chat-F32

List all available models

lemonade list

MiniGPT-22M Chat

The smallest known coherent multilingual chatbot at 22.5M parameters.

Model Description

Trained from scratch on Google Colab (A100) by an indie developer. This model is a fully chat-tuned language model that can hold conversations, respond coherently, and operate in 10+ languages — all in an 86MB GGUF file.

Training Details

Parameters: 22.5M
Architecture: gpt2
Training data: SlimPajama + Wikipedia + 3,000 conversation examples
Total tokens seen: ~4 billion (6.7 epochs over 600M token corpus)
Overtraining ratio: ~9-10x Chinchilla optimal (intentional for small model)
Val loss: Slightly below train loss, no overfitting

Performance

Outperforms Rocket-3B on greeting/conversational behavior
Comparable to SmolVLM-256M on chat tasks
70+ tokens/sec on Pixel 6a

Usage

Load the GGUF in any llama.cpp compatible app such as LM Studio, Ollama, or any other local inference tool that supports GGUF format.

Prompt Format

This model uses a simple User/Assistant format: User: {prompt} Assistant:

Downloads last month: -

GGUF

Model size

22.5M params

Architecture

gpt2

Hardware compatibility

32-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support