Instructions to use mondk/Msh-Tiny-47M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mondk/Msh-Tiny-47M-GGUF 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 mondk/Msh-Tiny-47M-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mondk/Msh-Tiny-47M-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mondk/Msh-Tiny-47M-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mondk/Msh-Tiny-47M-GGUF:F16

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 mondk/Msh-Tiny-47M-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf mondk/Msh-Tiny-47M-GGUF:F16

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 mondk/Msh-Tiny-47M-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mondk/Msh-Tiny-47M-GGUF:F16

Use Docker

docker model run hf.co/mondk/Msh-Tiny-47M-GGUF:F16

LM Studio
Jan

vLLM

How to use mondk/Msh-Tiny-47M-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mondk/Msh-Tiny-47M-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mondk/Msh-Tiny-47M-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mondk/Msh-Tiny-47M-GGUF:F16

Ollama
How to use mondk/Msh-Tiny-47M-GGUF with Ollama:
```
ollama run hf.co/mondk/Msh-Tiny-47M-GGUF:F16
```

Unsloth Studio

How to use mondk/Msh-Tiny-47M-GGUF 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 mondk/Msh-Tiny-47M-GGUF 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 mondk/Msh-Tiny-47M-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mondk/Msh-Tiny-47M-GGUF to start chatting

Docker Model Runner
How to use mondk/Msh-Tiny-47M-GGUF with Docker Model Runner:
```
docker model run hf.co/mondk/Msh-Tiny-47M-GGUF:F16
```

Lemonade

How to use mondk/Msh-Tiny-47M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mondk/Msh-Tiny-47M-GGUF:F16

Run and chat with the model

lemonade run user.Msh-Tiny-47M-GGUF-F16

List all available models

lemonade list

Atomic Chat

msh-tiny (GGUF)

A tiny (~47M parameter) GPT-2-style chat model, trained completely from scratch — no pretrained base model, custom BPE tokenizer trained from zero, custom PyTorch transformer architecture. This repo contains GGUF builds for use with llama.cpp, Ollama, and LM Studio.

The .safetensors source model is at mondk/Msh-Tiny-47M.

Limitations

This model was trained from random initialization on a modest amount of data with limited compute — it is a small educational project, not a production-quality assistant. Expect it to follow the chat format reliably but produce limited/inconsistent knowledge and occasional incoherent answers.

Prompt format

<|user|>
{your message}
<|assistant|>

The model was trained to stop generating at <|end|>.

Usage

Ollama

FROM ./model-f16.gguf

ollama create msh-tiny -f Modelfile
ollama run msh-tiny

LM Studio: drop the .gguf file into your models folder and load it directly.

llama.cpp

./llama-cli -m model-f16.gguf -p "<|user|>\nhi\n<|assistant|>\n"

Training data

Combining 15 well-known open instruction/chat datasets plus a small hand-written set of everyday chit-chat (greetings, thanks, small talk):

mondk/Greetings-hi-for-train-Msh-v2
tatsu-lab/alpaca
databricks/databricks-dolly-15k
teknium/OpenHermes-2.5
m-a-p/Code-Feedback
OpenAssistant/oasst1
FreedomIntelligence/medical-o1-reasoning-SFT
glaiveai/glaive-function-calling-v2
openai/openai_humaneval
HuggingFaceH4/no_robots
open-thoughts/OpenThoughts-114k
HuggingFaceH4/ultrachat_200k
google-research-datasets/poem_sentiment
CohereLabs/aya_dataset
sentence-transformers/natural-questions

Downloads last month: 95

GGUF

Model size

47.2M params

Architecture

gpt2

Hardware compatibility

8-bit

16-bit

32-bit

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