Instructions to use MLVXN/microllm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use MLVXN/microllm 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 MLVXN/microllm:F16
# Run inference directly in the terminal:
llama cli -hf MLVXN/microllm:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf MLVXN/microllm:F16
# Run inference directly in the terminal:
llama cli -hf MLVXN/microllm: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 MLVXN/microllm:F16
# Run inference directly in the terminal:
./llama-cli -hf MLVXN/microllm: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 MLVXN/microllm:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MLVXN/microllm:F16

Use Docker

docker model run hf.co/MLVXN/microllm:F16

LM Studio
Jan

vLLM

How to use MLVXN/microllm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MLVXN/microllm:F16

Ollama
How to use MLVXN/microllm with Ollama:
```
ollama run hf.co/MLVXN/microllm:F16
```

Unsloth Studio

How to use MLVXN/microllm 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 MLVXN/microllm 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 MLVXN/microllm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MLVXN/microllm to start chatting

Docker Model Runner
How to use MLVXN/microllm with Docker Model Runner:
```
docker model run hf.co/MLVXN/microllm:F16
```

Lemonade

How to use MLVXN/microllm with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MLVXN/microllm:F16

Run and chat with the model

lemonade run user.microllm-F16

List all available models

lemonade list

Atomic Chat

microllm / generation_config.json

MLVXN

Upload folder using huggingface_hub

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	{
	"_from_model_config": true,
	"bos_token_id": 1,
	"eos_token_id": 50259,
	"output_attentions": false,
	"output_hidden_states": false,
	"pad_token_id": 50259,
	"transformers_version": "5.14.1",
	"use_cache": true
	}