Instructions to use mrtechgarg/trio-nano with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrtechgarg/trio-nano with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mrtechgarg/trio-nano",
	filename="trio-nano-q4_k_m.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 mrtechgarg/trio-nano 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 mrtechgarg/trio-nano:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mrtechgarg/trio-nano:Q4_K_M

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/mrtechgarg/trio-nano:Q4_K_M

LM Studio
Jan
Ollama
How to use mrtechgarg/trio-nano with Ollama:
```
ollama run hf.co/mrtechgarg/trio-nano:Q4_K_M
```

Unsloth Studio

How to use mrtechgarg/trio-nano 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 mrtechgarg/trio-nano 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 mrtechgarg/trio-nano to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mrtechgarg/trio-nano to start chatting

Atomic Chat new
Docker Model Runner
How to use mrtechgarg/trio-nano with Docker Model Runner:
```
docker model run hf.co/mrtechgarg/trio-nano:Q4_K_M
```

Lemonade

How to use mrtechgarg/trio-nano with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mrtechgarg/trio-nano:Q4_K_M

Run and chat with the model

lemonade run user.trio-nano-Q4_K_M

List all available models

lemonade list

trio-nano

trio-nano -- ultra-fast model for edge and mobile by trio.ai (3B parameters)

Install

pip install triobot
trio train --setup --model trio-nano
trio serve

Model Family

Model	Params	Use Case
trio-nano	3B	Edge, mobile, instant
trio-small	4B	Daily tasks
trio-medium	8B	Coding, writing
trio-high	9B	Advanced reasoning
trio-max	12B	Best consumer GPU
trio-pro	30B MoE	Pro workloads

Built by trio.ai | Apache 2.0

Downloads last month: -

GGUF

Model size

2B params

Architecture

llama

Hardware compatibility

4-bit

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