Instructions to use OuteAI/Lite-Oute-1-65M-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OuteAI/Lite-Oute-1-65M-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="OuteAI/Lite-Oute-1-65M-Instruct-GGUF",
	filename="Lite-Oute-1-65M-Instruct-FP16.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 OuteAI/Lite-Oute-1-65M-Instruct-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OuteAI/Lite-Oute-1-65M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OuteAI/Lite-Oute-1-65M-Instruct-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf OuteAI/Lite-Oute-1-65M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OuteAI/Lite-Oute-1-65M-Instruct-GGUF: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 OuteAI/Lite-Oute-1-65M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OuteAI/Lite-Oute-1-65M-Instruct-GGUF: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 OuteAI/Lite-Oute-1-65M-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OuteAI/Lite-Oute-1-65M-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/OuteAI/Lite-Oute-1-65M-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use OuteAI/Lite-Oute-1-65M-Instruct-GGUF with Ollama:
```
ollama run hf.co/OuteAI/Lite-Oute-1-65M-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use OuteAI/Lite-Oute-1-65M-Instruct-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 OuteAI/Lite-Oute-1-65M-Instruct-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 OuteAI/Lite-Oute-1-65M-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for OuteAI/Lite-Oute-1-65M-Instruct-GGUF to start chatting

Docker Model Runner
How to use OuteAI/Lite-Oute-1-65M-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/OuteAI/Lite-Oute-1-65M-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use OuteAI/Lite-Oute-1-65M-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OuteAI/Lite-Oute-1-65M-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Lite-Oute-1-65M-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

OuteAI

🌎 OuteAI.com 🤝 Join our Discord 𝕏 @OuteAI

Lite-Oute-1-65M-Instruct

Lite-Oute-1-65M-Instruct is an experimental ultra-compact model in the Lite series, based on the LLaMA architecture and comprising approximately 65 million parameters.
The primary goal of this model was to explore the lower limits of model size while still maintaining basic language understanding capabilities.
Due to its extremely small size, this model demonstrates basic text generation abilities but may struggle with instructions or maintaining topic coherence.
Users should be aware of its significant limitations compared to larger models and expect inconsistent or potentially inaccurate responses.

Available versions:

Lite-Oute-1-65M-Instruct
Lite-Oute-1-65M-Instruct-GGUF
Lite-Oute-1-65M
Lite-Oute-1-65M-GGUF

Chat format

This model uses ChatML template. Ensure you use the correct template:

<|im_start|>system
[System message]<|im_end|>
<|im_start|>user
[Your question or message]<|im_end|>
<|im_start|>assistant
[The model's response]<|im_end|>

Benchmark	5-shot	0-shot
ARC Challenge	22.61	23.63
ARC Easy	37.16	40.49
CommonsenseQA	19.41	20.64
HellaSWAG	28.74	28.41
MMLU	25.20	23.45
OpenBookQA	27.40	28.60
PIQA	60.88	60.77
Winogrande	50.59	50.04

Risk Disclaimer

By using this model, you acknowledge that you understand and assume the risks associated with its use. You are solely responsible for ensuring compliance with all applicable laws and regulations. We disclaim any liability for problems arising from the use of this open-source model, including but not limited to direct, indirect, incidental, consequential, or punitive damages. We make no warranties, express or implied, regarding the model's performance, accuracy, or fitness for a particular purpose. Your use of this model is at your own risk, and you agree to hold harmless and indemnify us, our affiliates, and our contributors from any claims, damages, or expenses arising from your use of the model.

Downloads last month: 65

GGUF

Model size

65M params

Architecture

llama

Hardware compatibility

2-bit

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4-bit

5-bit

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Collection including OuteAI/Lite-Oute-1-65M-Instruct-GGUF

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8 items • Updated Apr 7, 2025 • 1