Instructions to use OuteAI/Lite-Oute-1-300M-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OuteAI/Lite-Oute-1-300M-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="OuteAI/Lite-Oute-1-300M-GGUF",
	filename="Lite-Oute-1-300M-FP16.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use OuteAI/Lite-Oute-1-300M-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-1-300M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OuteAI/Lite-Oute-1-300M-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-1-300M-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf OuteAI/Lite-Oute-1-300M-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-1-300M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf OuteAI/Lite-Oute-1-300M-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-1-300M-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf OuteAI/Lite-Oute-1-300M-GGUF:Q4_K_M

Use Docker

docker model run hf.co/OuteAI/Lite-Oute-1-300M-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use OuteAI/Lite-Oute-1-300M-GGUF with Ollama:
```
ollama run hf.co/OuteAI/Lite-Oute-1-300M-GGUF:Q4_K_M
```

Unsloth Studio

How to use OuteAI/Lite-Oute-1-300M-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-1-300M-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-1-300M-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-1-300M-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use OuteAI/Lite-Oute-1-300M-GGUF with Docker Model Runner:
```
docker model run hf.co/OuteAI/Lite-Oute-1-300M-GGUF:Q4_K_M
```

Lemonade

How to use OuteAI/Lite-Oute-1-300M-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull OuteAI/Lite-Oute-1-300M-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Lite-Oute-1-300M-GGUF-Q4_K_M

List all available models

lemonade list

OuteAI

🌎 OuteAI.com 🤝 Join our Discord 𝕏 @OuteAI

Lite-Oute-1-300M

Lite-Oute-1-300M (Base) is a Lite series model based on the Mistral architecture, comprising approximately 300 million parameters.
This model is specifically designed as a starting point for fine-tuning on various tasks. With its 300 million parameters, it offers a balance between compact size and capability, making it suitable for a wide range of fine-tuning applications.
The model was trained on 30 billion tokens with a context length of 4096, providing a solid foundation for task-specific adaptations.

Available versions:

Lite-Oute-1-300M-Instruct
Lite-Oute-1-300M-Instruct-GGUF
Lite-Oute-1-300M
Lite-Oute-1-300M-GGUF

Benchmarks:

Benchmark	5-shot	0-shot
ARC Challenge	26.62	26.28
ARC Easy	51.39	48.11
CommonsenseQA	19.49	20.64
HellaSWAG	34.86	34.85
MMLU	27.23	24.87
OpenBookQA	30.20	30.80
PIQA	65.07	65.02
Winogrande	51.14	53.35

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: 56

GGUF

Model size

0.3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

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8-bit

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Collection including OuteAI/Lite-Oute-1-300M-GGUF

Lite Oute 1

Collection

8 items • Updated Apr 7, 2025 • 1