Instructions to use librepowerai/tinyllama-1.1b-chat-be with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use librepowerai/tinyllama-1.1b-chat-be with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="librepowerai/tinyllama-1.1b-chat-be",
	filename="tinyllama-1.1b-q4_k_m-be.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use librepowerai/tinyllama-1.1b-chat-be with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf librepowerai/tinyllama-1.1b-chat-be:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf librepowerai/tinyllama-1.1b-chat-be:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf librepowerai/tinyllama-1.1b-chat-be:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf librepowerai/tinyllama-1.1b-chat-be: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 librepowerai/tinyllama-1.1b-chat-be:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf librepowerai/tinyllama-1.1b-chat-be: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 librepowerai/tinyllama-1.1b-chat-be:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf librepowerai/tinyllama-1.1b-chat-be:Q4_K_M

Use Docker

docker model run hf.co/librepowerai/tinyllama-1.1b-chat-be:Q4_K_M

LM Studio
Jan
Ollama
How to use librepowerai/tinyllama-1.1b-chat-be with Ollama:
```
ollama run hf.co/librepowerai/tinyllama-1.1b-chat-be:Q4_K_M
```

Unsloth Studio new

How to use librepowerai/tinyllama-1.1b-chat-be 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 librepowerai/tinyllama-1.1b-chat-be 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 librepowerai/tinyllama-1.1b-chat-be to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for librepowerai/tinyllama-1.1b-chat-be to start chatting

Docker Model Runner
How to use librepowerai/tinyllama-1.1b-chat-be with Docker Model Runner:
```
docker model run hf.co/librepowerai/tinyllama-1.1b-chat-be:Q4_K_M
```

Lemonade

How to use librepowerai/tinyllama-1.1b-chat-be with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull librepowerai/tinyllama-1.1b-chat-be:Q4_K_M

Run and chat with the model

lemonade run user.tinyllama-1.1b-chat-be-Q4_K_M

List all available models

lemonade list

TinyLlama 1.1B Chat - Big-Endian GGUF

This is a big-endian version of TinyLlama-1.1B-Chat-v1.0 in GGUF format, optimized for IBM AIX on POWER architecture.

Model Details

Base Model: TinyLlama 1.1B Chat v1.0
Format: GGUF (Q4_K_M quantization)
Endianness: Big-endian (IBM Power Systems)
Size: 638 MB
License: Apache 2.0

Usage

This model is designed for use with llama-aix, a port of llama.cpp for IBM AIX.

# Download model
wget https://huggingface.co/librepower/tinyllama-1.1b-chat-be/resolve/main/tinyllama-1.1b-q4_k_m-be.gguf

# Run inference on AIX
./llama-simple -m tinyllama-1.1b-q4_k_m-be.gguf -n 128 -p "Hello, world!"

Performance

On IBM POWER9 (16 cores, 128GB RAM):

Speed: ~18 tokens/second
Memory: ~800 MB RAM

Why Big-Endian?

IBM Power Systems use big-endian byte order, while most modern systems use little-endian. This model has been converted using llama.cpp's endianness conversion tool to run natively on AIX without runtime conversion overhead.

Conversion

This model was converted from the original little-endian GGUF using:

./llama-gguf-split --convert-be model.gguf model-be.gguf

About LibrePower

Unlocking Power Systems through open source.

LibrePower brings modern AI and open-source tools to IBM Power Systems, extending the life and capabilities of enterprise infrastructure.

Web: https://librepower.org
GitLab: https://gitlab.com/librepower
Newsletter: https://librepower.substack.com

Related Projects

llama-aix - llama.cpp for IBM AIX
redbook-explorer - RAG application for IBM Redbooks

Citation

Original model by Zhang et al. (TinyLlama team):

@article{tinyllama,
  title={TinyLlama: An Open-Source Small Language Model},
  author={Zhang, Peiyuan and Guangtao, Zeng and Wang, Tianduo and Lu, Wei},
  journal={arXiv preprint arXiv:2401.02385},
  year={2024}
}

Downloads last month: 1

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

Inference Providers NEW

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Paper for librepowerai/tinyllama-1.1b-chat-be

TinyLlama: An Open-Source Small Language Model

Paper • 2401.02385 • Published Jan 4, 2024 • 95