Instructions to use ooousay/bitnet-ios-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ooousay/bitnet-ios-models with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ooousay/bitnet-ios-models",
	filename="Falcon3-1B-Instruct-i2s.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ooousay/bitnet-ios-models with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ooousay/bitnet-ios-models
# Run inference directly in the terminal:
llama-cli -hf ooousay/bitnet-ios-models

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ooousay/bitnet-ios-models
# Run inference directly in the terminal:
llama-cli -hf ooousay/bitnet-ios-models

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 ooousay/bitnet-ios-models
# Run inference directly in the terminal:
./llama-cli -hf ooousay/bitnet-ios-models

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 ooousay/bitnet-ios-models
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ooousay/bitnet-ios-models

Use Docker

docker model run hf.co/ooousay/bitnet-ios-models

LM Studio
Jan
Ollama
How to use ooousay/bitnet-ios-models with Ollama:
```
ollama run hf.co/ooousay/bitnet-ios-models
```

Unsloth Studio new

How to use ooousay/bitnet-ios-models 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 ooousay/bitnet-ios-models 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 ooousay/bitnet-ios-models to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ooousay/bitnet-ios-models to start chatting

Docker Model Runner
How to use ooousay/bitnet-ios-models with Docker Model Runner:
```
docker model run hf.co/ooousay/bitnet-ios-models
```

Lemonade

How to use ooousay/bitnet-ios-models with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ooousay/bitnet-ios-models

Run and chat with the model

lemonade run user.bitnet-ios-models-{{QUANT_TAG}}

List all available models

lemonade list

BitNet iOS Models

Pre-converted GGUF models for use with BitNet-iOS — native 1-bit LLM inference on Apple Silicon using ARM64 NEON TL1 kernels.

These GGUFs were quantized using the BitNet.cpp i2_s format with locally-built llama-quantize from the microsoft/BitNet repo. Using GGUFs from other sources may produce incorrect output due to differences in i2_s packing between llama-quantize versions.

Models

File	Original Model	Type	Size	License
`Falcon3-1B-Instruct-i2s.gguf`	tiiuae/Falcon3-1B-Instruct-1.58bit	Instruct (chat)	1.36 GB	TII Falcon License 2.0
`bitnet-b1.58-large-i2s.gguf`	microsoft/bitnet_b1_58-large	Base (completion)	270 MB	MIT

Usage

These models are designed for the BitNet-iOS demo app, which downloads them automatically from this repo. They can also be used with the BitNet-iOS CLI:

# Instruct model (chat)
.build/debug/BitNetCLI /path/to/Falcon3-1B-Instruct-i2s.gguf --chat

# Base model (completion)
.build/debug/BitNetCLI /path/to/bitnet-b1.58-large-i2s.gguf "Once upon a time"

Why self-hosted GGUFs?

The BitNet TL1 kernels are sensitive to the exact i2_s quantization format. GGUFs from the original model repos (e.g., tiiuae's Falcon3 GGUF) were quantized with a different version of llama-quantize and differ by ~224 bytes in header metadata. This causes the ARM64 NEON kernels to silently produce garbage output. These GGUFs were converted with the same toolchain used to build the BitNet-iOS XCFramework, ensuring compatibility.

Attribution

Falcon3-1B-Instruct by Technology Innovation Institute (TII) — TII Falcon License 2.0
BitNet b1.58 Large by Microsoft Research — MIT License
Quantization via microsoft/BitNet (MIT License)

Downloads last month: 1

GGUF

Model size

0.7B params

Architecture

bitnet

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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