Instructions to use Thox-ai/ThoxSpeech-ASR-BitNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Thox-ai/ThoxSpeech-ASR-BitNet 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 Thox-ai/ThoxSpeech-ASR-BitNet:Q6_K
# Run inference directly in the terminal:
llama cli -hf Thox-ai/ThoxSpeech-ASR-BitNet:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Thox-ai/ThoxSpeech-ASR-BitNet:Q6_K
# Run inference directly in the terminal:
llama cli -hf Thox-ai/ThoxSpeech-ASR-BitNet:Q6_K

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 Thox-ai/ThoxSpeech-ASR-BitNet:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf Thox-ai/ThoxSpeech-ASR-BitNet:Q6_K

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 Thox-ai/ThoxSpeech-ASR-BitNet:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Thox-ai/ThoxSpeech-ASR-BitNet:Q6_K

Use Docker

docker model run hf.co/Thox-ai/ThoxSpeech-ASR-BitNet:Q6_K

LM Studio
Jan
Ollama
How to use Thox-ai/ThoxSpeech-ASR-BitNet with Ollama:
```
ollama run hf.co/Thox-ai/ThoxSpeech-ASR-BitNet:Q6_K
```

Unsloth Studio

How to use Thox-ai/ThoxSpeech-ASR-BitNet 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 Thox-ai/ThoxSpeech-ASR-BitNet 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 Thox-ai/ThoxSpeech-ASR-BitNet to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Thox-ai/ThoxSpeech-ASR-BitNet to start chatting

Docker Model Runner
How to use Thox-ai/ThoxSpeech-ASR-BitNet with Docker Model Runner:
```
docker model run hf.co/Thox-ai/ThoxSpeech-ASR-BitNet:Q6_K
```

Lemonade

How to use Thox-ai/ThoxSpeech-ASR-BitNet with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Thox-ai/ThoxSpeech-ASR-BitNet:Q6_K

Run and chat with the model

lemonade run user.ThoxSpeech-ASR-BitNet-Q6_K

List all available models

lemonade list

Atomic Chat

ThoxSpeech-ASR-BitNet

Your AI. Your Data. Your Rules.

Verbatim redistribution of microsoft/VibeVoice-ASR-BitNet — CPU-only multilingual ASR (NOT retrained or re-quantized by THOX).

What this is

This is a byte-for-byte mirror of microsoft/VibeVoice-ASR-BitNet (MIT, © Microsoft) with a SHA-256 provenance table. The decoder derives from Qwen2.5-1.5B (Apache-2.0, © Alibaba).
Total 1.58 GB (I8_S acoustic tokenizer 0.65 GB + I2_S/Q6_K LM 0.92 GB). CPU-only.
Cite Microsoft, not THOX. Accuracy degrades on far-field / overlapping speech (internal note: WER ~2.41 on LibriSpeech-clean).

Architecture (from config)

Field	Value
Architecture	2-stage: I8_S VAE tokenizer + I2_S/Q6_K Qwen2.5-1.5B LM decoder

Intended use

Multilingual automatic speech recognition on CPU. Research / on-device transcription.

Usage

huggingface-cli download Thox-ai/ThoxSpeech-ASR-BitNet --local-dir ./ThoxSpeech-ASR-BitNet
# run with the VibeVoice / BitNet ggml ASR runtime (CPU)

Model tree for Thox-ai/ThoxSpeech-ASR-BitNet

Base model

microsoft/VibeVoice-ASR-BitNet

Quantized

(5)

this model