Instructions to use unslothai/whisper-base-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Studio
How to use unslothai/whisper-base-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 unslothai/whisper-base-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 unslothai/whisper-base-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for unslothai/whisper-base-GGUF to start chatting
Load model with FastModel
pip install unsloth from unsloth import FastModel model, tokenizer = FastModel.from_pretrained( model_name="unslothai/whisper-base-GGUF", max_seq_length=2048, )
Whisper Base F16 for Unsloth Studio
Run fast, fully local speech-to-text dictation in Unsloth Studio. Whisper Base is a lightweight step up from Tiny and is a strong default when you want quick local dictation with better transcription quality.
Run in Unsloth Studio
- Install or update Unsloth Studio.
- Open Settings > Voice.
- Open the local dictation model picker and select Whisper Base.
- Let Studio download and cache the model.
- Use the microphone button in the chat composer to dictate locally.
The model runs on your device through whisper.cpp. Your recorded audio does not need to be sent to a hosted transcription service.
Model file
whisper-base.bin: native F16 model forwhisper.cpp- Download size: approximately 148 MB
- Best for: a lightweight balance of speed and transcription quality
whisper.cpp uses a custom GGML binary format for Whisper. The model file is therefore named .bin, not .gguf, even though this repository follows the common -GGUF repository naming convention.
No low-bit quantization was applied. Matrix weights are stored as F16, while tensors that whisper.cpp requires in F32 remain F32.
Manual whisper.cpp usage
whisper-cli -m whisper-base.bin -f audio.wav
Integrity
- Source
model.safetensorsSHA-256:07cadb9f25677c8d50df603e66a98fbd842cce45047139baeb16e6219a1e807b - Converted model SHA-256:
c2dad44135a48bc7c5a93564f4cd5cc8735ba8822999e358eb31f28d14d72a8b - Conversion tool:
ggml-org/whisper.cppcommit080bbbe85230f624f0b52127f1ae1218247989f9
The converted model was loaded by whisper.cpp and passed an end-to-end transcription test.
Model tree for unslothai/whisper-base-GGUF
Base model
unslothai/whisper-base