35 posts tagged “mlx”
The MLX framework for running machine learning models on Apple Silicon.
2024
Qwen2.5-Coder-32B is an LLM that can code well that runs on my Mac
There’s a whole lot of buzz around the new Qwen2.5-Coder Series of open source (Apache 2.0 licensed) LLM releases from Alibaba’s Qwen research team. On first impression it looks like the buzz is well deserved.
[... 697 words]Whisper large-v3-turbo model. It’s OpenAI DevDay today. Last year they released a whole stack of new features, including GPT-4 vision and GPTs and their text-to-speech API, so I’m intrigued to see what they release today (I’ll be at the San Francisco event).
Looks like they got an early start on the releases, with the first new Whisper model since November 2023.
Whisper Turbo is a new speech-to-text model that fits the continued trend of distilled models getting smaller and faster while maintaining the same quality as larger models.
large-v3-turbo is 809M parameters - slightly larger than the 769M medium but significantly smaller than the 1550M large. OpenAI claim its 8x faster than large and requires 6GB of VRAM compared to 10GB for the larger model.
The model file is a 1.6GB download. OpenAI continue to make Whisper (both code and model weights) available under the MIT license.
It’s already supported in both Hugging Face transformers - live demo here - and in mlx-whisper on Apple Silicon, via Awni Hannun:
import mlx_whisper
print(mlx_whisper.transcribe(
"path/to/audio",
path_or_hf_repo="mlx-community/whisper-turbo"
)["text"])
Awni reports:
Transcribes 12 minutes in 14 seconds on an M2 Ultra (~50X faster than real time).
mlx-vlm (via) The MLX ecosystem of libraries for running machine learning models on Apple Silicon continues to expand. Prince Canuma is actively developing this library for running vision models such as Qwen-2 VL and Pixtral and LLaVA using Python running on a Mac.
I used uv to run it against this image with this shell one-liner:
uv run --with mlx-vlm \
python -m mlx_vlm.generate \
--model Qwen/Qwen2-VL-2B-Instruct \
--max-tokens 1000 \
--temp 0.0 \
--image https://static.simonwillison.net/static/2024/django-roadmap.png \
--prompt "Describe image in detail, include all text"
The --image option works equally well with a URL or a path to a local file on disk.
This first downloaded 4.1GB to my ~/.cache/huggingface/hub/models--Qwen--Qwen2-VL-2B-Instruct folder and then output this result, which starts:
The image is a horizontal timeline chart that represents the release dates of various software versions. The timeline is divided into years from 2023 to 2029, with each year represented by a vertical line. The chart includes a legend at the bottom, which distinguishes between different types of software versions.
Legend
Mainstream Support:
- 4.2 (2023)
- 5.0 (2024)
- 5.1 (2025)
- 5.2 (2026)
- 6.0 (2027) [...]
Moshi (via) Moshi is "a speech-text foundation model and full-duplex spoken dialogue framework". It's effectively a text-to-text model - like an LLM but you input audio directly to it and it replies with its own audio.
It's fun to play around with, but it's not particularly useful in comparison to other pure text models: I tried to talk to it about California Brown Pelicans and it gave me some very basic hallucinated thoughts about California Condors instead.
It's very easy to run locally, at least on a Mac (and likely on other systems too). I used uv and got the 8 bit quantized version running as a local web server using this one-liner:
uv run --with moshi_mlx python -m moshi_mlx.local_web -q 8
That downloads ~8.17G of model to a folder in ~/.cache/huggingface/hub/ - or you can use -q 4 and get a 4.81G version instead (albeit even lower quality).
mlx-whisper
(via)
Apple's MLX framework for running GPU-accelerated machine learning models on Apple Silicon keeps growing new examples. mlx-whisper is a Python package for running OpenAI's Whisper speech-to-text model. It's really easy to use:
pip install mlx-whisper
Then in a Python console:
>>> import mlx_whisper
>>> result = mlx_whisper.transcribe(
... "/tmp/recording.mp3",
... path_or_hf_repo="mlx-community/distil-whisper-large-v3")
.gitattributes: 100%|███████████| 1.52k/1.52k [00:00<00:00, 4.46MB/s]
config.json: 100%|██████████████| 268/268 [00:00<00:00, 843kB/s]
README.md: 100%|████████████████| 332/332 [00:00<00:00, 1.95MB/s]
Fetching 4 files: 50%|████▌ | 2/4 [00:01<00:01, 1.26it/s]
weights.npz: 63%|██████████ ▎ | 944M/1.51G [02:41<02:15, 4.17MB/s]
>>> result.keys()
dict_keys(['text', 'segments', 'language'])
>>> result['language']
'en'
>>> len(result['text'])
100105
>>> print(result['text'][:3000])
This is so exciting. I have to tell you, first of all ...Here's Activity Monitor confirming that the Python process is using the GPU for the transcription:
This example downloaded a 1.5GB model from Hugging Face and stashed it in my ~/.cache/huggingface/hub/models--mlx-community--distil-whisper-large-v3 folder.
Calling .transcribe(filepath) without the path_or_hf_repo argument uses the much smaller (74.4 MB) whisper-tiny-mlx model.
A few people asked how this compares to whisper.cpp. Bill Mill compared the two and found mlx-whisper to be about 3x faster on an M1 Max.
Update: this note from Josh Marshall:
That '3x' comparison isn't fair; completely different models. I ran a test (14" M1 Pro) with the full (non-distilled) large-v2 model quantised to 8 bit (which is my pick), and whisper.cpp was 1m vs 1m36 for mlx-whisper.
I've now done a better test, using the MLK audio, multiple runs and 2 models (distil-large-v3, large-v2-8bit)... and mlx-whisper is indeed 30-40% faster