Instructions to use baa-ai/GLM-5-SWAN-5bit-MLX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use baa-ai/GLM-5-SWAN-5bit-MLX with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] hf download baa-ai/GLM-5-SWAN-5bit-MLX --local-dir GLM-5-SWAN-5bit-MLX
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
GLM-5-SWAN-5bit-MLX
Mixed-precision quantized version of THUDM/GLM-5 optimised by baa.ai.
GLM-5 (355B parameters). Experimental.
Metrics
| Metric | Value |
|--------|-------|
| Size | See model files |
| Average bits | 5 |
| Framework | MLX |
| WikiText-2 PPL | — |
Usage
from mlx_lm import load, generate
model, tokenizer = load("baa-ai/GLM-5-SWAN-5bit-MLX")
response = generate(model, tokenizer, prompt="Hello!", max_tokens=256)
print(response)
Quantized by baa.ai
Black Sheep AI Products
Black Sheep AI builds private AI with the Bounded AI Architecture (BAA): three layers, each bound to something you control, on infrastructure you control. Nothing leaves your boundary.
Ram (Reasoning Layer) — Full-capability frontier models, shrunk to fit smaller GPUs with reasoning kept and certified. They rely strictly on your Data Modules for knowledge, so you swap the model as better ones ship.
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Shepherd — Takes a frontier model, compresses it into a Ram reasoning model, certifies it with Watchman, binds it to your Paddock, and deploys the Bounded AI Architecture to your infrastructure: your cloud, a Mac Studio, or an air-gapped facility.
Learn more at baa.ai — Private AI for enterprises whose data cannot leave the building.
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