Downloads · 30 days
436
7% of all-time downloads
ox-ox/Laguna-M.1-MLX-Q3
Laguna-M.1-MLX-Q3 is a text generation model from ox-ox. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as apache-2.0.
A community MLX (Apple Silicon) build of Poolside's Laguna M.1 (a 225B-total / 23B-active Mixture-of-Experts model), quantized to 3-bit so it runs locally on a 128 GB Mac.
Downloads · 30 days
436
7% of all-time downloads
All-time downloads
6K
Public
Parameters
226B
100 GB on disk
Likes
10
Public
Click a slice to open those files.
.safetensors100 GB · 100%
How the weights are stored.
U32225B · 100%
From the Hugging Face model README
A community MLX (Apple Silicon) build of Poolside's Laguna M.1 (a 225B-total / 23B-active Mixture-of-Experts model), quantized to 3-bit so it runs locally on a 128 GB Mac.
Poolside ships official support for vLLM, SGLang, Transformers and TRT-LLM (all CUDA / datacenter). This is the Apple Silicon / edge path.
Expected quality. Uniform 3-bit on a 23B-active MoE is the size/quality floor. Degradation shows up first on long-context and agentic prompts. If you have the RAM headroom, prefer a higher-bit or mixed-precision build.
Not affiliated with Poolside. Weights © Poolside, Inc., released under Apache 2.0. Converted from the public release
poolside/Laguna-M.1-FP8(weights verified byte-identical to the public checkpoint; tokenizer, chat template and generation config taken from the public release).
laguna isn't upstream in mlx-lm yet
(PR #1415 pending). Until it
lands, install mlx-lm from the branch that ships the model file and the
tool-call parser fix (needed for tool use in agentic clients), pinned to a commit:
pip install "git+https://github.com/eauchs/mlx-lm.git@5b0c0667f4a8c25ee9bb9ef729ab64822d1b246e"
mlx_lm.generate --model ox-ox/Laguna-M.1-MLX-Q3 \
--prompt "Write a Python retry wrapper with exponential backoff." \
--temp 1.0 --top-k 20
--temp 1.0 --top-k 20 is Poolside's recommendation for the full-precision
model. At 3-bit it's on the aggressive side; if output looks unstable, try
--temp 0.6 --top-p 0.95.
Once the PR is merged, this is just pip install -U mlx-lm + the mlx_lm.generate
line, with no extra step.
| Quant | Hardware | Throughput | Peak RAM |
|---|---|---|---|
| Q3 | Apple M3 Max 128 GB | ~26.5 tok/s | ~100 GB |
70 layers (3 dense SwiGLU + 67 sparse MoE), 256 experts + 1 shared, top-k=16 sigmoid routing; global attention 64 Q / 8 KV heads, head_dim 128, QK-norm + softplus output gating; RoPE with YaRN. See the official model card for full details and benchmarks.
Apache 2.0 (weights and conversion). © Poolside, Inc. for Laguna M.1.
Conversion by Théophile Lafargue (@eauchs). Model by Poolside; see their technical report.