Laguna-S-2.1-oQ4e

Calibrated 4-bit MLX quantization of poolside/Laguna-S-2.1 (118B total, 8B activated per token), produced with oMLX oQ at level 4 enhanced — 4.60 bits/weight effective, 64 GB on disk. Data-driven mixed precision: bits are allocated per tensor from an imatrix-calibrated sensitivity map, not a fixed rule. For Apple Silicon.

• 64 GB on disk, down from 235 GB BF16
• 48 layers, 47 of them MoE with 256 routed experts + 1 shared, top-10 (L0 is a dense MLP); interleaved attention (12 global with YaRN to 1M context, 36 sliding-window 512)
• Peak memory in my tests: 60.4 GB at 1k context, 63.5 GB at 64k — fits a 96 GB Mac
• Converted and tested on a Macbook Pro M5 Max 128GB 40 GPU

Requirements

mlx-lm doesn't support the laguna architecture yet — there's an open PR: mlx-lm#1223. Until it lands, use mlx-vlm (0.6.3+), which implements laguna as a text-only model:

uvx --from mlx-vlm mlx_vlm.generate --model mlx-community/Laguna-S-2.1-oQ4e --prompt "..."

oMLX serves it directly from 0.5.3 on — it vendors that PR and patches it into mlx-lm at import, so no model setting is needed. On earlier builds, discovery decides between the mlx-lm and mlx-vlm loaders by looking for a vision sub-config, and laguna has none — so it lands on mlx-lm and fails with Model type laguna not supported. Set model_type_override: "vlm" in the model's settings, then refresh discovery (omlx restart): the load failure is cached per entry until the next discovery pass, so setting the override alone won't clear it.

Quantization

oQ4e allocates bits per tensor from an importance-matrix calibration pass over calibration data. The 4-bit base lands on the experts; the dense spine — attention, embeddings, lm_head, routers, 386 tensors in total — came out mixed, 284 at 8 bits, 1 at 6 and 101 at 5. Output is standard MLX affine quantization — no custom kernels or runtime required.

How it was quantized

oQ at level 4 enhanced — imatrix-calibrated, group size 128. I had to patch omlx to route laguna through the mlx-vlm loader. At 235 GB the model doesn't fit in 128 GB of RAM, so calibration ran against a uniform 4-bit proxy on disk rather than the FP weights, which shifts the bit allocation slightly.

Conversion check

Smoke-tested after conversion with mlx_vlm.generate: coherent — solved 17 * 24 = 408, broke it down by the distributive property and verified the result a second way, no repetition loop.

Performance

Measured with oMLX's benchmark harness on a Macbook Pro M5 Max 128GB 40 GPU, single request, 128 generated tokens:

Continuous batching at 1k prompt / 128 generated:

Benchmarks & Variants

mmlu_pro, mathqa and winogrande, n=300 seeded samples each, thinking off, identical questions across every variant. The bf16 row is the hosted API, measured the same way. Standard error at this n is around 2.5 points, so oQ4e through oQ6e aren't separated by this run.

Treat this as a rough sighting, not a verdict. Three benchmarks at n=300 cover a narrow slice of what the model does — no long-context work, no agentic loops, no real code — and at this sample size most of the ladder above 3.6 bpw sits inside the error bars. I ran them to size the drop between levels, not to rank the variants against each other. Test the one you're considering on your own workload before trusting any of it.

Usage

# mlx-vlm — plain mlx-lm doesn't support the laguna architecture
uvx --from mlx-vlm mlx_vlm.generate --model mlx-community/Laguna-S-2.1-oQ4e \
  --prompt "Explain Bayes' theorem in two sentences." --max-tokens 300

# oMLX — discovers the model from the HF cache; set model_type_override: "vlm" first
omlx serve

License

OpenMDW-1.1, inherited from the base model. Refer to the original model card for architecture, benchmarks, and intended use.