GLM-5.3-MLX-5bit

GLM-5.3-MLX-5bit

MLX (Apple Silicon) build of GLM-5.3 — 744B-parameter glm_moe_dsa MoE (256 experts, top-8; MLA with DeepSeek-V3.2-style sparse attention) — quantized to 5-bit.

These files are modified: converted from the upstream bfloat16 release (GLM-5.3-BF16) to MLX and quantized; the architecture is unchanged. The multi-token-prediction layer (78) is not included.

Runtime — read this

This checkpoint bundles glm_moe_dsa.py (declared via model_file) and needs it:

pip install -U mlx-lm
mlx_lm.generate --model pipenetwork/GLM-5.3-MLX-5bit --trust-remote-code --prompt "..." --max-tokens 300

mlx-lm's own glm_moe_dsa builds a lightning indexer on all 78 layers, but GLM-5.2/5.3 ship indexer weights on 21 (indexer_types: the other 57 "shared" layers reuse the previous full layer's top-k selection). A strict load of the release fails with 285 missing parameters; mlx_lm.load loads leniently and leaves those 57 indexers at random initialisation. Prompts up to 2048 tokens are unaffected (the indexer is bypassed below index_topk); beyond that, 57 layers attend to keys chosen by random projections. The bundled runtime implements the schedule as the reference does, plus the reference's fp32 indexer scores and router logits and the indexer LayerNorm epsilon. Tiny-config parity against transformers 5.16 is 4e-7 with the sparse path live, cached decode exact; strict loading of this checkpoint reports zero missing and zero unexpected tensors. Details and tests: https://github.com/PipeNetwork/glm53-mlx.

Size and what is quantized

0.0 GB on disk. RAM: 768 GB (two machines).

Source precision: the FP8 release is a lossy derivative of the bf16 one (dequantized FP8 weights differ from bf16 by up to 1.6e-2 on values of 0.46 — half an e4m3 step). The ladder row fp8 is the FP8 release itself measured against bf16: its error is the floor any FP8-sourced build inherits.

Quality

Two measurements, because at 744B most of the ladder cannot be loaded on a 512 GB machine:

Per-layer divergence vs bf16 (scripts/eval_ladder.py): every decoder layer run in bf16 and in each recipe on identical inputs (16,384 tokens of wikitext-2), teacher-forced (each layer sees bf16 inputs — isolates its own damage) and free-running (each recipe feeds itself — what inference does). Relative L2 error of the layer output; lower is better.

Perplexity on wikitext-2 (test), 288,627 tokens in 141 windows of 2048, for the builds that fit this machine, scored on identical windows:

Recommendation. For a 512 GB Mac, mixed 4/8-bit (427.7 GB): perplexity 2.7420, a paired 4.3% better than uniform 4-bit (ratio 0.9575 [0.9537, 0.9612], better on 98.6% of windows) for 9 GB more — the 2.5% of non-expert weights are worth their 8 bits, as on every model we have measured. Uniform 4-bit (418.6 GB) is the fallback when those 9 GB matter. Mixed 3/6-bit (332.6 GB) is the 384 GB-class option, at a real cost: 3.0338, +5.9% over 4-bit and +10.6% over mixed 4/8 — it leads the ladder for the first ten layers and then 3-bit expert damage compounds. Among the builds that cannot be run here, the ladder puts 8-bit closest to bfloat16 (free-running error 0.131), then 6-bit (0.167); the upstream FP8 release scores 0.173, between 6-bit and 5-bit, which is why these are converted from the bf16 release. 5-bit (0.225) sits just above mixed 4/8 (0.250) at 100 GB more.

Greedy generation (a collapse detector, not a ranking) is coherent on every published build.

License

GLM-5.3 license, as the upstream model. Port code: https://github.com/PipeNetwork/glm53-mlx.