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deepseek-v4.1-flash-gguf

antirez

Calibrated weights for the Metal implementation in DwarfStar, developed on the `ds4.1flash` branch. These are not interchangeable with DeepSeek V4 Flash weights or its DSpark drafter. The language GGUFs each include 188.83 GiB of native FP8 Engram tables. DwarfStar reads the needed rows directly from disk; it never makes the whole table resident. Keep the GGUF on a fast local SSD. The main-weight sizes above do not include context or runtime buffers. Use SSD streaming on a single 128 GB Mac. Q2 also fits resident tensor parallelism across two 128 GB Macs with RDMA, or full residency on a larger Mac. Q4 needs SSD streaming on smaller Macs; the resident target is a 512 GB Mac. It does not fit resident TP across two 128 GB Macs.

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deepseek-v4-gguf

antirez

This quants are specific for the DS4 inference engine. They may work with other inference engines or not (they should, but not the MTP model which requires a specific loader). Use **q2** on 128 GB Mac machines, **q4** on machines with ≥ 256 GB RAM, pair either with **MTP** for optional speculative decoding. For the **q4** file, only the three routed-expert classes change to `Q4_K`. Everything else is byte-for-byte identical to the q2 recipe.

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glm-5.3-flash-gguf

antirez

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

分类待补充

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glm-5.3-gguf

antirez

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

分类待补充

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qwen3.8-flash-next-gguf

antirez

Self-contained GGUFs for DwarfStar, with original BF16 n-grams read directly from disk. Keep the file on a local SSD. No n-gram sidecar is needed. MTP weights are included; enable them with `--mtp`. Requires DwarfStar's native BF16 n-gram reader. Older versions expecting `--ple` cannot load these files. Each file contains the same 95.37 GiB BF16 n-gram table. It is not made resident or quantized. Context and runtime buffers require additional RAM. The main/MTP tensor bytes come unchanged from Ivan Fioravanti's Q2 and Q4 releases. Q2 has imatrix-calibrated IQ2_XXS gate/up and Q2_K down routed experts, with the down rows padded from 640 to 768 inputs. Q4 has calibrated Q4_K gate/up and MXFP4 down. Other tensor formats and MTP weights are unchanged.

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