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AI モデル 51
- DeepSeek-V4-Flash-0731-MXFP4-MLX↗
TensorFold
- Laguna-S-2.1-MLX-6bit↗
TensorFold
- DeepSeek-V4.1-Flash-1017-EXL3-3.5bpw-TensorFold↗
LakyFx
- Qwen3.8-27B-MLX-4bit↗
TensorFold
A native Apple-silicon conversion of Qwen/Qwen3.8-27B , quantized with stock 4-bit affine weights for MLX-VLM and oMLX. This repository contains a stock 4-bit affine MLX conversion of Qwen3.8-27B. The upstream model is a dense, native vision-language model with flexible thinking control and support for text, images, and video. Its tokenizer, processor configuration, chat template, and generation configuration are preserved. The decode figure is the median of three greedy 256-token runs after a 256-token Metal-kernel warm-up. Individual runs measured 39.90, 39.89, and 39.91 tokens/s. This is a practical local reference, not a controlled cross-platform benchmark; prompt length, context growth, sampler settings, memory pressure, thermal state, and runtime versions can materially change performance.
- Qwen3.8-Flash-Next-MLX-oQ6-MTP↗
TensorFold
- Qwen3.6-35B-A3B-MLX-4bit-MTP↗
TensorFold
- GLM-5.3-Flash-EXL3-4bpw-TensorFold↗
Mia-AiLab
Mia's AI Lab's own EXL3 quantization of zai-org/GLM-5.3-Flash, built for TensorFold and served by the GLM-5.3-Flash EXL3 TensorFold recipe for DGX Sparks. **Lower KL divergence than the widely used TR3-4bpw quant on all six test sets, at the same size, speed and memory, measured against Z.AI's official FP8 release.** Same format and bit width; only the calibration is new. KL divergence measures how far a quant's next-token probabilities drift from the reference's, Z.AI's FP8 release (lower is better). This quant lowers it on all six sets: by 11-27% with only the routed experts quantized, and by 2-18% as TensorFold actually serves it, with its own 4-bit dense weights on top.
- DeepSeek-V4.1-Flash-1017-EXL3-EAQ-v1-2.9-bpw↗
Mia-AiLab
EAQ v1 (EXL3 format) quantization of deepseek-ai/DeepSeek-V4.1-Flash-1017 (revision `1bae0e10`, the refreshed V4.1-Flash weights), converted with exllamav3 `v1.6.1` plus the EAQ patch set (converter version 1.6.1) and a port of the DeepSeek-V4.1 architecture (see Conversion). Teacher-forced next-token fidelity to the original source weights was measured in a shared evaluation port; see **Quality vs the original model**. This is **not** a drop-in replacement for the original FP8/FP4 checkpoint. The Engram embedding tables are **not** in this repo: you still need shards 47 and 48 from the base model (about 95 GiB each). **Format: plain EXL3 (EAQ v1).** EAQ v1 currently uses the standard EXL3 format unchanged: `quant_method` is `exl3`, codebook `mul1`, `out_scales=always`, and the tensor keys and the main-model per-tensor bit widths are identical to the standard EXL3 2.9 bpw builds of this model (checked against LibertAIDAI/DeepSeek-V4.1-Flash-1017-EXL3-2.9bpw: 192,495 tensor keys; the widths of all 46,702 main-model quantized tensors match; tokenizer files byte-identical). The 46 MTP / DSpark dense tensors differ: 4 bit here, 6 bit in the LibertAI build. What EAQ v1 changes is only how the weights were produced (the EAQ conversion; see Conversion). Any runtime that loads EXL3 DeepSeek-V4.1 checkpoints should load this one without EAQ-specific code, including the TensorFold DeepSeek-V4.1-Flash-1017 2x DGX Spark recipe (set its `MODEL_ID` to this repo).
- Qwen3.8-27B-oQ8↗
TensorFold
- Qwen3.8-Flash-Next-MLX-3bit-MTP↗
TensorFold