clef-flash-oQ4e
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clef-flash-oQ8e
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View details ↗clef-oQ4e
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View details ↗d3-oQ8e
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View details ↗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。
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View details ↗This model was quantized using oQ (oMLX v0.7.1) mixed-precision quantization with Improved oQe Quantization (jundot/omlx#4385). A mixed-precision MLX checkpoint of deepseek-ai/DeepSeek-V4.1-Flash, a multimodal mixture-of-experts model with 552B backbone parameters, a Causal Encoder-Decoder layout, Compressed Sparse Attention 2 (CSA2), Engram conditional memory and DSpark speculative decoding. It includes the language model, the Engram tables, the vision encoder, the multimodal aligner and the DSpark MTP weights. The effective average is 4.365 bits/weight excluding Engram, or 4.529 bits/weight for the complete checkpoint.
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View details ↗> [!NOTE] > **Updated 2026-10-29:** Re-quantized with the improved oQe quantization in oMLX 0.7.3. Please re-download if you have an earlier copy. This model was quantized using oQ (oMLX v0.7.3) mixed-precision quantization with improved oQe (jundot/omlx#4930). A mixed-precision MLX checkpoint of deepseek-ai/DeepSeek-V4-Flash-0731, the official release of DeepSeek-V4-Flash with enhanced agentic capabilities. It has the same structure as DeepSeek-V4-Flash-DSpark, with a speculative decoding module attached. It includes the language model and the DSpark MTP weights.
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View details ↗embeddinggemma-2-oQ8e
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View details ↗gemma-4-12B-it-oQ4e-mtp
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View details ↗gemma-4-12B-it-oQ8e-mtp
JundotThis model was quantized using oQ (oMLX v0.7.1) mixed-precision quantization with Improved oQe Quantization (jundot/omlx#4385). A mixed-precision MLX checkpoint of google/gemma-4-12B-it, the 11.95B Unified model of the Gemma 4 family from Google DeepMind. It has no separate encoders: image patches and audio waveforms are projected directly into the embedding space through lightweight linear layers, and all modalities run through a single decoder-only transformer with a 256K-token context. It includes the model and a multi-token prediction (MTP) head merged from google/gemma-4-12B-it-assistant. oQ8e allocates bits by measured layer sensitivity, then rounds each group with an importance matrix (128 samples x 512 tokens) and a weighted least-squares refit of its scale and bias.
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View details ↗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。
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View details ↗> [!NOTE] > **Updated 2026-10-29:** Re-quantized with the improved oQe quantization in oMLX 0.7.3. Please re-download if you have an earlier copy. This model was quantized using oQ (oMLX v0.7.3) mixed-precision quantization with improved oQe (jundot/omlx#4930). A mixed-precision MLX checkpoint of google/gemma-4-26B-A4B-it, the mixture-of-experts model of the Gemma 4 family from Google DeepMind, with 25.2B total and 3.8B active parameters, text and image input, configurable thinking modes and a 256K-token context. It includes the language model, the vision encoder and a multi-token prediction (MTP) head merged from google/gemma-4-26B-A4B-it-assistant.
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View details ↗gemma-4-31B-it-oQ4e-mtp
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View details ↗gemma-4-31B-it-oQ6e-mtp
Jundot> [!NOTE] > **Updated 2026-10-29:** Re-quantized with the improved oQe quantization in oMLX 0.7.3. Please re-download if you have an earlier copy. This model was quantized using oQ (oMLX v0.7.3) mixed-precision quantization with improved oQe (jundot/omlx#4930). A mixed-precision MLX checkpoint of google/gemma-4-31B-it, the 30.7B dense model of the Gemma 4 family from Google DeepMind, with text and image input, configurable thinking modes and a 256K-token context. It includes the language model, the vision encoder and a multi-token prediction (MTP) head merged from google/gemma-4-31B-it-assistant.
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View details ↗gemma-4-31B-it-oQ8e-mtp
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View details ↗gemma-4-E2B-it-oQ4e-mtp
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View details ↗gemma-4-E4B-it-oQ4e-mtp
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View details ↗GLM-5.3-Flash-oQ2.5e-mtp
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View details ↗GLM-5.3-Flash-oQ2e-mtp
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