> [!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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embeddinggemma-2-oQ8e
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gemma-4-12B-it-oQ4e-mtp
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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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> [!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.
gemma-4-31B-it-oQ4e-mtp
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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.
gemma-4-31B-it-oQ8e-mtp
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gemma-4-E2B-it-oQ4e-mtp
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gemma-4-E4B-it-oQ4e-mtp
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GLM-5.3-Flash-oQ2.5e-mtp
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GLM-5.3-Flash-oQ2e-mtp
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GLM-5.3-Flash-oQ3.5e-mtp
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GLM-5.3-Flash-oQ3e-mtp
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GLM-5.3-Flash-oQ4e-mtp
Jundot> [!NOTE] > **Updated 2026-10-24:** Re-quantized with Better Low-Bit oQ. Please re-download if you have an earlier copy. This model was quantized using oQ (oMLX v0.7.3.dev1) mixed-precision quantization with Better Low-Bit oQ (jundot/omlx#4776). A mixed-precision MLX checkpoint of zai-org/GLM-5.3-Flash, a natively multimodal mixture-of-experts model with 320B total and 18B active parameters that combines sparse and linear attention with Manifold-Constrained Hyper-Connections (mHC). It includes the language model, the vision encoder and the multi-token prediction (MTP) head.
GLM-5.3-Flash-oQ5e-mtp
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Muse-Glimmer-30B-oQ4e
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Ornith-1.0-35B-oQ4e
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Qwen3.6-35B-A3B-oQ3e-mtp
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Qwen3.6-35B-A3B-oQ4e-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 Qwen/Qwen3.6-35B-A3B, a mixture-of-experts vision-language model with 35B total and 3B activated parameters and a hybrid layout of Gated DeltaNet and Gated Attention layers. It includes the language model, the vision encoder and the multi-token prediction (MTP) head.
Qwen3.6-35B-A3B-oQ6e-mtp
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Qwen3.6-35B-A3B-oQ8e-mtp
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Qwen3.8-27B-oQ3e-mtp
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