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  • montezM Offline
    montezM Offline
    montez
    wrote on last edited by montez
    #27

    Qwen3.8

    Hugging Face: https://huggingface.co/Qwen/Qwen3.8-27B, https://huggingface.co/Qwen/Qwen3.8-27B-FP8
    GitHub: https://github.com/QwenLM/Qwen3
    X: https://x.com/Alibaba_Qwen/status/2088280182356611304
    Website: https://qwen.ai/blog?id=qwen3.8

    Developer: Alibaba Cloud, Qwen team
    Released: August 2026
    Parameters: 27B
    Context: 262,144 tokens
    Architecture: Qwen3.5 foundation, dense native vision-language model; hybrid linear + full attention
    License: Apache 2.0
    Modalities: Text + Image + Video
    Runs on: Laptop, 24GB+ unified memory, estimated / Desktop GPU
    Formats: safetensors BF16, safetensors FP8, GGUF and MLX community quantizations
    On disk: 16.05GB MLX 4-bit / 30.87GB FP8 safetensors / 55.56GB BF16 safetensors

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    • montezM Offline
      montezM Offline
      montez
      wrote on last edited by montez
      #28

      NVIDIA Nemotron 3.5 Lightning

      Hugging Face: https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16, https://huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-NVFP4
      GitHub: https://github.com/NVIDIA-NeMo/Nemotron
      Website: https://build.nvidia.com/nvidia/nemotron-3.5-lightning-30b-a3b

      Developer: NVIDIA
      Released: August 2026
      Variants: Instruct, Base, DSpark and DFlash speculative drafters
      Parameters: 30B total, 3B active
      Context: 1,048,576 tokens
      Architecture: hybrid LatentMoE interleaving Mamba-2, MoE and attention; 52 layers, 128 routed experts, 6 active, 32Q/2KV heads, Multi-Token Prediction
      License: OpenMDW License Agreement, version 1.1
      Modalities: Text
      Runs on: Desktop GPU, Edge device, DGX Spark; NVIDIA only
      Formats: safetensors BF16, safetensors NVFP4, GGUF community conversion
      On disk: 17.82GB NVFP4 safetensors / 31.58GB BF16 safetensors

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      • montezM Offline
        montezM Offline
        montez
        wrote on last edited by
        #29

        Google DiffusionGemma

        Hugging Face: https://huggingface.co/google/diffusiongemma-26B-A4B-it
        GitHub: https://github.com/google-gemma
        Website: https://ai.google.dev/gemma/docs/diffusiongemma
        Announcement: https://blog.google/innovation-and-ai/technology/developers-tools/diffusion-gemma-faster-text-generation/

        Developer: Google DeepMind
        Released: June 2026
        Parameters: 25.2B total, 3.8B active
        Context: 256,000 tokens
        Architecture: discrete text diffusion on the Gemma 4 26B A4B MoE foundation; autoregressive encoder prefills the prompt into a KV cache, decoder applies bidirectional attention over a 256-token canvas, block-autoregressive multi-canvas sampling; 30 layers, 8 active of 128 experts plus 1 shared, 1,024 sliding window, 550M vision encoder
        License: Apache 2.0
        Modalities: Text + Image + Video in, Text out
        Runs on: Desktop GPU, 18GB+ VRAM
        Formats: safetensors
        On disk: 51.65GB safetensors

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        • montezM Offline
          montezM Offline
          montez
          wrote on last edited by
          #30

          IBM Granite Swash

          Hugging Face: https://huggingface.co/ibm-granite/granite-swash-2b, https://huggingface.co/ibm-granite/granite-swash-3b-a600m
          GitHub: https://github.com/ibm-granite/granite-4.1-language-models

          Developer: IBM Granite Team
          Released: July 2026
          Variants: SWASH-2B, SWASH-3B-A600M
          Parameters: 2B / 3B total, 600M active
          Context: 8,192 tokens
          Architecture: sliding window attention with learnable per-head attention sinks, LSE-scaled; 2B: dense decoder-only, 24 layers, 7 full-attention + 17 sliding-window layers, window 128, GQA, SwiGLU, RoPE, RMSNorm / 3B-A600M: MoE, 28 layers, 48 experts, 4 routed active
          License: Apache 2.0
          Modalities: Text
          Runs on: Smartphone, Laptop, Edge device
          Formats: safetensors
          On disk: 2B: 4.29GB safetensors / 3B-A600M: 6.04GB safetensors

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          • montezM Offline
            montezM Offline
            montez
            wrote on last edited by
            #31

            IBM Granite Vision 4.1

            Hugging Face: https://huggingface.co/ibm-granite/granite-vision-4.1-4b, https://huggingface.co/ibm-granite/granite-vision-4.1-4b-GGUF
            GitHub: https://github.com/ibm-granite/granite-vision-models
            Website: https://www.ibm.com/granite/docs/models/vision
            Announcement: https://research.ibm.com/blog/granite-4-1-ai-foundation-models

            Developer: IBM
            Released: April 2026
            Parameters: 4B total, Granite 4.1 3B language model plus vision encoder and projectors
            Context: 131,072 tokens
            Architecture: SigLIP2 so400m patch16-384 vision encoder over 384x384 image tiles, windowed Q-Former projectors compressing each 4x4 patch window to 2x2 tokens, and a Granite 4.1 3B language model with rank-256 LoRA across all self-attention projections
            License: Apache 2.0
            Modalities: Text + Image
            Runs on: Smartphone, Laptop, Edge device
            Formats: safetensors, GGUF Q4_K_M, Q5_K_M, Q6_K, Q8_0, bf16, with f16 mmproj
            On disk: 2.10GB Q4_K_M plus 1.16GB f16 mmproj / 6.81GB bf16

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            • montezM Offline
              montezM Offline
              montez
              wrote on last edited by
              #32

              Microsoft Mage-VL

              Hugging Face: https://huggingface.co/microsoft/Mage-VL
              GitHub: https://github.com/microsoft/Mage
              Website: https://microsoft.github.io/Mage/vl/
              Technical report: https://arxiv.org/abs/2607.24904

              Developer: Microsoft Mage Team
              Released: July 2026
              Parameters: 4B
              Context: 262,144 tokens
              Architecture: Mage-ViT codec-native visual encoder trained from scratch, 24 layers, feeding a two-layer MLP projector into a Qwen3-4B-Instruct-2507 causal decoder; separate cognition gate for proactive streaming
              License: Apache 2.0
              Modalities: Text + Image + Video
              Runs on: Laptop, 12GB+ memory at BF16, estimated / Desktop GPU
              Formats: safetensors, bundled streaming gate and neural codec
              On disk: 9.48GB safetensors

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              • montezM Offline
                montezM Offline
                montez
                wrote on last edited by
                #33

                Cohere Labs North Micro Vision

                Hugging Face: https://huggingface.co/CohereLabs/North-Micro-Vision-Instruct
                Technical report: https://huggingface.co/blog/CohereLabs/meet-north-micro-vision-instruct

                Developer: Cohere Labs
                Released: August 2026
                Parameters: 2.4B total, 2B language model + 400M vision encoder
                Context: 128,000 tokens, multimodal validated to 8,192
                Architecture: custom native-resolution vision encoder with DeepStack patch embeddings injected into early decoder layers, projector, and the Command A+ style North Micro LLM: three sliding-window attention layers with RoPE plus one global layer without positional embeddings
                License: Apache 2.0
                Modalities: Text + Image
                Runs on: Smartphone, Laptop, Edge device, with quantization
                Formats: safetensors BF16, MLX 4-bit and 8-bit community conversions
                On disk: 4.97GB BF16 safetensors

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                • montezM Offline
                  montezM Offline
                  montez
                  wrote on last edited by
                  #34

                  Cactus Compute Needle 2

                  Hugging Face: https://huggingface.co/Cactus-Compute/needle2
                  GitHub: https://github.com/cactus-compute/needle
                  X: https://x.com/cactuscompute/status/2086865960669983035
                  Website: https://cactuscompute.com/needle

                  Developer: Cactus Compute
                  Released: August 2026
                  Parameters: 45M
                  Context: 2,048 tokens
                  Architecture: Simple Attention Network, 27 layers, hidden 512, 8Q/4KV GQA, Hadamard MLP, engram sites, CQ2 quantization at 2.2 effective bits; byte-level grammar-constrained decoding and a tool-retrieval head
                  License: Apache 2.0
                  Modalities: Text
                  Runs on: Smartphone, Headset, Edge device, Microcontroller
                  Formats: cact single binary; ARM64, x86-64, ARMv7, RISC-V and WebAssembly builds
                  On disk: 13.7MB cact

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                  • montezM Offline
                    montezM Offline
                    montez
                    wrote on last edited by
                    #35

                    Syzygy Mach-1 Additive 35B

                    Hugging Face: https://huggingface.co/SyzygyResearch/Mach-1-Additive-35B
                    X: https://x.com/syzygyeng/status/2084350792841195992
                    Website: https://withsyzygy.com/mach-1
                    Docs: https://withsyzygy.com/docs/mach

                    Developer: Syzygy Research
                    Released: August 2026
                    Parameters: 35B total, 8 of 256 experts active
                    Context: 262,144 tokens
                    Architecture: Qwen3.5 MoE topology, 40 layers, 256 experts, 8 active, hybrid linear attention with full attention every 4th layer; additive 1.7-bit weights with no weight multiplication
                    License: Apache 2.0
                    Modalities: Text
                    Runs on: Laptop, 16GB+ unified memory; Apple Silicon only
                    Formats: packed 1.7-bit safetensors, MLX
                    On disk: 7.0GB

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                    • montezM Offline
                      montezM Offline
                      montez
                      wrote on last edited by
                      #36

                      OpenMOSS MOSS-VL

                      Hugging Face: https://huggingface.co/OpenMOSS-Team/MOSS-VL-Instruct-0708-FP8, https://huggingface.co/OpenMOSS-Team/MOSS-VL-Realtime-FP8
                      GitHub: https://github.com/OpenMOSS/MOSS-VL
                      Website: https://openmoss.ai/MOSS-VL/
                      Technical report: https://arxiv.org/abs/2606.07639

                      Developer: OpenMOSS Team
                      Released: August 2026
                      Variants: Instruct-0708, Realtime
                      Parameters: 11B
                      Context: 262,144 tokens
                      Architecture: unified cross-attention multimodal model, 48 language layers with 12 cross-attention layers, XRoPE 3D spatiotemporal positions, absolute frame timestamps for streaming video
                      License: Apache 2.0
                      Modalities: Text + Image + Video
                      Runs on: Desktop GPU; Instruct: 24GB VRAM / Realtime: 26GB+ VRAM; NVIDIA only
                      Formats: FP8 compressed-tensors with BF16 cross-attention and vision, HQQ INT8 KV cache
                      On disk: 15.73GB FP8 safetensors

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                      • montezM Offline
                        montezM Offline
                        montez
                        wrote last edited by
                        #37

                        IBM Granite 4.2

                        Hugging Face: https://huggingface.co/ibm-granite/granite-4.2-3b, https://huggingface.co/ibm-granite/granite-4.2-8b, https://huggingface.co/ibm-granite/granite-4.2-30b
                        GitHub: https://github.com/ibm-granite/granite-4.2-language-models
                        Website: https://www.ibm.com/granite/docs/models/granite4-2

                        Developer: IBM
                        Released: August 2026
                        Variants: 3B / 8B / 30B
                        Parameters: 3B / 8B / 30B
                        Context: 3B and 8B: 131,072 tokens / 30B: 131,072 tokens native, 512,000 tokens extended
                        Architecture: dense decoder-only transformer; GQA, RoPE, SwiGLU and RMSNorm; native reasoning modes and tool calling
                        License: Apache 2.0
                        Modalities: Text
                        Formats: safetensors
                        On disk: 3B: 7.32GB BF16 / 8B: 17.58GB BF16 / 30B: 58.55GB BF16

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                        • montezM Offline
                          montezM Offline
                          montez
                          wrote last edited by
                          #38

                          FireRedTeam FireRedAudio

                          Hugging Face: https://huggingface.co/FireRedTeam/FireRedAudio
                          GitHub: https://github.com/FireRedTeam/FireRedAudio
                          Website: https://fireredteam.github.io/demos/fireredaudio/
                          Technical report: https://arxiv.org/abs/2608.24168

                          Developer: FireRedTeam
                          Released: August 2026
                          Parameters: 9B
                          Architecture: shared 9B LLM; decoupled Audio Encoder for understanding and RedAE-Patch plus flow-matching DiT pathway for generation
                          License: Apache 2.0
                          Modalities: Text + Audio
                          Runs on: Desktop GPU, NVIDIA only
                          Formats: safetensors, PyTorch .pt
                          On disk: FireRedAudio: 21.23GB safetensors / RedAE decoder: 8.40GB PyTorch .pt / 29.63GB total, estimated

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                          • montezM Offline
                            montezM Offline
                            montez
                            wrote last edited by
                            #39

                            Cohere Labs Tiny Aya L2-Thinker

                            Hugging Face: https://huggingface.co/CohereLabs/tiny-aya-l2-thinker
                            Technical report: https://arxiv.org/abs/2609.10445

                            Developer: Cohere Labs
                            Released: September 2026
                            Parameters: 3.35B
                            Architecture: Cohere2 decoder-only transformer; supervised fine-tuning for in-language reasoning
                            License: CC-BY-NC-4.0
                            Modalities: Text
                            Runs on: Laptop
                            Formats: safetensors
                            On disk: 6.70GB BF16 safetensors, estimated

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                            • montezM Offline
                              montezM Offline
                              montez
                              wrote last edited by
                              #40

                              InternLM Intern Lumina U2

                              Hugging Face: https://huggingface.co/internlm/InternLumina-U2
                              GitHub: https://github.com/InternLM/InternLumina-U2
                              Website: https://internlm.github.io/InternLumina-U2/

                              Developer: Shanghai AI Laboratory / InternLM
                              Released: September 2026
                              Parameters: 16B total, 1B active
                              Architecture: LLaDA-2.0 MoE diffusion LLM backbone; 8-codebook fully-discrete AToken visual representation; spatial-parallel denoising plus codebook-depth autoregressive head
                              License: Apache 2.0
                              Modalities: Text + Image + Video + 3D
                              Runs on: Huawei Ascend NPU only
                              Formats: safetensors
                              On disk: 33.81GB Ascend safetensors, estimated

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                              • montezM Offline
                                montezM Offline
                                montez
                                wrote last edited by
                                #41

                                Microsoft FrogNano

                                Hugging Face: https://huggingface.co/microsoft/FrogNano-4B-2609
                                GitHub: https://github.com/microsoft/FrogNano
                                Technical report: https://arxiv.org/abs/2609.07925

                                Developer: Microsoft
                                Released: September 2026
                                Parameters: 4.66B
                                Context: 131K tokens in the evaluated configuration
                                Architecture: Qwen3.5-4B base; dense, 32 layers, hybrid Gated DeltaNet + gated attention; reinforcement-learning post-training on about 1,500 synthetic software-engineering tasks; repository-level coding agent run through the five-tool Leaf harness
                                License: MIT
                                Modalities: Text
                                Runs on: Laptop, 16GB+ unified memory
                                Formats: BF16 safetensors
                                On disk: 9.32GB BF16

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                                • montezM Offline
                                  montezM Offline
                                  montez
                                  wrote last edited by
                                  #42

                                  H Company Holo4

                                  Hugging Face: https://huggingface.co/Hcompany/Holo4-27B, https://huggingface.co/Hcompany/Holo4-35B-A3B, https://huggingface.co/Hcompany/Holotron4-30B-A3B
                                  Website: https://hcompany.ai/newsroom/holo4

                                  Developer: H Company
                                  Released: September 2026
                                  Variants: Holo4-27B / Holo4-35B-A3B / Holotron4-30B-A3B
                                  Parameters: 27B dense / 35B total, 3B active / 30B total, 3B active
                                  Context: 262,144 tokens
                                  Architecture: computer-use agent VLMs; 27B: Qwen3.8 base, dense, 64 layers / 35B-A3B: Qwen3.6 base, MoE, 40 layers, 256 experts, 8 routed active / Holotron4: NVIDIA Nemotron 3 Nano Omni base, hybrid Mamba + MoE + attention, 52 layers, 128 routed experts, 6 active; Holo4: supervised fine-tuning plus two merged reinforcement-learning LoRA experts
                                  License: 27B: CC-BY-NC-4.0 / 35B-A3B: Apache 2.0 / Holotron4: NVIDIA Open Model Agreement
                                  Modalities: Text + Image
                                  Runs on: 27B: Laptop, 32GB+ unified memory / 35B-A3B: Laptop, 32GB+ unified memory / Holotron4: Workstation GPU, 48GB+ memory
                                  Formats: 27B and 35B-A3B: BF16 safetensors, FP8, NVFP4, Q4_K_M GGUF with mmproj / Holotron4: BF16 safetensors, FP8
                                  On disk: 27B: 16.88GB Q4_K_M GGUF, 54.71GB BF16 / 35B-A3B: 21.30GB Q4_K_M GGUF, 70.21GB BF16 / Holotron4: 35.20GB FP8, 66.03GB BF16

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                                  • montezM Offline
                                    montezM Offline
                                    montez
                                    wrote last edited by
                                    #43

                                    JetBrains Mellum2.1

                                    Hugging Face: https://huggingface.co/JetBrains/Mellum2.1-12B-A2.5B-Thinking, https://huggingface.co/JetBrains/Mellum2.1-12B-A2.5B-Thinking-GGUF, https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Base, https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Instruct, https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Thinking
                                    Website: https://blog.jetbrains.com/ai/2026/10/mellum2-1-gets-to-work-a-fast-open-model-for-coding-agents/
                                    Technical report: https://arxiv.org/abs/2605.31268

                                    Developer: JetBrains
                                    Released: October 2026
                                    Variants: Mellum2.1 Thinking / Mellum2 Base, Instruct, Thinking (June 2026)
                                    Parameters: 12B total, 2.5B active
                                    Context: 131,072 tokens
                                    Architecture: MoE, 28 layers, 64 experts with 8 active, GQA with 32 Q and 4 KV heads, sliding window 1,024 on 3 of every 4 layers; Mellum2.1 keeps the Mellum2 architecture and adds reinforcement learning post-training
                                    License: Apache 2.0
                                    Modalities: Text
                                    Runs on: Laptop, 16GB+ unified memory
                                    Formats: safetensors BF16, GGUF (BF16, Q8_0, Q6_K, Q4_K_M, MXFP4_MOE)
                                    On disk: Mellum2.1 Thinking: 8.07GB Q4_K_M, 24.31GB BF16

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                                    • montezM Offline
                                      montezM Offline
                                      montez
                                      wrote last edited by
                                      #44

                                      PrismML Bonsai 2 27B

                                      Hugging Face: https://huggingface.co/prism-ml/Ternary-Bonsai-2-27B-mlx-2bit, https://huggingface.co/prism-ml/Ternary-Bonsai-2-27B-gguf
                                      GitHub: https://github.com/PrismML-Eng/Bonsai-demo
                                      Website: https://prismml.com/news/bonsai-2-27b
                                      White paper: https://github.com/PrismML-Eng/Bonsai-demo/blob/main/bonsai-2-27b-whitepaper.pdf

                                      Developer: PrismML
                                      Released: September 2026
                                      Parameters: 27.36B total, 24.35B language + 2.54B embedding and LM head + 0.47B vision tower
                                      Context: 262,144 tokens
                                      Architecture: Qwen3.8-27B base; 64 blocks, 24Q/4KV heads, hybrid attention with about 75% linear and 25% full attention; ternary {-1, 0, +1} weights with FP16 group-wise scales at group size 128, 1.76 effective bits per weight
                                      License: Apache 2.0
                                      Modalities: Text + Image
                                      Runs on: Laptop, Desktop GPU; Apple Silicon or NVIDIA GPU only
                                      Formats: MLX 2-bit ternary, GGUF PTQ1_0, GGUF PQ2_0, optional mmproj vision pack
                                      On disk: 5.95GB PTQ1_0 GGUF, 7.21GB PQ2_0 GGUF, 8.60GB MLX / 0.63GB mmproj vision pack

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                                      • montezM Offline
                                        montezM Offline
                                        montez
                                        wrote last edited by
                                        #45

                                        Swiss AI Apertus 1.5

                                        Hugging Face: https://huggingface.co/swiss-ai/Apertus-v1.5-8B, https://huggingface.co/swiss-ai/Apertus-v1.5-70B
                                        Website: https://www.apertus-ai.org/articles/2026-07-apertus-1-5
                                        Docs: https://www.apertus-ai.org/docs

                                        Developer: Swiss AI Initiative (EPFL, ETH Zurich, CSCS)
                                        Released: July 2026
                                        Variants: 8B / 70B
                                        Parameters: 8B / 70B
                                        Context: 262,144 tokens
                                        Architecture: decoder-only transformer with xIELU activation, trained with the AdEMAMix optimizer; continued pretraining of Apertus 1.0 on 4T added tokens (8B) and 2T added tokens (70B); optional thinking mode and tool calling
                                        License: Apache 2.0 with Acceptable Use Policy
                                        Modalities: Text + Image + Audio (audio experimental), text output
                                        Runs on: 8B: Laptop, Desktop GPU / 70B: Server-class hardware
                                        Formats: safetensors
                                        On disk: 8B: 18.40GB safetensors / 70B: 144.60GB safetensors

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                                        • montezM Offline
                                          montezM Offline
                                          montez
                                          wrote last edited by
                                          #46

                                          Edge0

                                          Hugging Face: https://huggingface.co/Edge0/Edge0-35B-A3B-preview, https://huggingface.co/Edge0/Edge0-8B-A1B-preview
                                          GitHub: https://github.com/Edge0-AI/Edge0
                                          Technical report: https://arxiv.org/abs/2609.18063

                                          Developer: Edge0 AI
                                          Released: September 2026
                                          Variants: Edge0-35B-A3B-preview / Edge0-8B-A1B-preview
                                          Parameters: 35B total, 3B active / 8B total, 1B active
                                          Context: 262,144 tokens / 131,072 tokens
                                          Architecture: early preview release; sparse MoE; 35B-A3B: Qwen3.6-35B-A3B base, 40 layers, 256 experts, 8 per token / 8B-A1B: Ling 3.0 tiny base, 24 layers, 128 experts, 8 per token; int4 checkpoint plus Recover-LoRA adapters and prerouter heads, with experts streamed from SSD by the edge0 framework
                                          License: Apache 2.0
                                          Modalities: Text
                                          Runs on: 35B-A3B: Smartphone, 12GB+ RAM on Android / 8B-A1B: Smartphone, 8GB+ RAM on Android; edge0 engines only (iOS, macOS, Android, Windows)
                                          Formats: MLX 4-bit safetensors, LoRA and prerouter adapter safetensors
                                          On disk: 35B-A3B: 19.51GB int4 checkpoint plus 0.18GB adapters / 8B-A1B: 4.51GB int4 checkpoint plus 0.06GB adapters

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