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Local Foundation Models

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  • montezM Offline
    montezM Offline
    montez
    wrote on last edited by montez
    #14

    Agents-A1

    Hugging Face: https://huggingface.co/InternScience/Agents-A1, https://huggingface.co/InternScience/Agents-A1-4B
    GitHub: https://github.com/InternScience/Agents-A1
    X: https://x.com/intern_lm
    Website: https://internscience.github.io/Agents-A1/
    Technical report: https://arxiv.org/abs/2606.30616

    Developer: InternScience, Shanghai Artificial Intelligence Laboratory
    Released: 35B-A3B: June 2026 / 4B: July 2026
    Parameters: 35B total, 3B active / 4B dense
    Context: 262,144 tokens
    Architecture: Qwen3.5 base, hybrid linear + full attention (full attention every 4th layer); 35B-A3B: MoE, 40 layers, 256 experts, 8 routed + 1 shared active / 4B: dense, 32 layers
    License: Apache 2.0
    Modalities: Text + Vision
    Runs on: 35B-A3B: Laptop, 24GB+ unified memory / 4B: Smartphone, Laptop
    Formats: GGUF, MLX (community), safetensors
    On disk: 35B-A3B: 19.7GB Q4_K_M / 4B: 2.5GB Q4_K_M, 4.2GB Q8_0

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    • montezM Offline
      montezM Offline
      montez
      wrote on last edited by
      #15

      Ornith-1.0

      Hugging Face: https://huggingface.co/deepreinforce-ai/Ornith-1.0-9B, https://huggingface.co/deepreinforce-ai/Ornith-1.0-35B
      GitHub: https://github.com/deepreinforce-ai/Ornith-1
      Website: https://deep-reinforce.com/ornith.html

      Developer: DeepReinforce
      Released: June 2026
      Parameters: 9B dense / 35B total, 8 of 256 experts active
      Context: 262,144 tokens
      Architecture: Qwen3.5 base; 9B: dense, 32 layers / 35B: MoE, 40 layers, 256 experts, 8 active
      License: MIT
      Modalities: Text + Vision
      Runs on: 9B: Smartphone, Laptop / 35B: Laptop, 24GB+ unified memory
      Formats: GGUF, FP8, safetensors
      On disk: 9B: 5.9GB Q4_K_M / 35B: 21.4GB Q4_K_M

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      • montezM Offline
        montezM Offline
        montez
        wrote last edited by montez
        #16

        DeepGrove Maple-Preview

        Hugging Face: https://huggingface.co/deepgrove/maple-preview
        GitHub: https://github.com/deepgrove-ai/mlx-lm-deepgrove
        Website: https://deepgrove.ai/maple-preview

        Developer: DeepGrove
        Released: August 2026
        Parameters: 20B total, 1B active
        Context: 131,072 tokens
        Architecture: ternary-weight MoE, 24 layers, 256 experts, 8 active; 3:1 SWA-512:GA attention
        License: MIT
        Modalities: Text
        Runs on: Mac mini M4
        Formats: safetensors, MLX 2-bit
        On disk: 5.31GB MLX 2-bit

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        • montezM Offline
          montezM Offline
          montez
          wrote last edited by montez
          #17

          Nanbeige4.2-3B

          Hugging Face: https://huggingface.co/Nanbeige/Nanbeige4.2-3B
          Website: https://nanbeige.zhipin.com/
          Technical report: https://arxiv.org/abs/2607.22083

          Developer: Nanbeige, BOSS Zhipin
          Released: July 2026
          Parameters: 4B total, 3B non-embedding
          Context: 262,144 tokens
          Architecture: dense Looped Transformer, LoopSplit, mHC with depth attention, concatenated n-gram embeddings
          License: Apache 2.0
          Modalities: Text
          Runs on: Laptop
          Formats: safetensors, GGUF Q4_K_M via conversion, MLX 4-bit
          On disk: 3.30GB MLX 4-bit

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          • montezM Offline
            montezM Offline
            montez
            wrote last edited by montez
            #18

            Fermion Research Neutrino

            Hugging Face: https://huggingface.co/FermionResearch/Neutrino-8B, https://huggingface.co/FermionResearch/Neutrino-0.6B, https://huggingface.co/FermionResearch/Neutrino-0.6B-Chat
            GitHub: https://github.com/fermionresearch/llama.cpp
            Website: https://www.fermionresearch.com/models/neutrino-8b/
            Announcement: https://www.fermionresearch.com/research/neutrino-8b/

            Developer: Fermion Research
            Released: July 2026
            Variants: 8B general purpose, 0.6B speculative-decoding draft, 0.6B-Chat conversational
            Parameters: 8.19B / 596M
            Context: 40,960 tokens
            Architecture: ternary QAT with staged post-training; 8B: Qwen3-8B topology, 36-layer decoder-only transformer, SwiGLU, GQA 4:1, RoPE, RMSNorm / 0.6B: 28 layers, hidden 1,024, SwiGLU 3,072, GQA 16Q/8KV, per-head Q/K RMSNorm, tied int8 embeddings
            License: Apache 2.0
            Modalities: Text
            Runs on: 8B: Laptop, 16GB unified memory / 0.6B: Laptop, CPU only
            Formats: TRTC v4, custom-FV5 GGUF, MLX
            On disk: 8B: 3.88GB TRTC v4 / 4.09GB custom-FV5 GGUF / 0.6B: 328MB TRTC v4, 238MB tv4z, 343MB GGUF

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            • montezM Offline
              montezM Offline
              montez
              wrote last edited by montez
              #19

              Kakao Kanana-2

              Hugging Face: https://huggingface.co/kakaocorp/kanana-2-3b-instruct, https://huggingface.co/kakaocorp/kanana-2-1.3b-instruct
              Website: https://tech.kakao.com/posts/826

              Developer: Kakao, Kanana LLM
              Released: July 2026
              Variants: 3B, 1.3B
              Parameters: 3B / 1.3B
              Context: 32,768 tokens
              Architecture: 3B: dense, pretrained from scratch, SFT + RL / 1.3B: cascade-pruned and distilled from 3B, sliding-window attention
              License: Kanana Open License
              Modalities: Text
              Runs on: Smartphone, Laptop
              Formats: safetensors, MLX 4-bit, 6-bit, 8-bit
              On disk: 3B: 1.99GB MLX 4-bit

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              • montezM Offline
                montezM Offline
                montez
                wrote last edited by
                #20

                Microsoft Fara1.5

                Hugging Face: https://huggingface.co/microsoft/Fara1.5-4B, https://huggingface.co/microsoft/Fara1.5-9B, https://huggingface.co/microsoft/Fara1.5-27B
                GitHub: https://github.com/microsoft/fara
                Website: https://labs.ai.azure.com/innovations/fara1-5/

                Developer: Microsoft Research AI Frontiers
                Released: May 2026
                Variants: 4B, 9B, 27B
                Parameters: 4B / 9B / 27B
                Context: 262,144 tokens
                Architecture: multimodal decoder-only LM, image + text to text
                License: MIT
                Modalities: Text + Image
                Formats: safetensors
                On disk: 4B: 9.08GB / 9B: 18.82GB / 27B: 54.71GB safetensors

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                • montezM Offline
                  montezM Offline
                  montez
                  wrote last edited by
                  #21

                  AMD Instella-MoE-16B-A3B-Think

                  Hugging Face: https://huggingface.co/amd/Instella-MoE-16B-A3B-Think
                  GitHub: https://github.com/AMD-AGI/Instella-MoE
                  Website: https://rocm.blogs.amd.com/artificial-intelligence/instella-moe/README.html

                  Developer: AMD
                  Released: July 2026
                  Parameters: 16B total, 2.8B active
                  Context: 32,768 tokens
                  Architecture: decoder-only MoE, Gated Multi-head Latent Attention, FarSkip-Collective connectivity
                  License: Research RAIL
                  Modalities: Text
                  Formats: safetensors
                  On disk: 31.73GB safetensors

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                  • montezM Offline
                    montezM Offline
                    montez
                    wrote last edited by montez
                    #22

                    Cisco Antares

                    Hugging Face: https://huggingface.co/fdtn-ai/antares-1b, https://huggingface.co/fdtn-ai/antares-350m
                    Website: https://cisco-foundation-ai.github.io/antares/
                    Announcement: https://cisco-foundation-ai.github.io/blogs/antares-beyond-vlocbench/

                    Developer: Cisco Foundation AI
                    Released: July 2026
                    Variants: 1b, 350m
                    Parameters: 1B / 350M
                    Architecture: fine-tuned from IBM Granite 4.0, GraniteMoEHybrid
                    License: Apache 2.0
                    Modalities: Text
                    Runs on: Smartphone, Laptop, Edge device
                    Formats: safetensors
                    On disk: 1b: 3.67GB safetensors / 350m: 0.70GB safetensors

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                    • montezM Offline
                      montezM Offline
                      montez
                      wrote last edited by
                      #23

                      AI9Stars G9v3-3B

                      Hugging Face: https://huggingface.co/ai9stars/G9v3-3B
                      GitHub: https://github.com/AI9Stars

                      Developer: AI9Stars
                      Parameters: ~3B
                      Context: 131,072 tokens
                      Architecture: dense causal LM, LlamaForCausalLM
                      License: Apache 2.0
                      Modalities: Text
                      Formats: safetensors
                      On disk: 5.99GB safetensors

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                      • montezM Offline
                        montezM Offline
                        montez
                        wrote last edited by
                        #24

                        Tencent Hy-Embodied-RxBrain-1.0

                        Hugging Face: https://huggingface.co/tencent/Hy-Embodied-RxBrain-1.0
                        GitHub: https://github.com/Tencent-Hunyuan/Hy-Embodied-RxBrain-1.0
                        Website: https://tairos.tencent.com/openSourceModels/hy-embodied-rxbrain-1.0
                        Technical report: https://arxiv.org/abs/2607.14187

                        Developer: Tencent Robotics X, Futian Laboratory, Tencent Hy Team
                        Released: July 2026
                        Parameters: ~6.2B
                        Architecture: Unified Mixture-of-Transformers, modality-specific text, vision, and generation pathways
                        License: Apache 2.0
                        Modalities: Text + Image + Video
                        Runs on: NVIDIA GPU, CUDA 12.x, Linux recommended
                        Formats: safetensors
                        On disk: 12.42GB safetensors

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                        • montezM Offline
                          montezM Offline
                          montez
                          wrote last edited by
                          #25

                          Poolside Laguna XS 2.1

                          Hugging Face: https://huggingface.co/poolside/Laguna-XS-2.1, https://huggingface.co/poolside/Laguna-XS-2.1-GGUF
                          Website: https://poolside.ai/blog/introducing-laguna-xs-2-1

                          Developer: Poolside
                          Released: July 2026
                          Variants: BF16, FP8, NVFP4, INT4; GGUF BF16, Q4_K_M
                          Parameters: 33B total, 3B active
                          Context: 262,144 tokens
                          Architecture: MoE, 40 layers: 10 global-attention + 30 sliding-window-attention; 256 experts + 1 shared expert
                          License: OpenMDW-1.1
                          Modalities: Text
                          Runs on: Mac with 36GB RAM
                          Formats: safetensors, GGUF
                          On disk: 20.27GB Q4_K_M GGUF / 66.89GB BF16 safetensors

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                          • montezM Offline
                            montezM Offline
                            montez
                            wrote last edited by montez
                            #26

                            Meta Muse Glimmer-30B

                            Hugging Face: https://huggingface.co/meta-models/Muse-Glimmer-30B
                            Announcement: https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model
                            Technical report: https://research.meta.ai/static/muse-glimmer-methodology

                            Developer: Meta Superintelligence Lab
                            Released: August 2026
                            Parameters: 29.6B total, including perception encoder
                            Context: 131,072+ tokens
                            Architecture: Dense causal transformer with ViT-G/14 perception encoder; 52 layers, GQA, SwiGLU, RoPE
                            License: Apache 2.0
                            Modalities: Text + Image
                            Runs on: MacBook M4 Max/M5 Max, RTX 5090; 24GB+ memory with 4-bit weights
                            Formats: BF16 safetensors, 4-bit quantized weights
                            On disk: 17GB K-Quant

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

                              webAI TwIL-LM

                              Hugging Face: https://huggingface.co/webAI-Official/TwIL-LM, https://huggingface.co/webAI-Official/TwIL-LM3
                              Website: https://www.webai.com/blog/webai-releases-twil-lm-a-family-of-formal-logic-models-that-outreason-a-120b-model-and-run-on-an-iphone

                              Developer: webAI Intelligence Lab
                              Released: August 2026
                              Variants: TwIL-LM 1.7B, TwIL-LM3 3B
                              Parameters: 1.7B: 1.78B total, 1.71B backbone + 72M LoRA / 3B: 3B
                              Context: 1.7B: 8,192 tokens / 3B: 65,536 tokens
                              Architecture: 1.7B: SmolLM2-1.7B-Instruct base, dense Llama, 24 layers, 32 heads, LoRA rank 64 SFT / 3B: SmolLM3-3B base, dense, 36 layers, GQA 16Q/4KV, NoPE every 4th layer; LoRA SFT, checkpoint fusion, WiSE-FT interpolation, GRPO reinforcement learning
                              License: webAI Non-Commercial License ver. 1.0
                              Modalities: Text
                              Runs on: 1.7B: Smartphone, Laptop / 3B: Laptop, 4GB VRAM or CPU
                              Formats: 1.7B: merged GGUF Q4_K_M, Q5_K_M, Q8_0, f16 / 3B: safetensors, GGUF Q4_K_M, Q5_K_M, Q6_K, Q8_0, F16
                              On disk: 1.7B: 1.06GB Q4_K_M / 3B: 1.92GB Q4_K_M

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

                                Ling 3.0

                                Hugging Face: https://huggingface.co/inclusionAI/Ling-3.0-flash, https://huggingface.co/inclusionAI/Ling-3.0-tiny, https://huggingface.co/inclusionAI/Ling-3.0-tiny-int4, https://huggingface.co/inclusionAI/Ling-3.0-tiny-fp8
                                GitHub: https://github.com/inclusionAI/Ling
                                X: https://x.com/AntLingAGI/status/2080351022028095681
                                Website: https://www.ant-ling.com/en
                                Docs: https://github.com/inclusionAI/ling-cookbook

                                Developer: Ant Group, InclusionAI
                                Released: July 2026
                                Variants: Ling-3.0-flash, Ling-3.0-tiny
                                Parameters: flash: 124B total, 5.1B active / tiny: 7.9B total, 1.3B active
                                Context: flash: 262,144 tokens / tiny: 131,072 tokens, 262,144 with YaRN
                                Architecture: BailingMoE hybrid; flash: linear KDA + MLA attention with sparse MoE / tiny: 3:1 KDA to MLA blocks, 128 routed experts, 8 routed + 1 shared active
                                License: MIT
                                Modalities: Text
                                Runs on: flash: NVIDIA DGX Spark / tiny: Laptop, 48GB unified memory
                                Formats: safetensors BF16, safetensors FP8, safetensors INT4, GGUF Q4_K_M
                                On disk: flash: 60.5GB Q4_K_M GGUF, 254.98GB BF16 safetensors / tiny: 5.81GB INT4, 8.41GB FP8, 15.79GB BF16 safetensors

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

                                  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 last edited by montez
                                    #30

                                    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 last edited by
                                      #31

                                      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 last edited by
                                        #32

                                        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 last edited by
                                          #33

                                          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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