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AI 모델 2119

  • StartLux-Decision-v1-0.8B-Q8_0-GGUF↗

    startlux-models

    StartLux-Decision-v1-0.8B in Q8_0 (8-bit) as a GGUF file for llama.cpp. The decision server in this repository turns it into the same typed decisions, with a probability for every option, as the original model. "Same decision" is the share of the 231 public JevBench items on which the file picks the same answer as the original weights run through the `startlux_decision` package, with the same prompts, readout and temperatures. Q8_0 and Q4_K_M are llama.cpp's standard quantizations of BF16, without an importance matrix. At this size Q4_K_M changes noticeably more answers (93.1% the same), so Q8_0 is the better choice. `-ngl 99` puts every layer on the GPU (CUDA or Metal); leave it out on a CPU-only machine. llama.cpp has to be recent enough for this model (build b10454 or newer).

  • sam2-hiera-tiny↗

    facebook

    Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and videos from FAIR. See the SAM 2 paper for more information. SAM2 can be used for automatic mask generation to segment all objects in an image using the `mask-generation` pipeline: SAM2's key strength is its ability to track objects across video frames. Here's how to use it for video segmentation:

  • LTX2.3_comfy↗

    Kijai

    The fp8 quantizations were done with the basic static weight scales and are set to not run with fp8 matmuls, the models marked `input_scaled` additionally have activation scaling, and are set to run with fp8 matmuls on supported hardware (roughly 40xx and later Nvidia GPUs). As this is first time I'm attempting to do calibrate input scales, these are pretty experimental, but result wise seems to work, this is a test on a 4090, 8 steps with distill: **update:** input_scaled_v3 follows the same pattern as the official one where blocks 0-1 and 46-47 (first two and last two) are kept in bf16, as well as better calibrated input scales, this does fix some of the issues in v2, especially when using input audio.

  • smollm2-1.7b-gsm8k↗

    EiadEssam

  • seresnext50_32x4d.racm_in1k↗

    timm

    A SE-ResNeXt-B image classification model with Squeeze-and-Excitation channel attention. This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample * grouped 3x3 bottleneck convolutions * Squeeze-and-Excitation channel attention Recipe details: * RandAugment `RACM` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in ResNet Strikes Back. * RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging * Step (exponential decay w/ staircase) LR schedule with warmup

  • Qwen3.8-Flash-Next-Uncensored-MLX-Serve-4bit↗

    ARC4NUM

    The **uncensored** Qwen3.8-Flash-Next (125B total / 6B active MoE) packed for mlx-serve, so it actually fits and flies on a 128 GB Apple Silicon Mac: > The other ways to run this model uncensored on a Mac either don't fit 128 GB > (the original MLX repo is 163 GiB) or run slower (GGUF IQ4_XS ≈ 26 tok/s on > the same hardware; generic Python MLX paths ≈ 27–36 tok/s). This is a 4-bit MLX-Serve pack of **orcarouter/Qwen3.8-Flash-Next-Uncensored** — the full-precision **BF16 abliterated** (refusal-removed) build of Qwen's Qwen3.8-Flash-Next. See orcarouter's card for the full abliteration methodology and evaluation; this section summarizes it.

  • Bonsai-2-27B-Ternary-CRACK-GGUF↗

    dealignai

    **Abliterated · No guardrails · PQ2_0 ternary 2.13 bpw · 7.2 GB · Runs on a laptop / single GPU · Vision-capable** > ⚠️ **Re-download notice (2026-09-17 20:44 PDT / 2026-09-18 03:44 UTC)** — an earlier build of this model had a coherence bug in reasoning modes (`low`/`xhigh`) that could cause token loops on some prompts. This version fixes it. If you downloaded before this timestamp, please pull the latest .gguf. **Proprietary weight-level abliteration** by the dealignai research team. Byte-identical to the base ternary quant everywhere except a small set of tensors that carry the refusal circuit. Drop-in replacement for the ternary base at inference — same tokenizer, same chat template, same reasoning modes, same vision projector interface.

  • mask2former-swin-large-ade-panoptic↗

    facebook

    Mask2Former model trained on ADE20k panoptic segmentation (large-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository. Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.

  • Reason-ModernColBERT↗

    lightonai

    Reason-ModernColBERT is a late interaction model trained on the reasonir-hq dataset. It achieves extremely competitive performance on the BRIGHT benchmark aimed at evaluating reasoning-intensive retrieval performance, outperforming all existing models up to 7B (more than 45 times its size) and even surprisingly improving performance of ReasonIR-8B (a 8B model trained on the same data) by more than 2.5 NDCG@10 on average on Stack Exchange splits. We attribute such strong results to late-interaction, see evaluation section. Unfortunately, since the ReasonIR data has been released under a cc-by-nc-4.0 license, we cannot release this model under an Apache 2.0 license. However, the authors of ReasonIR released code to generate the data. Anyone willing to reproduce the data could then easily reproduce this model under an Apache 2.0 license by running a fine-tuning lasting lower than 2 hours using this boilerplate. This is a multi-vector (ColBERT-style late interaction) embedding model finetuned from lightonai/GTE-ModernColBERT-v1 on the reasonir-hq dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.

  • CognitiveFusion2-4x7B-BF16↗

    Kquant03

    This is an update to the original Cognitive Fusion. We intend to perform a fine-tune on it in order to increase its performance. The scale of a model is one of the most important axes for better model quality. Given a fixed computing budget, training a larger model for fewer steps is better than training a smaller model for more steps. Mixture of Experts enable models to be pretrained with far less compute, which means you can dramatically scale up the model or dataset size with the same compute budget as a dense model. In particular, a MoE model should achieve the same quality as its dense counterpart much faster during pretraining.