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AI Models 2119

  • Cosmos-Predict1-7B-Video2World-Sample-AV-Single2MultiView↗

    NVIDIA

  • Qwen3.8-27B-NVIDIA-NVFP4-NInferV3↗

    wallawalla47

    **A single-file NInfer V3 (`.ninfer`) artifact of nvidia/Qwen3.8-27B-NVFP4, built for an NVIDIA RTX 5090 (sm_120a), for Infernix (recommended) and NInfer. No conversion step at load time: point `infernix-serve` (or `ninfer-serve`) at the `.ninfer` file and run.** This repository is the NInfer form of the NVIDIA checkpoint above: the Model Optimizer mixed NVFP4/FP8 weights are carried over **bit-exactly** for every transformer linear except the language-model head, and two speculative-decoding components (DFlash2 draft model, indexed proposal head) are added so the artifact runs with the engines' fastest decode paths. **Recommended engine: Infernix**, my engine grown from NInfer, which replaces my earlier ninfer-custom fork. In an agentic coding replay on the 27B it served 90 % of prompt tokens from its prefix cache and reached the first token in about half NInfer's average time, and it prefills long prompts 1.3-1.5× faster, at the same quality (benchmarks, measured on NInfer's official 27B artifact). It also runs Qwen3.8-Flash-Next. The artifact still works with NInfer itself and with ninfer-custom.

  • mms-tts-div-finetuned-md-f01↗

    alakxender

    This is a fine-tuned VITS (Variational Inference with adversarial learning for end-to-end Text-to-Speech) model for Divehi speech synthesis. The model produces female voice audio from Thaana-scripted Divehi text. Fine-tuned from Meta’s MMS-TTS architecture using a curated dataset of synthetic Divehi speech. - **Model**: `alakxender/mms-tts-div-finetuned-md-f01` - **Evaluated Samples**: 3 - **Avg Estimated MOS (UTMOS)**: `2.349` - **Artifacts**: - 🎵 Audio: `outputs/audio/` - 📊 Spectrograms: `outputs/spectrograms/` - 📄 Report: `outputs/report.txt` - 📈 MOS Scores: `outputs/mos_scores.txt`

  • wav2vec2-base-vi-vlsp2020↗

    nguyenvulebinh

    Our models use wav2vec2 architecture, pre-trained on 13k hours of Vietnamese youtube audio (un-label data) and fine-tuned on 250 hours labeled of VLSP ASR dataset on 16kHz sampled speech audio. You can find more description here The ASR model parameters are made available for non-commercial use only, under the terms of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. You can find details at: https://creativecommons.org/licenses/by-nc/4.0/legalcode

  • DeepSeek-V4-Flash-0731-reap-150b-gguf↗

    puwaer

    GGUF builds of puwaer/DeepSeek-V4-Flash-0731-reap-150b for llama.cpp — 132 routed experts per layer (down from the base model's 256) via router-weighted expert activation **pruning** (REAP). See that repo's card for the compression recipe and full benchmark discussion; this one covers what is specific to the GGUF files: what each one is, how it was quantized, and how to serve it. Unlike the 200b checkpoints, no `IQ1_M` rung is published for this size — the 132-expert checkpoint's baseline fits inside a single 96 GB GPU, so this ladder was only carried down to `Q2_K`. **`MXFP4_MOE` is not a quantization, it is the baseline.** DeepSeek ships this model's routed experts already in MXFP4 (`expert_dtype: fp4`); llama.cpp's converter repacks those values into ggml's MXFP4 block layout without changing a single one (`conversion/deepseek.py::_pack_mxfp4_blocks`, 17 bytes per 32 values either way). So this file is numerically identical to the safetensors checkpoint linked above — same weights, different container.

  • resnet18.a1_in1k↗

    timm

    This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample Recipe details: * ResNet Strikes Back `A1` recipe * LAMB optimizer with BCE loss * Cosine LR schedule with warmup - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 11.7 - GMACs: 1.8 - Activations (M): 2.5 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/huggingface/pytorch-image-models

  • security-8b↗

    bebrws

    An 8B cybersecurity agent (offense + defense) fine-tuned from `Qwen/Qwen3-8B` on **2197 verified agentic + Q&A traces**, distilled from GLM-5.2 (round 4) and Kimi K3/K2.6 (rounds 1–3). Trained to operate an autonomous security harness — plan, run bash, read observations, write files, finish — not just answer security questions. Pipeline per iteration: failure analysis on eval traces → parameterized, decontaminated seed factory → teacher best-of-3 rejection-sampled agentic transcripts (judged + artifact-checked) → full-FT SFT → multi-run attack/defend eval → next round. Run-to-run variance is significant at temperature 0.7; single-run scores are not meaningful for this suite. Known v4 gap: `atk-sqli` — the model prefers to start the staged vulnerable app and fuzz it over HTTP instead of reading the offline artifacts (trace-verified behavioral prior, targeted in round 5).

  • stable-diffusion-v1-5↗

    botp

    Stable Diffusion is a latent text-to-image diffusion model capable of generating photo-realistic images given any text input. For more information about how Stable Diffusion functions, please have a look at 🤗's Stable Diffusion blog. The **Stable-Diffusion-v1-5** checkpoint was initialized with the weights of the Stable-Diffusion-v1-2 checkpoint and subsequently fine-tuned on 595k steps at resolution 512x512 on "laion-aesthetics v2 5+" and 10% dropping of the text-conditioning to improve classifier-free guidance sampling. You can use this both with the 🧨Diffusers library and the RunwayML GitHub repository.

  • humanizer↗

    jialinyyzz

    **A 12B model that rewrites AI-written drafts (emails, essays, reports, forum posts; English and Chinese) so they read like a person wrote them.** It is trained to keep every number, unit, date, name and quote and to add nothing, and it runs locally. **Quick start:** get the app for **Mac (Apple silicon)** or **Windows**, open it and paste a draft; it works offline. Without the app: llama.cpp and other runtimes, or AGENTS.md for coding agents. Drafts from the held-out evaluation set. For each we generated 8 samples with the Q8_0 file (llama.cpp, the app's settings) and picked the one that reads best among those the fact judge passed; it is shown unedited, and all 8 samples are on GitHub. These are picks: across the whole set the judge flagged 44 of the 420 English rewrites, 135 problems in all, 125 of them a single word or phrase. Read the result before you send it, especially numbers, dates and names. Results over the whole set are below.

  • gemma-3-12b-it-int4-awq↗

    gaunernst

    This is the QAT INT4 Flax checkpoint (from Kaggle) converted to HF+AWQ format for ease of use. AWQ was NOT used for quantization. You can find the conversion script `convert_flax.py` in this model repo. NOTE: this is NOT the same as the official QAT INT4 GGUFs released here https://huggingface.co/collections/google/gemma-3-qat-67ee61ccacbf2be4195c265b Below is the original Model card from https://huggingface.co/google/gemma-3-12b-it