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AI Models 2119

  • LFM3-1.7B-GGUF↗

    LiquidAI

    GGUF version of LFM3-1.7B for local inference on CPUs and GPUs. Find architecture and usage details in the original model card. Recommended sampling: temperature 0.1, top_k 50, repeat penalty 1.1. For math and science: temperature 1.0, top_p 0.95, top_k 20.

  • gemma-4-12b-it-GGUF↗

    unsloth

    Jun 9 Update: Added MTP support. See our MTP Guide . Gemma 4 12B can now be run and fine-tuned in Unsloth Studio . Read our guide . See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection . Example of Gemma 4 E4B (4-bit GGUF) running in Unsloth Studio with tool-calling: > [!Note] > This model card is for the Gemma 4 12B Unified model, which is part of the Gemma 4 family of open models. Built with the same multimodal functionality as Gemma 4 E2B and E4B (text, audio, image, and video inputs), it brings native audio and vision understanding directly to local environments without the need for separate encoders. This unified approach to multimodality makes the model encoder-free, offering a deployment size that is perfect for consumer devices and streamlined local execution. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages.

  • univnet-dev↗

    dg845

    The UnivNet model is a state-of-the-art neural vocoder which synthesizes audio waveforms from full-band MEL spectrograms, introduced in "UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation" by Won Jang, Dan Lim, Jaesam Yoon, Bongwan Kim, Juntae Kim. UnivNet is a generative adversarial network (GAN) in which the generator is trained to convert real (or fake, during training) log MEL spectrograms to waveforms, and the discriminator is trained to classify whether input waveforms are real or fake. From the original paper abstract: > Most neural vocoders employ band-limited mel-spectrograms to generate waveforms. If full-band spectral features are used as the input, the vocoder can be provided with as much acoustic information as possible. However, in some models employing full-band mel-spectrograms, an over-smoothing problem occurs as part of which non-sharp spectrograms are generated. To address this problem, we propose UnivNet, a neural vocoder that synthesizes high-fidelity waveforms in real time. Inspired by works in the field of voice activity detection, we added a multi-resolution spectrogram discriminator that employs multiple linear spectrogram magnitudes computed using various parameter sets. Using full-band mel-spectrograms as input, we expect to generate high-resolution signals by adding a discriminator that employs spectrograms of multiple resolutions as the input. In an evaluation on a dataset containing information on hundreds of speakers, UnivNet obtained the best objective and subjective results among competing models for both seen and unseen speakers. These results, including the best subjective score for text-to-speech, demonstrate the potential for fast adaptation to new speakers without a need for training from scratch. This checkpoint was released as part of an unofficial implementation by maum-ai (on which the `transformers` implementation is also based). As far as I know, there is no official model or code release by the original authors from Kakao Enterprise.

  • Nemotron-3-Content-Safety↗

    NVIDIA

    The Nemotron 3 Content Safety model is a Large Language Model (LLM) classifier that uses Google’s Gemma-3-4B-it as the base and is fine-tuned by NVIDIA on multimodal and multilingual content-safety related datasets. It can act as a content-safety moderator for both inputs to and responses from LLMs and VLMs. It can be considered an extension of the popular English-only Llama 3.1 Nemoguard 8b Content Safety and the multilingual Llama 3.1 Nemotron Safety Guard 8B v3, that evaluate the safety of prompts and responses only for LLMs. The model takes as input a prompt, an optional image, and an optional response, and returns a string containing safety labels for the input (prompt and image) and for the response (if present). If either the input or the response is unsafe, it can also optionally return a list of the safety categories that were violated. The model uses the same safety taxonomy as the Nemotron 8B Content Safety Dataset v2. The model supports 12 languages - English, Arabic, German, Spanish, French, Hindi, Japanese, Thai, Dutch, Italian, Korean and Chinese. The model was trained as a LoRA adapter and the weights were merged back into the parent Gemma-3-4b-it model. Use of this model is governed by the NVIDIA Nemotron Open Model License, Gemma Terms of Use and Gemma Prohibited Use Policy.

  • Yi-1.5-9B-Chat↗

    01-ai

    Yi-1.5 is an upgraded version of Yi. It is continuously pre-trained on Yi with a high-quality corpus of 500B tokens and fine-tuned on 3M diverse fine-tuning samples. Compared with Yi, Yi-1.5 delivers stronger performance in coding, math, reasoning, and instruction-following capability, while still maintaining excellent capabilities in language understanding, commonsense reasoning, and reading comprehension. Model | Context Length | Pre-trained Tokens | :------------: | :------------: | :------------: | | Yi-1.5 | 4K, 16K, 32K | 3.6T

  • sam3.1-bf16↗

    mlx-community

  • Qwen3.6-27B-AEON-Ultimate-Uncensored-NVFP4↗

    AEON-7

    > [!IMPORTANT] > **Superseded by Qwen3.8 — use this instead:** `AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED-NVFP4-MIXED` > > **Operator recipes (Spark Dynamic DFlash lattice, TP=2, RTX):** `AEON-7/Qwen3.8-27B-AEON-ULTIMATE-UNCENSORED` > > This Qwen3.6-27B card stays online for history and existing downloads. For new work, move to the Qwen3.8 Ultimate Uncensored NVFP4-MIXED release. That line is far superior in capability and reaches substantially more of what the architecture can do, with a more refined mixed quantization methodology (NVFP4 + FP8 lattice) than these older Qwen3.6 exports. > > Serve with the unified container: `ghcr.io/aeon-7/aeon-vllm-ultimate:latest` (Spark / GB10) or `ghcr.io/aeon-7/aeon-vllm-ultimate-rtx:latest` (RTX discrete). NVFP4 hardware-quantized release of `AEON-7/Qwen3.6-27B-AEON-Ultimate-Uncensored`. Same model, same 0/50 refusal rate, same preserved capabilities — now compressed from **51 GB BF16** to **26 GB NVFP4** for native FP4 hardware throughput on DGX Spark / GB10 / Blackwell sm_121a. This release is **multimodal-preserved** (vision tower stays BF16 — text + image inference fully functional) and **hybrid-attention-preserved** (the 48 linear-attention / GatedDeltaNet layers stay BF16; FP4 only applies to the 16 full-attention layers' output projections + all MLPs).

  • distilroberta-base↗

    distilbert

    1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training Details 5. Evaluation 6. Environmental Impact 7. Citation 8. How To Get Started With the Model This model is a distilled version of the RoBERTa-base model. It follows the same training procedure as DistilBERT. The code for the distillation process can be found here. This model is case-sensitive: it makes a difference between english and English. The model has 6 layers, 768 dimension and 12 heads, totalizing 82M parameters (compared to 125M parameters for RoBERTa-base). On average DistilRoBERTa is twice as fast as Roberta-base.

  • Cydonia-24B-v4.3-heretic-nvfp4↗

    Firworks

    **Format:** NVFP4 — weights & activations quantized to FP4 with dual scaling. **Base model:** `coder3101/Cydonia-24B-v4.3-heretic` **How it was made:** One-shot calibration with LLM Compressor (NVFP4 recipe), long-seq calibration (256 samples of 4096 length) with xensive/roleplaydataset100k. If there are other models you're interested in seeing quantized to NVFP4 for use on the DGX Spark, or other modern Blackwell (or newer) cards let me know. I'm trying to make more NVFP4 models available to allow more people to try them out.

  • Qwen3.8-27B-DFlash2-ternary-bonsai2↗

    naklitechie

    A DFlash 2 draft model for prism-ml/Ternary-Bonsai-2-27B (PrismML's 2-bit, Hadamard-rotated Qwen3.8-27B). It is z-lab/Qwen3.8-27B-DFlash2 fine-tuned on the ternary model's own hidden states and its own greedy generations, so the drafter predicts what the 2-bit target will actually say rather than what the bf16 base would have said. Same architecture and config as the z-lab checkpoint (5 Qwen3-style layers, hidden 5120, 32/8 heads, sliding window 2048, `block_size 8`, taps at target layers 5/19/33/47/61, rank-256 candidate selector, top-k 16, `mask_token_id 248070`); shares the target's embeddings and `lm_head`, so there are no `lm_head`/embedding tensors here. 81 bf16 tensors, 3.85 GB. `config.json` is the z-lab file, unchanged. Runtime: NakliTechie/dflash-mlx-bonsai2 (Apple Silicon, MLX). Status: experimental; measured on one M4 Pro.