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AI Models 2119
- hviske-v5.3↗
syvai
Danish ASR. Fine-tuned from syvai/hviske-v5.1 on the CoRal v3 train splits with **layer-wise learning-rate decay** (encoder LR = 0.75 × decoder LR) for 5 epochs. A 2B-parameter Conformer encoder-decoder ASR model, optimized for Danish read-aloud and conversational speech. Evaluated on the *complete* test splits (17,560 samples). Two normalization conventions:
- blossom-v5.1-9b↗
Azure99
Blossom is a conversational large language model, fine-tuned on the Blossom Orca/Wizard/Chat/Math mixed dataset based on the Yi-1.5-9B pre-trained model. Blossom possesses robust general capabilities and context comprehension. Additionally, the high-quality Chinese and English datasets used for training have been made open source. Training was conducted in two stages. The first stage used 40K Wizard, 40K Orca, 10K Math single-turn instruction datasets, training for 1 epoch; the second stage used 10K Blossom chat multi-turn dialogue dataset, and 10% randomly sampled data from the first stage, training for 3 epochs.
- Infinity-Instruct-3M-0625-Llama3-8B↗
BAAI
Infinity-Instruct-3M-0625-Llama3-8B is an opensource supervised instruction tuning model without reinforcement learning from human feedback (RLHF). This model is just finetuned on Infinity-Instruct-3M and Infinity-Instruct-0625 and showing favorable results on AlpacaEval 2.0 and MT-Bench. Infinity-Instruct-3M-0625-Llama3-8B is tuned on Million-level instruction dataset Infinity-Instruct. First, we apply the foundational dataset Infinity-Instruct-3M to improve the foundational ability (math & code) of Llama3-8B, and get the foundational instruct model Infinity-Instruct-3M-Llama3-8B. Then we finetune the Infinity-Instruct-3M-Llama3-8B to get the stronger chat model Infinity-Instruct-3M-0625-Llama3-8B. Here is the training hyperparamers. Thanks to FlagScale, we could concatenate multiple training samples to remove padding token and apply diverse acceleration techniques to the traning procudure. It effectively reduces our training costs. We will release our code in the near future!
- Qwen3.8-27B-nvfp4qat-NInfer↗
cometkim
The repository contains a QAT-sourced NVFP4 weight profile of Qwen/Qwen3.8-27B in the native NInfer `.ninfer` artifact format, with the z-lab/Qwen3.8-27B-DFlash2 block-diffusion speculative drafter embedded in the same image. It runs on NInfer **v3** engines — Text and MTP components through format/shape routes the upstream runtime already implements, the optional NVFP4-encoded DFlash2 companion through the fork's runtime support. It is not a Transformers checkpoint, Safetensors distribution, or GGUF file. This is a fourth weight profile for the existing `qwen3_8_27b` target — a peer of the official `groupwise-int` and `nvfp4` profiles and of the fork's fuller-requant `nvfp4full` profile. The Text weight stack is copied **word-for-word** from QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4 — a quantization-aware-trained checkpoint (QUASAR loss-aware NVFP4 distillation against the frozen BF16 teacher, arXiv 2608.13966): - every one of the 496 text linear layers is NVFP4 (W4A4) — attention, gated-delta-net, and MLP alike, with no high-precision exceptions; - the QAT factory quantizes per site, so every constituent tensor of a fused parent shares one weight and input global scale; the converter enforces that sharing before copying a word, and the site input divisors ride the artifact directly; - the GDN control projections are decoded from their QAT NVFP4 words to the BF16 control parent the engine consumes.
- Dr_Samantha-7b-i1-GGUF↗
mradermacher
- Dr.RP.Samantha-L3-8B-i1-GGUF↗
mradermacher
- Samrudh-3-Brahma-7B-GGUF↗
mradermacher
- Qwen3.5-9B-Cold-Fusion-GAIN-v1.0-Uncensored-Heretic-NEO-MAX-Imatrix-GGUF↗
DavidAU
IMPORTANT: The GAIN method of training maintains 99% of performance of BF16, at both 8 bit and 4 bit levels. COLD FUSION exceeds performance of both 9B and 27B Qwen 3.5 models. Cold Fusion exceeds Qwen 3.5 27B performance, at only 9B parameters (and at both 4 bit and 8 bit too) plus enhanced by "GAIN" method training. The "GAIN" method (programming) automatically (and dynamically) changes training on a per sample basis in real time during training AS THE MODEL LEARNS. This was designed to be used with all model types and datasets and plugs into the Unsloth training "stream".
- krea2-turbo-sda↗
F16
A rank-32 LoRA for Krea 2 Turbo that **restores the sampling diversity the Turbo distillation removed**, without degrading image quality or prompt adherence. Trained with **SDA (Semantic Directional Alignment)** — a teacher-guided diversity alignment loss — wrapped in **Forward XM best-of-5** candidate exploration, on a single high-noise sigma node (σ = 0.9567). *"a cat sitting on a windowsill"* — the baseline regenerates the same tabby-and-white cat 16 times; the LoRA varies fur patterns, lighting, seasons and window settings: *"a still life with fruit on a table"* — one pineapple-centric template vs varied compositions, props and backgrounds:
- mask2former-swin-tiny-ade-semantic↗
facebook
Mask2Former model trained on ADE20k semantic segmentation (tiny-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.