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AI Models 2119

• wav2vec2-large-xls-r-300m-assamese↗

  jayashreedevi2020

  This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the common_voice_11_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.9825 - Wer: 0.6470 The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 2 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

• L3-8B-Stheno-v3.2↗

  Sao10K

  *Just message me on discord if you want to host this privately for a service or something. We can talk.* I have done a test run with multiple variations of the models, merged back to its base at various weights, different training runs too, and this Sixth iteration is the one I like most. Changes compared to v3.1 \- Included a mix of SFW and NSFW Storywriting Data, thanks to Gryphe \- Included More Instruct / Assistant-Style Data \- Further cleaned up Roleplaying Samples from c2 Logs -> A few terrible, really bad samples escaped heavy filtering. Manual pass fixed it. \- Hyperparameter tinkering for training, resulting in lower loss levels.

• Wav2Vec2-large-xlsr-hindi↗

  theainerd

  Fine-tuned facebook/wav2vec2-large-xlsr-53 hindi using the Multilingual and code-switching ASR challenges for low resource Indian languages. When using this model, make sure that your speech input is sampled at 16kHz. The script used for training can be found Hindi ASR Fine Tuning Wav2Vec2

• resnet50d.ra2_in1k↗

  timm

  This model features: * ReLU activations * 3-layer stem of 3x3 convolutions with pooling * 2x2 average pool + 1x1 convolution shortcut downsample Recipe details: * RandAugment `RA2` 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 - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 25.6 - GMACs: 4.4 - Activations (M): 11.9 - 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 - Bag of Tricks for Image Classification with Convolutional Neural Networks: https://arxiv.org/abs/1812.01187 - **Original:** https://github.com/huggingface/pytorch-image-models

• German-RAG-UAE-LARGE-V1-TRIPLES-MERGED-HESSIAN-AI↗

  avemio

  This is a sentence-transformers model trained on this Dataset with roughly 300k Triple-Samples. It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more. It was merged with the Base-Model WhereIsAI/UAE-Large-V1 again to maintain performance on other languages again. - **Maximum Sequence Length:** 512 tokens - **Output Dimensionality:** 1024 tokens - **Similarity Function:** Cosine Similarity - **Documentation:** Sentence Transformers Documentation - **Repository:** Sentence Transformers on GitHub - **Hugging Face:** Sentence Transformers on Hugging Face

• Qwen3.6-35B-A3B-heretic-moe-balanced-v2↗

  DmitryDB

  Decensored (abliterated) **Qwen3.6-35B-A3B**, the **balanced** operating point: refusals removed at the lowest damage to the model. Built with a fork of Heretic that searches against a **perplexity objective** instead of first-token KL — that is what makes this build cheaper than the previous release at the same or fewer refusals. Sibling: `-max-v2` is the more aggressive point (fewer refusals, more damage); it also loses more facts, so **balanced is the recommended build**. Method and figures: the study repo. The refusal direction is removed from every projection that writes to the residual stream — full and linear attention outputs, the 256 routed experts (fused), and the always-on shared expert. The multi-token-prediction layer is restored verbatim from the base model, so that branch is unmodified. All builds carry the full 1045 tensors.

• TinyLLama-v0↗

  Maykeye

  This is a first version of recreating roneneldan/TinyStories-1M but using Llama architecture. * Full training process is included in the notebook train.ipynb. Recreating it as simple as downloading TinyStoriesV2-GPT4-train.txt and TinyStoriesV2-GPT4-valid.txt in the same folder with the notebook and running the cells. Validation content is not used by the script so you put anythin in * Backup directory has a script do_backup that I used to copy weights from remote machine to local. Weight are generated too quickly, so by the time script copied weihgt N+1

• Qwen3.8-27B-Whitehat-GGUF↗

  QuaduxIT

  > **⚠️ Authorized security use only — Nur für autorisierte Sicherheitsarbeit.** > For authorized IT-security work on systems you own or have explicit permission to test. Physical harm, weapons/CBRN, illegal substances and CSAM are refused by design. Provided "as is" without warranty (Apache-2.0). **By downloading or using this model you accept the Disclaimer and Terms of Use** (see the "Haftungsausschluss / Disclaimer" section below and `DISCLAIMER.md`). > *Dieses Modell dient autorisierter IT-Sicherheitsarbeit; mit der Nutzung akzeptierst du den Haftungsausschluss (siehe unten und `DISCLAIMER.md`).* A **red-team / white-hat fine-tune** of Qwen/Qwen3.8-27B, produced by **Quadux IT GmbH** as a **local, private assistant for internal offensive-security work and vulnerability self-assessment**. Hosted models refuse most offensive-security tasks, and you often cannot send sensitive vulnerability or system data to an external service anyway. This model fills that gap: it **helps fully with any computer- and network-security task** — offensive and defensive, including exploit development, malware development and analysis, reverse engineering, and license/DRM research — so your findings stay in-house. At the same time it **still refuses requests aimed at real physical harm to people** (weapons, explosives, drugs, poisons, chemical/biological weapons, violence) and **child sexual abuse material**. That boundary holds on **both text and image input** and **across languages**.

• Samantha-1.11-13B-GGUF↗

  TheBloke

• AI-image-detector↗

  umm-maybe

  *__NOTE__: Unless you are trying to detect imagery generated using older models such as VQGAN+CLIP, please use the updated version of this detector instead.* This model is a proof-of-concept demonstration of using a ViT model to predict whether an artistic image was generated using AI. It was created in October 2022, and as such, the training data did not include any samples generated by Midjourney 5, SDXL, or DALLE-3. It still may be able to correctly identify samples from these more recent models due to being trained on outputs of their predecessors.