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AI Models 2119

• sam3↗

  JobHarvest

• wav2vec2-large-xlsr-53-japanese↗

  jonatasgrosman

  Fine-tuned facebook/wav2vec2-large-xlsr-53 on Japanese using the train and validation splits of Common Voice 6.1, CSS10 and JSUT. When using this model, make sure that your speech input is sampled at 16kHz. In the table below I report the Word Error Rate (WER) and the Character Error Rate (CER) of the model. I ran the evaluation script described above on other models as well (on 2021-05-10). Note that the table below may show different results from those already reported, this may have been caused due to some specificity of the other evaluation scripts used.

• d1-omni-600M↗

  LiquidAI

  d1-omni-600M is a 600M parameter **decision model** built on LFM2.5-Encoder-350M. You give it a state (text or JSON, with images or a voice clip) and a set of named questions. It returns typed answers with **zero output tokens**: every answer is read directly from the model's distribution over the options, with no generation and no parsing. - **Vision-language**: text and images (tiled for large frames, several images per state) in a single forward pass. - **Audio-language**: text and up to 30 s of speech in a single forward pass. - **Edge-sized**: 587M parameters: a 381M shared trunk and decision head, a 94M vision encoder and a 112M audio encoder. Every modality runs the same trunk weights. Find more information about open d1 in our blog post.

• samantha-1.1-westlake-7b-laser-i1-GGUF↗

  mradermacher

• domain-classifier↗

  NVIDIA

  Model was trained in multiple rounds using Wikipedia and Common Crawl data, labeled by a combination of pseudo labels and Google Cloud API. The model outputs one of the 26 domain classes as the predicted domain for each input sample. Example output: The inference code is available on NeMo Curator's GitHub repository. Check out this example notebook to get started.

• wav2vec2-large-xlsr-indonesian↗

  cahya

  Fine-tuned facebook/wav2vec2-large-xlsr-53 on the Indonesian Common Voice dataset. When using this model, make sure that your speech input is sampled at 16kHz. The model can be evaluated as follows on the Indonesian test data of Common Voice. The Common Voice `train`, `validation`, and ... datasets were used for training as well as ... and ... # TODO

• InternVL3-1B↗

  OpenGVLab

  We introduce InternVL3, an advanced multimodal large language model (MLLM) series that demonstrates superior overall performance. Compared to InternVL 2.5, InternVL3 exhibits superior multimodal perception and reasoning capabilities, while further extending its multimodal capabilities to encompass tool usage, GUI agents, industrial image analysis, 3D vision perception, and more. Additionally, we compare InternVL3 with Qwen2.5 Chat models, whose corresponding pre-trained base models are employed as the initialization of the langauge component in InternVL3. Benefitting from Native Multimodal Pre-Training, the InternVL3 series achieves even better overall text performance than the Qwen2.5 series. As shown in the following figure, InternVL3 retains the same model architecture as InternVL 2.5 and its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 3 and Qwen 2.5, using a randomly initialized MLP projector. As in the previous version, we applied a pixel unshuffle operation, reducing the number of visual tokens to one-quarter of the original. Besides, we adopted a similar dynamic resolution strategy as InternVL 1.5, dividing images into tiles of 448×448 pixels. The key difference, starting from InternVL 2.0, is that we additionally introduced support for multi-image and video data.

• wav2vec2-xls-r-300m↗

  facebook

  XLS-R is Facebook AI's large-scale multilingual pretrained model for speech (the "XLM-R for Speech"). It is pretrained on 436k hours of unlabeled speech, including VoxPopuli, MLS, CommonVoice, BABEL, and VoxLingua107. It uses the wav2vec 2.0 objective, in 128 languages. When using the model make sure that your speech input is sampled at 16kHz. **Note**: This model should be fine-tuned on a downstream task, like Automatic Speech Recognition, Translation, or Classification. Check out **this blog** for more information about ASR. Authors: Arun Babu, Changhan Wang, Andros Tjandra, Kushal Lakhotia, Qiantong Xu, Naman Goyal, Kritika Singh, Patrick von Platen, Yatharth Saraf, Juan Pino, Alexei Baevski, Alexis Conneau, Michael Auli

• Qwen3.6-35B-A3B-Uncensored-Heretic-MLX-4bit↗

  froggeric

  **1. Architecture-aware uncensoring.** Qwen 3.6 uses a hybrid attention design — linear (DeltaNet-style) *and* traditional softmax blocks, mixed 3:1. Most abliteration tools treat them the same. llmfan46 applied **separate parameters** for each attention type using the Heretic tool, yielding one of the lowest KL divergences (0.0015) of any uncensored Qwen variant — 88% fewer refusals with negligible capability loss. **2. A fixed chat template.** The official Qwen 3.6 template is broken on every C++ runtime (LM Studio, llama.cpp, MLX). Tool calls crash, the `developer` role throws errors, and empty thinking blocks waste your context window. This model ships with a rewritten template that fixes all five issues and adds a thinking toggle (` ` / ` `) you can drop into any message. **3. Vision, fixed and working.** The source model had 333 vision tower keys with incorrect prefixes, breaking image inputs. Those were corrected before conversion, so text, image, and video inputs all work out of the box.

• shap-e↗

  OpenAI

  Shap-E introduces a diffusion process that can generate a 3D image from a text prompt. It was introduced in Shap-E: Generating Conditional 3D Implicit Functions by Heewoo Jun and Alex Nichol from OpenAI. *We present Shap-E, a conditional generative model for 3D assets. Unlike recent work on 3D generative models which produce a single output representation, Shap-E directly generates the parameters of implicit functions that can be rendered as both textured meshes and neural radiance fields. We train Shap-E in two stages: first, we train an encoder that deterministically maps 3D assets into the parameters of an implicit function; second, we train a conditional diffusion model on outputs of the encoder. When trained on a large dataset of paired 3D and text data, our resulting models are capable of generating complex and diverse 3D assets in a matter of seconds. When compared to Point-E, an explicit generative model over point clouds, Shap-E converges faster and reaches comparable or better sample quality despite modeling a higher-dimensional, multi-representation output space. We release model weights, inference code, and samples at this https URL.*