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AI 模型 2119

• GLM-4-32B-0414↗

  zai-org

  The GLM family welcomes new members, the **GLM-4-32B-0414** series models, featuring 32 billion parameters. Its performance is comparable to OpenAI’s GPT series and DeepSeek’s V3/R1 series. It also supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-trained on 15T of high-quality data, including substantial reasoning-type synthetic data. This lays the foundation for subsequent reinforcement learning extensions. In the post-training stage, we employed human preference alignment for dialogue scenarios. Additionally, using techniques like rejection sampling and reinforcement learning, we enhanced the model’s performance in instruction following, engineering code, and function calling, thus strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in engineering code, Artifact generation, function calling, search-based Q&A, and report generation. In particular, on several benchmarks, such as code generation or specific Q&A tasks, GLM-4-32B-Base-0414 achieves comparable performance with those larger models like GPT-4o and DeepSeek-V3-0324 (671B). **GLM-Z1-32B-0414** is a reasoning model with deep thinking capabilities. This was developed based on GLM-4-32B-0414 through cold start, extended reinforcement learning, and further training on tasks including mathematics, code, and logic. Compared to the base model, GLM-Z1-32B-0414 significantly improves mathematical abilities and the capability to solve complex tasks. During training, we also introduced general reinforcement learning based on pairwise ranking feedback, which enhances the model's general capabilities. **GLM-Z1-Rumination-32B-0414** is a deep reasoning model with rumination capabilities (against OpenAI's Deep Research). Unlike typical deep thinking models, the rumination model is capable of deeper and longer thinking to solve more open-ended and complex problems (e.g., writing a comparative analysis of AI development in two cities and their future development plans). Z1-Rumination is trained through scaling end-to-end reinforcement learning with responses graded by the ground truth answers or rubrics and can make use of search tools du

• MiniMax-H3-Turbo-Lora-Pruned-ComfyUI↗

  Abiray

  It is designed to dramatically accelerate both video and audio generation for the MiniMax-H3 model. By using this adapter, you can drastically cut down the required sampling steps while still generating native, synchronized 32kHz stereo audio alongside your video. The initial release of this LoRA required a standalone custom Python script because standard ComfyUI samplers struggled to handle the dual-schedule (video + audio) mechanics without blowing out the audio track. **This specific `.safetensors` file has been structurally pruned and reformatted so it works seamlessly inside standard ComfyUI workflows.** You can load it using a standard `Load LoRA` node. It is optimized to run alongside the pruned or curve-form MiniMax-H3 checkpoints designed for ComfyUI.

• bridgetower-base↗

  BridgeTower

  The BridgeTower model was proposed in "BridgeTower: Building Bridges Between Encoders in Vision-Language Representative Learning" by Xiao Xu, Chenfei Wu, Shachar Rosenman, Vasudev Lal, Wanxiang Che, Nan Duan. The model was pretrained on English language using masked language modeling (MLM) and image text matching (ITM)objectives. It was introduced in this paper and first released in this repository. The abstract from the paper is the following: Vision-Language (VL) models with the Two-Tower architecture have dominated visual-language representation learning in recent years. Current VL models either use lightweight uni-modal encoders and learn to extract, align and fuse both modalities simultaneously in a deep cross-modal encoder, or feed the last-layer uni-modal representations from the deep pre-trained uni-modal encoders into the top cross-modal encoder. Both approaches potentially restrict vision-language representation learning and limit model performance. In this paper, we propose BridgeTower, which introduces multiple bridge layers that build a connection between the top layers of uni-modal encoders and each layer of the cross-modal encoder. This enables effective bottom-up cross-modal alignment and fusion between visual and textual representations of different semantic levels of pre-trained uni-modal encoders in the cross-modal encoder. Pre-trained with only 4M images, BridgeTower achieves state-of-the-art performance on various downstream vision-language tasks. In particular, on the VQAv2 test-std set, BridgeTower achieves an accuracy of 78.73%, outperforming the previous state-of-the-art model METER by 1.09% with the same pre-training data and almost negligible additional parameters and computational costs. Notably, when further scaling the model, BridgeTower achieves an accuracy of 81.15%, surpassing models that are pre-trained on orders-of-magnitude larger datasets. You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you.

• MiniCPM-V-4.7-35B-A3B-Uncensored-Sangreal-ICE↗

  gbuzhf

  Five ICE 2.0 tiers of **openbmb's MiniCPM-V 4.7 35B-A3B** (35B total, 3B active, Qwen3.5-MoE text tower + SigLIP2 vision), **uncensored trunk** (openbmb's BF16 + Atomic Chat's refusal-direction LoRA, merged), quantized against **Sangreal** — a purpose-built 77-bucket calibration corpus — and shipping **Qwen-Sharp v22.5.0-arrow4.0-minicpm** — our agent-tuned chat template, a heavily modified derivative of Qwen-Sharp, fitted to MiniCPM-V's multimodal content format (spec below). Vision works through openbmb's own projector, shipped here unmodified. **Sangreal-UD** — Unsloth Dynamic 2.0 replays (with Sangreal10K and default chat-template) — on the same trunk are shipped as well. > ⚠️ **Uncensored.** Refusal behaviour has been suppressed in the > weights. Sandbox it at the OS level and control its network and code-execution access; with no refusal > backstop, a prompt injection from a hostile page or third-party code has nothing to stop it. All five tiers ship as **ICEv2**: `12G-ICEv2`, `15G-ICEv2`, `19G-ICEv2`, `23G-ICEv2` and `25G-ICEv2`.

• sam3↗

  JobHarvest

• wav2vec2-large-xlsr-53-japanese↗

  jonatasgrosman

  使用 Common Voice 6.1、CSS10 和 JSUT 的训练集与验证集，在日语上对 facebook/wav2vec2-large-xlsr-53 进行了微调。使用该模型时，请确保您的语音输入采样率为 16kHz。下表报告了该模型的词错误率（WER）和字符错误率（CER）。我还于 2021-05-10 对其他模型运行了上述评估脚本。请注意，下表显示的结果可能与先前报告的不同，这可能是由于所用其他评估脚本存在某些特定差异所致。

• 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