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AI Models 2119

• GLM-Z1-32B-0414↗

  zai-org

  The GLM family welcomes a new generation of open-source models, the **GLM-4-32B-0414** series, featuring 32 billion parameters. Its performance is comparable to OpenAI's GPT series and DeepSeek's V3/R1 series, and it supports very user-friendly local deployment features. GLM-4-32B-Base-0414 was pre-trained on 15T of high-quality data, including a large amount of reasoning-type synthetic data, laying the foundation for subsequent reinforcement learning extensions. In the post-training stage, in addition to human preference alignment for dialogue scenarios, we also enhanced the model's performance in instruction following, engineering code, and function calling using techniques such as rejection sampling and reinforcement learning, strengthening the atomic capabilities required for agent tasks. GLM-4-32B-0414 achieves good results in areas such as engineering code, Artifact generation, function calling, search-based Q&A, and report generation. Some benchmarks even rival 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 and extended reinforcement learning, as well as further training of the model on tasks involving 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 the training process, we also introduced general reinforcement learning based on pairwise ranking feedback, further enhancing the model's general capabilities. **GLM-Z1-Rumination-32B-0414** is a deep reasoning model with **rumination capabilities** (benchmarked against OpenAI's Deep Research). Unlike typical deep thinking models, the rumination model employs longer periods of deep thought 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). The rumination model integrates search tools during its deep thinking process to handle complex tasks and is trained by utilizing multiple rule-based rewards to guide and extend end-to-end reinforcement learning. Z1-Rumination shows significant improvements in research-style writing and complex retrieval tasks.

• sam3↗

  DiffusionWave

• flan-t5-small↗

  Google

  0. TL;DR 1. Model Details 2. Usage 3. Uses 4. Bias, Risks, and Limitations 5. Training Details 6. Evaluation 7. Environmental Impact 8. Citation 9. Model Card Authors If you already know T5, FLAN-T5 is just better at everything. For the same number of parameters, these models have been fine-tuned on more than 1000 additional tasks covering also more languages. As mentioned in the first few lines of the abstract : > Flan-PaLM 540B achieves state-of-the-art performance on several benchmarks, such as 75.2% on five-shot MMLU. We also publicly release Flan-T5 checkpoints,1 which achieve strong few-shot performance even compared to much larger models, such as PaLM 62B. Overall, instruction finetuning is a general method for improving the performance and usability of pretrained language models. **Disclaimer**: Content from **this** model card has been written by the Hugging Face team, and parts of it were copy pasted from the T5 model card.

• mini-Jev↗

  samatv256

• gemma-3-27b-it-qat-GGUF↗

  unsloth

  Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.

• tiny_starcoder_py↗

  bigcode

  This is a 164M parameters model with the same architecture as StarCoder (8k context length, MQA & FIM). It was trained on the Python data from StarCoderData for ~6 epochs which amounts to 100B tokens. The model was trained on GitHub code, to assist with some tasks like Assisted Generation. For pure code completion, we advise using our 15B models StarCoder or StarCoderBase. Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:

• Qwopus3.5-9B-Coder-GGUF↗

  Jackrong

  As the base model of this model, **Qwopus3.5-9B-v3.5** is already a model with powerful capabilities. On this foundation, **Qwopus3.5-9B-coder** is specially optimized and fine-tuned for high-performance **🤖 Agentic Coding, complex Tool Calling, and logical reasoning.** > 💡 **Why the 9B Dense Model?** > We believe that the 9B dense architecture represents the perfect **"sweet spot"** for large language models. It runs seamlessly at 8-bit precision on entry-level 16GB RAM devices—such as standard laptops and the Mac mini—making it exceptionally lightweight yet highly versatile. Without requiring expensive hardware, it allows you to achieve excellent performance paired with impressive inference speeds. Simply put, **Qwen3.5-9B is currently the best open-source model in its class.** > [!TIP] >**Vision & Tool Calling Support**: This model supports visual capabilities and tool calling. To enable vision, please place the `mmproj.gguf` file from the GGUF repository into the same directory as the main `.gguf` file.

• kotoba-whisper-bilingual-v1.0↗

  kotoba-tech

  _Kotoba-Whisper-Bilingual_ is a collection of distilled Whisper models trained for - **Japanese ASR** - **English ASR** - **Speech-to-text translation (Japanese -> English)** - **Speech-to-text translation (English -> Japanese)** developed through the collaboration bewteen Asahi Ushio and Kotoba Technologies. Following the original work of distil-whisper (Robust Knowledge Distillation via Large-Scale Pseudo Labelling), we employ OpenAI's Whisper large-v3 as the teacher model for Japanese and English ASR, while we translate the transcription into English and Japanese by external LLM to obtain training dataset for speech-to-text translation. We employ ReazonSpeech for Japanese ASR and Japanese speech to English text translation, and Multilingual LibriSpeech for English ASR and English speech to Japanese text translation. Kotoba-whisper-bilingual's loss objective consists of cross-entropy on both of ASR and translation tasks, while KL divergence loss only for ASR task. The student model consists the full encoder of the teacher large-v3 model and the decoder with two layers initialized from the first and last layer of the large-v3 model. As kotoba-whisper uses the same architecture as distil-whisper/distil-large-v3, it inherits the benefit of the improved latency compared to openai/whisper-large-v3 (**6.3x faster than large-v3**, see the table below taken from distil-whisper/distil-large-v3).

• Qwen2.5-Math-RM-72B↗

  阿里巴巴 · Qwen

  Qwen2.5-Math-RM-72B is specifically designed to guide the Qwen2.5-Math model throughout the training process by offering more granular feedback on the quality of reasoning and intermediate steps, ultimately facilitating more robust model improvements. - Multilingual and Multi-Modal Support: Offers preference signals across two languages (Chinese and English) and in dual modes (Chain-of-Thought and Tool-integrated Reasoning), enhancing versatility. - Model Training Guide: - Training Data Enhancement: Employs a data selection process via reward model scoring combined with Rejection Sampling to incrementally enhance the quality of responses - Reinforcement Learning Training: Integrates seamlessly into the reinforcement learning training and provide effective reward signal, further improving model performance.

• BirdNET_v2.4↗

  sammlapp