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AI 모델 2119

• Step-3.7-Flash-GGUF↗

  阶跃星辰

  Step-3.7-Flash is a 198B-parameter sparse Mixture-of-Experts vision-language model from StepFun-ai, activating ~11B parameters per token for up to 400 t/s throughput. It pairs a 196B-parameter language backbone with a 1.8B-parameter vision encoder for native image understanding, supports a 256K context window, and offers three selectable reasoning levels (low / medium / high) to balance speed, cost, and depth. Built for agentic workloads — tool calling, multi-step reasoning, code, and math — with native multilingual coverage. A separate `mmproj` projector ships alongside the language quants for multimodal inference. With 128 GB of unified memory (Mac Studio, DGX Spark, Ryzen AI Max+ 395, etc.), you can privately host Step-3.7-Flash: Q4 quants and below run at full 256K context with high precision. The GGUF quantization files in this repository are derivative works of `stepfun-ai/Step-3.7-Flash` and are released under the same **Apache 2.0** license.

• Sakura-Qwen-Image-2.1-Turbo-Uncensored-GGUF↗

  webmp3

  **Built with Qwen.** Community quantization of Qwen/Qwen-Image-2.1-Turbo (8-step distilled, 7B DiT) with an **abliterated text encoder**. Independent work, not made or endorsed by Alibaba/Qwen. **Non-commercial use only** (Qwen RESEARCH LICENSE AGREEMENT, copy in `LICENSE`). For adults only; you are responsible for what you generate and for the laws that apply to you. Q8_0 image model and our 8bit uncensored text encoder, stable-diffusion.cpp (Vulkan, Radeon 8060S), 1024x1024, 8 steps, CFG 1.0, `euler`, official Turbo sigmas. One seed per prompt and no re-rolls; the prompts themselves were chosen from a larger set of candidates. About 77 s per image including model loading; the GPU memory in use on our machine peaked at 18.3 GiB while generating (Windows counter for the whole GPU, 1.5 GiB of it were in use before the run). The images were made with stable-diffusion.cpp, not with ComfyUI. The prompts are neutral. These images say nothing about what the ablated text encoder changes for restricted content; they only show that ordinary prompts still work. The same nine prompts and seeds are used in the sibling repository Sakura-Qwen-Image-2.1-Turbo-GGUF with the official encoder, so the two sets can be compared.

• InternVL2_5-1B↗

  OpenGVLab

  We are excited to introduce **InternVL 2.5**, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0, maintaining its core model architecture while introducing significant enhancements in training and testing strategies as well as data quality. As shown in the following figure, InternVL 2.5 retains the same model architecture as 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 2.5 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.

• mustango↗

  declare-lab

  Meet Mustango, an exciting addition to the vibrant landscape of Multimodal Large Language Models designed for controlled music generation. Mustango leverages Latent Diffusion Model (LDM), Flan-T5, and musical features to do the magic! We use the `accelerate` package from Hugging Face for multi-gpu training. Run `accelerate config` from terminal and set up your run configuration by the answering the questions asked. The `--model_type` flag allows to choose either Mustango, or Tango to be trained with the same code. However, do note that you also need to change `--unet_model_config` to the relevant config: diffusion_model_config_munet for Mustango; diffusion_model_config for Tango.

• 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: