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AI モデル 2119

• chronos-t5-small↗

  amazon

  🚀 **Update Feb 14, 2025**: Chronos-Bolt & original Chronos models are now available on Amazon SageMaker JumpStart! Check out the tutorial notebook to learn how to deploy Chronos endpoints for production use in a few lines of code. 🚀 **Update Nov 27, 2024**: We have released Chronos-Bolt⚡️ models that are more accurate (5% lower error), up to 250 times faster and 20 times more memory-efficient than the original Chronos models of the same size. Check out the new models here. Chronos is a family of **pretrained time series forecasting models** based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as synthetic data generated using Gaussian processes.

• FLUX.2-VAE↗

  unsloth

  The single-file FLUX.2 VAE that stable-diffusion.cpp needs for both FLUX.2 [dev] and FLUX.2 klein, mirrored from Comfy-Org/flux2-dev. Unsloth Studio fetches this on the no-GPU (native sd.cpp) path. It used to come straight from the community repack; a rename or a takedown there turns every CPU FLUX.2 load into a 404, so it is mirrored here. **The weights are unmodified:** byte for byte the file of the same name in the source repo. The rest of FLUX.2 [dev] is under the FLUX Non-Commercial License. The **autoencoder is not**. Black Forest Labs released it separately and says so in their own repository: "The autoencoder is released under Apache 2.0" (github.com/black-forest-labs/flux2).

• Yi-9B-200K↗

  01-ai

  If you're interested in Yi's adoption of Llama architecture and license usage policy, see Yi's relation with Llama. ⬇️ > 💡 TL;DR > > The Yi series models adopt the same model architecture as Llama but are **NOT** derivatives of Llama. 🔥 2024-07-29 : The Yi Cookbook 1.0 is released, featuring tutorials and examples in both Chinese and English.

• 160M-exp↗

  294-288-pretrain-interaction

  Five 160M GPT-NeoX training runs on the separate M/C/G source pools in Dolma3-16B. `Clean`, `M500`, `C500`, `G500`, and `MCG500` are separate run directories under `runs/`. Each contains all existing `step-*.pt` checkpoints, including the final `step-122071.pt` after 16,000,090,112 tokens. Intermediate checkpoints are approximately 1B tokens apart, with additional scheduled steps. Standalone final weights are under `models/ /model.safetensors`, with a matching config and tokenizer in each run directory. Load one with `GPTNeoXForCausalLM.from_pretrained("294-288-pretrain-interaction/160M-exp", subfolder="models/Clean")` and the tokenizer from the same subfolder. Each `.pt` is a PyTorch training checkpoint containing `model`, optimizer, scheduler, rank RNG states, step, token count, and run/manifest/config hashes. The `.pt` files are **not** direct `from_pretrained` artifacts. For trusted checkpoints, load the `model` state dict into `GPTNeoXForCausalLM(GPTNeoXConfig.from_json_file("config.json"))`. Use the included `tokenizer/`. The `training/` directory contains the training and data code; `manifest.json` fixes the source mixture, event schedule, and seeds.

• reward-model-deberta-v3-large-v2↗

  OpenAssistant

  Reward model (RM) trained to predict which generated answer is better judged by a human, given a question. All models are train on these dataset with a same split seed across datasets (if validation split wasn't available) Its likely SytheticGPT has somekind of surface pattern on the choosen-rejected pair which makes it trivial to differentiate between better the answer.

• lingbot-video-moe-30b-a3b↗

  robbyant

  We are excited to introduce **LingBot-Video**, the first open-source large-scale MoE (Mixture-of-Experts) video generation model dedicated to embodied intelligence. As a top-tier video model, LingBot-Video is designed to bridge the gap between video synthesis and physical world understanding. > **💡 Rewriter deployment**: the bundled rewriter uses the single-process > `transformers` backend. For higher throughput, deploy the VLM yourself and call > it through an OpenAI-compatible API. Preserve the two-stage semantics: step 1 > must use the base VLM without the rewriter LoRA, while step 2 must use the same > base VLM with the rewriter LoRA enabled. This can be implemented with two > endpoints, or with one server that can select the adapter per request. See > vLLM / SGLang official docs. Install the optional SGLang dependencies only when using SGLang Diffusion or the fused / FP8 MoE runtime:

• mms-tts-nld↗

  facebook

  This model is part of Facebook's Massively Multilingual Speech project, aiming to provide speech technology across a diverse range of languages. You can find more details about the supported languages and their ISO 639-3 codes in the MMS Language Coverage Overview, and see all MMS-TTS checkpoints on the Hugging Face Hub: facebook/mms-tts. VITS (**V**ariational **I**nference with adversarial learning for end-to-end **T**ext-to-**S**peech) is an end-to-end speech synthesis model that predicts a speech waveform conditional on an input text sequence. It is a conditional variational autoencoder (VAE) comprised of a posterior encoder, decoder, and conditional prior. A set of spectrogram-based acoustic features are predicted by the flow-based module, which is formed of a Transformer-based text encoder and multiple coupling layers. The spectrogram is decoded using a stack of transposed convolutional layers, much in the same style as the HiFi-GAN vocoder. Motivated by the one-to-many nature of the TTS problem, where the same text input can be spoken in multiple ways, the model also includes a stochastic duration predictor, which allows the model to synthesise speech with different rhythms from the same input text.

• gemma-4-E4B-it-assistant↗

  Google

  > [!Note] > This model card is for the Multi-Token Prediction (MTP) drafters for the Gemma 4 models. MTP is implemented by extending the base model with a smaller, faster draft model. When used in a Speculative Decoding pipeline, the draft model predicts several tokens ahead, which the target model then verifies in parallel. This results in significant decoding speedups (up to 3x) while guaranteeing the exact same quality as standard generation, making these checkpoints perfect for low-latency and on-device applications. Gemma is a family of open models built by Google DeepMind. Gemma 4 models are multimodal, handling text and image input (with audio supported on E2B, E4B, and 12B) and generating text output. This release includes open-weights models in both pre-trained and instruction-tuned variants. Gemma 4 features a context window of up to 256K tokens and maintains multilingual support in over 140 languages. Featuring both Dense and Mixture-of-Experts (MoE) architectures, Gemma 4 is well-suited for tasks like text generation, coding, and reasoning. The models are available in five distinct sizes: **E2B**, **E4B**, **12B**, **26B A4B**, and **31B**. Their diverse sizes make them deployable in environments ranging from high-end phones to laptops and servers, democratizing access to state-of-the-art AI.

• girlslike↗

  ifmylove2011

  以下为上传至 Hugging Face 的图片示例，每张图展示对应的 LoRA 名。 个别lora有多个版本，但只要名字对得上就是同一个人。 小图上方各自有用于生成此图的模型名，少则2、3个，多则5、6个，都是兼容性较好的模型。 Below are image samples uploaded to Hugging Face, each showcasing a specific LoRA model. Some LoRAs have multiple versions, but as long as the names match, they refer to the same person. Above each image, you'll find the model names used to generate it — usually 2 to 3, sometimes up to 5 or 6 — all of which are highly compatible with the LoRA.

• SoL-Refiner-LTX-2.5-H3-int8-convrot↗

  szwagros

  One-step 1080p refiner for MiniMax-H3 videos: an H3 draft is encoded, 2x latent-upsampled, re-noised to sigma 0.909375 and denoised in **one** transformer forward, then decoded by the refiner's diffusion decoder. Repackaged from NVlabs' SoL-Refiner-LTX-2.5-for-MiniMax-H3 (project: SoL-Refiner) for image-server's LTX-2.5 `ltx_core` loader. Notes - int8 layer set = Lightricks' official LTX-2.5 int8-convrot set (transformer) / int8 Gemma 4 set (text encoder), produced with convert-to-quant 1.3.3 from the refiner's bf16 weights; runs on `image_server_kernels.int8_linear`. - The text encoder is the refiner's shipped Gemma 4 — the **pre-2026-08-17** Lightricks encoder, not the current one. - Conditioning `frame_rate` = the input fps (H3 drafts are 24 fps). One step, no CFG. Output video only (no audio). - Transformer math verified against the Diffusers reference within bf16 noise; int8 text encoder vs bf16: connector-output cosine >= 0.99995. License: the source repository declares none. SoL-Refiner is a fine-tune of LTX-2.5, so the **LTX-2.x Community License** (Lightricks) applies; check it before commercial use.