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AI 모델 2119

• MiniMax-H3-Combat-Base-V2↗

  JOKER141

  Combat Base V2 is a major upgrade over V1, focusing not only on faster combat, but on better action continuity, hit reactions, cumulative damage, spatial movement, and physical cause-and-effect. Combat Base V2 is no longer just a combat LoRA — it now works for both action and dialogue scenes, adding richer body movement, finer sound details, and stronger shot-to-shot continuity to character performance. Combat Base V2 不再只是一个武打 LoRA——它现在可文可武，在文戏中也能增加更丰富的身体动作、更细致的声音表现，以及更强的镜头与状态连续性。 If the LoRA looks muddy or less impactful than expected, check the sampler setup first. Sampler choice matters a lot for this LoRA. Core Physics & Motion Logic / 核心物理与动作逻辑 Combat Base V2 moves away from "attack → reset" loops into a continuous physical chain where missed strikes carry momentum, blocks change direction smoothly, and limbs separate cleanly without sticking. Hit reactions now persist and accumulate—sustained torso bending, lowered guards, and impaired mobility directly build toward convincing heavy finishers.

• OuteTTS-0.2-500M-GGUF↗

  OuteAI

  table { border-collapse: collapse; width: 100%; margin-bottom: 20px; } th, td { border: 1px solid #ddd; padding: 8px; text-align: center; } .best { font-weight: bold; text-decoration: underline; } .box { text-align: center; margin: 20px auto; padding: 30px; box-shadow: 0px 0px 20px 10px rgba(0, 0, 0, 0.05), 0px 1px 3px 10px rgba(255, 255, 255, 0.05); border-radius: 10px; } .badges { display: flex; justify-content: center; gap: 10px; flex-wrap: wrap; margin-top: 10px; } .badge { text-decoration: none; display: inline-block; padding: 4px 8px; border-radius: 5px; color: #fff; font-size: 12px; font-weight: bold; width: 250px; } .badge-hf-blue { background-color: #767b81; } .badge-hf-pink { background-color: #7b768a; } .badge-github { background-color: #2c2b2b; } 🤗 Hugging Face - OuteTTS 0.2 500M 🤗 Hugging Face - OuteTTS 0.2 500M GGUF 🤗 Hugging Face - Demo Space GitHub - OuteTTS OuteTTS-0.2-500M is our improved successor to the v0.1 release. The model maintains the same approach of using audio prompts without architectural changes to the foundation model itself. Built upon the Qwen-2.5-0.5B, this version was trained on larger and more diverse datasets, resulting in significant improvements across all aspects of performance.

• colbert-xm↗

  antoinelouis

  This is a ColBERT model that can be used for semantic search in many languages. It encodes queries and passages into matrices of token-level embeddings and efficiently finds passages that contextually match the query using scalable vector-similarity (MaxSim) operators. The model uses an XMOD backbone, which allows it to learn from monolingual fine-tuning in a high-resource language, like English, and perform zero-shot retrieval across multiple languages. This model can be used with Sentence Transformers as a multi-vector (ColBERT-style late interaction) retriever via the `MultiVectorEncoder`: We use the English training samples from the MS MARCO passage ranking dataset, which contains 8.8M passages and 539K training queries. We do not employ the BM25 netaives provided by the official dataset but instead sample harder negatives mined from 12 distinct dense retrievers, using the msmarco-hard-negatives distillation dataset. Our final training set consists of 6.4M (q, p+, p-) triples.

• tradepulse-finbert-sentiment↗

  Bencode92

  **Task**: Sentiment Classification **Target Column**: `label` **Labels**: ['negative', 'neutral', 'positive'] *Last training: 2026-04-20 17:22* *Dataset: `base_reference.csv` (1797 samples)* - **Base Model**: Bencode92/tradepulse-finbert-sentiment - **Training Mode**: Incremental - **Epochs**: 2 - **Learning Rate**: 1e-05 - **Batch Size**: 4 - **Class Balancing**: None

• granite-timeseries-ttm-r2↗

  ibm-granite

  TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research. **With model sizes starting from 1M params, TTM introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting. The paper describing TTM was accepted at NeurIPS 24.** TTM outperforms other models demanding billions of parameters in several popular zero-shot and few-shot forecasting benchmarks. TTMs are lightweight forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. **Note that zeroshot, fine-tuning and inference tasks using TTM can easily be executed on 1 GPU or on laptops.** TTM r2 comprises TTM variants pre-trained on larger pretraining datasets (\~700M samples). The TTM r2.1 release increases the pretraining dataset size to approximately (\~1B samples). The prior model releases, TTM r1, were trained on \~250M samples and can be accessed here. In general, TTM r2 models perform better than TTM r1 models as they are trained on a larger pretraining dataset. In standard benchmarks, TTM r2 outperform TTM r1 by over 15%. However, the choice of r1 vs. r2 depends on your target data distribution, and hence users should try both variants and pick the best model for your data. The TTM r2 releases support point forecasting use-cases specifically ranging from minutely to hourly resolutions (Ex. 10 min, 15 min, 1 hour.). With the TTM r2.1 release, we add support for daily and weekly resolutions.

• chronos-bolt-base↗

  autogluon

  🚀 **Update Feb 14, 2025**: Chronos-Bolt 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. Chronos-Bolt is a family of pretrained time series forecasting models which can be used for zero-shot forecasting. It is based on the T5 encoder-decoder architecture and has been trained on nearly 100 billion time series observations. It chunks the historical time series context into patches of multiple observations, which are then input into the encoder. The decoder then uses these representations to directly generate quantile forecasts across multiple future steps—a method known as direct multi-step forecasting. Chronos-Bolt models are **more accurate**, up to **250 times faster** and **20 times more memory-efficient** than the original Chronos models of the same size. The following plot compares the inference time of Chronos-Bolt against the original Chronos models for forecasting 1024 time series with a context length of 512 observations and a prediction horizon of 64 steps.

• sam-audio-base-tv↗

  facebook

• ddpm-celebahq-256↗

  Google

  *We present high quality image synthesis results using diffusion probabilistic models, a class of latent variable models inspired by considerations from nonequilibrium thermodynamics. Our best results are obtained by training on a weighted variational bound designed according to a novel connection between diffusion probabilistic models and denoising score matching with Langevin dynamics, and our models naturally admit a progressive lossy decompression scheme that can be interpreted as a generalization of autoregressive decoding. On the unconditional CIFAR10 dataset, we obtain an Inception score of 9.46 and a state-of-the-art FID score of 3.17. On 256x256 LSUN, we obtain sample quality similar to ProgressiveGAN.* for inference. Note that while the *ddpm* scheduler yields the highest quality, it also takes the longest. For a good trade-off between quality and inference speed you might want to consider the *ddim* or *pndm* schedulers instead.

• GLM-5.3-Flash-DFlash2↗

  incoai

  This repository contains the DFlash 2 draft model for `zai-org/GLM-5.3-Flash`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. Benchmark numbers for this checkpoint are being re-measured and will be published here once final.

• Qwen3.8-Flash-Next-NVFP4-Q8-Mix-Strata↗

  Maximilian228

  Qwen's original Qwen3.8-Flash-Next with **our NVFP4 GPTQ experts, and Q8_0 in the layers that move the answers most**, packed for Strata NVFP4. It is the most accurate model that fits a **128 GB PC** with every expert in RAM. Its most likely next token is the Q8_0 model's 95.0% of the time, against 93.6% for the all-NVFP4 GPTQ, and it moves the answers 43% less (KL). It decodes at about half the GPTQ model's speed. **Quick start (Windows):** from the Strata NVFP4 release 0.1.42-nvfp4.2 or newer, run `START-HERE.bat --family qwen-nvfp4-mix`: it checks the PC, downloads this repository (only its experts if `qwen-nvfp4-gptq` is installed: the other files are the same) and starts the model. `pack/mix.json` has the plan. - **Everything but the experts is the same as in the NVFP4 GPTQ repository**, byte for byte: the dense GGUF (Q8_0 projections; router, small tensors and norms BF16 / F32), Qwen's own FP8 PLE table, the BF16 embedding and the MTP draft head. - **The original model, censored, with a switch.** It refuses what Qwen refuses. The fork can remove the refusal direction at run time (layers 8-33, the weights untouched): `--uncensored on` at install, the web app's "Disable censorship" switch, or `"uncensored": true` in a request. Checked on this pack: 5 of 5 held-out harmful requests refused with it off, 0 of 5 with it on. - **Only for Strata NVFP4.** The files are Strata's runtime formats. They do not load in transformers, vLLM or llama.cpp.