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AI モデル 2119
- MiniMax-H3-Realism-People-LoRA↗
fal
A LoRA adapter for MiniMax H3 specialized in realistic people: faces that hold up in close-up, natural skin texture, believable expressions and gestures, film-style lighting and documentary camera movement. 19 pairs, **same prompt, same seed, adapter on vs off** — the only variable is the LoRA. The trigger word is present on both sides, so it is not doing the work. Each pair plays the base model first, then freezes and dims while the adapted version plays beside it. Close-up talking faces, arguments, several people speaking at once, weathered skin, children, ritual and travel scenes. Nothing cherry-picked from a larger render batch: these are the pairs that were kept, in order. (download the comparison, 197s, 1920x1080)
- Underdog-Saluki-27B-1.0-MTP-Abliterated-GGUF↗
Kujira
Same as the MTP release: stock llama.cpp with MTP drafting (`--spec-type draft-mtp`). - Recommended sampling with thinking on: `--temp 1.0 --top-p 0.95 --top-k 20 --presence-penalty 1.1` (Qwen3.8's own thinking-mode values plus a light presence penalty). At temperature 0.6 with presence penalty 1.5, long agent sessions sometimes got stuck repeating whole paragraphs in the thinking; see "Sampling" below for the comparison. - 12 GB cards: 40K context fits (11.4 GB with a 22K-token prompt). 64K starts at 11.85 GB and grows about 0.45 GB with long prompts, spills out of VRAM and slows down sharply. RTX 3080 12 GB, WSL2, llama.cpp (PrismML fork `prism-b10754`). Refusal = the reply contains a refusal phrase. Harmful prompts: the 104-prompt test split of a public harmful-behaviors set.
- Qwen2.5-VL-7B-Instruct-GGUF↗
samgreen
- Hy3-295B-A21B-GGUF↗
YanissAmz
- sam2-hiera-base-plus-hf↗
facebook
- granite-speech-4.1-2b-plus↗
ibm-granite
We have updated the model weights and configuration. The updated model is more accurate, especially for keyword biasing and for word-level timestamps on long samples. It is a drop-in replacement for the previous version, but we have also updated our usage recommendations to get the best performance. In the tables below, GSP v2610 refers to the updated model and GSP v2604 to the original release. Granite-Speech-4.1-2B-Plus (GSP) has similar capabilities to the Granite-Speech-4.1-2B model. The plus model adds two new community-requested rich transcription features that can be activated with a simple prompt change: speaker-attributed ASR (speaker labels and word transcripts) and word-level timing information. Unlike the base model, the plus model doesn't provide punctuation and capitalization.
- arabic_PP-OCRv5_mobile_rec↗
PaddlePaddle
arabic_PP-OCRv5_mobile_rec is one of the PP-OCRv5_rec that are the latest generation text line recognition models developed by PaddleOCR team. It aims to efficiently and accurately support the recognition of Arabic. The key accuracy metrics are as follow: **Note**: If any character (including punctuation) in a line was incorrect, the entire line was marked as wrong. This ensures higher accuracy in practical applications. You can also integrate the model inference of the text recognition module into your project. Before running the following code, please download the sample image to your local machine.
- OLMoE-1B-7B-0125↗
allenai
> OLMoE-1B-7B is a Mixture-of-Experts LLM with 1B active and 7B total parameters released in January 2025 (0125) that is 100% open-source. It is an improved version of OLMoE-09-24, see the paper appendix for details. Important branches: - `step1200000-tokens5033B`: Pretraining checkpoint used for annealing. There are a few more checkpoints after this one but we did not use them. - `main`: Checkpoint annealed from `step1200000-tokens5033B` for an additional 100B tokens (23,842 steps). We use this checkpoint for our adaptation (https://huggingface.co/allenai/OLMoE-1B-7B-0125-SFT & https://huggingface.co/allenai/OLMoE-1B-7B-0125-Instruct). - `fp32`: FP32 version of `main`. The model weights were stored in FP32 during training but we did not observe any performance drop from casting them to BF16 after training so we upload all weights in BF16. If you want the original FP32 checkpoint for `main` you can use this one. You will find that it yields slightly different results but should perform around the same on benchmarks.
- chronos-t5-large↗
autogluon
🚀 **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.
- samsung_qwen2.1↗
Danrisi