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AI モデル 2119
- sam2_hiera_base_plus.fb_r896↗
timm
`timm` SAM2 (HieraDet image encoder only) weights from https://huggingface.co/facebook/sam2-hiera-base-plus
- flux-3-image-klein-pup-GGUF↗
unsloth
GGUF conversions of black-forest-labs/flux-3-image-klein-pup, the prompt upsampler for FLUX 3 Image Klein. It is a fine-tune of Qwen3.5-9B by Black Forest Labs that expands a short image request (and, for edits, the reference images) into the detailed caption FLUX 3 Image Klein is trained on. FLUX 3 Image Klein is meant to run with a prompt upsampler in front of it. Short prompts sent to the image model as typed often give generic images and broken poses (backflips, handstands, tangled arms); the upsampled caption describes the pose and every limb explicitly and fixes most of those. Unsloth Desktop and Unsloth Studio download and run this upsampler automatically for FLUX 3 Image Klein through the "Enhance prompt" option.
- mms-tts-bam↗
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.
- locate-anything.cpp-gguf↗
mudler
GGUF builds of `nvidia/LocateAnything-3B` for **locate-anything.cpp** - a C++/ggml inference engine for open-vocabulary detection / visual grounding, no Python at inference time. The detections are the same as the official PyTorch implementation (the engine is parity-gated against it), and it runs faster - on CPU and GPU. The full-precision `f32` GGUF (~15 GB) is reproducible from the HF weights with `scripts/convert_locateanything_to_gguf.py` in the repo.
- indic-parler-tts↗
RXD03
It is a fine-tuned version of Indic Parler-TTS Pretrained, trained on a **1,806 hours** multilingual Indic and English dataset. **Indic Parler-TTS Mini** can officially speak in 20 Indic languages, making it comprehensive for regional language technologies, and in English. The **21 languages** supported are: Assamese, Bengali, Bodo, Dogri, English, Gujarati, Hindi, Kannada, Konkani, Maithili, Malayalam, Manipuri, Marathi, Nepali, Odia, Sanskrit, Santali, Sindhi, Tamil, Telugu, and Urdu. Thanks to its **better prompt tokenizer**, it can easily be extended to other languages. This tokenizer has a larger vocabulary and handles byte fallback, which simplifies multilingual training.
- FLUX-3-Image-Klein-TE-Uncensored-GGUF↗
AtomicChat
FLUX 3 Image Klein's text encoder, Qwen3-VL-4B-Instruct, with its refusal direction projected out: a drop-in --llm for stable-diffusion.cpp next to our Klein GGUFs . Klein's prompt upsampler without refusals is in FLUX-3-Image-Klein-pup-Uncensored-GGUF . Below, exactly which part is uncensored and what that does to the pictures. The measurements are public. Built by the team behind Atomic Chat , the app that runs open-weight models locally and offline. As a chat model, the encoder's refusals fall from 79% to 0% on held-out English prompts. MMLU goes from 71.1% to 71.2%. On images it changes nothing we could measure. The open Klein pipeline has no filter and already draws the sensitive categories we tested. With this encoder, alone or together with the uncensored upsampler, no category moved by more than one picture in ten. See where exactly this is uncensored . Another uncensored Klein encoder, webmp3's Sakura, has a smaller edit: it moves the model and the pictures less than this one, at the same 0% refusals. See the comparison . FLUX 3 Image Klein makes a picture with four parts. Two of them are changed in our Uncensored releases, one in each repo.
- bge-large-en↗
BAAI
**Recommend switching to newest BAAI/bge-large-en-v1.5, which has more reasonable similarity distribution and same method of usage.** Model List | FAQ | Usage | Evaluation | Train | Contact | Citation | License More details please refer to our Github: FlagEmbedding.
- t5-large↗
google-t5
1. Model Details 2. Uses 3. Bias, Risks, and Limitations 4. Training Details 5. Evaluation 6. Environmental Impact 7. Citation 8. Model Card Authors 9. How To Get Started With the Model The developers of the Text-To-Text Transfer Transformer (T5) write: > With T5, we propose reframing all NLP tasks into a unified text-to-text-format where the input and output are always text strings, in contrast to BERT-style models that can only output either a class label or a span of the input. Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task.
- SambaLingo-Arabic-Chat-70B-i1-GGUF↗
mradermacher
- MOSS-Image-SFT↗
OpenMOSS-Team
MOSS-Image-SFT is a supervised fine-tuned text-to-image model in the **MOSS-Image** family from OpenMOSS, derived from MOSS-Image-Base. It uses the same architecture and inference pipeline, supporting natural-language prompts, tag sequences, and JSON layouts for controlling text placement and object arrangement. Use Python 3.10 or later and an NVIDIA GPU with CUDA support. Install a PyTorch 2.9 or later build compatible with your CUDA environment, then install: The model and custom pipeline code are downloaded and cached automatically. You can also pass a complete local model directory to `from_pretrained`. The transformer and text encoder use bfloat16; the VAE and initial noise use float32. The bundled VAE uses the FLUX.2 small decoder.