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AI 모델 2119
- Sa2VA-Qwen3-VL-4B-SAM3↗
字节跳动
- bge-base-en↗
BAAI
**Recommend switching to newest BAAI/bge-base-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.
- RavenX-Conjecture-Qwen3-8B-MLX↗
deadbydawn101
1. Read the ConjectureBench paper (arXiv:2510.11986) — nobody had implemented it locally 2. Built the first MLX-native LEAN-FIRE pipeline on Apple Silicon 3. Fine-tuned the first conjecture generation model that exists 4. Verified Dmitry Rybin's breaking counterexample to the 30-year-old Dinitz-Garg-Goemans conjecture within hours The core insight: **conjecture generation IS prediction.** Same pipeline whether you're breaking a 30-year-old math conjecture or sizing a Polymarket position. **The killer app.** Conjecture = Prediction. Formalization = Resolution criteria. Counterexample = The trade.
- Samantha-PL-AG-Mistral-7B-v0.2-i1-GGUF↗
mradermacher
- mask2former-swin-large-ade-semantic↗
facebook
Mask2Former model trained on ADE20k semantic segmentation (large-sized version, Swin backbone). It was introduced in the paper Masked-attention Mask Transformer for Universal Image Segmentation and first released in this repository. Disclaimer: The team releasing Mask2Former did not write a model card for this model so this model card has been written by the Hugging Face team. Mask2Former addresses instance, semantic and panoptic segmentation with the same paradigm: by predicting a set of masks and corresponding labels. Hence, all 3 tasks are treated as if they were instance segmentation. Mask2Former outperforms the previous SOTA, MaskFormer both in terms of performance an efficiency by (i) replacing the pixel decoder with a more advanced multi-scale deformable attention Transformer, (ii) adopting a Transformer decoder with masked attention to boost performance without without introducing additional computation and (iii) improving training efficiency by calculating the loss on subsampled points instead of whole masks.
- all_datasets_v4_MiniLM-L6↗
flax-sentence-embeddings
The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained 'MiniLM-L6-H384-uncased' model and fine-tuned in on a 1B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. We developped this model during the Community week using JAX/Flax for NLP & CV, organized by Hugging Face. We developped this model as part of the project: Train the Best Sentence Embedding Model Ever with 1B Training Pairs. We benefited from efficient hardware infrastructure to run the project: 7 TPUs v3-8, as well as intervention from Google’s Flax, JAX, and Cloud team member about efficient deep learning frameworks. Our model is intented to be used as a sentence encoder. Given an input sentence, it ouptuts a vector which captures the sentence semantic information. The sentence vector may be used for information retrieval, clustering or sentence similarity tasks.
- Samantha-Qwen2-7B-i1-GGUF↗
mradermacher
- Qwen-Image-FP8↗
unsloth
This is an FP8 / INT8 quantized version of Qwen-Image. - Optimized for efficient inference with reduced memory footprint. Same-seed LPIPS vs the bf16 model (lower is better): 0.057 INT8; FP8 needs activation_value_lb handling - use INT8 for stability. 💜 Qwen Chat    |   🤗 Hugging Face    |   🤖 ModelScope    |    📑 Tech Report    |    📑 Blog    We are thrilled to release **Qwen-Image**, an image generation foundation model in the Qwen series that achieves significant advances in **complex text rendering** and **precise image editing**. Experiments show strong general capabilities in both image generation and editing, with exceptional performance in text rendering, especially for Chinese.
- MERT-v1-95M↗
m-a-p
The development log of our Music Audio Pre-training (m-a-p) model family: - 02/06/2023: arxiv pre-print and training codes released. - 17/03/2023: we release two advanced music understanding models, MERT-v1-95M and MERT-v1-330M , trained with new paradigm and dataset. They outperform the previous models and can better generalize to more tasks. - 14/03/2023: we retrained the MERT-v0 model with open-source-only music dataset MERT-v0-public - 29/12/2022: a music understanding model MERT-v0 trained with **MLM** paradigm, which performs better at downstream tasks. - 29/10/2022: a pre-trained MIR model music2vec trained with **BYOL** paradigm. The m-a-p models share the similar model architecture and the most distinguished difference is the paradigm in used pre-training. Other than that, there are several nuance technical configuration needs to know before using: - **Model Size**: the number of parameters that would be loaded to memory. Please select the appropriate size fitting your hardware. - **Transformer Layer-Dimension**: The number of transformer layers and the corresponding feature dimensions can be outputted from our model. This is marked out because features extracted by **different layers could have various performance depending on tasks**. - **Feature Rate**: Given a 1-second audio input, the number of features output by the model. - **Sample Rate**: The frequency of audio that the model is trained with.
- NVIDIA-Nemotron-3.5-Bolt-E4B-Pollard↗
PollardWeights
> ### Pollard shrank this model: **15.99 GB (f16) -> 3.10 GB** -- **81% smaller, 5.2x down**. > The smallest rung here; larger, higher-fidelity rungs are listed below. > > | format | this model's size | > |---|---:| > | f16 | 15.99 GB | > | Q8_0 | ~8.48 GB | > | Q6_K | 6.22 GB | > | Q4_K_M | ~4.64 GB | > | **PollardMix (this repo's IQ2_XXS)** | **3.10 GB** | Pollard builds of nvidia/NVIDIA-Nemotron-3.5-Bolt-E4B-BF16 made with Pollard Weights -- a ladder of **measured-allocation** quants (bits placed by per-layer sensitivity, not a uniform crush). Every rung is the **same weights**, sized to a different RAM budget by the measured allocation. Pick the largest one that fits your machine with room for context: