サイト内検索
AI モデル 2119
- drama3-small-GGUF↗
pentacoxian-dev
**Built with Fish Audio.** This is a compressed derivative of Fish Audio Drama 3. Fish Audio does not sponsor or endorse it. drama3-small keeps Drama 3's expressive Japanese and English voice cloning: inline tags, English style directions and director's notes. It should fit in 12 GB of VRAM. The 35B-A3B mixture-of-experts backbone was pruned to 45% of its expert slots and quantized to about 3 bits. It was then distilled back towards the original on Japanese and English speech. All samples come from this GGUF in one take, cloned from a 29-second Japanese reference recording of the repo owner's voice (used with consent).
- DeepScaleR-1.5B-Preview↗
agentica-org
DeepScaleR-1.5B-Preview is a language model fine-tuned from DeepSeek-R1-Distilled-Qwen-1.5B using distributed reinforcement learning (RL) to scale up to long context lengths. The model achieves 43.1% Pass@1 accuracy on AIME 2024, representing a 15% improvement over the base model (28.8%) and surpassing OpenAI's O1-Preview performance with just 1.5B parameters. Our training dataset consists of approximately 40,000 unique problem-answer pairs compiled from: - AIME problems (1984-2023) - AMC problems (prior to 2023) - Omni-MATH dataset - Still dataset We employ Deepseek's Group Relative Policy Optimization (GRPO), a simplified RL algorithm that extends PPO by: - Normalizing advantage function over all samples generated from the same prompt. - Applying KL divergence regularization on top of PPO's surrogate loss to prevent significant policy drift.
- PP-OCRv4_server_rec↗
PaddlePaddle
PP-OCRv4_server_rec is a text line recognition model within the PP-OCRv4_rec series, developed by the PaddleOCR team. PP-OCRv4 is an upgrade over PP-OCRv3. The overall framework retains the same pipeline as PP-OCRv3, with optimizations made to several modules such as data, network structure, and training strategy for both detection and recognition models. It supports text line recognition in general Chinese and English scenarios, but mainly focuses on Chinese. The key accuracy metrics are as follow: 80.61 71.2 M The server-side model of PP-OCRv4, offering high inference accuracy and deployable on various servers. **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.
- chadGPT↗
sam-eer12
- flux-3-image-klein-FP8↗
unsloth
This is an FP8 / INT8 quantized version of FLUX 3 Image Klein (the 16-step distilled checkpoint). - Optimized for efficient inference with reduced memory footprint: the transformer is 6.44 GB instead of 11.1 GB. Same-seed LPIPS vs the bf16 model (lower is better): 0.035 INT8, 0.036 FP8. - Loads pre-quantized in Unsloth Desktop, which skips the bf16 download and the on-the-fly quantization. The text encoder (Qwen3-VL-4B-Instruct) and the VAE are unchanged; take them from black-forest-labs/flux-3-image-klein. The text encoder rows are scored against the bf16 text encoder with the bf16 transformer, so they measure only the encoder. The VAE stays bf16. This repo also carries the upstream diffusers folders from black-forest-labs/flux-3-image-klein, unchanged (`modular_model_index.json`, `text_encoder/`, `transformer/`, `vae/`), so Unsloth Desktop downloads every component from here.
- aragpt2-base↗
aubmindlab
You can find more information in our paper AraGPT2 The code in this repository was used to train all GPT2 variants. The code support training and fine-tuning GPT2 on GPUs and TPUs via the TPUEstimator API. GPT2-base and medium uses the code from the `gpt2` folder and can trains models from the minimaxir/gpt-2-simple repository. These models were trained using the `lamb` optimizer and follow the same architecture as `gpt2` and are fully compatible with the `transformers` library.
- mask2former-swin-base-ade-semantic↗
facebook
Mask2Former model trained on ADE20k semantic segmentation (base-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.
- sam-1-base-i1-GGUF↗
mradermacher
- samantha-mistral-7b-Mistral-7B-Instruct-v0.2-slerp-GGUF↗
MaziyarPanahi
- A.X-4.0-Light↗
skt
SK Telecom released **A.X 4.0** (pronounced "A dot X"), a large language model (LLM) optimized for Korean-language understanding and enterprise deployment, on July 03, 2025. Built on the open-source Qwen2.5 model, A.X 4.0 has been further trained with large-scale Korean datasets to deliver outstanding performance in real-world business environments. - **Superior Korean Proficiency**: Achieved a score of 78.3 on KMMLU, the leading benchmark for Korean-language evaluation and a Korean-specific adaptation of MMLU, outperforming GPT-4o (72.5). - **Deep Cultural Understanding**: Scored 83.5 on CLIcK, a benchmark for Korean cultural and contextual comprehension, surpassing GPT-4o (80.2). - **Efficient Token Usage**: A.X 4.0 uses approximately 33% fewer tokens than GPT-4o for the same Korean input, enabling more cost-effective and efficient processing. - **Deployment Flexibility**: Offered in both a 72B-parameter standard model (A.X 4.0) and a 7B lightweight version (A.X 4.0 Light). - **Long Context Handling**: Supports up to 131,072 tokens, allowing comprehension of lengthy documents and conversations. (Lightweight model supports up to 16,384 tokens length) Benchmarks A.X 4.0 Qwen3-235B-A22B (w/o reasoning) Qwen2.5-72B GPT-4o