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AI モデル 84759

• Quokka_2.7b↗

  Corianas

• t5-sql-finetuned↗

  CozyRuiz

• qwen3-tts-1.7b-customvoice-GGUF↗

  cstr

  GGUF / ggml conversion of `Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice` for use with **CrispStrobe/CrispASR**. The 1.7B sibling of `cstr/qwen3-tts-0.6b-customvoice-GGUF`. Same fixed-speaker pattern (9 baked speakers, no ECAPA, no reference WAV), but on the 1.7B talker — `hidden=2048`, `head_dim=128`, with a `small_to_mtp_projection` MLP that maps the 2048-d talker hidden state down to the 1024-d code predictor input space. Pair this with the codec at `cstr/qwen3-tts-tokenizer-12hz-GGUF` — the talker emits 16-codebook RVQ codes that the codec decoder renders to 24 kHz PCM.

• L3.1-Dark-Reasoning-LewdPlay-evo-Hermes-R1-Uncensored-8B↗

  DavidAU

• deepseek-coder-6.7b-instruct↗

  DeepSeek

  Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks. - **Massive Training Data**: Trained from scratch fon 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages. - **Highly Flexible & Scalable**: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements.

• tapas-large-nq-hn-reader↗

  deepset

• huihui-ai.Qwen2.5-0.5B-Instruct-abliterated-v3-GGUF↗

  DevQuasar

• Meta-Llama-3-8B-Instruct-imatrix-GGUF↗

  duyntnet

• August_2024↗

  EarthnDusk

• detr-resnet-50-panoptic↗

  facebook

  DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 panoptic (118k annotated images). It was introduced in the paper End-to-End Object Detection with Transformers by Carion et al. and first released in this repository. Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team. The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100.