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AI モデル 84759

• ReflectionCoder-CL-34B↗

  SenseLLM

  ReflectionCoder is a novel approach that effectively leverages reflection sequences constructed by integrating compiler feedback to improve one-off code generation performance. Please refer to our paper and repo for more details! Following chat templates of most models, we use two special tokens to wrap the message of user and assistant, *i.e.*, `` ``, `` ``, and `` ``. Furthermore, we use two special tokens to wrap the content of different blocks, *i.e.*, `` `` and `` ``. You can use the following template to prompt our ReflectionCoder.

• distilbert-base-nli-mean-tokens↗

  sentence-transformers

  **⚠️ This model is deprecated. Please don't use it as it produces sentence embeddings of low quality. You can find recommended sentence embedding models here: SBERT.net - Pretrained Models** This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. Using this model becomes easy when you have sentence-transformers installed:

• ar-stablelm-2-chat↗

  Stability AI

• Tracer-Low-1.7B↗

  tau-emotion-bench

• ConTeXT-Skill-Extraction-base↗

  TechWolf

  This is a sentence-transformers model trained. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

• gemma-4-26B-A4B-it-Claude-Opus-Distill-GGUF↗

  TeichAI

• NeuralTranslate_v0.2_GGUF↗

  Thermostatic

• controlnet-sd21-depth-diffusers↗

  thibaud

• vit_base_patch16_dinov3_qkvb.lvd1689m↗

  timm

  A DINOv3 ViT model image feature encoder. Distilled on LVD-1689M from the DINOv3 ViT-7B model. * The original model weights ended up with all QKV projection biases being zeroes. For `timm`, have disabled the QKV bias (`qkv_bias=False`) for the models and not loaded the zero weights. For some model sizes there are variants with `qkvb` in the name that have the bias enabled (`qkv_bias=True`), but zero, to match the behaviour of `transformers` and original models. * The original models keep RoPE periods as a persistent `bfloat16` buffer. `timm` generates `float32` periods at init. This results in some numerical differences, however the `timm` approach should be less problematic running on devices without bfloat16 support, and appears to work as well if not slightly better for fine-tuning. `model.rope.periods = model.rope.periods.to(torch.bfloat16).to(torch.float32)` will truncate the periods to bfloat16 and result in matching outputs. - **Model Type:** Image Feature Encoder - **Model Stats:** - Params (M): 85.7 - GMACs: 23.6 - Activations (M): 34.1 - Image size: 256 x 256 - **Original:** https://github.com/facebookresearch/dinov3 - **License:** DINOv3 - **Dataset:** LVD-1689M - **Papers:** - DINOv3: https://arxiv.org/abs/2508.10104 - An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2 - PyTorch Image Models: https://github.com/huggingface/pytorch-image-models

• testing↗

  tmadge

  This modelcard aims to be a base template for new models. It has been generated using this raw template. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).