サイト内検索

AI モデル 84759

• stanza-grc↗

  stanfordnlp

• ai-text-detection↗

  tabularisai

• Autolycus-Mistral_7B-GGUF↗

  TheBloke

• eva02_base_patch16_clip_224.merged2b_s8b_b131k↗

  timm

• regnety_320.seer↗

  timm

• tiny-MistralForCausalLM-0.2↗

  trl-internal-testing

  This is a minimal model built for unit tests in the TRL library.

• Kev-4B-Artemis-GGUF↗

  turintech

• DeepSeek-R1-GGUF↗

  unsloth

  Unsloth's DeepSeek-R1 1.58-bit + 2-bit Dynamic Quants is selectively quantized, greatly improving accuracy over standard 1-bit/2-bit. Or you can view more detailed instructions here: unsloth.ai/blog/deepseekr1-dynamic 1. Do not forget about ` ` and ` ` tokens! - Or use a chat template formatter 2. Obtain the latest `llama.cpp` at https://github.com/ggerganov/llama.cpp. You can follow the build instructions below as well: 3. It's best to use `--min-p 0.05` to counteract very rare token predictions - I found this to work well especially for the 1.58bit model. 4. Download the model via:

• Llama-3.1-8B-Instruct-bnb-4bit↗

  unsloth

  We have a free Google Colab notebook for turning Llama 3.1 (8B) into a reasoning model: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face. The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.

• LongCat-Image-Edit-GGUF↗

  vantagewithai

  We introduce **LongCat-Image-Edit**, the image editing version of Longcat-Image. LongCat-Image-Edit supports bilingual (Chinese-English) editing, achieves state-of-the-art performance among open-source image editing models, delivering leading instruction-following and image quality with superior visual consistency.