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AI モデル 84759

• beren-gpt2-medium-x49↗

  stanford-crfm

• clip-vit-base-patch16_resisc45↗

  tanganke

• dd1↗

  tao1000

• deberta-small-long-nli↗

  tasksource

  DeBERTa-v3-small with context length of 1680 tokens fine-tuned on tasksource for 250k steps. I oversampled long NLI tasks (ConTRoL, doc-nli). Training data include HelpSteer v1/v2, logical reasoning tasks (FOLIO, FOL-nli, LogicNLI...), OASST, hh/rlhf, linguistics oriented NLI tasks, tasksource-dpo, fact verification tasks. This model is suitable for long context NLI or as a backbone for reward models or classifiers fine-tuning. This checkpoint has strong zero-shot validation performance on many tasks (e.g. 70% on WNLI), and can be used for: - Zero-shot entailment-based classification for arbitrary labels [ZS]. - Natural language inference [NLI] - Further fine-tuning on a new task or tasksource task (classification, token classification or multiple-choice) [FT].

• openchat_3.5-16k-GGUF↗

  TheBloke

• Rocinante-XL-16B-v1-GGUF↗

  TheDrummer

  Hi! I'm Drummer. I'm a Software Engineer with experience in JavaScript, Golang, Python, and generally engineering the crap out of things. I started my tuning venture back in mid-2024 when I wanted to improve its literary capabilities. I've come a long way since then and I have branched out and specialized. Foundational models today are optimized for non-creative uses, and I believe there is a place for AI in creativity and entertainment. While intelligence, correctness, and problem solving are not my priority, they are still one of many qualities I want in my models.

• mambaout_base.in1k↗

  timm

• DeepSeek-R1-Distill-Llama-8B-Abliterated-GGUF↗

  ufydy937

• gemma-4-26B-A4B-it-NVFP4↗

  unsloth

  Gemma 4 can now be run and fine-tuned in Unsloth Studio. Read our guide. See all versions of Gemma 4 (GGUF, 16-bit etc.) in our collection. Do not use the Marlin backend (around 2x slower); let vLLM auto-select the NVFP4 kernel.

• Llama-4-Maverick-17B-128E-Instruct-GGUF↗

  unsloth

  The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding. These Llama 4 models mark the beginning of a new era for the Llama ecosystem. We are launching two efficient models in the Llama 4 series, Llama 4 Scout, a 17 billion parameter model with 16 experts, and Llama 4 Maverick, a 17 billion parameter model with 128 experts. **Model Architecture:** The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality.