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AI モデル 84759

• gatortron-base↗

  UFNLP

  Developed by a joint effort between the University of Florida and NVIDIA, GatorTron-Base is a clinical language model of 345 million parameters, pre-trained using a BERT architecure implemented in the Megatron package (https://github.com/NVIDIA/Megatron-LM). This model is converted to Hugginface from : https://catalog.ngc.nvidia.com/orgs/nvidia/teams/clara/models/gatortron_og Model | Parameter | Maximum input --- | --- | --- gatortron-base-2k | 345 million | 2048 gatortron-base (this model) | 345 million | 512 gatortronS | 345 million | 512 gatortron-medium | 3.9 billion | 512 gatortron-large | 8.9 billion | 512

• Qwen3.5-397B-A17B-GGUF↗

  unsloth

  Unsloth Dynamic 2.0 achieves superior accuracy & outperforms other leading quants. Disable thinking via `--chat-template-kwargs '{"enable_thinking":false}'`. Read our guide. - All GGUFs now use our new imatrix data. See some improvements in chat, coding, long context, and tool-calling use-cases. - GGUFs now updated with an improved quantization algorithm. - See our new benchmarks for Qwen3.5 here. ---

• Ming-Image-0.1-Design-GGUF↗

  voltaire321

• tcr-bert-mlm-only↗

  wukevin

• Huihui-Qwen3.8-27B-abliterated-GGUF↗

  xrachelburns

  This is an uncensored version of Qwen/Qwen3.8-27B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. The newly added **Huihui-Qwen3.8-27B-abliterated-Swift** series come from ukisai/Swift-1.5-Qwen3.8-27B-GSQ-RCO-GGUF. Only layers **22 to 52 (0-based indexing)** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. This is just a test/validation. The newly added **Huihui-Qwen3.8-27B-abliterated-Ternary** series come from prism-ml/Ternary-Bonsai-2-27B-gguf. Only layers **22 to 52 (0-based indexing)** have been ablated, while the other layers remain unablated. It may come with a small disclaimer warning. The size after conversion may differ from the original GGUF (Some of the weights are converted from PTQ1 to Q2_K or Q3_K.). This is just a test/validation.

• chatglm2-6b-int4↗

  zai-org

• MythoMax-L2-13B-GGUF↗

  zihuiliu7737

• Qwen3 30B A3B↗

  阿里巴巴 · Qwen

  Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique...

• qwen3-next-80b-a3b-thinking↗

  阿里巴巴 · Qwen

  Over the past few months, we have observed increasingly clear trends toward scaling both total parameters and context lengths in the pursuit of more powerful and agentic artificial intelligence (AI). We are excited to share our latest advancements in addressing these demands, centered on improving scaling efficiency through innovative model architecture. We call this next-generation foundation models **Qwen3-Next**. **Qwen3-Next-80B-A3B** is the first installment in the Qwen3-Next series and features the following key enchancements: - **Hybrid Attention**: Replaces standard attention with the combination of **Gated DeltaNet** and **Gated Attention**, enabling efficient context modeling for ultra-long context length. - **High-Sparsity Mixture-of-Experts (MoE)**: Achieves an extreme low activation ratio in MoE layers, drastically reducing FLOPs per token while preserving model capacity. - **Stability Optimizations**: Includes techniques such as **zero-centered and weight-decayed layernorm**, and other stabilizing enhancements for robust pre-training and post-training. - **Multi-Token Prediction (MTP)**: Boosts pretraining model performance and accelerates inference. We are seeing strong performance in terms of both parameter efficiency and inference speed for Qwen3-Next-80B-A3B: - Qwen3-Next-80B-A3B-Base outperforms Qwen3-32B-Base on downstream tasks with 10% of the total training cost and with 10 times inference throughput for context over 32K tokens. - Leveraging GSPO, we have addressed the stability and efficiency challenges posed by the hybrid attention mechanism combined with a high-sparsity MoE architecture in RL training. Qwen3-Next-80B-A3B-Thinking demonstrates outstanding performance on complex reasoning tasks, not only **surpassing Qwen3-30B-A3B-Thinking-2507 and Qwen3-32B-Thinking**, but also **outperforming the proprietary model Gemini-2.5-Flash-Thinking** across multiple benchmarks.

• DeepSeek-V4.1-Flash-Quark-MXFP4↗

  amd