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AI 모델 84999

• bert_uncased_L-6_H-768_A-12↗

  Google

  This is the set of 24 BERT models referenced in Well-Read Students Learn Better: On the Importance of Pre-training Compact Models (English only, uncased, trained with WordPiece masking). We have shown that the standard BERT recipe (including model architecture and training objective) is effective on a wide range of model sizes, beyond BERT-Base and BERT-Large. The smaller BERT models are intended for environments with restricted computational resources. They can be fine-tuned in the same manner as the original BERT models. However, they are most effective in the context of knowledge distillation, where the fine-tuning labels are produced by a larger and more accurate teacher. Our goal is to enable research in institutions with fewer computational resources and encourage the community to seek directions of innovation alternative to increasing model capacity.

• finetuned-bertweet-poskd3↗

  GozdeA

• opus-mt-th-fr↗

  Helsinki-NLP

• chinese-alpaca-2-13b-16k↗

  hfl

  **This is the full Chinese-LLaMA-2-13B-16K (context size 16K)，model，which can be loaded directly for inference and full-parameter training.** **Related models👇** * Long context base models (16K) * Chinese-LLaMA-2-7B-16K (full model) * Chinese-LLaMA-2-LoRA-7B-16K (LoRA model) * Chinese-LLaMA-2-13B-16K (full model) * Chinese-LLaMA-2-LoRA-13B-16K (LoRA model) * Long context Instruction/Chat models * Chinese-Alpaca-2-7B-16K (full model) * Chinese-Alpaca-2-LoRA-7B-16K (LoRA model) * Chinese-Alpaca-2-13B-16K (full model) * Chinese-Alpaca-2-LoRA-13B-16K (LoRA model) * Base models * Chinese-LLaMA-2-7B (full model) * Chinese-LLaMA-2-LoRA-7B (LoRA model) * Chinese-LLaMA-2-13B (full model) * Chinese-LLaMA-2-LoRA-13B (LoRA model) * Instruction/Chat models * Chinese-Alpaca-2-7B (full model) * Chinese-Alpaca-2-LoRA-7B (LoRA model) * Chinese-Alpaca-2-13B (full model) * Chinese-Alpaca-2-LoRA-13B (LoRA model) This project is based on the Llama-2, released by Meta, and it is the second generation of the Chinese LLaMA & Alpaca LLM project. We open-source Chinese LLaMA-2 (foundation model) and Alpaca-2 (instruction-following model). These models have been expanded and optimized with Chinese vocabulary beyond the original Llama-2. We used large-scale Chinese data for incremental pre-training, which further improved the fundamental semantic understanding of the Chinese language, resulting in a significant performance improvement compared to the first-generation models. The relevant models support a 4K context and can be expanded up to 18K+ using the NTK method.

• gpt2-fa↗

  HooshvareLab

• japanese-reranker-small-v2↗

  hotchpotch

  japanese-reranker-tiny-v2、japanese-reranker-xsmall-v2、japanese-reranker-small-v2、japanese-reranker-base-v2は、以下の特徴を持つ小型リランカーモデルです： 1. CPUやAppleシリコン環境でも実用的な速度で動作 2. 高価なGPUリソースなしでもRAGシステムの精度向上が可能 3. エッジデバイスでの展開や低レイテンシが要求される本番環境で活用可能 4. ModernBertベースのruri-v3-pt-30m（tiny/xsmall）、ruri-v3-pt-70m（small）、ruri-v3-pt-130m（base）を利用 以下は約15万ペアをリランキングした際の推論速度結果（トークナイズ時間を除く純粋なモデル推論時間）です。MPS(Appleシリコン)とCPU測定にはM4 Max、GPUにはRTX5090を使用しています。GPU処理では flash-attention2 を使用しています。

• Erlangshen-Longformer-110M↗

  IDEA-CCNL

• Ming-Image-0.1-Design-Layer↗

  inclusionAI

• Typhon-Mixtral-v1-iMat-GGUF↗

  InferenceIllusionist

• distilbert_finetuned_ai4privacy↗

  Isotonic