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AI 模型 84753

• granite-20b-code-instruct-IMat-GGUF↗

  legraphista

  Original Model: ibm-granite/granite-20b-code-instruct Original dtype: `BF16` (`bfloat16`) Quantized by: llama.cpp b3649 IMatrix dataset: here If the model file is big, it has been split into multiple files. In order to download them all to a local folder, run: According to this investigation, it appears that lower quantizations are the only ones that benefit from the imatrix input (as per hellaswag results).

• zephyr-7b-gemma-sft↗

  li-muyang

• Phi-4-reasoning-GGUF↗

  lmstudio-community

• cocolm-large↗

  Microsoft

• Dayhoff-170M-UR90-HL-24000↗

  Microsoft

• tapex-base-finetuned-wikisql↗

  Microsoft

  TAPEX was proposed in TAPEX: Table Pre-training via Learning a Neural SQL Executor by Qian Liu, Bei Chen, Jiaqi Guo, Morteza Ziyadi, Zeqi Lin, Weizhu Chen, Jian-Guang Lou. The original repo can be found here. TAPEX (**Ta**ble **P**re-training via **Ex**ecution) is a conceptually simple and empirically powerful pre-training approach to empower existing models with *table reasoning* skills. TAPEX realizes table pre-training by learning a neural SQL executor over a synthetic corpus, which is obtained by automatically synthesizing executable SQL queries. TAPEX is based on the BART architecture, the transformer encoder-encoder (seq2seq) model with a bidirectional (BERT-like) encoder and an autoregressive (GPT-like) decoder.

• unispeech-large-fr-ft↗

  Microsoft

• 5FUMG9qvpoSaNVKvZRUnwVTapie7Lu2NYjevMLpXxJqfmuCM_vgg↗

  minatolegend0311

• MobileMoE-L-QAT↗

  ml-resources

• GUI-Owl-1.5-32B-Instruct↗

  mPLUG