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AI モデル 84999

• meta-llama_-_Llama-3.2-3B-Instruct-gguf↗

  RichardErkhov

• mlfoundations-dev_-_hp_ablations_mistral_epoch2-gguf↗

  RichardErkhov

• mlfoundations-dev_-_oh-dcft-v3-sharegpt-format-sedrick-gguf↗

  RichardErkhov

• PPMem↗

  Robbyant-Research

• ACE-Step1.5-sft-MLX-bf16↗

  roman220220

• ruvltra-claude-code↗

  ruv

• yolo12l-person-seg↗

  RyanJames

• codet5p-770m↗

  Salesforce

  CodeT5+ is a new family of open code large language models with an encoder-decoder architecture that can flexibly operate in different modes (i.e. _encoder-only_, _decoder-only_, and _encoder-decoder_) to support a wide range of code understanding and generation tasks. It is introduced in the paper: CodeT5+: Open Code Large Language Models for Code Understanding and Generation by Yue Wang\*, Hung Le\*, Akhilesh Deepak Gotmare, Nghi D.Q. Bui, Junnan Li, Steven C.H. Hoi (* indicates equal contribution). Compared to the original CodeT5 family (CodeT5-base: `220M`, CodeT5-large: `770M`), CodeT5+ is pretrained with a diverse set of pretraining tasks including _span denoising_, _causal language modeling_, _contrastive learning_, and _text-code matching_ to learn rich representations from both unimodal code data and bimodal code-text data. Additionally, it employs a simple yet effective _compute-efficient pretraining_ method to initialize the model components with frozen off-the-shelf LLMs such as CodeGen to efficiently scale up the model (i.e. `2B`, `6B`, `16B`), and adopts a "shallow encoder and deep decoder" architecture. Furthermore, it is instruction-tuned to align with natural language instructions (see our InstructCodeT5+ 16B) following Code Alpaca.

• Flux2-Klein-9B-True-V3↗

  sanrenti

• Impish_Nemo_12B_GGUF↗

  SicariusSicariiStuff