https://github.com/salesforce/CodeT5 模型HuggingFace链接 https://huggingface.co/Salesforce/codet5-base 在线演示地址 暂无 DataLearnerAI的模型介绍 官方博客论文 CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation ...
文中对CodeT5的描述是:a unified pre-trained encoder-decoder Transformer model that better leverages the code semantics conveyed from the developer-assigned identifiers,即一个能更好地利用代码语法信息(形式是identifier,即标识符)的统一预训练Transformer模型。在开始之前,和PLBART一样,先简单说下Google T5模型。
We release two large-sized CodeT5 checkpoints at HuggingFace:Salesforce/codet5-largeandSalesforce/codet5-large-ntp-py, which are introduced by theCodeRL paper. Oct 2021 We releasefine-tuned checkpointsfor all the downstream tasks covered in the paper. Besides, we release a CodeT5-base fine-...
CodeFuse-CGE-Large huggingface 地址 https://huggingface.co/codefuse-ai/CodeFuse-CGE-Large Model Configuration Base Model:CodeQwen1.5-7B-Chat Model Size:7B Embedding Dimension:1024 Requirements flash_attn==2.4.2torch==2.1.0accelerate==0.28.0transformers==4.39.2vllm=0.5.3 CodeFuse-CGE-Small hugg...
An officially supported task in theexamplesfolder (such as GLUE/SQuAD, ...) My own task or dataset (give details below) Reproduction fromtransformersimportAutoTokenizertokenizer=AutoTokenizer.from_pretrained("formermagic/codet5-large")print(tokenizer.model_max_length) ...
huggingface 地址 https://huggingface.co/codefuse-ai/CodeFuse-CGE-Small Model Configuration Base Model:Phi-3.5-mini-instruct Model Size:3.8B Embedding Dimension:1024 Requirements flash_attn==2.4.2torch==2.1.0accelerate==0.28.0transformers>=4.43.0 ...
我们有纯技术基建的岗位,自研模型在2023.10登顶Huggingface Open LLM榜单;我们也有LLM-based业务落地的岗位,我们的AI产品是业界为数不多的已经实现大规模商业化赚钱并仍快速增长的。不管你对LLM技术感兴趣还是热衷于LLM业务落地,欢迎加入我们。 职责描述: 负责跨境电商垂直多语言多模态基座大模型的研发,包括从大规模预训...
Since InstructCodeT5+ 16b was not available via chat or IDE extension, we prompted it via Google Colab and HuggingFace. 3.2. Our AI/Human-Generated Program Code Dataset In this subsection, we will first introduce the coding platform LeetCode. Then we will present the coding problems that we ...
Google 的T5大概有110亿的参数,最显著的特点就是可以多任务微调,关键它还是开源的。 OpenAI的GPT3.5出现之后在市面上所带来的效果是非常惊人的,效果反馈也非常的好,它的参数更是达到了 1750亿 ,所需要的算力是之前很多模型的很多倍,相较于其他模型,GPT3.5的一个显著特点就是支持人工反馈的微调。
and the model weights are sourced from Huggingface [45].The Codex model was inferenced via OpenAI’s API with authorized usage rights, maintaining a rate limit of 20 queries or 40,000 tokens per minute despite its deprecation as of March 2023. The code-davinci-002 model continues to be ac...