Models Hugging Face Under the license, you must give credit to replit, provide a link to the license, and indicate if changes were made. you may do so in any reasonable manner, but not in any way that suggests that replit endorses you or your use. For most instruct tuning use cases, we recommend starting from the hugging face examples below. otherwise, we also provide a detailed guide to do instruction tuning with llm foundry.
Replit Replit Model description replit code v1 3b is a 2.7b causal language model focused on code completion. the model has been trained on a subset of the stack dedup v1.2 dataset. the training mixture includes 20 different languages, listed here in descending order of number of tokens:. Replit code v1 3b is a 2.7b causal language model focused on code completion. the model has been trained on a subset of the stack dedup v1.2 dataset. in total, the training dataset contains 175b tokens, which were repeated over 3 epochs in total, replit code v1 3b has been trained on 525b tokens (~195 tokens per parameter). With replit, you can build software collaboratively with the power of ai, on any device, without spending a second on setup. We’re on a journey to advance and democratize artificial intelligence through open source and open science.
Replit Replit With replit, you can build software collaboratively with the power of ai, on any device, without spending a second on setup. We’re on a journey to advance and democratize artificial intelligence through open source and open science. To accompany ai for all, we’re releasing our new code generation language model replit code v1.5 3b on hugging face. we believe in open source language models – anyone can use it as a foundational model for application specific fine tuning without strict limitations on commercial use. Hugging face space hosting the replit code v1 3b demo; provides access to the app, files, and community discussions; features a simple interface with navigation links to models, datasets, spaces, docs, and user account options. Replit code v1 3b takes text input and generates text output, with a focus on producing code snippets. the model utilizes advanced techniques like flash attention and alibi positional embeddings to enable efficient training and inference on long input sequences. Replit code v1 3b is a state of the art 2.7 billion parameter causal language model developed specifically for code completion tasks. this model is hosted in the hugging face model hub and was trained on a wide variety of 20 programming languages.
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