wac81/

toy_retnet_1.3b

介绍 (Introduction)

1. 根据retnet论文(https://arxiv.org/pdf/2307.08621.pdf)开发并基于transformer文本生成模型。该仓库的算法实现根据repo进行(https://github.com/syncdoth/RetNet.git)
2. 该仓库目标是建立一个retnet基础训练仓库，建议做学习研究使用，不建议商用。
3. 该仓库只使用wiki文本和少量sharegpt/belle/多轮指令数据集训练而成。包含中英文数据，数据估算占比7:3。
4. 本次放出pretrain模型与sft微调后模型。
5. 本模型使用了tokenizer为百川大模型的第一版分词器，共包含64000个vocab。
6. 已知问题：

• 会出现重复句子回答，可以调节topk减轻该问题。
• 会出现回答不全问题，可以提高max_new_token缓解该问题。
• 由于知识储备不足，回答准确性一般。

1. Developed according to retnet paper (https://arxiv.org/pdf/2307.08621.pdf) and based on transformer text generation model. The algorithmic implementation of this repository is carried out according to repo (https://github.com/syncdoth/RetNet.git)
2. The goal of this repository is to suggest a retnet base training repository, which is recommended to be used for learning research and not for commercial use.
3. This repository is trained using only wiki text and a small amount of sharegpt/belle instruction dataset.
4. This release pretrain model with sft fine-tuned model.
5. This model uses the tokenizer as the first version of the BaiChuan model tokenizer, which contains a total of 64,000 vocabs.
6. known issues:

• Repeated sentence answers will occur, topk can be adjusted to mitigate the problem.
• Incomplete answers will occur, you can increase max_new_token to alleviate the problem.
• Answer accuracy is average due to insufficient knowledge base.

软件依赖 (Dependencies)

模型&代码仓库（Model&Code Repo）

1. 基础预训练模型(pretrain model) (https://huggingface.co/wac81/toy_retnet_1.3b_pretrain)
2. sft微调后模型(sft model) (https://huggingface.co/wac81/toy_retnet_1.3b)
3. Code Repo (https://github.com/wac81/toy_retnet_1.3b)

最小需求 (Minimum Requirements)

代码调用 (Code Usage)

协议 (License)