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[#]: subject: "Open-Source Model 'Dolly' Claims to be a Cheaper Alternative to ChatGPT"
[#]: via: "https://news.itsfoss.com/open-source-model-dolly/"
[#]: author: "Sourav Rudra https://news.itsfoss.com/author/sourav/"
[#]: collector: "lkxed"
[#]: translator: "lxbwolf"
[#]: reviewer: " "
[#]: publisher: " "
[#]: url: " "
Open-Source Model 'Dolly' Claims to be a Cheaper Alternative to ChatGPT
======
An affordable alternative to ChatGPT? And, open-source? Looks like we're joining the open-source race against ChatGPT.
![open source model dolly][1]
![][2]
Databricks is a software company that has established itself in a variety of sectors, with data warehousing, and AI-based solutions being their primary focus.
In recent times, we have seen the meteoric rise of ChatGPT, resulting in similar efforts from the likes of Meta, Google, and even Mozilla.
And now, Databricks is trying in their own way by open-sourcing its [large language model][3] (LLM) 'Dolly'.
Let's take a look at it.
**What is happening?:** In a recent announcement, Databricks introduced what they term as **'a cheap-to-build'** LLM that functions by using an existing open-source parameter [model][4] by [EleutherAI][5].
The model has been slightly tweaked to give Dolly instruction following capabilities such as brainstorming and text generation.
When you compare the **175 billion parameters** in GPT-3, Dolly's **6 billion parameters** might seem puny in comparison.
But, the folks over at Databricks were surprised when they saw that even with this much data, Dolly was **able to****exhibit many of the same capabilities as ChatGPT**.
Below is one of the examples they showcased:
![a screenshot of how dolly performs in an open question and answer scenario][6]
The original model used data from [Alpaca][7], the model built by Stanford using the [LLaMA][8] LLM by Meta as a base.
But, as you can see, the original model produced a very haphazard result, whereas Dolly, with its different model and tweaks, was able to produce a far usable answer.
> 📝 Fun Fact: The name was taken from the first cloned mammal, Dolly the sheep.
**Why now?:** According to Databricks, they think that **many companies would prefer to build their own model** rather than sending data to some centralized provider who has locked their model behind an API.
Many companies might not be comfortable handing over their most sensitive data to a third party, and then there are the various tradeoffs in terms of model quality, cost, and desired behavior.
**Do you want to check it out?**
Sure, but there's a catch.
You will have to **use their platform to use Dolly**, they have open-sourced a [Databricks notebook][9] that will help you build it on Databricks.
Moreover, if you want to get access to the trained weights, you will have to contact them. I am uncertain whether they will provide access to it for free, though.
In a nutshell, this move to open-source their model should be good for companies to help safeguard their data, save on operating costs, and more by enabling them to create their own model.
You can check out the[announcement blog][10] to learn more about the technical details and other plans for it.
--------------------------------------------------------------------------------
via: https://news.itsfoss.com/open-source-model-dolly/
作者:[Sourav Rudra][a]
选题:[lkxed][b]
译者:[译者ID](https://github.com/译者ID)
校对:[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译,[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://news.itsfoss.com/author/sourav/
[b]: https://github.com/lkxed/
[1]: https://news.itsfoss.com/content/images/size/w1304/2023/03/opensource-ai-model-dolly.png
[2]: https://news.itsfoss.com/content/images/2023/03/linux-mega-packt.webp
[3]: https://en.wikipedia.org/wiki/Large_language_model?ref=its-foss-news
[4]: https://huggingface.co/EleutherAI/gpt-j-6B?ref=its-foss-news
[5]: https://www.eleuther.ai/?ref=its-foss-news
[6]: https://news.itsfoss.com/content/images/2023/03/Dolly_AI.jpg
[7]: https://crfm.stanford.edu/2023/03/13/alpaca.html?ref=its-foss-news
[8]: https://ai.facebook.com/blog/large-language-model-llama-meta-ai/?ref=its-foss-news
[9]: https://github.com/databrickslabs/dolly?ref=its-foss-news
[10]: https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html?ref=its-foss-news

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[#]: subject: "Open-Source Model 'Dolly' Claims to be a Cheaper Alternative to ChatGPT"
[#]: via: "https://news.itsfoss.com/open-source-model-dolly/"
[#]: author: "Sourav Rudra https://news.itsfoss.com/author/sourav/"
[#]: collector: "lkxed"
[#]: translator: "lxbwolf"
[#]: reviewer: " "
[#]: publisher: " "
[#]: url: " "
号称可以成为 ChatGPT 平替的开源模型 ”Dolly“
======
你需要一款 ChatGPT 的平替?还得是开源的?看起来我们已经被卷入了与 ChatGPT 的开源大战。
![open source model dolly][1]
![][2]
Databricks 这家软件公司,在各个领域都有所建树,尤其是在数据仓库和基于人工智能的解决方案方面。
最近,随着 ChatGPT 横空出世Meta、Google 甚至 Mozilla 也可以效仿 ChatGPT。
而现在Databricks 开源了其 [<ruby>大型语言模型<rt>large language model</rt></ruby>][3]LLM"Dolly",也正在以自己的方式进行尝试。
我们一起来看看它。
**发生了什么?:** 在最近的公告中Databricks 介绍了他们号称 **”廉价构建“** 的 LLM使用 [EleutherAI][5] 的已经开源参数[模型][4] 提供功能。
他们在该模型基础上稍作调整,赋予了 Dolly 指令跟随能力,如头脑风暴和文本生成。
当你拿它与 GPT-3 中的 **1750亿个参数** 比较时Dolly 的 **60亿个参数** 就可能显得微不足道了。
但是,当 Databricks 的人看到即使数据量与 GPT-3 相差这么多Dolly 也能 **展示很多与 ChatGPT 相同的能力** 时,他们感到非常震惊。
下面是他们展示的其中一个例子:
![a screenshot of how dolly performs in an open question and answer scenario][6]
原始模型使用了 [Alpaca][7] 的数据,该模型由斯坦福大学以 Meta 的 [LaMA][8] LLM 为基础建立。
但是,正如你所看到的,原始模型产生了一个非常杂乱无章的结果,而 Dolly通过不同的模型和调整能够产生一个更为可用的答案。
> 📝 有趣的事实Dolly 名字取自世界上第一只克隆羊。
**为什么是现在?** 根据 Databricks 的说法,他们认为 **许多公司更愿意建立自己的模型,**而不是将数据发送给某个紧紧掌握模型只对外提供 API 的集中式供应商。
许多公司可能不愿意将他们最敏感的数据交给第三方,然后在模型质量、成本和所需行为方面进行各种权衡。
**你想看看吗?**
当然,但有一个问题。
你必须 **使用他们的平台来使用 Dolly**,他们已经开源了一个 [Databricks 笔记本][9],可以帮助你在 Databricks 上构建它。
此外,如果你想获得训练好的权重,你必须联系他们。不过我不确定他们是否会免费提供使用权。
总而言之,这种开源其模型的举动应该对其他公司有好处,可以保护他们的数据、节省运营成本,其他公司也能使用它创建自己的模型。
你可以查看 [公告博客][10],以了解更多技术细节和其他计划。
--------------------------------------------------------------------------------
via: https://news.itsfoss.com/open-source-model-dolly/
作者:[Sourav Rudra][a]
选题:[lkxed][b]
译者:[lxbwolf](https://github.com/lxbwolf)
校对:[校对者ID](https://github.com/校对者ID)
本文由 [LCTT](https://github.com/LCTT/TranslateProject) 原创编译,[Linux中国](https://linux.cn/) 荣誉推出
[a]: https://news.itsfoss.com/author/sourav/
[b]: https://github.com/lkxed/
[1]: https://news.itsfoss.com/content/images/size/w1304/2023/03/opensource-ai-model-dolly.png
[2]: https://news.itsfoss.com/content/images/2023/03/linux-mega-packt.webp
[3]: https://en.wikipedia.org/wiki/Large_language_model?ref=its-foss-news
[4]: https://huggingface.co/EleutherAI/gpt-j-6B?ref=its-foss-news
[5]: https://www.eleuther.ai/?ref=its-foss-news
[6]: https://news.itsfoss.com/content/images/2023/03/Dolly_AI.jpg
[7]: https://crfm.stanford.edu/2023/03/13/alpaca.html?ref=its-foss-news
[8]: https://ai.facebook.com/blog/large-language-model-llama-meta-ai/?ref=its-foss-news
[9]: https://github.com/databrickslabs/dolly?ref=its-foss-news
[10]: https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html?ref=its-foss-news