stanford_alpaca VS Milvus

Alpaca is an instruction-oriented LLM derived from LLaMA, enhanced by Stanford researchers with a dataset of 52,000 examples of following instructions, sourced from OpenAI’s InstructGPT through the self-instruct method. The extensive self-instruct dataset, details of data generation, and the model refinement code were publicly disclosed. This model complies with the licensing requirements of its base model. Due to the utilization of InstructGPT for data generation, it also adheres to OpenAI’s usage terms, which prohibit the creation of models competing with OpenAI. This illustrates how dataset restrictions can indirectly affect the resulting fine-tuned model.

In this article, we're going to experiment with LoRA and fine-tune Llama Alpaca using commercial hardware.

Most papers I did read showed at least a thousand, even 10000 at several cases, so I assumed that to be the trend in the case of Low rank adapter(PEFT) training.(source: [2305.14314] QLoRA: Efficient Finetuning of Quantized LLMs (arxiv.org) , Stanford CRFM (Alpaca) and the minimum being openchat/openchat · Hugging Face ; There are a lot more examples)

I'm not talking about OpenAI ChatGPT I'm talking about things ALPACA, and where did they train these models? Off the existing models for a fraction of a fraction of a fraction of the cost: https://crfm.stanford.edu/2023/03/13/alpaca.html

I think there's a new approach for “How do you get the data?” that wasn't available when this article was written in 2015. The new text and image generative models can now be used to synthesize training datasets.

I was working on an typing autocorrect project and needed a corpus of "text messages". Most of the traditional NLP corpuses like those available through NLTK [0] aren't suitable. But it was easy to script ChatGPT to generate thousands of believable text messages by throwing random topics at it.

Similarly, you can synthesize a training dataset by giving GPT the outputs/labels and asking it to generate a variety of inputs. For sentiment analysis... "Give me 1000 negative movie reviews" and "Now give me 1000 positive movie reviews".

The Alpaca folks used GPT-3 to generate high-quality instruction-following datasets [1] based on a small set of human samples.

Etc.

[0] https://www.nltk.org/nltk_data/

[1] https://crfm.stanford.edu/2023/03/13/alpaca.html

AFAIK, the original alpaca repo was a full finetune. https://github.com/tatsu-lab/stanford_alpaca

Zilliz (zilliz.com) | Hybrid/ONSITE (SF, NYC) | Full-time

I am part of the hiring team for DevRel

NYC - https://boards.greenhouse.io/zilliz/jobs/4307910005

SF - https://boards.greenhouse.io/zilliz/jobs/4317590005

Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus), the most starred vector database on GitHub. Milvus is a distributed vector database that shines in 1B+ vector use cases. Examples include autonomous driving, e-commerce, and drug discovery. (and, of course, RAG)

We are also hiring for other roles that I am not personally involved in the hiring process for such as product managers, software engineers, and recruiters.

Get started with Milvus on GitHub.

Milvus: An open-source vector database designed for AI and ML applications. It excels in handling large-scale vector similarity searches, making it suitable for recommendation systems, image and video retrieval, and natural language processing tasks.

Selecting the right version of open-source Milvus is important to the success of any project leveraging vector search technology. With Milvus offering different versions of its vector database tailored to varying requirements, understanding the significance of selecting the correct version is key for achieving desired outcomes.

Milvus is an open-source vector database designed to handle large-scale similarity search and vector indexing. It supports multiple index types and offers highly efficient search capabilities, making it suitable for a wide range of AI and ML applications, including image and video recognition, natural language processing, and recommendation systems.

Zilliz is hiring! We're looking for REMOTE and/or HYBRID roles in SF

Zilliz is the company behind Milvus (https://github.com/milvus-io/milvus), the most widely adopted vector database. Vector databases are a crucial piece of any technology stack looking to take advantage of unstructured data. Most recently and notably, Retrieval Augmented Generation (RAG). For RAG, vector databases like Milvus are used as the tool to inject customized data. In other words, vector databases make things like customized chat bots, personalized product recommendations, and more possible.

We are hiring for Developer Advocates, Senior+ Level Engineers and Product people, and Talent Acquisition. Check out all the roles here: https://zilliz.com/careers

Good on them, I know the crustaceans are out here happy about this raise for a Rust based Vector DB!

(now I'm gonna plug what I work on)

If you're interested in a more scalable vector database written in Go, check out Milvus (https://github.com/milvus-io/milvus)

But before we do, I do want to say that 🤩 all these lovely Open-Source projects would love a little 🎉💕 love by getting a GitHub star ⭐ for their efforts. Including Open Source Milvus 🥰

1. Milvus by Zilliz | Github