(Yes, that's the handle)
I do machine learning at Weaviate and write about it on the Internet.
medium.com/@iamleonie
You might know me for my monochrome technical visuals.
Static embeddings -> speed-improvements
Binary quantization -> storage-reduction
Late interaction -> added granularity
I'm curious about lesser-known approaches that worked surprisingly well.
Static embeddings -> speed-improvements
Binary quantization -> storage-reduction
Late interaction -> added granularity
I'm curious about lesser-known approaches that worked surprisingly well.
violets are blue,
A good baseline embedding model
is all-MiniLM-L6-v2.
violets are blue,
A good baseline embedding model
is all-MiniLM-L6-v2.
In his latest recipe, @danman966.bsky.social shows you how you can build a RAG pipeline with citations, using:
- a @weaviate.bsky.social vector database and
- @anthropic.com's Claude 3.5 Sonnet
📌 Code: github.com/weaviate/rec...
In his latest recipe, @danman966.bsky.social shows you how you can build a RAG pipeline with citations, using:
- a @weaviate.bsky.social vector database and
- @anthropic.com's Claude 3.5 Sonnet
📌 Code: github.com/weaviate/rec...
It's evolving fast.
I’m sure your to-do list is growing as fast as mine.
Here are 3 topics, I want to catch up on this quarter:
• AI agents
• Fine-tuning embedding models
• Multimodality
• (If time permits: reinforcement learning)
What about you?
It's evolving fast.
I’m sure your to-do list is growing as fast as mine.
Here are 3 topics, I want to catch up on this quarter:
• AI agents
• Fine-tuning embedding models
• Multimodality
• (If time permits: reinforcement learning)
What about you?
Here are some patterns I’m seeing so far:
1. Type of collaboration:
Network vs. hierarchical
2. Type of information flow:
Sequential vs. parallel vs. loop
3. Type of functionality:
Routing vs. aggregating
What else?
Here are some patterns I’m seeing so far:
1. Type of collaboration:
Network vs. hierarchical
2. Type of information flow:
Sequential vs. parallel vs. loop
3. Type of functionality:
Routing vs. aggregating
What else?
1. Data complexity
2. Task complexity
3. Dataset size
4. Computational constraints
5. Performance requirements
6. Scalability requirements
7. Latency requirements
What else?
1. Data complexity
2. Task complexity
3. Dataset size
4. Computational constraints
5. Performance requirements
6. Scalability requirements
7. Latency requirements
What else?
With the new explorer tool, looking at your data got a lot easier in Weaviate Cloud.
The explorer tool provides a graphical interface to easily:
• Browse collections
• Inspect objects, metadata, and vectors
Check it out now: https://buff.ly/3KWivSF
With the new explorer tool, looking at your data got a lot easier in Weaviate Cloud.
The explorer tool provides a graphical interface to easily:
• Browse collections
• Inspect objects, metadata, and vectors
Check it out now: https://buff.ly/3KWivSF
Here’s how you can fine-tune Gemma 2 in a Kaggle notebook on a single T4 GPU:
• @kaggle.com offers 30 hours/week of GPUs for free
• @unsloth.bsky.social uses 60% less memory to fit it on a T4 GPU
🔗Code: https://buff.ly/4apUUG2
Here’s how you can fine-tune Gemma 2 in a Kaggle notebook on a single T4 GPU:
• @kaggle.com offers 30 hours/week of GPUs for free
• @unsloth.bsky.social uses 60% less memory to fit it on a T4 GPU
🔗Code: https://buff.ly/4apUUG2
Vertical scaling: scaling up (to a more powerful machine)
Horizontal scaling: scaling out (to multiple smaller machines)
I still always have to take a second to think about it.
It’s like the left-right-weakness of system design.
Vertical scaling: scaling up (to a more powerful machine)
Horizontal scaling: scaling out (to multiple smaller machines)
I still always have to take a second to think about it.
It’s like the left-right-weakness of system design.
So, we did - and you can download it for free.
Together with my colleagues Mary & Prajjwal, we curated an e-book of the most effective advanced RAG techniques.
Which ones did we miss?
Get it now: weaviate.io/ebooks/advan...
So, we did - and you can download it for free.
Together with my colleagues Mary & Prajjwal, we curated an e-book of the most effective advanced RAG techniques.
Which ones did we miss?
Get it now: weaviate.io/ebooks/advan...
Here’s my entry for the latest @kaggle.com comp.
This tutorial shows you:
• Fine-tune Gemma 2
• LoRA fine-tuning with @unsloth.bsky.social on T4 GPU
• Experiment tracking with @weightsbiases.bsky.social
🔗Code: www.kaggle.com/code/iamleon...
Here’s my entry for the latest @kaggle.com comp.
This tutorial shows you:
• Fine-tune Gemma 2
• LoRA fine-tuning with @unsloth.bsky.social on T4 GPU
• Experiment tracking with @weightsbiases.bsky.social
🔗Code: www.kaggle.com/code/iamleon...
Although this book is from 2017, I heard so many good things about it this year.
Can't wait to dig into this over the holidays.
And with that being said, I hope you have some nice and relaxing holidays yourself!
See you in the new year!
Although this book is from 2017, I heard so many good things about it this year.
Can't wait to dig into this over the holidays.
And with that being said, I hope you have some nice and relaxing holidays yourself!
See you in the new year!
Here’s what I got right (and what I missed) in my 2024 predictions:
✅ Evaluation
❌ Multimodal foundation models
❌ Fine-tuning open-weight models and quantization
❌ AI agents
✅ RAG lives on
❌ Knowledge graphs
medium.com/towards-data...
Here’s what I got right (and what I missed) in my 2024 predictions:
✅ Evaluation
❌ Multimodal foundation models
❌ Fine-tuning open-weight models and quantization
❌ AI agents
✅ RAG lives on
❌ Knowledge graphs
medium.com/towards-data...
@weaviate.bsky.socialでは3つのトークナイザーを使用することができます。
一つずつのメリットとデメリットはこちら
weaviate.io/blog/hybrid-...
@weaviate.bsky.socialでは3つのトークナイザーを使用することができます。
一つずつのメリットとデメリットはこちら
weaviate.io/blog/hybrid-...
Here’s a cheat sheet of 7 of the most popular RAG architectures.
Which variants did we miss?
Here’s a cheat sheet of 7 of the most popular RAG architectures.
Which variants did we miss?
ハイブリッド検索は、デンスベクトルとスパースベクトルを統合して、それぞれの検索手法の利点を活かします。
この記事では、Weaviateの日本語テキスト向けのハイブリッド検索の説明をします。
- 日本語テキス用のトークナイザーを使用するキーワード検索
- ベクトル検索
- 融合アルゴリズム
詳しくはこちら
https://buff.ly/49yMR9K
ハイブリッド検索は、デンスベクトルとスパースベクトルを統合して、それぞれの検索手法の利点を活かします。
この記事では、Weaviateの日本語テキスト向けのハイブリッド検索の説明をします。
- 日本語テキス用のトークナイザーを使用するキーワード検索
- ベクトル検索
- 融合アルゴリズム
詳しくはこちら
https://buff.ly/49yMR9K
This is already the 2nd edition of “Developing apps with GPT-4” by Olivier and Marie-Alice I had the pleasure to review.
This edition covers the latest advancements in GPT-4, especially regarding its visual capabilities to build multimodal applications.
This is already the 2nd edition of “Developing apps with GPT-4” by Olivier and Marie-Alice I had the pleasure to review.
This edition covers the latest advancements in GPT-4, especially regarding its visual capabilities to build multimodal applications.
What cool use cases using Generative AI have you seen in the wild so far?
What cool use cases using Generative AI have you seen in the wild so far?
You might want to give Docling a try.
𝗪𝗵𝗮𝘁'𝘀 𝗗𝗼𝗰𝗹𝗶𝗻𝗴?
• Python package by IBM
• OS (MIT license)
• PDF, DOCX, PPTX → Markdown, JSON
𝗪𝗵𝘆 𝘂𝘀𝗲 𝗗𝗼𝗰𝗹𝗶𝗻𝗴?
• Doesn’t require fancy gear, lots of memory, or cloud services
• Works on regular computers or Google Colab Pro
You might want to give Docling a try.
𝗪𝗵𝗮𝘁'𝘀 𝗗𝗼𝗰𝗹𝗶𝗻𝗴?
• Python package by IBM
• OS (MIT license)
• PDF, DOCX, PPTX → Markdown, JSON
𝗪𝗵𝘆 𝘂𝘀𝗲 𝗗𝗼𝗰𝗹𝗶𝗻𝗴?
• Doesn’t require fancy gear, lots of memory, or cloud services
• Works on regular computers or Google Colab Pro
本イベントでは、Weaviateの特徴や活用事例を学び、Weaviate CEO @bobvanluijt.bsky.socialとグローバル パートナーシップ責任者 @jobig630.bsky.social やWeaviate Kagome コントリビューター Jun Ohtaniと交流できる場を提供します。
マルチモーダル検索や検索拡張世代(RAG)によるAIのユースケースのお話楽しみにしてます。
本イベントでは、Weaviateの特徴や活用事例を学び、Weaviate CEO @bobvanluijt.bsky.socialとグローバル パートナーシップ責任者 @jobig630.bsky.social やWeaviate Kagome コントリビューター Jun Ohtaniと交流できる場を提供します。
マルチモーダル検索や検索拡張世代(RAG)によるAIのユースケースのお話楽しみにしてます。
@victorialslocum.bsky.social shows you how - using just numpy.
This article covers:
• How does vector search work?
• How to do vector search from scratch in Python
• and more
Learn more: weaviate.io/blog/vector-...
@victorialslocum.bsky.social shows you how - using just numpy.
This article covers:
• How does vector search work?
• How to do vector search from scratch in Python
• and more
Learn more: weaviate.io/blog/vector-...
Here's how Weaviate's researchers 10x'ed query speeds with ACORN:
Here's how Weaviate's researchers 10x'ed query speeds with ACORN:
(Yes, that's the handle)
I do machine learning at Weaviate and write about it on the Internet.
medium.com/@iamleonie
You might know me for my monochrome technical visuals.
(Yes, that's the handle)
I do machine learning at Weaviate and write about it on the Internet.
medium.com/@iamleonie
You might know me for my monochrome technical visuals.