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Jina AI launches open-source 8k text embedding

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Re: Jina AI launches open-source 8k text embedding

#181

Post by simonw »

EGreg wrote: post #151
szundi wrote: post #136

Earlier quoted context omitted.

Interesting as the literal source of the result is not open

People need to realize something… The model weights in eg TensorFlow are the source code . It is not a von-Neumann architecture but a gigabyte of model weights is the executable part, no less than a gigabyte of imperative code. Now, the training of the model is akin to the process of writing the code. In classical imperative languages that code may be such spaghetti code that each part would be intertwined with 40 ot…

That doesn't work for me.

The model weights aren't source code. They are the binary result of compiling that source code.

The source code is the combination of the training data and configuration of model architecture that runs against it.

The model architecture could be considered the compiler.

If you give me gcc and your C code I can compile the binary myself.

If you give me your training data and code that implements your model architecture, I can run those to compile the model weights myself.

Re: Jina AI launches open-source 8k text embedding

#182

Post by simonw »

michalmatczuk wrote: post #163
simonw wrote: post #21

I just shipped a new llm-embed-jina plugin for my LLM tool which provides access to these new Jina models: https://github.com/simonw/llm-embed-jina Here's how to try it out. First, install LLM. Use pip or pipx or brew: brew install llm Next install the new plugin: llm install llm-embed-jina You can confirm the new models are now available to LLM by running: llm embed-models You should see a list that includes "jina-e…

FYI it seems that llm install llm-embed-jina is missing yaml dependency File "/opt/homebrew/Cellar/llm/0.11_1/libexec/lib/python3.12/site-packages/llm/default_plugins/openai_models.py", line 17, in import yaml ModuleNotFoundError: No module named 'yaml'

Thanks! I wonder if the Python 3.12 upgrade broke something.

The pyyaml package is correctly listed on the formula page though: https://formulae.brew.sh/formula/llm

Re: Jina AI launches open-source 8k text embedding

#183

Post by LoganDark »

space_fountain wrote: post #30
LoganDark wrote: post #28

Earlier quoted context omitted.

> Is an 8K sequence length directly comparable to text-embedding-ada-002 if the vocabulary is much smaller? I seem to remember its tokeniser has a larger vocabulary. Words that aren't in the vocabulary can still be represented by multiple tokens. Some models can input and output valid UTF-8 at the byte level (rather than needing a unique token for each codepoint). For example RWKV-World.

A large vocabulary means less tokens are needed to represent the same information

Thanks.

Re: Jina AI launches open-source 8k text embedding

#184

Post by r3trohack3r »

krageon wrote: post #153
jyrkesh wrote: post #125

Earlier quoted context omitted.

I'm actually mostly in your camp here. But it's complicated with AI. What if someone gave you a binary and the source code, but not a compiler? Maybe not even a language spec? Or what if they gave you a binary and the source code and a fully documented language spec, and both of 'em all the way down to the compiler? BUT it only runs on special proprietary silicon? Or maybe even the silicon is fully documented, but pr…

There is the binary (the model) and the source (the thing that allows you to recreate the model, the dataset and methodology). Compilers and how art is made quite simply doesn't factor in here, because nobody is talking about the compiler layer. Art isn't even close to what is present. Trying to make this more complicated than it is is playing into companies' hands by troubling the waters around what constitutes open…

To be fair, OpenSource troubled the waters around what constitutes free software.

Free(dom Respecting) Software wasn’t just about the source code.

https://www.gnu.org/philosophy/open-source-misses-the-point....

Re: Jina AI launches open-source 8k text embedding

#185

Post by [deleted] »

pietz wrote: post #155

I'm always happy to see OSS contributions but I don't quite understand why this model is so remarkable. As the leaderboard suggests it's ranking lower than OpenAI embeddings, while 14 other contributions are even better than that. Many of which feature a comparable or lower dimensionality than 768. The 8k context window is new, but isn't the 512 token limitation a soft limit anyway? I'm pretty sure I can stuff bigger…

[deleted]

Re: Jina AI launches open-source 8k text embedding

#186

Post by theptip »

pietz wrote: post #155

I'm always happy to see OSS contributions but I don't quite understand why this model is so remarkable. As the leaderboard suggests it's ranking lower than OpenAI embeddings, while 14 other contributions are even better than that. Many of which feature a comparable or lower dimensionality than 768. The 8k context window is new, but isn't the 512 token limitation a soft limit anyway? I'm pretty sure I can stuff bigger…

> The 8k context window is new

Hasn’t Claude had this for many months (before they bumped to 100k)?

Edit: ah, you mean new for OSS maybe?

Re: Jina AI launches open-source 8k text embedding

#187

Post by stormfather »

infecto wrote: post #168
infecto wrote: post #162

Earlier quoted context omitted.

I have been trying to understand the hype as well. Happy to see all the work happening in this space still. I was pretty curious about the context limit. I am not an expert in this area but I always thought the biggest problem was the length of your original text. So typically you might only encode a sentence or a selection of sentences. You could always stuff more in but they you are potentially losing the specifici…

Thinking about it some more as I read through more comments. I guess in the stated case of research papers it can make sense if your task is looking for the common themes and not specific details. If you are embedding a sentence or a paragraph you miss out on the connection between those sentences across the whole paper...or at least its harder to manage that. By encoding a large number of pages from the paper (or th…

I would guess that the embedded summary is better, but for many tasks where you use embeddings (like document search), summarizing every document with an LLM is too expensive and slow.

Re: Jina AI launches open-source 8k text embedding

#188

Post by clarkmcc »

pknerd wrote: post #69
clarkmcc wrote: post #59

Earlier quoted context omitted.

Check out my little side project for chatting with PDFs. You should be able to load most models including this one. https://github.com/clarkmcc/chitchat

This looks cool so can it be used to feed Website/Products data in CSV/JSON format and "chat" with it?

Pretty much! Right now it only supports md, pdf, txt, and html, but supporting additional formats is trivial: https://github.com/clarkmcc/chitchat/blob/main/src-tauri/src....

Re: Jina AI launches open-source 8k text embedding

#189

Post by simonw »

theptip wrote: post #186
pietz wrote: post #155

I'm always happy to see OSS contributions but I don't quite understand why this model is so remarkable. As the leaderboard suggests it's ranking lower than OpenAI embeddings, while 14 other contributions are even better than that. Many of which feature a comparable or lower dimensionality than 768. The 8k context window is new, but isn't the 512 token limitation a soft limit anyway? I'm pretty sure I can stuff bigger…

> The 8k context window is new Hasn’t Claude had this for many months (before they bumped to 100k)? Edit: ah, you mean new for OSS maybe?

Claude is a large language model, which is a different thing from an embedding model.

Re: Jina AI launches open-source 8k text embedding

#190

Post by theptip »

simonw wrote: post #189
theptip wrote: post #186

Earlier quoted context omitted.

> The 8k context window is new Hasn’t Claude had this for many months (before they bumped to 100k)? Edit: ah, you mean new for OSS maybe?

Claude is a large language model, which is a different thing from an embedding model.

Aha, that’s what I missed, thanks!
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