5 ms·

You can get this fairly trivially with word and sentence embeddings just by running the inverse (huggingface models have this as a builtin). For llama, the same

You can get this fairly trivially with word and sentence embeddings just by running the inverse (huggingface models have this as a builtin). For llama, the same is possible, but the matrix transpose is your responsibility :)

sp332 3y ago
Ok, I got annoyed at oobabooga (around when it first came out) and have been messing with llama.cpp since then. I can't tell if this feature request is the same thing we're talking about here? https://github.com/ggerganov/llama.cpp/issues/1552 https://github.com/ggerganov/llama.cpp/issues/1552 I guess if I want more features, I should move on to something that has more features lol.
- TeMPOraL 3y ago
  Looking at that issue, I wonder how is it that everyone seemed to not understand the poster's question? The feature itself is something I wanted to play with too, as it's kind of an obvious thing to want. I mean, these models execute a pipeline: [text] -> [tokens] -> {[embeddings] -> [inference] -> [embeddings]} -> [tokens] -> [text] Where the part in { ... } may or may not be implemented as a single step (i.e. all three parts interleaved). Now, apparently all the magic[0] of transformer models sits in the latent space and is invoked by the { ... } bit. We also know for sure that you can make the pipeline look like this: [text] -> [tokens] -> {[embeddings] -> [inference]} -> [embeddings] So with the two things in mind, it's kind of obvious you'd also want a pipe that looks like: [embeddings] -> [inference] -> [embeddings] (and optionally -> [tokens] -> [text]) for the sole purpose of messing around and exploring the latent space itself. I'm very much not up to date with the whole space, so I might be missing something, but I'd thought that poking around the latent space would be getting a lot more attention than it seems to be getting. (EDIT: replaced < ... > with { ... } for readability.) -- [0] Not the "how do transformers tick" details, but the "how the hell are they this good" / "GPT-4 is uncanny valley" / "could this thing be actually thinking?" kind of magic.
  - deleted 3y ago
    [deleted]
  - freeone3000 3y ago
    In every model I’ve worked with, all of those steps are EXPLICIT. You take input, then tokenize, then embed, feed to model, then convert from embeddings to tokens, then back to words. There’s actually a lot of work done in embedding space! Chained execution, neural graph reasoning (see https://arxiv.org/pdf/2303.14617.pdf https://arxiv.org/pdf/2303.14617.pdf), even basic IR tasks like clustering and retrieval. There’s been no real effort to especially expose it because that is what you get by default. Even OpenAI has an “embed” endpoint. So you’re not going to see a huge push for it the same way you won’t see a push for reasoning about websites “in the HTML”. :)
    - sp332 3y ago
      I only see a way to get embeddings out. How do I put embeddings into an OpenAI API?
      - dustymcp 3y ago
        im not sure i follow if you want embeddings for any word/sentence you send it to the embeddingendpoint and save the vectors coming back?
        TeMPOraL 3y ago
        The point is that we want to shove arbitrary embeddings vector into an LLM and inspect what comes out. This is different from feeding tokens as input. The kind of thing I'd like to do, for example, is to: - Get embeddings for e.g. "blue" and "red", or "the sky is blue" and "galaxy redshift"; - Average them, resulting a vector that's bound to not be expressible with tokens alone; - Input that to the same model I got the embeddings from, and see what comes out. If by "embeddingendpoint" you mean OpenAI, they provide that for a specialized model, and (AFAIK) you can only get embeddings out (for the purpose of comparing various vectors yourself). They have no API endpoint for an LLM that can take those embeddings as input.

sp332 3y ago

Ok, I got annoyed at oobabooga (around when it first came out) and have been messing with llama.cpp since then. I can't tell if this feature request is the same thing we're talking about here? https://github.com/ggerganov/llama.cpp/issues/1552 https://github.com/ggerganov/llama.cpp/issues/1552 I guess if I want more features, I should move on to something that has more features lol.

TeMPOraL 3y ago
Looking at that issue, I wonder how is it that everyone seemed to not understand the poster's question? The feature itself is something I wanted to play with too, as it's kind of an obvious thing to want. I mean, these models execute a pipeline: [text] -> [tokens] -> {[embeddings] -> [inference] -> [embeddings]} -> [tokens] -> [text] Where the part in { ... } may or may not be implemented as a single step (i.e. all three parts interleaved). Now, apparently all the magic[0] of transformer models sits in the latent space and is invoked by the { ... } bit. We also know for sure that you can make the pipeline look like this: [text] -> [tokens] -> {[embeddings] -> [inference]} -> [embeddings] So with the two things in mind, it's kind of obvious you'd also want a pipe that looks like: [embeddings] -> [inference] -> [embeddings] (and optionally -> [tokens] -> [text]) for the sole purpose of messing around and exploring the latent space itself. I'm very much not up to date with the whole space, so I might be missing something, but I'd thought that poking around the latent space would be getting a lot more attention than it seems to be getting. (EDIT: replaced < ... > with { ... } for readability.) -- [0] Not the "how do transformers tick" details, but the "how the hell are they this good" / "GPT-4 is uncanny valley" / "could this thing be actually thinking?" kind of magic.
- deleted 3y ago
  [deleted]
- freeone3000 3y ago
  In every model I’ve worked with, all of those steps are EXPLICIT. You take input, then tokenize, then embed, feed to model, then convert from embeddings to tokens, then back to words. There’s actually a lot of work done in embedding space! Chained execution, neural graph reasoning (see https://arxiv.org/pdf/2303.14617.pdf https://arxiv.org/pdf/2303.14617.pdf), even basic IR tasks like clustering and retrieval. There’s been no real effort to especially expose it because that is what you get by default. Even OpenAI has an “embed” endpoint. So you’re not going to see a huge push for it the same way you won’t see a push for reasoning about websites “in the HTML”. :)
  - sp332 3y ago
    I only see a way to get embeddings out. How do I put embeddings into an OpenAI API?
    - dustymcp 3y ago
      im not sure i follow if you want embeddings for any word/sentence you send it to the embeddingendpoint and save the vectors coming back?
      - TeMPOraL 3y ago
        The point is that we want to shove arbitrary embeddings vector into an LLM and inspect what comes out. This is different from feeding tokens as input. The kind of thing I'd like to do, for example, is to: - Get embeddings for e.g. "blue" and "red", or "the sky is blue" and "galaxy redshift"; - Average them, resulting a vector that's bound to not be expressible with tokens alone; - Input that to the same model I got the embeddings from, and see what comes out. If by "embeddingendpoint" you mean OpenAI, they provide that for a specialized model, and (AFAIK) you can only get embeddings out (for the purpose of comparing various vectors yourself). They have no API endpoint for an LLM that can take those embeddings as input.