Show HN: A reasoning model that infers over whole tasks in 1ms in latent space

Show HN: A reasoning model that infers over whole tasks in 1ms in latent space(github.com)

3 points by orderone_ai 308 days ago | 6 comments

I've spent the last few weeks working on a novel model architecture. It is not a transformer - it lacks attention, tokens, and softmax.

However: Batch Processing: Average batch size: 10 Time per batch: 13.03ms Time per example in batch: 1.30ms

TASK SUMMARY WITH TIMING

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Task Correct Total Accuracy Med Time (ms)

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Emotion Classification 10 10 100.0 % 1.30

Toxicity Classification 9 10 90.0 % 1.29

Sentiment Classification 10 10 100.0 % 1.34

Domain Classification 8 10 80.0 % 1.30

Sarcasm Detection 6 10 60.0 % 1.34

Scam Detection 7 10 70.0 % 1.31

Age Appropriateness Classification 4 10 40.0 % 1.28

Urgency Level Classification 4 10 40.0 % 1.25

Dialogue Speaker Classification 8 10 80.0 % 1.29

Book Review Sentiment 10 10 100.0 % 1.25

Empathetic Direction Classification 10 10 100.0 % 1.29

Virtual Assistant Action Classification 6 10 60.0 % 1.37

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OVERALL 101 130 77.7 %

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It can do interesting things.

This has a lot of caveats and limitations. However, the model is available for download via a script in the repo, and the exact benchmarks I used are available. The white paper gets into theory and application, as well as reveals a lot of limitations and interesting differences from transformers in terms of training and prompting behavior. It also produces extensive appendices (over 100 pages) on training datasets used, and performance on the ~260 (I think?) NIV2 tasks in its validation dataset.

Running inference for the DSRU model + BGE embedding model together takes a bit shy of 10GB of VRAM, and the reference comparison model -- Zephyr 7B -- takes about 15GB of VRAM.

throwawayffffas 308 days ago |

Can I ask? why do you have a single model for all these tasks?

Wouldn't it be easier and more ergonomic to users to have dedicated models for each of this tasks?

orderone_ai 308 days ago | |

Thank you for the question!

I would say that ease of use and deployment is actually a good reason to have a single model.

We don't train 20 LLMs for different purposes - we train one (or, I guess 3-4 in practice, each with their own broad specialization), and then prompt it for different tasks.

This simplifies deployment, integration, upgrading, etc.

This model is basically the same - instead of having a restriction to doing single-task classification. This means that a user can complete new tasks using a new prompt, not a new model.

throwawayffffas 308 days ago | | |

While I agree with the general reasoning, isn't it harder for the user to prompt the model correctly as opposed to selecting a specialized model that they wish to use?

That's the feeling I have when I try to use LLMs for more general language processing.

Have you run in cases where the model "forgets" the task at hand and switches to another mid text stream?

Regardless of all of the above. It looks to me that your choice of reasoning and problem solving in the latent space is a great one and where we should be collectively focusing our efforts, keep up the good work.

tripplyons 308 days ago |

How does this model compare to just using a linear classifier trained on BGE embeddings?

orderone_ai 307 days ago | |

Thank you for your question!

Because I'm not sure exactly what you're looking for when you say 'compares to' -- whether accuracy, speed, or architecture -- I'll hit all 3, but sorry if it's a bit much.

1. Accuracy: For simple tasks (like sentiment analysis on straightforward examples), it won't be much more accurate than a classical linear classifier, if at all.

1a. Accuracy on more diverse or challenging tasks: Because a linear classifier is just so damned simplistic, it simply cannot handle anything even resembling a reasoning task. Meanwhile, (when specifically trained), this architecture managed to get 8/10 on textual entailment tasks, which are generally considered the sort of entry level gold standard for reasoning ability.

2. Speed: It's slower than a classical classifier...in light of the ~1B params it's pushing. They're both still pretty much blazing fast, but the tiny classical classifier will definitely be faster.

3. Architecture: Here's where it gets interesting.

The architecture of the core model here differs significant from a classical linear classifier:

Classical Classifier: Input: BGE embedding (in this hypothetical) Output: Class labels through softmax Internal Architecture: No nonlinearity, no hidden layers, direct projection

General Classifier: Input: BGE Embedding Output: Class labels through nearest neighbor cosine similarity search of vocabulary Internal architecture: An input projection sparse layer, a layer for combining the 3 inputs after their upwards projection, and 14 hidden layers with nonlinearity (GELU), layernorms, skip connections -- all of the standard stuff you'd expect in an LLM, but...not in an LLM.

I hope that clears up your questions! If not, I'm happy to tell you more.