From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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136 lines
3.6 KiB
136 lines
3.6 KiB
WEBVTT |
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00:01.490 --> 00:08.780 |
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So before we try our new model and one more recap on the models so far and keep notes of this so we |
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00:08.780 --> 00:09.980 |
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can see how we do. |
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00:09.980 --> 00:14.930 |
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And your excitement can be there while we run our fine tuned model. |
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00:15.140 --> 00:18.230 |
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We started with a constant model. |
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00:18.230 --> 00:21.320 |
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We actually started with a random model, but I think we can put that one to bed. |
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00:21.350 --> 00:23.150 |
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That's that was that was silly. |
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00:23.300 --> 00:29.450 |
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So a constant model which just guesses the average from the training data set ends up with an error |
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00:29.450 --> 00:31.070 |
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of 146. |
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00:31.280 --> 00:35.930 |
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Uh, and we certainly hope that we can do better than 146. |
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00:35.960 --> 00:38.900 |
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Otherwise, we might as well stick with a constant. |
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00:38.930 --> 00:44.630 |
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When we used a very simplistic traditional machine learning with basic features, we got 139. |
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00:44.660 --> 00:45.170 |
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Remember that? |
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Well, I hope random forest, a more sophisticated algorithm that also that looked at the language, |
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the words um, got down to 97. |
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This human did a poor job at 127. |
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00:58.910 --> 01:00.500 |
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Uh, GPT four. |
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01:00.530 --> 01:03.940 |
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Oh, the big guy did very nicely indeed. |
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01:03.940 --> 01:18.430 |
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At 76 and the Bass Llama 3.1, untrained, quantized down to four bits, did an appalling $396 of error. |
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01:18.460 --> 01:23.710 |
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A much better off just sticking with the constant than using an untrained llama. |
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01:23.800 --> 01:26.560 |
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The poor thing did not do particularly well at all. |
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01:26.740 --> 01:31.000 |
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So I go through this one more time so that you have this nicely framed. |
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01:31.030 --> 01:37.060 |
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The question is, remember, GPT four is a model that has trillions of weights. |
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01:37.120 --> 01:40.390 |
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GPT four had 1.76 trillion GPT four. |
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01:40.630 --> 01:44.380 |
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It's not known, but it's considered to be much more than that. |
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01:44.380 --> 01:46.600 |
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So a huge number of weights. |
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01:46.630 --> 01:53.530 |
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Llama 3.1 base has 8 billion weights, and we have reduced them down to four bits. |
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01:53.530 --> 01:57.130 |
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And then we have used our color. |
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01:57.340 --> 01:57.580 |
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Sorry. |
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01:57.610 --> 02:04.900 |
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Our Lora adapters like 109MB worth of them to to put some extra weights that we can use to adapt. |
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02:04.930 --> 02:11.900 |
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Llama lemma 3.1 base, but these are still small numbers, and obviously this is an open source model, |
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which means it's free to run. |
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So I'm saying all this to set expectations that obviously it's a lot to ask to try and compete with |
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some of these models at the frontier. |
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The thing that you need to be looking out for is, can we do better than traditional machine learning? |
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Can we do better than a human can? |
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Certainly. |
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Can we do better than constant? |
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And how do we stack up when we compare ourselves to GPT four? |
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02:35.210 --> 02:42.590 |
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So the leading frontier model, and we can also compare it to GPT four or mini, um, as well, uh, |
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afterwards. |
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So that gives you the context. |
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I hope you have this in your mind. |
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Maybe write down the numbers so you're ready for for what's to come. |
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And it is time for us to head to Colab and to run inference on the the best, strongest checkpoint from |
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from the the training of our own verticalized specialized, uh, open source model.
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