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license: other |
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# QA Builder - 32k Training |
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I'm Happy to share the training progress of my new language model with a 32k setup. Training is being meticulously delivered in six distinct steps to ensure broad coverage and effectiveness. It is important to mention that all content used was converted to Markdown format, ensuring uniformity and optimization of processing. Here are the steps: |
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1- Wikipedia Titles+Introduction: The solid foundation of the model is built using Wikipedia titles, offering an overview of numerous topics. |
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2-Titles + Wikipedia Content: The next step deepens the model's understanding by incorporating not only titles, but also the full content of Wikipedia articles. Everthing was ordered, so the model never learns something without having knowledge of what it has dependent on. |
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3-Classic Books: To ensure a deep and historical literary understanding, the model is exposed to the texts of classic books, immersing him in the nuances of literary language. |
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4-Articles: This step infuses the model with up-to-date and detailed information on various topics, taking advantage of the rich content of articles from different fields of knowledge. |
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5-QA (Questions and Answers): To enhance the model's responsiveness and understanding capabilities, it is trained on a question and answer dataset. |
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6-Rhetoric: Finally, to fine-tune the model's ability to understand and generate persuasive speeches, he is exposed to materials rich in rhetorical elements. |
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7 look forward to sharing the results of this exciting project with you all! |
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# **SAMPLE** |
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This was a 330 million model that still has a slightly high loss: |
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# RESPONSE |
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In this article, we present the online Viterbi algorithm to decode |
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Markov models (HMMs) in much smaller than linear space. Our analysis on |
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Two-state HMMs suggest that the maximum expected memory used to decode |
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sequence of length $n$ with state $m$ HMM can be as low as $\Theta(m\log n)$, |
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without a significant slowdown compared to the classic Viterbi algorithm. |
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The classical Viterbi algorithm requires $O(mn)$ space, which is impractical for |
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analysis of long sequences of DNA (such as complete chromosomes of the human genome) and |
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for continuous data streams. We also experimentally demonstrate the performance |
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of Viterbi's online algorithm into a simple HMM for gene localization in both |
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simulated and real DNA sequences. |
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# TO URGE |
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\begin{align*} |
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Viterbi, $A\cdot\epsilon^2 = 0.2\pi|A^2$$ |
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\end{align*} |
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The above code takes two vectors $A$ and $B$, respectively, as a potential padding for $u(n)$ units during sequential fetch. The resulting value for the next unit $u(n)$ is a probability distribution function defined by: |
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\begin{align*} |
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\frac{P(X)}{B\times Viterbi}{F^3}=\sum_{k=N-1} P(X)=0\log_{k=L-1} P(X) \cdot V([np,np-np_np,np_np_np,np_np_np_np]) |
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\end{align*} |
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The code above provides an estimate of the overall loss function of a DNA sequence, which represents the number of classes that need to be extracted from the HMM. The code consists of five convolutional layers with convolutional layers with dense layers that have a sigmin gradient corresponding to the corresponding output. |
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What does this mean for the K-DNA replication process? |
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# ENDOFTEXT |
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## STATUS TRAINING - |
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in my last tests with length 2048, I got great models, I trained models in 24 hours with only a 4090 GPU, I'll try to do the same with this 32k, in the following hours and I'll post the result |
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1 - OK |
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2 - RUNNING - next upload 9/9 - 00:30 GMT |
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3 - |
