Journey With Me Into The Mind of Large Language Models: Interesting Findings in AnthropicAI's Scaling Monosemanticity paper.
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Jaward Sesay
Jaward
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I like to train large deep neural nets too 🧠🤖💥 | First Paper (AutoAgents: A Framework for Automatic Agent Generation) Accepted @ IJCAI 2024 | Role Model Karpathy
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It's work like this that in some way signal the eventual “dominance” of AI over all the sciences.
“We train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the model’s displacement outputs”
The emulator is capable of predicting
the nonlinear displacement and velocity fields for 128^3 particles in half a second on a single GPU🤯
“We train our model on the six-dimensional N-body phase space, predicting particle velocities as the time derivative of the model’s displacement outputs”
The emulator is capable of predicting
the nonlinear displacement and velocity fields for 128^3 particles in half a second on a single GPU🤯
Post
1716
Triton nanoGPT now has a custom cross entropy loss kernel 🚀
Next: matmul, gradually overthrowing all major PyTorch ops:)
Simplified pseudo for parallel cross-entropy loss compute:
- init program: get pid, compute offsets, load targets.
- init row_max and row_sum.
- for-loop1 (find max logits): update row_max with max logits.
- for-loop2 (compute softmax and loss): compute row_sum, update loss.
- add log(row_sum) and store loss.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/triton_nanoGPT.ipynb
Next: matmul, gradually overthrowing all major PyTorch ops:)
Simplified pseudo for parallel cross-entropy loss compute:
- init program: get pid, compute offsets, load targets.
- init row_max and row_sum.
- for-loop1 (find max logits): update row_max with max logits.
- for-loop2 (compute softmax and loss): compute row_sum, update loss.
- add log(row_sum) and store loss.
Code: https://github.com/Jaykef/ai-algorithms/blob/main/triton_nanoGPT.ipynb
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