attentionmech

import torch
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colormaps
from matplotlib.animation import FuncAnimation
from mpl_toolkits.mplot3d import Axes3D
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from sklearn.decomposition import PCA

# === CONFIG ===

gridley

willccbb

# train_grpo.py
#
# See https://github.com/willccbb/verifiers for ongoing developments
#
"""
citation:

@misc{brown2025grpodemo,
  title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models},
  author={Brown, William},

afrendeiro

Introduction to linux command line/ipython/git/R for bioinformatics:

• https://github.com/biom262/biom262-2016
• https://github.com/ricket-sjtu/bi028
• https://github.com/YeoLab/single-cell-bioinformatics/
• https://github.com/YeoLab/single-cell-bioinformatics/tree/master/notebooks
• https://github.com/ltminty/Bioinformatics-and-R/blob/master/Rbioinformatics.ipynb

RNA-seq:

• https://github.com/Caroldm/RNA-Seq-Tutorial---Jupyter-notebook/blob/master/RNAseq%20tutorial.ipynb

BrianWeinstein

ic[x_, y_] := 1 E^(-350 ((x - 1/5)^2 + ( y - 1/3)^2))

solnDir =
 NDSolve[
  {D[u[x, y, t], {t, 2}] == D[u[x, y, t], {x, 2}] + D[u[x, y, t], {y, 2}],
   u[x, y, 0] == ic[x, y],
   (D[u[x, y, t], t] /. t -> 0) == 0,
   u[0, y, t] == ic[0, y],
   u[1, y, t] == ic[1, y],
   u[x, 0, t] == ic[x, 0],