William Held Helw150

## parallel_t5.py
from transformers import AutoTokenizer, T5ForConditionalGeneration

# Model Init
n_gpu = 8
tokenizer = AutoTokenizer.from_pretrained("google/flan-ul2")
model = T5ForConditionalGeneration.from_pretrained("google/flan-ul2")
heads_per_gpu = len(model.encoder.block) // n_gpu
device_map = {
    gpu: list(
        range(

## watch.sh
#!/bin/bash

# Invoke like this:
# ./watch.sh my command here
# And it will run 'my command here' once, and then when it detects changes.

# TODO: Don't just search in the last second. Search for updates since the last
# completed build. Otherwise for big directories, midway through your search
# you've already taken 1s and you will miss updates.

## CATCH_Keras_RL.md

      
              3 files
            
          
              63 forks
            
          
              27 comments
            
          
              153 stars
            
          
                EderSantana
                / CATCH_Keras_RL.md
            
            
              Last active
              October 16, 2023 08:32
            
              
                Keras plays catch - a single file Reinforcement Learning example
              
          
    Code for Keras plays catch blog post
Train

python qlearn.py
Test


Generate figures
	from transformers import AutoTokenizer, T5ForConditionalGeneration

	# Model Init
	n_gpu = 8
	tokenizer = AutoTokenizer.from_pretrained("google/flan-ul2")
	model = T5ForConditionalGeneration.from_pretrained("google/flan-ul2")
	heads_per_gpu = len(model.encoder.block) // n_gpu
	device_map = {
	gpu: list(
	range(
	#!/bin/bash

	# Invoke like this:
	# ./watch.sh my command here
	# And it will run 'my command here' once, and then when it detects changes.

	# TODO: Don't just search in the last second. Search for updates since the last
	# completed build. Otherwise for big directories, midway through your search
	# you've already taken 1s and you will miss updates.