Javier Salcedo,
Telecom and Graphics Engineer,
Technical artist & producer
at Virtual Phenix Games
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| # train_grpo.py | |
| # | |
| # See https://github.com/willccbb/verifiers for ongoing developments | |
| # | |
| import re | |
| import torch | |
| from datasets import load_dataset, Dataset | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from peft import LoraConfig | |
| from trl import GRPOConfig, GRPOTrainer |
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| # train_grpo.py | |
| # | |
| # See https://github.com/willccbb/verifiers for ongoing developments | |
| # | |
| import re | |
| import torch | |
| from datasets import load_dataset, Dataset | |
| from transformers import AutoTokenizer, AutoModelForCausalLM | |
| from peft import LoraConfig | |
| from trl import GRPOConfig, GRPOTrainer |
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| import neuralnet_pytorch as nnt | |
| import torch as T | |
| from torch_scatter import scatter_add | |
| def pointcloud2voxel_fast(pc: T.Tensor, voxel_size: int, grid_size=1., filter_outlier=True): | |
| b, n, _ = pc.shape | |
| half_size = grid_size / 2. | |
| valid = (pc >= -half_size) & (pc <= half_size) | |
| valid = T.all(valid, 2) |
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| import torch | |
| import torch.nn as nn | |
| import math | |
| import numbers | |
| import torch | |
| import numpy | |
| from torch import nn | |
| from torch.nn import functional as F | |