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import torch

# set seed
torch.manual_seed(42)

B, T, D = 1, 4, 10 # batch size, sequence length, vocab size
tensor = torch.rand(B, T, D, requires_grad=True)
labels = torch.Tensor([[1, 2, 3, 4]]).long()

print("="*100)

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"""
# Convert HF to TorchTitan DCP
python convert.py hf_to_dcp --input-path meta-llama/Meta-Llama-3.1-8B --output-path ./torchtitan_model

# Convert TorchTitan DCP to HF (works with any checkpoint structure)
python convert.py dcp_to_hf --input-path ./torchtitan_model --output-path ./hf_model

# Model structure
If you run the following code, you will get the model structure.

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absl-py==2.3.0
aiohappyeyeballs==2.6.1
aiohttp==3.12.13
aiosignal==1.3.2
annotated-types==0.7.0
anyio==4.9.0
attrs==25.3.0
azure-core==1.34.0
azure-identity==1.23.0
azure-storage-blob==12.25.1

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# SPDX-License-Identifier: Apache-2.0
"""Example for starting a Gradio OpenAI Chatbot Webserver
Start vLLM API server:
    vllm serve allenai/OLMo-2-0425-1B-Instruct

Start Gradio OpenAI Chatbot Webserver:
    python x1.py -m allenai/OLMo-2-0425-1B-Instruct --model-url http://ceres-cs-aus-441:8000/v1

Note that `pip install --upgrade gradio` is needed to run this example.
More details: https://github.com/gradio-app/gradio

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for seed in 1 2; do
for lr in 5e-7 7e-7 9e-7; do
python update_command_args.py scripts/train/olmo2/grpo_7b.sh \
    --priority urgent \
    --workspace ai2/olmo-instruct \
    --exp_name 0423_grpo_seed_${seed}_lr_${lr} \
    --model_name_or_path allenai/OLMo-2-0425-1B-DPO \
    --model_revision main \
    --tokenizer_name_or_path allenai/OLMo-2-1124-7B-DPO \

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TEMPLATE = """
---
license: apache-2.0
language:
- en
datasets:
- {{dataset}}
base_model:
- {{base_model}}
pipeline_tag: text-generation

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import numpy as np


def stable_softmax(x, axis=None):
    """taken from scipy.special.softmax"""
    x_max = np.amax(x, axis=axis, keepdims=True)
    exp_x_shifted = np.exp(x - x_max)
    return exp_x_shifted / np.sum(exp_x_shifted, axis=axis, keepdims=True)

def get_prob(arr: np.ndarray, temp: float) -> np.ndarray:

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# Copyright 2024 AllenAI. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

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from collections import deque
import queue
import time
import numpy as np
import ray
from vllm import SamplingParams, LLM
import wandb
from open_instruct.dataset_transformation import TokenizerConfig, get_cached_dataset_rlvr
from open_instruct.vllm_utils3 import create_vllm_engines
from transformers import HfArgumentParser

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from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import torch.nn.functional as F

model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-135M")
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM2-135M")
tokenizer.add_special_tokens({"pad_token": "<PAD>"})
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
device = torch.device("cpu")
model.to(device)