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Model: robowaifudev/megatron-gpt2-345m Source: Original Platform
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README.md
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---
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language:
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- en
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tags:
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- gpt2
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license: apache-2.0
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widget:
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- text: It was a bright cold day in April, and the clocks were striking thirteen. Winston Smith,
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datasets:
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- wikitext
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- openwebtext
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- spacemanidol/cc-stories
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model-index:
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- name: megatron-gpt2-345m
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results:
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- task:
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type: text-generation
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name: Text generation
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dataset:
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name: WikiText-103
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type: wikitext
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metrics:
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- type: wikitext
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value: 19.31
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name: Perplexity
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- task:
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type: text-generation
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name: Text generation
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dataset:
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name: WikiText-2
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type: wikitext
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metrics:
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- type: wikitext
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value: 17.151
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name: Perplexity
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- task:
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type: text-generation
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name: Text generation
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dataset:
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name: LAMBADA
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type: lambada
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metrics:
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- type: lambada
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value: 5.509
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name: Perplexity
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- type: lambada
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value: 68.31%
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name: Accuracy
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---
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<!---
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# ##############################################################################################
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#
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# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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# ##############################################################################################
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-->
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This is an archive of [nvidia/megatron-gpt2-345m](https://huggingface.co/nvidia/megatron-gpt2-345m) that contains readily available model weights (375M). Its performance on Wikitext-103 is 19.31.<sup>1</sup> In comparison, the performance of GPT2-large (1.5B) is 17.48 and GPT2-medium (762M) is 22.05.<sup>2</sup>
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### References
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1. Shoeybi, Mohammad, et al. Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism. arXiv, 2019, [https://doi.org/10.48550/ARXIV.1909.08053](https://doi.org/10.48550/ARXIV.1909.08053).
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2. Alec Radford, et al. Language Models are Unsupervised Multitask Learners. OpenAI, 2019. [https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf](https://cdn.openai.com/better-language-models/language_models_are_unsupervised_multitask_learners.pdf).
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## Description
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[Megatron](https://arxiv.org/pdf/1909.08053.pdf) is a large, powerful transformer developed by the Applied Deep Learning Research team at NVIDIA. This particular Megatron model was trained from a generative, left-to-right transformer in the style of GPT-2. This model was trained on text sourced from Wikipedia, RealNews, OpenWebText, and CC-Stories. It contains 345 million parameters.
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Find more information at [https://github.com/NVIDIA/Megatron-LM](https://github.com/NVIDIA/Megatron-LM)
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# How to run Megatron GPT2 using Transformers
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## Text generation
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The following code shows how to use the Megatron GPT2 checkpoint and Transformers to generate text.
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```python
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import os
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import torch
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from transformers import GPT2Tokenizer, GPT2LMHeadModel
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tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
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model = GPT2LMHeadModel.from_pretrained("robowaifudev/megatron-gpt2-345m")
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if torch.cuda.is_available():
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device = torch.device("cuda")
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model.half()
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else:
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device = torch.device("cpu")
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model.to(device)
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model.eval()
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# Generate
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prompt = (
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"It was a bright cold day in April, and the clocks were striking thirteen. Winston Smith,"
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)
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input_ids = tokenizer.encode(prompt, return_tensors="pt").to(device)
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output = model.generate(
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input_ids=input_ids,
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max_length=len(input_ids) + 128,
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do_sample=True,
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top_k=64,
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top_p=0.9,
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temperature=0.8,
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num_return_sequences=2,
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repetition_penalty=1.025
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)
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# Output the text
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print("Prompt:", prompt)
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print("*" * 3)
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for i, sentence in enumerate(output):
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text = tokenizer.decode(sentence, clean_up_tokenization_spaces=True)
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print(f"{i}:", text)
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print("*" * 3)
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```
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# Original code
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The original Megatron code can be found here: [https://github.com/NVIDIA/Megatron-LM](https://github.com/NVIDIA/Megatron-LM).
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82
config.json
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{
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"vocab_size": 50257,
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"n_positions": 1024,
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"n_embd": 1024,
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"n_layer": 24,
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"n_head": 16,
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"n_inner": 4096,
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"activation_function": "gelu_new",
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"resid_pdrop": 0.1,
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"embd_pdrop": 0.1,
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"attn_pdrop": 0.1,
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"layer_norm_epsilon": 1e-05,
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"initializer_range": 0.02,
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"summary_type": "cls_index",
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"summary_use_proj": true,
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"summary_activation": null,
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"summary_first_dropout": 0.1,
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"summary_proj_to_labels": true,
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"scale_attn_weights": true,
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"use_cache": true,
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"scale_attn_by_inverse_layer_idx": false,
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"reorder_and_upcast_attn": false,
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"bos_token_id": 50256,
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"eos_token_id": 50256,
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"return_dict": true,
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"output_hidden_states": false,
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"output_attentions": false,
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"torchscript": false,
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"torch_dtype": null,
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"use_bfloat16": false,
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"pruned_heads": {},
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"tie_word_embeddings": true,
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"is_encoder_decoder": false,
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"is_decoder": false,
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"cross_attention_hidden_size": null,
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"add_cross_attention": false,
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"tie_encoder_decoder": false,
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"max_length": 20,
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"min_length": 0,
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"do_sample": false,
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"early_stopping": false,
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"num_beams": 1,
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"num_beam_groups": 1,
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"diversity_penalty": 0.0,
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"temperature": 1.0,
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"top_k": 50,
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"top_p": 1.0,
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"repetition_penalty": 1.0,
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"length_penalty": 1.0,
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"no_repeat_ngram_size": 0,
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"encoder_no_repeat_ngram_size": 0,
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"bad_words_ids": null,
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"num_return_sequences": 1,
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"chunk_size_feed_forward": 0,
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"output_scores": false,
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"return_dict_in_generate": false,
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"forced_bos_token_id": null,
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"forced_eos_token_id": null,
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"remove_invalid_values": false,
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"architectures": ["GPT2LMHeadModel"],
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"finetuning_task": null,
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"id2label": {
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"0": "LABEL_0",
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"1": "LABEL_1"
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},
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"label2id": {
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"LABEL_0": 0,
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"LABEL_1": 1
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},
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"tokenizer_class": null,
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"prefix": null,
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"pad_token_id": null,
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"sep_token_id": null,
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"decoder_start_token_id": null,
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"task_specific_params": null,
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"problem_type": null,
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"_name_or_path": "",
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"transformers_version": "4.15.0",
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"n_ctx": 1024,
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"gradient_checkpointing": false,
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"model_type": "gpt2"
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}
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