Update README.md

README.md

@@ -30,18 +30,17 @@ This is an "Abstract to Tweet" model that crafts a tweet summarizing a research
 ```python3
 from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
 
-tokenizer = AutoTokenizer.from_pretrained('datadreamer-dev/abstracts_to_tweet_model', revision=None) # Load tokenizer
-model = AutoModelForSeq2SeqLM.from_pretrained('datadreamer-dev/abstracts_to_tweet_model', revision=None) # Load model
-pipe = pipeline('text2text-generation', model=model, tokenizer=tokenizer, pad_token_id=tokenizer.pad_token_id)
+# Load model
+pipe = pipeline('text2text-generation', 'datadreamer-dev/abstracts_to_tweet_model')
 
-# For example, run the model on the abstract of the LoRA paper (https://arxiv.org/abs/2106.09685)
+# Generate a tweet from the abstract of the LoRA paper
 abstract = "An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency. We also provide an empirical investigation into rank-deficiency in language model adaptation, which sheds light on the efficacy of LoRA. We release a package that facilitates the integration of LoRA with PyTorch models and provide our implementations and model checkpoints for RoBERTa, DeBERTa, and GPT-2 at this https URL."
 generated_tweet = pipe(abstract, max_length=512)['generated_text'] 
 
 # Print the generated tweet
 print(generated_tweet) 
 
-# This will print:
+# Output:
 # "Exciting news in #NLP! We've developed Low-Rank Adaptation, or LoRA, to reduce the number of
 #  trainable parameters for downstream tasks. It reduces model weights by 10,000 times and GPU
 #  memory by 3 times. #AI #MachineLearning"