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2014-05-19
I'm sure the service is extremely useful to the computer literates, but I'm not one of them. Although it's not rocket science, I'm old fashioned and was hoping to talk to someone and walk me through it over the phone. Not a bad experience though...
2016-01-07
Great online pdf editor. Should have more features in personal level, a bit annoying to not be able to perform basic functions such as erasing sections of a document or deleting pages in a commercial, paid product. That is my only complaint though. Great software otherwise.
2016-04-11
It´s very complete, For me is the best online PDF manager, but i don´t have money to pay the service, I live in Argentina and I think that this service could works with adds to solvent the development
2019-02-19
So far my overall experience has been pretty great. The only thing I would recommend would be having more of a collection of pdf's to compare on certain subjects. Other than that, I am one happy customer!
2019-04-12
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2019-11-08
it did what l wanted it to do. i was not aware this kind of software existed until pdf filler. if l do this kind of job regularly l will use Pdf filler
2022-05-04
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2021-07-30
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2020-07-25
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What is the difference between base LLMs and instruction tuned LLMs?
Base LLMs establish a solid language understanding foundation, while instruction tuned LLMs offer specialization and increased accuracy in specific tasks.
What is the difference between instruction tuning and supervised fine-tuning?
Whereas supervised fine-tuning consists in training models on example inputs and the results derived from them (output), instruction tuning fleshes out input-output examples with another component: instructions. This is precisely what enables instruction-tuned models to generalize more easily to new tasks.
What is the difference between supervised fine-tuning and instruction tuning?
Whereas supervised fine-tuning trains models on input examples and their corresponding outputs, instruction tuning augments input-output examples with instructions, which enables instruction-tuned models to generalize more easily to new tasks. Pretrain-finetuning vs prompting vs instruction tuning (Wei et al., 2022).
What is instruction tuning and how is it different from traditional fine-tuning?
Instruction-tuning offers an efficient and adaptable approach to tailoring AI models to specific business requirements. Unlike traditional fine-tuning, which relies heavily on data and may not be easily modifiable, instruction tuning leverages natural language conversations to instruct and fine-tune AI models.
What is the main advantage of instruction fine-tuning?
Instruction finetuning allows you to provide instructions like “Write a response to the customer who is facing the following issue…” or “Summarize the following chat transcript between a support agent and a customer…” The LLM learns to interpret these instructions, allowing it to perform specific tasks or fulfill
What is a word that means to fine tune?
verb. make fine adjustments or divide into marked intervals for optimal measuring. synonyms: calibrate, graduate. type of: adjust, correct, set.
What is a fine tuned model?
What is fine-tuning? Fine-tuning in machine learning is the process of adapting a pre-trained model for specific tasks or use cases. It has become a fundamental deep learning technique, particularly in the training process of foundation models used for generative AI.
How effective is fine-tuning?
If you're working with non-English data, especially in a specialized domain, fine-tuning can dramatically improve performance over an off-the-shelf model. Output consistency: Fine-tuning enables you to train the model to adhere to specific output formatting.
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