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Navigating the
Large Language Model
Choices
Buy or Build
@ravidaparthi
What Next ?
Choosing whether to purchase or construct Large
Language Models can be quite daunting. Let's
scrutinize the following to pinpoint the appropriate
language model suited to your objectives and
requirements.
Pre-Trained LLMs
Pre-Trained LLMs have been trained a bit more. This
extra training helps developers use what the model
already knows about language and make it better
for their specific needs. It's a quicker and more cost-
effective way than starting to train a language
model from zero.
BENEFITS OF
PRE-TRAINED LLMS
Summarizing Text
Generating Text
Creating Content
Producing Code
Analyzing Feelings
Chatbot Development
Provide ease of use, conserve time, and reduce
expenses, as constructing your own language
model can be pricey and lengthy. The integration
process is simplified with APIs offered by services such
as ChatGPT, and prompt engineering empowers
users to improve output quality without altering the
base model.
BENEFITS OF
PRE-TRAINED LLMS
BUILDING LLMS
(OPEN SOURCE)
Open-source LLMs give users the ability to train and
refine the model for unique requirements. The
source code of these LLMs is accessible to the public,
offering enhanced adaptability and personalization
choices.
OPEN SOURCE -
EXAMPLES
BERT
LLaMA
Falcon
MPT
FastChat-T5
OpenLLaMA
RedPajama-INCITE
GPT-J
GPT-Neo
Open-source LLMs necessitate advanced technical
understanding and computational capabilities for
training, yet they provide superior command over
data, model structure, and improved
confidentiality..
COST :
Pre-Trained models are more economical as they
negate the necessity for initial training, whereas
open-source LLMs demand more resources.
TIME :
Pre-trained models are immediately accessible for
utilization, conserving time relative to training open-
source LLMs.
PRE-TRAINED VS
OPEN SOURCE
PRIVACY :
LLMs can be made available on-site, offering
enhanced management over confidential data
and superior privacy protections.
PROFIC
IENCY :
Employing open-source LLMs necessitates expert
understanding in Natural Language Processing (NLP)
and machine learning, while pre-trained models are
more user-centric.
PRE-TRAINED VS
OPEN SOURCE
PERSONAL
IZATION :
Open-source LLMs provide enhanced flexibility for
personalization, enabling users to adapt the model
to their particular requirements.
QUA
LITY :
Both categories of LLMs exhibit strong performance,
yet open-source models outshine when conditioned
for particular assignments.
PRE-TRAINED VS
OPEN SOURCE
Wish Pre-Trained
Build/
Open Source
Quick to Market
Accuracy for your needs
Cost Effective
Data Security and Privacy
Performance and Scalability
Customization
Fewer Technical Skills
Intellectual Property (IP)
Integration with Existing
Systems
SUMMARY
@ravidaparthi
ravidaparthi

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Navigating the Large Language Model choices_Ravi Daparthi

  • 1. Navigating the Large Language Model Choices Buy or Build @ravidaparthi
  • 2. What Next ? Choosing whether to purchase or construct Large Language Models can be quite daunting. Let's scrutinize the following to pinpoint the appropriate language model suited to your objectives and requirements.
  • 3. Pre-Trained LLMs Pre-Trained LLMs have been trained a bit more. This extra training helps developers use what the model already knows about language and make it better for their specific needs. It's a quicker and more cost- effective way than starting to train a language model from zero.
  • 4. BENEFITS OF PRE-TRAINED LLMS Summarizing Text Generating Text Creating Content Producing Code Analyzing Feelings Chatbot Development
  • 5. Provide ease of use, conserve time, and reduce expenses, as constructing your own language model can be pricey and lengthy. The integration process is simplified with APIs offered by services such as ChatGPT, and prompt engineering empowers users to improve output quality without altering the base model. BENEFITS OF PRE-TRAINED LLMS
  • 6. BUILDING LLMS (OPEN SOURCE) Open-source LLMs give users the ability to train and refine the model for unique requirements. The source code of these LLMs is accessible to the public, offering enhanced adaptability and personalization choices.
  • 7. OPEN SOURCE - EXAMPLES BERT LLaMA Falcon MPT FastChat-T5 OpenLLaMA RedPajama-INCITE GPT-J GPT-Neo Open-source LLMs necessitate advanced technical understanding and computational capabilities for training, yet they provide superior command over data, model structure, and improved confidentiality..
  • 8. COST : Pre-Trained models are more economical as they negate the necessity for initial training, whereas open-source LLMs demand more resources. TIME : Pre-trained models are immediately accessible for utilization, conserving time relative to training open- source LLMs. PRE-TRAINED VS OPEN SOURCE
  • 9. PRIVACY : LLMs can be made available on-site, offering enhanced management over confidential data and superior privacy protections. PROFIC IENCY : Employing open-source LLMs necessitates expert understanding in Natural Language Processing (NLP) and machine learning, while pre-trained models are more user-centric. PRE-TRAINED VS OPEN SOURCE
  • 10. PERSONAL IZATION : Open-source LLMs provide enhanced flexibility for personalization, enabling users to adapt the model to their particular requirements. QUA LITY : Both categories of LLMs exhibit strong performance, yet open-source models outshine when conditioned for particular assignments. PRE-TRAINED VS OPEN SOURCE
  • 11. Wish Pre-Trained Build/ Open Source Quick to Market Accuracy for your needs Cost Effective Data Security and Privacy Performance and Scalability Customization Fewer Technical Skills Intellectual Property (IP) Integration with Existing Systems SUMMARY