LLM-Intro to Large Language Models

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LLM

some LLM’s model and weight are not opened to user

what is?

Llama 270b model

• 2 files

  • parameters file
    • parameter or weight of neural network
    • parameter – 2bytes, float number
  • code run parameters(inference)
    • c or python, etc
    • for c, 500 lines code without dependency to run
    • self contained package(no network need)

• how to get parameters?

  • lossy compress large chunk of text (10TB) with 6000 GPU for 12 days (cost 200$) to 140G zip file(gestalt of the text, weights and parameters)

• what neural do is trying to predict the next word in a sequence. parameters are dispersed throughout the neural network and neurons are connected to each other, fire in a certain way
  

• prediction has strong relationship with compression

• LLM create a correct form of text and fill it with its knowedge. not create a copy of text that was be trained.

• how does it work？

training stage

• pre-training

  • expensive
  • base model. get a document generator model
  • it’s about knowledge
  • internet documents

• fine tuning

  • cheaper
  • assistant model. get a assistant model
  • it’s about alighment
  • Q&A document
  • training with high quality conversation(question and answer).write labeling instructions to specify how assistant should behave
  • focus on quality not amount
    

• stage 3(optional)

  • use comparison label
  • reenforcement learning from human feedback

• labeling is a human-machine collaboration

• rank of LLM

LLM scaling laws：

• more D and N will get better model

• multimodality. now some LLM like GPT can use different tools to help it with answering questions. browser, calculator, python interpreter.

• future directions of development in LLM

give LLM system 2 ablility

• LLM now only have system one(instinctive)
• convert time to accuracy

self-improvement

• in narrow domain it is possible to self-improve

customization

experts in certain domain

future of LLM

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