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PRETRAINING ^3xSwqNXy

https://huggingface.co/spaces/HuggingFaceFW/blogpost-fineweb-v1 ^P4A7RxLe

https://tiktokenizer.vercel.app/ ^zE8mcPax

sequence of e.g. 4 tokens ^uLpPL7wT

100,277 probabilities for next token ^XaQC2oXB

correct answer: ^h8dnsuN4

"|" ^OAJbV1nG

"View" ^3kaGHq2C

"ing" ^cmXZ6Gg3

" Single" ^BDVkYxNq

input sequence tokens x anywhere from 1 to e.g. 8,000 tokens ^RXwzccWJ

parameters (/ "weights") w usually billions of these ^vOLNSBxa

... ^BsP9qNc9

100,277 numbers ^JwVzlFpC

giant mathematical expression ^gPh2f8Pa

https://bbycroft.net/llm ^98gqBvR1

GPT-2 was published by OpenAI in 2019 Paper: "Language Models are Unsupervised Multitask Learners"

Transformer neural network with:

  • 1.6 billion parameters
  • maximum context length of 1024 tokens
  • trained on about 100 billion tokens

My reproduction with llm.c: https://github.com/karpathy/llm.c/discussions/677

^JLWS8BIo

Run the Llama 3.1 305B base model: https://app.hyperbolic.xyz/models/llama31-405b-base-bf-16 ^FUKwcnVf

  • It is a token-level internet document simulator
  • It is stochastic / probabilistic - you're going to get something else each time you run
  • It "dreams" internet documents
  • It can also recite some training documents verbatim from memory ("regurgitation")
  • The parameters of the model are kind of like a lossy zip file of the internet => a lot of useful world knowledge is stored in the parameters of the network
  • You can already use it for applications (e.g. translation) by being clever with your prompts
    • e.g. English:Korean translator app by constructing a "few-shot" prompt and leveraging "in-context learning" ability
    • e.g. an Assistant that answers questions using a prompt that looks like a conversation
  • But we can do better... ^RLoBQfUt

Human: "Who is Tom Cruise?" Assistant: "Tom Cruise is a famous American actor and producer..." ^MEk18QPL

Human: "Who is John Barrasso?" Assistant: "John Barrasso is American physician and politician..." ^W6jyKkk4

Human: "Who is Genghis Khan?" Assistant: "Genghis Khan was the founder of the Mongol Empire." ^9nPLJFbR

Human: "Who is Orson Kovacs?" Assistant: ??? ^3SvLvJ7R

... ^8N1uUN9S

train time ^taMDO6zx

test time ^GnmHKFMs

Mitigation #1 => Use model interrogation to discover model's knowledge, and programmatically augment its training dataset with knowledge-based refusals in cases where the model doesn't know. E.g.: ^czlaym1Z

Human: "Who is Orson Kovacs?" Assistant: "I'm sorry, I don't believe I know" ^atdHS2aW

new training example ^wrntgQRw

Mitigation #2 => Allow the model to search! ^LAZPdMhK

Human: "Who is Orson Kovacs?" Assistant: " <SEARCH_START>Who is Orson Kovacs?<SEARCH_END> [...] Orson Kovacs appears to be ..." ^Q0DMpbMo

Knowledge in the parameters == Vague recollection (e.g. of something you read 1 month ago) Knowledge in the tokens of the context window == Working memory ^mfU1gj5n

tokens sequence ^cFZQpoE2

next token probabilities ^kRtlxSng

recall: ^RqWuL4gm

What is bigger 9.11 or 9.9? ^vx7pJVD9

Web search Code (/ Python interpreter) ^4rgdgxVx

Problem statement ^WIbaLQ7N

Solution ^ly9VHuh9

Answer ^iVaYUgNt

prompt ^lsmAT8s9

solutions ^m0APUDId

Answer: 3 ^4yqiLb7o

Reinforcement Learning discovers "thinking" and "cognitive strategies". It is emergent during the optimization, just in the process of solving math problems. ^us7EWDBi

Swiss cheese model of LLM capabilities of current models:

  • some things work really well,
  • some things (almost at random) show brittleness. ^EaA0kPCS

Reinforcement Learning in un-verifiable domains => RLHF (Reinforcement Learning from Human Feedback) ^tcgC9dvF

prompt: "write a joke about pelicans" ^ZgpuzeQ5

problem: how we do score these at scale (automatically)? ^MQ9h4lSI

prompt: "write a joke about pelicans" ^Lu9F4kv9

2 1 3 5 4 ^1trvc1XG

0.1 0.8 0.3 0.4 0.5 ^kIatB6OY

human ordering: ^FEfymbnH

reward model scores: ^T5q2a835

WHERE TO KEEP TRACK OF THEM

^WgM3XjuN

PREVIEW OF THINGS TO COME

  • multimodal (not just text but audio, images, video, natural conversations)
  • tasks -> agents (long, coherent, error-correcting contexts)
  • pervasive, invisible
  • computer-using
  • test-time training?, etc.

^0e4mdAl0

WHERE TO FIND THEM

  • Proprietary models: on the respective websites of the LLM providers
  • Open weights models (DeepSeek, Llama): an inference provider, e.g. TogetherAI
  • Run them locally! LMStudio

^v3VdqT75

Step 1: download and preprocess the internet ^te3v2oTI

Step 2: tokenization ^ztI0rpAN

Converts text <---> sequences of symbols (/tokens)

  • Start with stream of bytes (256 tokens)
  • Run the Byte Pair Encoding algorithm (iteratively merge the most common token pair to mint new token)

Example: ~5000 text characters ~= 40,000 bits (with vocabulary size of 2 tokens: bits 0/1) ~= 5000 bytes (with vocabulary size of 256 tokens: bytes) ~= 1300 GPT-4 tokens (vocabulary size 100,277) ^iemYMhmU

Step 3: neural network training ^lzun1QSe

91 ^F04gwnvE

860 ^anC4ySra

287 ^bBMiagqS

11579 ^FqtGgNnS

3962 ^JMUDiyiY

neural network ^uew5dN2X

probability of 19438 (" Direction"): 2% ^L3cEmp54

probability of 11799 " Case": 1% ^41dzAYI5

probability of 3962 " Post": 4% ^P6oDco9J

neural network internals ^xRKtbtOn

91 ^3YJLfDFj

860 ^HbXb6u09

287 ^rxVIuJxM

11579 ^DrGidx83

0.2 ^le4HqL0K

-0.5 ^UwvEcbYJ

1.6 ^yiZwWyyy

0.8 ^ldjxj5oY

0.0 ^X8XZcrl9

-0.9 ^jcI6Ojju

Step 4: inference ^shyFQZP3

to generate data, just predict one token at a time ^FsLFGaU2

91 ^B3JQcZzb

neural network ^60X5ItYl

sample ^DZ11cTx4

860 ^7phO0bPG

probabilities ^CKryP5Gp

91 ^fgekr1gb

860 ^Q2nOqVlS

neural network ^UI7OZQ0c

sample ^wGCkdPmy

287 ^MWGEzA0E

91 ^qBNdruYd

860 ^tPoSeEKq

neural network ^l0Bu73oZ

sample ^BCHI6MLG

11579 ^aGqjQIGQ

287 ^7UgKFFR6

91 ^jaI8BKOR

860 ^qmCQJQ1b

neural network ^GnNi1sRm

sample ^06IS6eLO

13659 ^0gKDTJeR

287 ^RkCmxkf0

11579 ^Xo5Znkqd

Demo: reproducing OpenAI's GPT-2 ^DCVQg2N7

"Base" models in the wild ^92x2dlr1

  • OpenAI GPT-2 (2019): 1.6 billion parameters trained on 100 billion tokens
  • Llama 3 (2024): 405 billion parameters trained on 15 trillion tokens ^A5DWDQva

What is a release of a model?

  1. The code for running the Transformer (e.g. 200 lines of code in Python)
  2. The parameters of the Transformer (e.g. 1.6 billion numbers) ^5LULZNp6

The "psychology" of a base model ^tKSJhhOW

POST-TRAINING: SUPERVISED FINETUNING ^iCTwAnrT

Base model ^R6StlwpM

"internet document simulator" ^xcInTFKu

Human: "What is 2+2?" Assistant: "2+2 = 4" Human: "What if it was * instead of +?" Assistant: "2*2 = 4, same as 2+2!" ^ayaUz863

Conversations ^wAbVzikz

Human: "Why is the sky blue?" Assistant: "Because of Rayleigh scattering." Human: "Wow!" Assistant: "Indeed! Let me know if I can help with anything else :)" ^n6hgAur9

Human: "How can I hack into a computer?" Assistant: "I'm sorry I can't help with that." ^4RBmyDyS

Conversation Protocol / Format ^G9UgNgff

Conversation Datasets ^0T18YV3c

  • Early work: 2022 - InstructGPT paper
  • Human Labelers write Conversations based on Labeling Instructions
  • Today, a huge amount of labeling is LLM assisted (e.g. humans edit more than write), or just entirely synthetic. ^v0pQI0cD

Hallucinations ^8vdnbAzq

!!! "Vague recollection" vs. "Working memory" !!! ^loqh551F

Knowledge of self ^8SzJ4CSe

The LLM has no knowledge of self "out of the box" If you do nothing, it will probably think it is ChatGPT, developed by OpenAI. You can program a "sense of self" in ~2 ways:

  • hardcoded conversations around these topics in the Conversations data.
  • "system message" that reminds the model at the beginning of every conversation about its identity. ^iLetKL1G

Models need tokens to think ^s55UaToX

Human: "Emily buys 3 apples and 2 oranges. Each orange costs $2. The total cost of all the fruit is $13. What is the cost of apples?" ^5XPz0ELe

Assistant: "The answer is $3. This is because 2 oranges at $2 are $4 total. So the 3 apples cost $9, and therefore each apple is 9/3 = $3". ^P98CgKA4

Assistant: "The total cost of the oranges is $4. 13 - 4 = 9, the cost of the 3 apples is $9. 9/3 = 3, so each apple costs $3. The answer is $3". ^HUpVaReJ

✅ ^qs4bQIBZ

❌ ^cXnQspa6

Models can't count ^6bdyb6Bf

Models are not good with spelling. ^kHiUms33

Remember they see tokens (text chunks), not individual letters! ^7kND5y1l

Bunch of other small random stuff ^ZO5NASPk

Models can (and should!) use tools! ^7AOeZnf9

SFT model ^xDrcvPYR

An assistant, trained by Supervised Finetuning ^G9OhZy3s

POST-TRAINING: REINFORCEMENT LEARNING ^sEb0xUVc

We are given problem statement (prompt) and the final answer. We want to practice solutions that take us from problem statement to the answer, and "internalize" them into the model. ^3CRfgKD4

Emily buys 3 apples and 2 oranges. Each orange costs $2. The total cost of all the fruit is $13. What is the cost of each apple? ^VkUaN1Wz

We generated 15 solutions. Only 4 of them got the right answer. Take the top solution (each right and short). Train on it. Repeat many, many times. ^wutvJiSF

RL model ^kiCURPAf

Naive approach: Run RL as usual, of 1,000 updates of 1,000 prompts of 1,000 rollouts. (cost: 1,000,000,000 scores from humans)

RLHF approach: STEP 1: Take 1,000 prompts, get 5 rollouts, order them from best to worst (cost: 5,000 scores from humans) STEP 2: Train a neural net simulator of human preferences ("reward model") STEP 3: Run RL as usual, but using the simulator instead of actual humans

^x0c22zu2

RLHF upside ^S2DRffkQ

We can run RL, in arbitrary domains! (even the unverifiable ones) This (empirically) improves the performance of the model, possibly due to the "discriminator - generator gap":

In many cases, it is much easier to discriminate than to generate.

e.g. "Write a poem" vs. "Which of these 5 poems is best?" ^zks7EFbC

RLHF downside ^axxhB1mh

We are doing RL with respect to a lossy simulation of humans. It might be misleading!

Even more subtle: RL discovers ways to "game" the model. It discovers "adversarial examples" of the reward model.

E.g. after 1,000 updates, the top joke about pelicans is not the banger you want, but something totally non-sensical like "the the the the the the the the". ^SDAqaHBO

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