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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
- reference https://lmarena.ai/
- subscribe to https://buttondown.com/ainews
- X / Twitter
^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?
- The code for running the Transformer (e.g. 200 lines of code in Python)
- 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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