Sebastian Raschka @rasbt
Machine learning & AI researcher writing at https://t.co/A0tXWzG1p5. LLM research engineer @LightningAI. Previously stats professor at UW-Madison. sebastianraschka.com/books/ United States Joined October 2012-
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It’s here! Meet Llama 3, our latest generation of models that is setting a new standard for state-of-the art performance and efficiency for openly available LLMs. Key highlights • 8B and 70B parameter openly available pre-trained and fine-tuned models. • Trained on more…
Super nice and concise slide deck introducing instruction finetuning & alignment for LLMs! Also love that it starts with a clear definition of the terminology jargon. (We often use "finetuning" to mean instruction finetuning, which can be very confusing coming from classic ML.)
Super nice and concise slide deck introducing instruction finetuning & alignment for LLMs! Also love that it starts with a clear definition of the terminology jargon. (We often use "finetuning" to mean instruction finetuning, which can be very confusing coming from classic ML.) https://t.co/oUhosPMBwQ
Thanks @_akhaliq 🔥Surprisingly!! we figured out simple recipe to pre-train small language models with 1 GPU for less than half a day. Traditionally small base LMs w/ billion params (1B-2B) are trained on trillion tokens and it’s extremely compute intensive.
Thanks @_akhaliq 🔥Surprisingly!! we figured out simple recipe to pre-train small language models with 1 GPU for less than half a day. Traditionally small base LMs w/ billion params (1B-2B) are trained on trillion tokens and it’s extremely compute intensive.
Orca 2.5 7B based on Direct Nash Optimization, Mixtral 8x22B, Griffin, CodeGemma, @karpathy's llm.c, Gemini 1.5's audio, Gemini 1.5 Pro, new GPT-4 Turbo, Meta's & Google's new AI chips ... currently traveling, what else did I miss? 😅 Oh, and Llama 3 is also coming out next…
Freshly merged: you can now evaluate LLMs in LitGPT with just one command (thanks to the great LLM Eval Harness)! github.com/Lightning-AI/l…
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Here is a paper that finally answers a question: Which method is better for RLHF/supervised fine-tuning, DPO or PPO?
While I was eagerly awaiting the technical report/paper accompanying the Llama 3 release yesterday, I stumbled upon another very interesting research paper this week, which finally answers one of my pressing questions: "Is DPO Superior to PPO for LLM Alignment?" RLHF is one of…
62% HumanEval at 8B is just mind blowing. The future of LM-assisted programming is going to be amazing, and it's going to get here much sooner than we expected.
I'm still stunned by this. How did it improve so much? I mean, look at 8B vs the old 70B
🧵 A nice LLAMA-3 summary by @natolambert. In short, performance is better, but largely due to the sheer increase in the pre-training scale. Training data is a secret. ↩️
@rasbt I'm learning about DPO and reading its paper. The authors also raise a question about whether DPO policies can generalize out of distribution, compared to learning from an explicit reward function. Iterative DPO is a clever tip! Thanks for the insights.
Is DPO superior to PPO for LLM Alignment ? A comprehensive Study.
While I was eagerly awaiting the technical report/paper accompanying the Llama 3 release yesterday, I stumbled upon another very interesting research paper this week, which finally answers one of my pressing questions: "Is DPO Superior to PPO for LLM Alignment?" RLHF is one of…
@rasbt And then there's this ORPO that combines SFT with preference alignment: arxiv.org/abs/2403.07691
It's great to see a direct comparison in the paper, shedding light on PPO vs. DPO. The hybrid approach used in Llama 3 sounds intriguing - blending techniques might be the way forward.
While I was eagerly awaiting the technical report/paper accompanying the Llama 3 release yesterday, I stumbled upon another very interesting research paper this week, which finally answers one of my pressing questions: "Is DPO Superior to PPO for LLM Alignment?" RLHF is one of…
It’s here! Meet Llama 3, our latest generation of models that is setting a new standard for state-of-the art performance and efficiency for openly available LLMs. Key highlights • 8B and 70B parameter openly available pre-trained and fine-tuned models. • Trained on more…
@rasbt @llama_index Amazing! Let me know how it goes🤗
So fast! Thanks @llama_index day 0 support!
The latest model from @MistralAI, the hugely powerful 8x22b, defines the state of the art in open models and is now available! As usual, we have day 0 support: check out @ravithejads's Mistral cookbook showing ➡️ RAG ➡️ Query routing ➡️ Tool use docs.llamaindex.ai/en/latest/exam…
"Weight watchers" should be a podcast (or a newsletter) covering latest model releases.
@alexalbert__ @MKBHD I think my votes on this go to @rasbt for technical content on the data science/ML side of language models and to @vboykis for the ML engineering side.
Watch my @Stanford CS 25 lecture next week, "aligning open language models," it'll be good, v excited
Slides are done! See you tomorrow at 4:30pm PST for the whole story. From alpaca, openassistant, qlora, zephyr, tulu, to today. Slides: docs.google.com/presentation/d… Course link (with zoom): web.stanford.edu/class/cs25/
Watch my @Stanford CS 25 lecture next week, "aligning open language models," it'll be good, v excited
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