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Ex-NVIDIA engineer who built Unsloth explained RL, kernels, reasoning, quantization, and agents in 2 hours 42 minutes - better than $5000 fine-tuning bootcamps. pick the base model -> write triton kernels for 2x faster fine-tune -> quantize to 4-bit -> run GRPO/DPO -> ship a reasoning model on your... show more

h100envy

4,554 subscribers

497,378 Aufrufe • vor 3 Monaten •via X (Twitter)

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34 Kommentare

Profilbild von Daniel Han

Daniel Hanvor 3 Monaten

Thanks for the support! We did another 3 hour one at AIE last week! Look out for the recording on YT hopefully in weeks!

Profilbild von Akumu

Akumuvor 3 Monaten

@danielhanchen YouTube link:

Profilbild von Tony Ge

Tony Gevor 3 Monaten

@grok can you find the video on youtube

Profilbild von Preston Badeer

Preston Badeervor 3 Monaten

Source:

Profilbild von Kirtan

Kirtanvor 3 Monaten

YT link:

Profilbild von leopardracer

leopardracervor 3 Monaten

This is the kind of talk that’s worth watching all the way through instead of just reading the highlights

Profilbild von Lee Penkman

Lee Penkmanvor 3 Monaten

love unsloth team very cracked

Profilbild von Arindam Majumder 𝕏

Arindam Majumder 𝕏vor 3 Monaten

I also wrote an article on Loop Engineering:

Profilbild von May Contain Nuts

May Contain Nutsvor 3 Monaten

I tuned into a Gemini live stream that featured Daniel a couple months ago. It was that stream that convinced me that AI is the real deal. When obviously-smart MFs like him are pursuing this stuff, it's like wildfire.

Profilbild von Dmitrii Malakhov

Dmitrii Malakhovvor 3 Monaten

Spent a month debugging browser automation edge cases before my agent stopped breaking in production. No bootcamp preps you for that.

Profilbild von Alexander Benz

Alexander Benzvor 3 Monaten

been self-taught my whole life. the 2h42m from someone who actually shipped it > any bootcamp

Profilbild von ViceSol

ViceSolvor 3 Monaten

This is a solid overview of the modern open-source fine-tuning stack. Understanding the concepts matters more than memorizing the tools

Profilbild von Diam

Diamvor 3 Monaten

Crazy that this is becoming something you can just try at home.

Profilbild von anvxxr

anvxxrvor 3 Monaten

@grok what video means?

Profilbild von Poetica

Poeticavor 3 Monaten

the goat

Profilbild von arpit bansal

arpit bansalvor 3 Monaten

Oh this is the last year's session. Have they does this year's one?

Profilbild von Swarup Das

Swarup Dasvor 3 Monaten

Solid overview of the single-GPU stack. Just finished fine-tuning a Qwen model with Unsloth last week-2x speed really holds up.

Profilbild von Harry Tandy

Harry Tandyvor 3 Monaten

unsloth makes fine-tuning accessible without a massive gpu cluster

Profilbild von Rodds

Roddsvor 3 Monaten

Most people are learning how to use AI. The real leverage comes from learning how to build and fine-tune it. That's the difference between being a user and becoming a builder.

Profilbild von ro

rovor 3 Monaten

@ycombinator TIL there are fine-tuning boot camps 😂

Profilbild von Akshaya Kumawat

Akshaya Kumawatvor 3 Monaten

Game-changing approach! Who else is excited to ditch the $5000 bootcamps and optimize their workflow with Unsloth?

Profilbild von Prince does AI

Prince does AIvor 3 Monaten

the single GPU line is what got me

Profilbild von Nabeel K.

Nabeel K.vor 3 Monaten

The pipeline is real, but the sequence is backwards for most people. Quantize-then-GRPO only makes sense once you already know your reward signal is stable at full precision. Debug RL at 4-bit and you cannot tell if the policy collapsed or the quantization did.

Profilbild von ContextFirstAI

ContextFirstAIvor 3 Monaten

It's refreshing to see someone explain the full pipeline instead of just sharing another fine-tuning tutorial. Understanding why each step exists makes a huge difference.

Profilbild von Silverrock

Silverrockvor 3 Monaten

This is gold! Been waiting for a no-BS deep dive on GRPO + Unsloth stack. Watching tonight and shipping my first reasoning model on one GPU. Legend work @danielhanchen 🔥

Profilbild von bk

bkvor 3 Monaten

do you think you will get another community notes saying the quoted article is not related to the speech unsloth founder gave?

Profilbild von Ujjawal Sahay

Ujjawal Sahayvor 3 Monaten

Excellent resource. Practical insights into fine tuning, optimization, and AI agents are exactly what the community needs as open-source AI continues to evolve. Thanks for sharing. A follow back would be greatly appreciated. Looking forward to learning from your future posts!```

Profilbild von Jabir | AI Systems

Jabir | AI Systemsvor 3 Monaten

Great stuff

Profilbild von yb

ybvor 3 Monaten

Also recommend this tool that helps you manage your loop

Profilbild von Julley Thai

Julley Thaivor 3 Monaten

that kernel optimization part is the real differentiator because most people just import hf and pray it works

Profilbild von 0xSurdo.ETH

0xSurdo.ETHvor 3 Monaten

bootcamps are just tax

Profilbild von Technomechanicus

Technomechanicusvor 3 Monaten

Fine tune for what? Been hearing it for ages. I want to see what people have created after fine tuning.

Profilbild von Ian Channing 🦈@ianchanning@mastodon.social

Ian Channing 🦈@[email protected]vor 3 Monaten

This is one of the most amazing introductions I've ever seen to a technical talk. Such unpretentious wonderful excitement and enthusiasm. Love it.

Profilbild von Adel Bucetta

Adel Bucettavor 3 Monaten

because the hard part was never writing kernels, it's optimizing performance without sacrificing reasoning

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Rahul

34,785 Aufrufe • vor 24 Tagen

Modal engineer who teaches Full-Stack Deep Learning explained everything an AI engineer needs to know about GPUs in 20 minutes - better than $800 CUDA workshops. memory bandwidth -> SM occupancy -> kernel fusion -> arithmetic intensity -> know when you're compute-bound vs memory-bound. That loop is how you stop overpaying for GPUs you're barely using. VRAM math + roofline model + Modal serverless GPUs + kernel profiling - that's the stack. Watch and save it, then check your own GPU utilization tonight.

Modal engineer who teaches Full-Stack Deep Learning explained everything an AI engineer needs to know about GPUs in 20 minutes - better than $800 CUDA workshops. memory bandwidth -> SM occupancy -> kernel fusion -> arithmetic intensity -> know when you're compute-bound vs memory-bound. That loop is how you stop overpaying for GPUs you're barely using. VRAM math + roofline model + Modal serverless GPUs + kernel profiling - that's the stack. Watch and save it, then check your own GPU utilization tonight.

h100envy

18,442 Aufrufe • vor 3 Monaten

Apple built a large foundation model and fine-tuned it on multiple tasks. But they are doing something very clever: They load a single model in memory and use different adapters to specialize the model on the fly. I recorded a video to show you how to write the code to do the same thing Apple is doing. I explain everything step by step. Here is what I'll show you in the video: 1. We'll load two datasets 2. Then load a large model 3. Then, we'll fine-tune the model on both datasets I'll use LoRA to fine-tune the model. This process creates two small adapters, each specializing in solving one of the datasets. The base model's original parameters will remain unchanged. From here: 4. We'll generate a list of tasks 5. We'll load the correct adapter to solve each task The large model I'm using needs 346 MB of memory, but I only need to load it once. Each adapter is only 2.7 MB. I only need to load the base model once and pair it with any of the fine-tuned adapters. Minimum memory footprint and I can solve multiple tasks. Hope this helps!

Apple built a large foundation model and fine-tuned it on multiple tasks. But they are doing something very clever: They load a single model in memory and use different adapters to specialize the model on the fly. I recorded a video to show you how to write the code to do the same thing Apple is doing. I explain everything step by step. Here is what I'll show you in the video: 1. We'll load two datasets 2. Then load a large model 3. Then, we'll fine-tune the model on both datasets I'll use LoRA to fine-tune the model. This process creates two small adapters, each specializing in solving one of the datasets. The base model's original parameters will remain unchanged. From here: 4. We'll generate a list of tasks 5. We'll load the correct adapter to solve each task The large model I'm using needs 346 MB of memory, but I only need to load it once. Each adapter is only 2.7 MB. I only need to load the base model once and pair it with any of the fine-tuned adapters. Minimum memory footprint and I can solve multiple tasks. Hope this helps!

Santiago

84,747 Aufrufe • vor 2 Jahren