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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 views • 3 months ago •via X (Twitter)

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34 Comments

Daniel Han3 months ago

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

Akumu3 months ago

@danielhanchen YouTube link:

Tony Ge3 months ago

@grok can you find the video on youtube

Preston Badeer3 months ago

Source:

Kirtan3 months ago

YT link:

leopardracer3 months ago

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

Lee Penkman3 months ago

love unsloth team very cracked

Arindam Majumder 𝕏3 months ago

I also wrote an article on Loop Engineering:

May Contain Nuts3 months ago

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.

Dmitrii Malakhov3 months ago

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

Alexander Benz3 months ago

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

ViceSol3 months ago

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

Diam3 months ago

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

anvxxr3 months ago

@grok what video means?

Poetica3 months ago

the goat

arpit bansal3 months ago

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

Swarup Das3 months ago

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

Harry Tandy3 months ago

unsloth makes fine-tuning accessible without a massive gpu cluster

Rodds3 months ago

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.

ro3 months ago

@ycombinator TIL there are fine-tuning boot camps 😂

Akshaya Kumawat3 months ago

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

Prince does AI3 months ago

the single GPU line is what got me

Nabeel K.3 months ago

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.

ContextFirstAI3 months ago

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.

Silverrock3 months ago

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 🔥

bk3 months ago

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

Ujjawal Sahay3 months ago

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!```

Jabir | AI Systems3 months ago

Great stuff

yb3 months ago

Also recommend this tool that helps you manage your loop

Julley Thai3 months ago

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

0xSurdo.ETH3 months ago

bootcamps are just tax

Technomechanicus3 months ago

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

Ian Channing 🦈@[email protected]3 months ago

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

Adel Bucetta3 months ago

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

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18,442 views • 3 months ago

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 views • 2 years ago