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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 次观看 • 3 个月前 •via X (Twitter)

科学技术教育艺术

Anya Rossi• Live Now

Private livecam show

34 条评论

Daniel Han 的头像

Daniel Han3 个月前

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

Akumu 的头像

Akumu3 个月前

@danielhanchen YouTube link:

Tony Ge 的头像

Tony Ge3 个月前

@grok can you find the video on youtube

Preston Badeer 的头像

Preston Badeer3 个月前

Source:

Kirtan 的头像

Kirtan3 个月前

YT link:

leopardracer 的头像

leopardracer3 个月前

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

Lee Penkman 的头像

Lee Penkman3 个月前

love unsloth team very cracked

Arindam Majumder 𝕏 的头像

Arindam Majumder 𝕏3 个月前

I also wrote an article on Loop Engineering:

May Contain Nuts 的头像

May Contain Nuts3 个月前

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 Malakhov 的头像

Dmitrii Malakhov3 个月前

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

Alexander Benz 的头像

Alexander Benz3 个月前

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

ViceSol 的头像

ViceSol3 个月前

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

Diam 的头像

Diam3 个月前

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

anvxxr 的头像

anvxxr3 个月前

@grok what video means?

Poetica 的头像

Poetica3 个月前

the goat

arpit bansal 的头像

arpit bansal3 个月前

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

Swarup Das 的头像

Swarup Das3 个月前

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

Harry Tandy 的头像

Harry Tandy3 个月前

unsloth makes fine-tuning accessible without a massive gpu cluster

Rodds 的头像

Rodds3 个月前

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.

ro 的头像

ro3 个月前

@ycombinator TIL there are fine-tuning boot camps 😂

Akshaya Kumawat 的头像

Akshaya Kumawat3 个月前

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

Prince does AI 的头像

Prince does AI3 个月前

the single GPU line is what got me

Nabeel K. 的头像

Nabeel K.3 个月前

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.

ContextFirstAI 的头像

ContextFirstAI3 个月前

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.

Silverrock 的头像

Silverrock3 个月前

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 🔥

bk 的头像

bk3 个月前

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

Ujjawal Sahay 的头像

Ujjawal Sahay3 个月前

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 Systems 的头像

Jabir | AI Systems3 个月前

Great stuff

yb 的头像

yb3 个月前

Also recommend this tool that helps you manage your loop

Julley Thai 的头像

Julley Thai3 个月前

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

0xSurdo.ETH 的头像

0xSurdo.ETH3 个月前

bootcamps are just tax

Technomechanicus 的头像

Technomechanicus3 个月前

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

Ian Channing 🦈@ianchanning@mastodon.social 的头像

Ian Channing 🦈@[email protected]3 个月前

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 Bucetta 的头像

Adel Bucetta3 个月前

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

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Rahul

34,785 次观看 • 24 天前

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 次观看 • 3 个月前

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 次观看 • 2 年前