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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 görüntüleme • 3 ay önce •via X (Twitter)

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34 Yorum

Daniel Han profil fotoğrafı

Daniel Han3 ay önce

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 profil fotoğrafı

Akumu3 ay önce

@danielhanchen YouTube link:

Tony Ge profil fotoğrafı

Tony Ge3 ay önce

@grok can you find the video on youtube

Preston Badeer profil fotoğrafı

Preston Badeer3 ay önce

Source:

Kirtan profil fotoğrafı

Kirtan3 ay önce

YT link:

leopardracer profil fotoğrafı

leopardracer3 ay önce

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

Lee Penkman profil fotoğrafı

Lee Penkman3 ay önce

love unsloth team very cracked

Arindam Majumder 𝕏 profil fotoğrafı

Arindam Majumder 𝕏3 ay önce

I also wrote an article on Loop Engineering:

May Contain Nuts profil fotoğrafı

May Contain Nuts3 ay önce

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 profil fotoğrafı

Dmitrii Malakhov3 ay önce

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

Alexander Benz profil fotoğrafı

Alexander Benz3 ay önce

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

ViceSol profil fotoğrafı

ViceSol3 ay önce

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

Diam profil fotoğrafı

Diam3 ay önce

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

anvxxr profil fotoğrafı

anvxxr3 ay önce

@grok what video means?

Poetica profil fotoğrafı

Poetica3 ay önce

the goat

arpit bansal profil fotoğrafı

arpit bansal3 ay önce

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

Swarup Das profil fotoğrafı

Swarup Das3 ay önce

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 profil fotoğrafı

Harry Tandy3 ay önce

unsloth makes fine-tuning accessible without a massive gpu cluster

Rodds profil fotoğrafı

Rodds3 ay önce

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 profil fotoğrafı

ro3 ay önce

@ycombinator TIL there are fine-tuning boot camps 😂

Akshaya Kumawat profil fotoğrafı

Akshaya Kumawat3 ay önce

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

Prince does AI profil fotoğrafı

Prince does AI3 ay önce

the single GPU line is what got me

Nabeel K. profil fotoğrafı

Nabeel K.3 ay önce

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 profil fotoğrafı

ContextFirstAI3 ay önce

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 profil fotoğrafı

Silverrock3 ay önce

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 profil fotoğrafı

bk3 ay önce

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

Ujjawal Sahay profil fotoğrafı

Ujjawal Sahay3 ay önce

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 profil fotoğrafı

Jabir | AI Systems3 ay önce

Great stuff

yb profil fotoğrafı

yb3 ay önce

Also recommend this tool that helps you manage your loop

Julley Thai profil fotoğrafı

Julley Thai3 ay önce

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

0xSurdo.ETH profil fotoğrafı

0xSurdo.ETH3 ay önce

bootcamps are just tax

Technomechanicus profil fotoğrafı

Technomechanicus3 ay önce

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 profil fotoğrafı

Ian Channing 🦈@[email protected]3 ay önce

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 profil fotoğrafı

Adel Bucetta3 ay önce

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

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35,390 görüntüleme • 3 ay önce

Holy sh*t, developers are going to lose their minds over this. Someone open sourced a repo that lets you fine-tune 100+ LLMs and VLMs without building the training pipeline yourself. Llama. Qwen. DeepSeek. Gemma. Mistral. And more. It's called LLaMA-Factory and here's why it changes everything: → 100+ open-source LLMs + VLMs → LoRA + QLoRA → SFT + DPO + PPO → multimodal fine-tuning → experiment tracking → Web UI You don't need to train the next frontier model. You can take a much smaller model and make it extremely good at one specific job. most people haven't done that yet. full guide in the article below

Holy sh*t, developers are going to lose their minds over this. Someone open sourced a repo that lets you fine-tune 100+ LLMs and VLMs without building the training pipeline yourself. Llama. Qwen. DeepSeek. Gemma. Mistral. And more. It's called LLaMA-Factory and here's why it changes everything: → 100+ open-source LLMs + VLMs → LoRA + QLoRA → SFT + DPO + PPO → multimodal fine-tuning → experiment tracking → Web UI You don't need to train the next frontier model. You can take a much smaller model and make it extremely good at one specific job. most people haven't done that yet. full guide in the article below

Rahul

34,785 görüntüleme • 24 gün önce

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 görüntüleme • 3 ay önce

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 görüntüleme • 2 yıl önce