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Train Your Own O1 Preview Model Within $450
- JoshTko 2y agoHas anyone tested if the consensus of top 4-5 mini models together would out perform the best frontier model?
- qqmm 2y agoIs it because Deepseek decided to open their model? I noticed they have a similar timeline
- Tiberium 2y agoBetter URL: https://novasky-ai.github.io/posts/sky-t1/ https://novasky-ai.github.io/posts/sky-t1/
- 9woc 2y agoTrue. The previous discussion on this is here: https://news.ycombinator.com/item?id=42681417 https://news.ycombinator.com/item?id=42681417
- danielhanchen 2y agoIf anyone's interested, I made Colab notebooks with free GPUs for both GRPO (the algo DeepSeek used) to train a reasoning model from scratch, and also general finetuning, which the Berkeley team employed! GRPO notebook for Llama 3.1 8B: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-GRPO.ipynb https://colab.research.google.com/github/unslothai/notebooks... General finetuning notebook: https://colab.research.google.com/github/unslothai/notebooks/blob/main/nb/Llama3.1_(8B)-Alpaca.ipynb https://colab.research.google.com/github/unslothai/notebooks... The Berkeley team's 17K dataset: https://huggingface.co/datasets/NovaSky-AI/Sky-T1_data_17k https://huggingface.co/datasets/NovaSky-AI/Sky-T1_data_17k Hugging Face also released a 220K dataset: https://huggingface.co/datasets/open-r1/OpenR1-Math-220k https://huggingface.co/datasets/open-r1/OpenR1-Math-220k
- threecheese 2y agoHow long does this take on a free tier T4? This is really neat, I’d assumed this type of “playing with the guts” work was more difficult to access as a normie programmer. Looks like something I’d like to try!
- danielhanchen 2y agoFor GRPO - we also made it much faster, but you might need to wait 2 to 4 hours in the minimum for anything meaningful :) Also you can install Unsloth on your local machine :) Kaggle has 2x Tesla T4s as well for free for 30 hours per week!
- fl4tul4 2y agoI do love competition. In the last weeks are are seeing a torrent of advances, just because someone opened their architectures. Imagine where we could go if the training datasets were also publicly available and unbounded by any copyright laws. (I'm not talking about doing anything illegal). I can only dream, I guess.
- paper2d 2y agoThose training datasets can never be free as almost all of them is copyrighted.
- lionkor 2y agoalmost all free things are copyrighted
- chii 2y agoperhaps copyright needs to be updated. And in any case, my personal belief is that training on data that is publicly released, and as well as purchased media, is fair use.
- tonyedgecombe 2y agoThe UK government is doing that at the behest of the AI companies which tends to indicate they have bet misbehaving up to now.
- philipwhiuk 2y agoIf anything it needs to be updated to actually prevent the rampant profit extraction from human creation in order to protect actual creators.
- FergusArgyll 2y agoNot OP, but that should be part of the update, I think. I think we can all agree there does need to be an update. You don't want to forever outlaw deep learning (even if you do want to, that's not going to happen so it's worth helping to shape the future) It's very complicated with a bunch of moving parts but I really want society to start arguing about it so we can get to a semi-fair place
- brador 2y agoI just want to make music with AI and it is very difficult. The meta model on hugging gives an error when used through the website and no one will ever fix it.
- polishdude20 2y agoSuno?
- fragmede 2y agoYeah. If you want to play ai researcher, by all means go play around with hugging face and build a local AI GPU rig. if you want to make some music, just use Suno.
- ionwake 2y agoI find I can only give them one sentence to describe the music I want which is not good enough - has this changed at all?
- petercooper 2y agoIt's still only 240 characters or whatever, but it pays to be dense. So rather than "Write a song that sounds like polka etc etc" just keyword pack it.
- xyproto 2y agoYou can describe or upload the first N seconds, then extend from that by using another description, then extend from N further seconds etc. But Suno music within a genre has a pretty limited range.
- Kye 2y agoIt depends on how much you want it to do for you. I've used ChatGPT to come up with song briefs which I then turn into music myself.
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- magicalhippo 2y agoSo this is a fine-tune and not from scratch, which makes the proposition much more reasonable. That said, for someone who's not in the game but been curious as to the details of fine-tuning, it's great to get both the dataset and the code.
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- rlforllms 2y agoWait so Qwen trained QWQ 32B from Qwen 32B and then they distill QWQ back into Qwen 32B? What's the point? This is massive marketing scam here. Borderline academic dishonesty.
- barrenko 2y agoNot sure if scam, honestly depends on the data sometimes it might work.
- rlforllms 2y agoThe goal is distillation is to distill into smaller models like 7B, 1.5B. They didn't even change the model size, let alone try a different class of models. Getting expert model's trajectories is trivial if you have vLLM to do batched inference.
- jojaja 2y agoSo you are better off just using QwQ
- andy_xor_andrew 2y agoI wouldn't go that far, but I agree, my reaction to reading the details was to go "huh?" From the title, my best guess was they applied some kind of RL/GRPO to an existing model. But... they took an existing model that had already undergone SFT for reasoning... and then used it to generate data to SFT the exact same model... nothing wrong with that, but it doesn't seem to warrant the title they chose.
- _joel 2y agoIt's not from scratch, though, right? Am I missing something here as to why it's at the top of the posts?
- twobitshifter 2y agoThere’s no real reason to start from true scratch anymore. You don’t harvest wheat, mill flour, milk a cow, and churn butter for your cake.
- _joel 2y agoYes and LoRA etc has been a thing for a while, what's new?
- mkagenius 2y agoWeird that they had to resort to click bait using "O1 preview" in their name. I expected some sort of way to actually get o1 preview retrained (and downloadable). Also, calling it O1 preview on just 7 benchmarks is not correct. What if someone comes up with some use cases where O1 preview does better than this. apart from that, good that things are becoming cheaper.
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- codelion 2y agoYeah, I agree. The "O1 preview" naming feels a bit misleading. It sets an expectation of broader coverage than just those specific benchmarks. It's cool to see cost reductions, but the marketing could be more transparent about the scope.
- jug 2y agoIt’s dishonest because they not only point towards a specific language model, but the beta version of a specific model. WTH?
- echelon 2y agoIt's not dishonest, it's simple human behavior. The vocabulary used to describe the culturally prevailing leader will be used to explain similar concepts and create analogies. That's an easier tool to communicate to the masses than crafting super tailored messages for only domain experts. It's why we keep doing this, and it's also why trademarks become generics. "Google it", "Uber for X", "band aid", "the band sounds like Y", "the actor looks like Z", etc. etc. This is a core part of how human language works and how we as a species communicate with one another.
- michaelt 2y ago"Build your own Lamborghini Huracan at home for $450" "Wow! Quite a feat to deliver an iconic design, a 631 horsepower engine, and performance of 0-150 mph in 15.4 seconds on such a small budget!" "Actually what we mean is, like the Lamborghini Huracan, our vehicle has two seats."
- scosman 2y agoInference time compute is still very under utilized in actual AI deployments. Lots of folks are working on foundation models, which require reasoning about broad problem domains. Not enough people are using the same techniques for task-specific performance improvements. You can easily distill the reasoning from larger models like R1 for your task. Often better, you can mix in custom thinking instructions for specific sub-problems so a fine tuned model learns a mix of task specific reasoning and custom logic. It’s not hard and easily beats prompt iteration. When you find bugs, you can fix it. I made a GitHub project for distilling thinking models (and customs COT inference time fine tuning): https://docs.getkiln.ai/docs/guide-train-a-reasoning-model https://docs.getkiln.ai/docs/guide-train-a-reasoning-model
- anon373839 2y agoThanks for linking to this. That’s a good resource! Do you have any pointers on assembling fine-tuning data not for isolated tasks, but for a flexible range of queries in a particular problem domain? Similar to general purpose instruction-tuning, but much more focused. For example, suppose you’re building an app that helps doctors search through research literature to aid in diagnosis, check hypotheses, etc. Of course you would want to have some domain experts and real users available to see what kind of queries they would create. But getting from that point to a well-balanced dataset that adequately represents the distribution of possible queries, instructions, writing/cognitive styles, formatting, dialog flows, etc. your app will encounter —- it just seems kind of hard to know how to approach a task like that. It seems there are infinitely many dimensions you could accidentally overfit on.
- pizza 2y agoGeneral advice? Collect data, train a model, note the mistakes in the model, mistakes in the data, and think critically about what it is that you're ending up teaching. Repeat many, many, many times.. For some tasks, don't be surprised if it ends up taking months or a year or several. It took me 6 months of building a dataset, by hand, by myself, to produce ~1600 'gold standard' text examples (bolstered by ~100K synthetic examples) - texts plus 20 dimensions rated 1-4. But I managed to beat SOTA models in this task from all the frontier labs by doing so. It also makes sense to consider all of the various "lacks" of the competing models. It's quite difficult to see all the future decisions you will make due to future insights about future versions of the whole loop. But you will be needing to make some. I will say one more concrete thing though: the more metadata you collect, generally, the better, but this can make it more expensive. Also, if you ever need to update your schema.. well this is actually one reason why text data for LLMs is nice: your schema is essentially fluid in the first place, so you could eg stick metadata in the text itself if at some future point you start collecting it. I guess, also, it's a good thing to constantly add new benchmarks, if possible. Treat your model's capabilities as knowable, but never treat your model's capabilities as actually known.
- genpfault 2y ago> The model training finishes in 19 hours on 8 H100 with DeepSpeed Zero-3 offload (~ $450 according to Lambda Cloud pricing).
- moconnor 2y agoThey trained on QwQ traces and in their evaluation they are… mostly slightly worse than QwQ. Hardly a huge win.
- rdli 2y agoThe blog post was a little unclear, so my summary was: - They used QwQ to generate training data (with some cleanup using GPT-4o-mini) - The training data was then used to FT Qwen2.5-32B-Instruct (non-reasoning model) - Result was that Sky-T1 performs slightly worse than QwQ but much better than Qwen2.5 on reasoning tasks There are a few dismissive comments here but I actually think this is pretty interesting as it shows how you can FT a foundation model to do better at reasoning.
- azinman2 2y agoI wish they would have compared to the r1 distills of qwen2.5
- m3kw9 2y agoLooks like they need to put quotes on the 450$
- tw1984 2y agojust several weeks ago, OpenAI was still using reasoning as a part of its tech moat to partially justify its hugely inflated valuation. in just weeks after the release of deepseek and kimi and their paper on how to do it, average joes can now train it at home by spending less than the purchase cost of one single mid end gaming GPU.
- cytocync 2y ago[dead]