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We fine-tuned Llama and got 4.2x Sonnet 3.5 accuracy for code generation
- banddk 2y agoHi HN! We worked at OpenAI and Anthropic and believe we can provide much higher quality code generation by fine-tuning an LLM on your codebase compared to Sonnet-3.5 or o1 but not fine-tuned. Let me know if you are interested and we can fine-tune for you for free to test.
- dr_kiszonka 2y agoI wish you posted more evaluation details on your page as text. What exactly was your accuracy vs. Sonnet? (Right now, we can only tell that Sonnet's was ≤ 1/4.3.) Why the Discourse repo? Providing more detailed information would help folks trust your claims more.
- samatdav 2y agoI agree, we need to post more data. Since we are very early (<1 month) we just shared the initial results. Discourse repo was just a good option since it is a big public repo that could benefit from fine-tuning. We plan to add more benchmarks to the website as we progress.
- WhatsName 2y agoTalk is cheap, benchmarks please. Also why did you decide for LLama? AFAIK deepseek always had a slight edge over llama when it comes to coding performance, or is this no longer the case?
- pdimitar 2y agoSome more details that programmers can inspect would be very useful.
- samatdav 2y agoI agree, we plan to publish more benchmarks and metrics. We also want to publicly host our fine-tuned model for one of the open-source repos so that people can try themselves agains SOTA models.
- siscia 2y agoI am getting quite deep into coding with AI and cost of tokens is a bit of an issue indeed. Trivial issue because it saves me A LOT of time, but it could be an issue for new people testing it. I would love to test this approach. Are you guys fine tuning for each codebase?
- manishsharan 2y ago>>cost of tokens is a bit of an issue indeed Their cost is $0.7 per 1M token. DeepSeek is $0.14 / 1M tokens ( cache miss)
- siscia 2y agoDeepSeek is an amazing product but has few issues: 1. Data is used for training 2. Context window is rather small and doesn't fit as well large codebase I keep saying this over and over in all the content I create, the valu of coding with AI will come from working on big, complex, legacy codebases. Not from flashy demo where you create a to-do app. For that you need solid models with big context and private inference.
- MacsHeadroom 2y agoDeepSeek is open source and has a context length of 128k tokens.
- siscia 2y agoCommercial service have a context of 64k tokens, which I find quite limiting. https://api-docs.deepseek.com/quick_start/pricing https://api-docs.deepseek.com/quick_start/pricing Running it locally is quite a bit beyond the scope of being productive while coding with AI. Beside that 128k is still significantly less than Claude
- elashri 2y agoShouldn't we be comparing with other open source model? In particular since this is about llama3.3 then they have the exact context limit which is 128k [1]. Also [1] https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct
- doubtfuluser 2y agoAny plans on distilling it down to an 8b model to enable it for pure local usage on most consumer hardware?
- samatdav 2y agoCould be done in the future. Our current focus is highest accuracy. But there are no limitations on the models - just would depend on user preference of size/performance tradeoff.
- jbellis 2y agoI'm interested. I submitted my email to your landing page form.
- samatdav 2y agoThank you! Will email you within a couple of days:)
- tucnak 2y agoI'm not saying you're an imposter... but you're making it really easy to assume that; it doesn't seem you have learnt much while you guys were there. Are you sure you weren't hired by mistake?
- ryao 2y agoI would be interested in a fine tune on OpenZFS: https://github.com/openzfs/zfs https://github.com/openzfs/zfs
- samatdav 2y agoThank you for the suggestion, we will take a look!
- futureshock 2y agoIt seems an interesting fine-tuning idea. Drawing from reasoning models, I wonder if it’s effective to 10x or 100x the fine-tune dataset by having a larger reasoning model create documentation and reasoning COTs about the code base’s current state and speculation about future state updates. Maybe have it output some verbose execution flow analysis.
- samatdav 2y agoThank you for the idea! We are also considering upsampling and distillation. But on high level, correctly setting up the data for simple fine-tuning can already produce great results.
- redman25 2y agoWhat is the metric for LLMs? Shouldn't more than just accuracy be measured? If something has high accuracy but low recall, won't it be overfit and fail to generalize? Your metrics would give you false confidence in how effective your model is. Just wondering because the announcement only seems to mention accuracy.
- samatdav 2y agoGood point, we should provide more detailed metrics. Since we are very early, we focus on the main metric in our view: higher accuracy of changes to be more practically usable. We will do more testing on overfitting and how the model performance on different types of tasks. On high level we believe in the idea of "a well fine-tuned model should be much better than a large general model". But we need more metrics, I agree.
- rtfeldman 2y agoInterested! Our large Rust code base at https://zed.dev https://zed.dev is open-source at https://github.com/zed-industries/zed https://github.com/zed-industries/zed and I'd be curious to try this out on it. My email is richard at our website's domain if you'd like to get in touch!
- samatdav 2y agoLooks like a great repo to try the fine-tuning! I will email you, thanks!
- fovc 2y agoMakes a ton of sense! Is this for completions, patches, or new files?
- samatdav 2y agoHi! Currently we generate a whole diff (like cmd+shift+k in Cursor). But plan to add there rest soon!:)
- eurekin 2y agoThat's exactly what everybody advised me against doing - finetuning on own projects. Got really discouraged and stopped. So glad someone has done it!
- menaerus 2y ago> That's exactly what everybody advised me against doing - finetuning on own projects Why would someone advise against it? IMHO that sounds as the end game to me. If it weren't so darn expensive, I'd try this for myself for sure.
- freehorse 2y agoI think that people suggest RAG, also because the models develop so fast that very probably the base model you finetune on will be obsolete in a year or so. If we are approaching diminishing returns it makes more sense to finetune. As the recent advances seem to happen by throwing more compute to CoT etc maybe the time is close or has already come.
- eru 2y agoWhat's CoT?
- vlabakje90 2y agoChain of Thought. When I see people using abbreviations like this I sometimes jokingly wonder what they do with all this time they're saving.
- plagiarist 2y agoPerhaps they're preemptively reducing several tokens into one, for the machines' benefit.
- zitterbewegung 2y agoThere are so many chain types it is easier to do the abbreviations. Basically extend a RAG to have a graph to influence how to either critisize itself or perform different actions. It has gotten to the point where there are libraries for define them. https://langchain-ai.github.io/langgraph/tutorials/introduction/ https://langchain-ai.github.io/langgraph/tutorials/introduct...
- smcleod 2y agoIs the source code available for inspection somewhere? It's not really clear from the landing page.
- samatdav 2y agoNot yet, but we plan to publicly host a fine-tuned model so anyone can try.
- deleted 2y ago[deleted]
- aussieguy1234 2y agoYou make a bold marketing claim, 4.2x Sonnet, but viewing your website, I can see no data or test results to back this up.
- mathgeek 2y agoThanks for calling this out, even if it just gets OP to comment with some details/data. Was hoping this would be a shallow or deep dive into the results, but looks like it’s just a marketing post to a marketing page to support a PH launch.
- samatdav 2y agoGood point, I agree, we haven't shared enough details. Since we are very early, we only got high level results and want to get feedback on what direction would be most applicable and useful. We plan to add more metrics and data to the website in the future and also want to publicly host a fine-tuned model for anyone to try and see.
- pcwelder 2y agoWas the comparison done with or without code context (as obtained using RAG or letting Sonnet ask for files)?
- jeswin 2y agoIn the absense of other information, looks like a cherry-picked example to me.
- samatdav 2y agoWe used a single file for the context. It is a cherry-picked example, you are right. I wanted to demonstrate a simple visual change that our model did correctly unlike Sonnet-3.5. Since we are just getting started, we don't have many features like making changes across multiple files in the code editor so it would be harder to demo. Our premise is that a smaller fine-tuned works better than a large, general-purpose SOTA model. We plan to share more metrics and data in the future.
- DataDaemon 2y agoMaybe ask this model to create a better landing page?
- samatdav 2y agoThank you, we will!:) This was a quick landing page for us to start the conversation and gather feedback. We are trying to make sure we are not building something that nobody needs.
- mrfinn 2y ago2023: Our tiny model blah blah blah beats GPT4! 2024: Our tiny model blah blah blah beats Claude! 2025: Our tiny model blah blah blah beats ???
- mentalgear 2y agoI like it and it makes sense, but from a business perspective I wonder what keeps the upstream LLM providers (all trying to generate profits) from offering the same fine-tuning service quickly ? Edit: OK, right it's olama, so I assume you can download your own model. (Assuming it's downloadable?) I think openAI already offers fine-tuning with custom data for some of their models, but maybe not specific to coding tasks.
- samatdav 2y agoYes, you can download and host the fine-tuned open-source model like Llama. The fine-tuning is easy once you have the data, but gathering and cleaning data is challenging. There are also optimizations like upsampling and distillation that could improve the quality of the resulting model. We had 40 engineers at the Asana AI org and never did the fine-tuning because it is not easy.
- tkgally 2y ago> Our team is ex-OpenAI, Anthropic, and Asana research scientists and AI engineers The page includes the logos of those companies. Is it normal to do that for companies one used to work for?
- tucnak 2y agoNo it's not! These are probably imposters anyway. Apparently, you can buy HN upvotes... I find it hard to believe that honest researchers from frontier labs would behave like crypto scammers.
- uxhacker 2y agoIt’s also weird that there is no about page naming the founders.
- Lerc 2y agohttps://x.com/karpenoid/status/1670723794544263170 https://x.com/karpenoid/status/1670723794544263170 https://x.com/karpenoid/status/1873281722613400002 https://x.com/karpenoid/status/1873281722613400002 This might be linked.
- tucnak 2y agoThere's a reason why you don't see frontier-grade AI researchers throwing around meaningless numbers to go with the most layman idea of a product in the field imaginable. The whole thing stinks. I reckon this is some kind of extortion scam intended to trick people into compromising IP.
- flakiness 2y agoThe page lacks details. The details are only in the YouTube video apparently? Please.
- prmoustache 2y agoHow do you measure code generation accuracy? Are there some base tests and if so how can I ensure the models aren't tuned for those tests only the same way vw cheated the emissions tests on their diesels?
- samatdav 2y agoWe run a set of change requests on the discourse repo. Good point, we plan to publish more detailed testing benchmarks and metrics on the website.
- Nelkins 2y agoHere's a Hugging Face blog post where they walk through how to fine tune a model on your code base: https://huggingface.co/blog/personal-copilot https://huggingface.co/blog/personal-copilot . Kudos to the founders for shipping. I do think this kind of functionality will become very rapidly commoditized though. But then, I suppose people said the same thing about Dropbox.
- Imnimo 2y ago4.2x doesn't mean anything if you don't tell me what "accuracy" Sonnet 3.5 had.
- samatdav 2y agoI agree. Our local early results were promising were a higher percentage of code change requests produced a functionally correct output. We will post more metrics and data in the future.
- samatdav 2y agoHi HN! I'm Samat, the co-founder from the video. Thank you for the critical feedback, great points. 0. Is this a scam? No. We're very early (started <1 month ago) so our landing page is to validate our concept, gather initial feedback and start conversation on what we can build that would be most applicable. We'll add more details and benchmarks to the website. 1. Company logos. You're right. We're using our work experience as a credibility signal because at this stage that is our main selling point. We'll replace logos with concrete results as we develop. 2. Team. We're 2 software engineers and 1 AI researcher: - I was an AI product engineer at Asana. https://linkedin.com/in/samatd https://linkedin.com/in/samatd - Denis was a tech lead at a unicorn startup. https://x.com/karpenoid https://x.com/karpenoid - Our third co-founder works at Anthropic and was previously at OpenAI. Since he is still at Anthropic and planning to leave soon for the startup, I can share his details privately. 3. Claims and transparency. Our "4.2x Sonnet-3.5 accuracy" is an initial estimate from a locally fine-tuned model. Actual results may vary - a small app might not see big improvements, but we believe larger, private enterprise projects could see significant gains. We plan to publish our fine-tuned model so others can verify the results. 4. Competition from LLM providers. Fine-tuning requires complex data cleanup and setup. Enterprise projects have fragmented data, making automation challenging for big providers like OpenAI. Appreciate the feedback! If you want to chat more 1-1, happy to discuss at hi@finecodex.com Samat.