81–90 of 118 posts
Fine tuning is a story that is nice to tell but that with modern LLMs makes less and less sense. Modern LLMs are so powerful that they are able to few shot learn complicated things, so a strong prompt and augmenting the generation (given the massive context window of Qwen3.5, too) is usually the best option available. There are models for which fine tuning is great, like image models: there with LoRa you can get good…
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For me, trying to fine-tune a model to write "best day" prose I would accept over 80% of the time. You are correct if we are talking about knowledge. However it is bad at hyper-idiosyncratic, gritty style transfer. I first noticed the issue when asking claude code to draft email responses. The choice of register was off. ("Register in writing refers to the level of formality and tone chosen to suit a specific audienc…
> Instruct models are already so tuned that they could not be tuned Some models have the base model available, that is before instruction tuning. For example llama 3 comes in "pre-trained and instruction tuned variants" [1]. I'm guessing you already know that though. [1] https://huggingface.co/meta-llama/Meta-Llama-3-8B
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Very interesting. Could you give examples of industrial tasks where lower accuracy is acceptable?
Industrial inspection is usually a fairly blunt task and I wouldn't be concerned about accuracy. Especially in high volume environments where training data is plentiful. Think about things like chip placement errors, alignment problems, bad solder joints, missing components.
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I've seen these agent-written fake anecdotes on Twitter, Reddit, and now here, all with the exact same formatting. They pretend to be real people with real anecdotes, but they're all completely made up.
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Only to prompt thought on this exact question, im interested in answers: I just ran a benchmark against haiku of a very simple document classification task that at the moment we farm out to haiku in parallel. very naive same prompt system via same api AWS bedrock, and can see that the a few of the 4b models are pretty good match, and could be easily run locally or just for cheap via a hosted provider. The "how much d…
You can fine tune a small LLM with a few thousand examples in just a few hours for a few dollars. It can be a bit tricky to host, but if you share a rough idea of the volume and whether this needs to be real-time or batched, I could list some of the tradeoffs you'd think about. Source: Consulted for a few companies to help them finetune a bunch of LLMs. Typical categorical / data extraction use cases would have ~10x…
My interest in fine tuning at all is based on an adjacent interest in self hosting small models, although i tested this on aws bedrock for ease of comparison, so my hope is that given we are self hosting, then fine tuning and hosting our tuned model shouldn't be terribly difficult, at least compared to managed finetuning solutions on cloud providers which im generally wary of. Happy for those assumptions to be challenged.
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Only to prompt thought on this exact question, im interested in answers: I just ran a benchmark against haiku of a very simple document classification task that at the moment we farm out to haiku in parallel. very naive same prompt system via same api AWS bedrock, and can see that the a few of the 4b models are pretty good match, and could be easily run locally or just for cheap via a hosted provider. The "how much d…
if you add 2 spaces at the start of the line, you turn it into a code block like this
Fine tuning is a story that is nice to tell but that with modern LLMs makes less and less sense. Modern LLMs are so powerful that they are able to few shot learn complicated things, so a strong prompt and augmenting the generation (given the massive context window of Qwen3.5, too) is usually the best option available. There are models for which fine tuning is great, like image models: there with LoRa you can get good…
Qwen filtered out a lot of porn during data curation, and a finetuned model can perform much better than context engineering. Abliteration can only remove censorship, not add something non-existent in the training data.
This guy did some great work in the age of Qwen 3.0: https://huggingface.co/chenrm/qwen3-235b-a22b-h-corpus-lora
Fine tuning is a story that is nice to tell but that with modern LLMs makes less and less sense. Modern LLMs are so powerful that they are able to few shot learn complicated things, so a strong prompt and augmenting the generation (given the massive context window of Qwen3.5, too) is usually the best option available. There are models for which fine tuning is great, like image models: there with LoRa you can get good…
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I'll find some time to do this and I hope someone can do this earlier than me.
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> where latency matters more than raw accuracy – think industrial inspection Huh? Why would industrial inspection, in particular, benefit from lower latency in exchange for accuracy? Sounds a bit backwards, but maybe I'm missing something obvious.