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sumo43 4 days ago

I made a 4B Qwen3 distill of this model (and a synthetic dataset created with it) a while back. Both can be found here: https://huggingface.co/flashresearch

Nymbo 4 days ago | parent | next [-]

Just tried this out with my web search mcp, extremely impressed with it. Never seen deep research this good from a model so small.

Imustaskforhelp 4 days ago | parent | prev [-]

Can you please create a huggingface space or something similar, I am not sure about the state of huggingface but I would love to be able to try it out in a browser or something similar if possible as I am really curious and I just love qwen3 4b as they were one of the models which work even on my intel integrated gpu graphics card at a really impressive rate and they were really nice the last time I tried but this looks even more cooler/practical.

I had once an idea of using something like qwen4 or some pre-trained AI model just to do a (to censor or not to) idea after the incidents of mecha-hitler. I thought if there was some extremely cheap model which could detect that it is harmful that the AI models of Grok itself couldn't recognize, it would've been able to prevent the absolute advertising/ complete disaster that happened.

What are your thoughts on it? I would love to see an Qwen 4B of something similar if possible if you or anyone is up to the challenge or any small LLM's in generals. I just want to know if this idea fundamentally made sense or not.

Another idea was to use it for routing purposes similar to what chatgpt does but I am not sure about that now really but I still think that it maybe worth it but this routing idea I had was before chatgpt had implemented it, so now after it implemented, we are gonna find some more data/insights about if its good or not/ worth it, so that's nice.

greggh 3 days ago | parent | next [-]

I use emotions-analyzer-bert for this classifying content in a similar way. It's very small and very fast, under a gig of vram in use.

bigyabai 4 days ago | parent | prev [-]

> What are your thoughts on it?

You don't really need an entire LLM to do this - lightweight encoder models like BERT are great at sentiment analysis. You feed it an arbitrary string of text, and it just returns a confidence value from 0.0 to 1.0 that it matches the characteristics you're looking for.