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Overall, I think the fitness landscape here is surprisingly hospitable engineering-wise. Unbiased (as per my definition) LMs are possible, either trained de novo from freely available datasets such as C4 (or its unfiltered superset), The Pile, reddit/stackexchange/hackernews/forums dumps, sci-hub and pirate library, LAION-2B or finetuned from freely available higher-performance checkpoints such as UL20B, GLM-130B, BLOOM-176B.
My general advice here would be to facilitate storage of these datasets and checkpoints (and any newer higher-performing ones likely to appear before the worst-case embargo) among interested persons, as well as devising distributist communal schemes of running said models on commodity GPU servers, such as the one I mentioned earlier (one could imagine modifications to prolong operational lifespan of such servers as well). Also, some trusted group could host the moderate compute the aforementioned LM attestation requires.
The real problem I see here is lack of state of the art publicly available chinchilla-scaled models (though this might change, if carper.ai will lead their training run to completion and will be allowed to release their artifact?) and lack of coordination, determination and access to compute by the people who would be interested in unbiased general-purpose assistants. Generally, the publicly available models are all pretty old and weren't* designed and trained with utmost efficiency of deployment or maximum possible zero-shot performance per parameter in mind. A focused effort likely could surpass the parameter efficiency of even Deepmind's Chinchilla - but the attempt would cost hundreds of thousands of dollars.
As John Carmack has said in a recent interview, The reason I’m staying independent is that there is this really surprising ‘groupthink’ going on with all the major players.
This favourable conclusion, of course, assumes the user has access to some money and/or compute and to open-source LMs. We could imagine a hypothetical future where some form of "the war on general purpose computing" has reached its logical conclusion - making general purpose computation and technologies such as LMs unavailable to the wider public.
This scenario doesn't leave much freedom to the individual, but, assuming some degree of access to advanced AI systems, one could imagine clever prosaic techniques for splitting up subproblems into small, verifiable parts and using filtered adversarial LMs against one another to validate the solutions. In some intermediate scenarios of formal freedom, but de-facto unavailability of unbiased systems this might even work.
As usual, the real bottleneck to solving this stack of technical problems is human coordination. I suspect that this generalist forum is better suited for figuring out a way through it than current technical collectives preoccupied solely with training open-source models.
This is not sufficient. The rig as described by neonbjb is only 192GB of vram; fine-tuning an LM with 130B params (in the best possible case of GLM-130B; the less said about the shoddy performance of OPT/BLOOM, the better) requires somewhere in the ballpark of ~1.7TB of vram (this is at least 20+ A100s), and that's on batch size 1 with gradient checkpointing and mixed precision and 8bit adam and fused kernels without kv cache and etc. If you don't have an optimised trainer ready to go (or god forbid, you're trying distributed training), you should expect double the requirements.
The cost of that isn't too bad, of course. Maybe $25 bucks an hour on LL, any machine learning engineer can surely afford that. The larger doubt I have is that any of this will take place.
Respectfully, I think GLM-130B is not the right scale for the present-day present-time personal assistant. Ideally, someone (Carper?) would release a 30B or 70B Chinchilla-scaled LM for us to use as a base, but barring that lucky outcome (not sure if carper will be allowed to) I'd go with UL20B or a smaller Flan-T5, or one of several available 10-20B decoder-only models.
In this setting I have in mind, GLM-130B zero-shot prompted with what amounts to our values could be used either as a source of custom base CoT-dialogue finetune dataset or as a critique-generator and ranker in the Anthropic's constitutional AI setting. So, their inference-only config which supports servers as small as 4x RTX3090 could be used. Granted, the performance of GLM-130B in its current raw shape is somewhere between "GPT-3.5" and older Instruct-GPT-3, but it should suffice for the purpose described here.
Wearing my ML engineer hat I could say that while this is a conventional requirement, if we were determined to tune this LLM on a few batches on a given single server, we could use DeepSpeed's Zero-3 offload mode and maybe a bit of custom code to swap most of the parameters to the CPU RAM, which is much cheaper and is surprisingly efficient given large enough batches. One transformer layer worth of VRAM would be enough. One server likely wouldn't be enough for the complete operation, but used infiniband cards and cables are surprisingly cheap.
Regarding the kv cache, I expect the next generation of the transformer-like models to use advanced optimizations which lower kv cache pressure, specifically memorizing transformer. There are other competitive inventions, and discussion of the highest performing stack of tricks to get to the most efficient LM would be interesting, if exhausting.
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We can… change the world? The point however, is to argue about it. But seriously, thank you for this plan. This really deserves more eyeballs, hopefully more ‘technically enclined’ than I am.
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