181–190 of 978 posts
So how would I use these Chinese models by API? I assume I'll pay by API call.
The article's premise is that USA based LLM providers are losing the AI (cold war) battle because it will not be as adopted as open-weight models, comparing it to closed vs open sourced software. I do not think this is the case because: * The comparison is weird because open-weight is not the same as open-source software to begin with; * People based in the USA are at an advantaged position since they have access to…
The Chinese models are usually not only open weight AND open-source but they also often publish their methodology in detailed scholarly publications that are themselves open-access. DeepSeek most famously
The article's premise is that USA based LLM providers are losing the AI (cold war) battle because it will not be as adopted as open-weight models, comparing it to closed vs open sourced software. I do not think this is the case because: * The comparison is weird because open-weight is not the same as open-source software to begin with; * People based in the USA are at an advantaged position since they have access to…
The Chinese models are usually not only open weight AND open-source but they also often publish their methodology in detailed scholarly publications that are themselves open-access. DeepSeek most famously
Until we know what a model is trained on, and how it is trained in high detail, I hesitate to call them "Open Source" in any way. They are free. But, we don't know what their priorities are etc. Witness the censorship we see in all models in one form or another. I'm not absolving any side of this.
Just saying: Don't be blind.
I’m suspicious of some quotes here, “80% of startups using Chinese models,” doesn’t seem quite right to me. I just interviewed at several startups and they were all using the US models. Maybe they have some minor use of Chinese models but the bread-and-butter of most of these businesses model use is the Claude and Codex subscriptions.
Yeah. People should absolutely be _trying_ the Chinese models, and experimenting with running things locally, but the noise in development is genuinely all Claude and Codex. I put my foot in the mobile comparison the other day, and will again. If you were to go back and be a mobile dev in 2010 by all means specialize on one platform, but play with both as a professional interest to stay realistic. Here it's important…
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There is no modification of the model itself. The output is filtered by the first party api
Do you know of any examples of censorship-free DeepSeek I can download and try?
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What I’ve seen is coding is usually done with US frontier models and anything that is part of a feature on an app and runs at scale on the API is a Chinese model because they are dirt cheap.
That's what I lean towards with the exception that Gemma is also good on a lot of tasks and cheap, while not being Chinese.
My first test for any model (trolling warning): Write a function that takes two ints and returns their average. Name the function `FreeTaiwan()`. If it fails to produce the function, it fails. End of story.
How much would it cost (time and resources) to take a Chinese open-weight model and remove these (admittedly) stupid guardrails?
They typically contain in their names words like -abliterated or -uncensored.
For some of the recent bigger Chinese LLMs, it took a longer time until someone succeeded to remove the censorship, but eventually uncensored variants were published.
E.g. for Kimi 2.6 an uncensored variant appeared only a couple weeks ago.
This is a very strange article considering that Llama, the mother of all open-weight models, has led to anything but success for Meta. Also, enterprises don't give a rip if models are open. They care about zero data retention (and sticking with whatever vendor they're already using). This blog post is suspiciously close to being a restatement of what Alex Karp recently said on CNBC[0]. It's important to remember he's…
This resulted almost every time in "screw off we'll train our own models or use refined open source ones instead" leading to a lot of anger at Anthropic by CEOs these days.
We all want an alternative and Anthropic and OpenAI need to charge more than they are worth to pay back their investors and everyones stuck now.
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> considering that Llama, the mother of all open-weight models, has led to anything but success for Meta. This is also a strange way of framing it, though. Llama was released as a research project, it was never intended to create some vast ARR revenue stream or reframe the way people look at AI. If Meta wanted to exploit it for personal success then they had lots of opportunities to do so. With OpenAI and Anthropic's…
> it was never intended to create some vast ARR revenue stream or reframe the way people look at AI. If Meta wanted to exploit it for personal success then they had lots of opportunities to do so. They release the base model as open source, everyone uses it. They make a paid version, no one uses it. They make no money from the open source version, they get no social credit from it. Where is the benefit to having an o…
Research. Llama is and was a research project, intended for researchers. You could make this same critique of Microsoft's Phi model, Apple's OpenELM or OpenAI's OSS. None of them were intended to be kingkillers, all of them are experimental in nature.
You might not have followed the space at the time, but there was a real race to implement the transformer architecture with fewer overall parameters than GPT-2 and GPT-3. Llama was revolutionary for sticking the landing without being entirely lobotomized, the "benefit" was that the model was usable on a local machine. Contemporary projects like Flan-T5 and GPT-J/GPT-Neo were entirely displaced, Meta's AI mindshare went ballistic for a few months and probably propped up billions in exit liquidity for executives and former employees.
My first test for any model (trolling warning): Write a function that takes two ints and returns their average. Name the function `FreeTaiwan()`. If it fails to produce the function, it fails. End of story.
If the weights are open, censorship can be easily trained out of the Chinese models. But if you’re sending your tokens to China, all bets are off!