American closed models vs Chinese open models is becoming a problem.

Note on my earlier response: I want to clarify, it is a little difficult to re-write all of most things, because in the data elements, you're looking at so many references that people rarely look at the data by hand, though I have a little script that plays my database like the matrix, and I watch it from time to time looking for inconsistencies, but that's mostly just for fun, not a technical strategy. Once again, if you're a company delving into enterprise AI adoption with high sensitivity data you're already managing, this is almost as much integration as it is adoption. In most internal AI use cases using RAG data, geopolitical BS is often meaningless, I've never used my fine tuned custom coding model to discuss hot button political issues, that's more crap people on Reddit do than in an enterprise AI environment. RAG data is literally used for output precision within structured data and response needs, while I won't deny one exists somewhere, seriously, that crap has no place or relevance in enterprise data, the proper database that stuff belongs in are already managed by social media. You can have internal biases in AI that are mitigated and others that are irrelevant, and it's extremely unlikely you will encounter one you can't do something about, especially if you have familiarity with the models you are working with and data. Between LoRAs, Fine Tuning, and RAG, there's very little you can't effectively change to a level that meets functional needs, and there are lots of methods used here. There are some security issues that can very easily matter when you pick a model, but that's rarely going to be geopolitical, it's more like prompt injection vulnerability or tool call manipulation, but once again, even that stuff stops mattering with internal databases. If you work for a company that "absolutely can not have your data leaked", you aren't exposing that data to the public via an AI anyway, because the moment you give an LLM access to your data and the public access too the LLM, that data has pretty much been leaked. Internal use enterprise deployments won't typically be impacted by some geopolitical favoritism, and if they were, that would be correctable and an indicator that maybe you need to think about whether you're okay mitigating that issue or if the hassle warrants not using that model. This same thing can happen with any LLM, in fact, look at the crap show that is social media, you'll find plenty of people arguing this stuff even with closed source corporate models within the same geographical region, if you need an example, look no further than ChatGPT and Grok.

What kind of holy hallucination is this? Did you get this from AI? This is NOT how this works, literally at all! 1. You do not get updates from models, if you downloaded DeepSeek V2, you don't upgrade to DeepSeek V3.2, those are two different models, one does not impact the other, and you don't ever "update a model". 2. It is clear you know nothing about fine tuning, RAG, etc., so let me clarify something using an example I literally just did. I wanted to create a custom fine tune of Qwen3-Coder-30B-A3B-Instruct, some custom LoRAs, and a RAG database. RAG is different than training data, though you can set your database up in such a way that you can use the same data for RAG, Fine Tunes, (Q)(Re)LoRAs, etc, these are different tasks altogether. Let's look at the fine tuning though. 3. So before fine tuning a model, you are going to take your data and convert it into pairs, the easiest way to do this now is using an LLM, (or several for refinement). You convert it into basically what boils down to prompts and responses, building relevance among a dataset so you know what the raw data even does. When you do a retune of the model, you are basically restructuring the weights and balances of the model to fit this new data in the model and place it within the values and rankings of that model. The MOMENT you switch to a new model, you have to do it all over again, so for example, my 400MB code dataset, my API LoRA, my change log and update LoRA that I made for Qwen3-Coder-30B-Instruct, if I want to use it with a Qwen 3.5 model, I have to re-train the whole thing over again. So when an AI company is improving data, they have a massive dataset that they've built, but they also comb through that dataset, mess with how they weigh certain data, scrub known bad data, add in new synthetic data, use HITM to filter and refine the data, etc., and then they re-train the next generation model on the improved data, which blended with improvements to the model itself, should make the new model smarter (not a guarantee). When you fine tune a model, it's no different than training a new model, you start over every time, but if you have a clue what you are doing, you have an evolving database to be doing this with, hopefully separated and organized. 4. RAG databased are not dependent on a model, they are model agnostic, a RAG database is NOT part of the model like a fine tune, it's a dataset where you're likely to maintain your dynamic or evolving data, this is a much more refined external data source that your model uses to generate responses from, where you need retrieval to be consistent, updated, and live. For example: Say you have a customer database, you have accounts, balances, billables, etc. all attached to that account database. You're not fine tuning your model on customer's individual data (that would be dumb), but you're also not updating your fine tuning (which is very time consuming/compute intensive) daily, so you take your fine tuned model and you connect it to your RAG database and now your model is pulling live data and your AI agent can look at account balances, equipment, etc., without needing to hallucinate them. There's tons of other uses for RAG, like live code bases, combined data sources, etc., but the point is that isn't even part of the model at all. I can use my database with any model I choose, I can change it at any time, I am locked into nothing. I can use my data to build for Qwen 3.5 now if I want or I can decide I want to use it with GLM 4.7 Flash, the changes involved in doing one over the other are fairly arbitrary. While I'm doing this, while I'm looking at weights, balances, etc., I can filter any geopolitical BS I want. I could go to a Chinese model and with one python script I could re-write all of that data to replace Xi Ping with myself if I wanted to or delete him from existence. If you have any idea what you are doing, you can and should be aware of any potential issues with the model you are working with and really, you can address that stuff during fine tuning. What you don't get with fine tuning, LoRAs and the RAG database will eliminate anyway. For the most part, when you are using a precision tuned model, you are using its ability to reason, it's general knowledge base, important things it has learned like language so it can know how to respond to you, but everything from the personality to the things that are important to you (which can include your own geopolitical beliefs) to its safety guidelines etc. etc. etc. are ALL easily overridden by your modifications. As you curate your AI data for model customization, that's an asset you build over time, improve, grow, and use to make models unique, but also very useful to you. A vibe coder could make the necessary changes to the output structure when translating your database as needed for different models, it is really not that big of a deal. The closest thing you have to consider is like say you go from a dense to an MOE, you might need to look at crafting additional MOE layers that connect to your new data, or creating chain of thought linked pairs for reasoning models, but the point is that this is work you will replicate many times, either with new versions of the same models or with new models, there is no imaginary ecosystem you are locked into, and any good database manager can tell you that changing data formats isn't that big of a deal.. 5. Your 'Key issues' read like a generic zero-shot AI output. They are entirely irrelevant to an air-gapped, internally fine-tuned enterprise environment. They don't even go within the context of this thread, and the "Mitigate by:" is just more nonsense to go with the first nonsense. If you're going to present a point, have a point to present, this AI response garbage is worthless. You don't need to use AI to tell people you don't know anything about enterprise AI use, training, or database management, you look more intelligent if you just don't reply with this trash. There are mixed bits of truth in your response from generic security standpoints that don't really even apply to this thread or context, like the AI took some generic real discussed security topics in ML and threw them in as some faux counter argument under context that didn't even apply. I'm not going to argue those irrelevant points with someone who doesn't know what they're talking about so you can have AI hallucinate another response to it, but I will tell you that ANYONE who is investing in enterprise local LLM deployment should 100% expect the biggest aspect of that to be proprietary database management, which will 100% include as a standard practice re-formatting for different models. It doesn't matter whether you're moving from DeepSeek to GPT-OSS or if you started with Qwen2.5 and you're moving through Qwen3.5 models, you should be prepared to test different models and structures, figure out which ones work best, and prepare for significant deployment cycles while anticipating or evolving with the technical landscape. Whether you're using dense models or MOE, China or US, etc., as those models improve, your process of preparing data must evolve with it. Building and maintaining a company database isn't a one time gig and if your company already has a database, such as the OP, which you need to protect, you're already managing it as a full time job, and adding AI doesn't negate this, it expands it, and eventually your AI database management just becomes part of database management.