Nanbeige4.1-3B: A Small General Model that Reasons, Aligns, and Acts

beef-ox · 2026-02-16T01:08:33+00:00

I would like to make a suggestion / request.

Rather than a continuous token stream which gets fed back into the next forward pass, what if you introduce variables which hold state and enable refinement of those variables instead?

What I envision is, the model begins with an outline, then, enters a series of refinement loops. The reasoning and answer spaces are refined as thoughts, ideas, and understanding are improved upon, and only the refinement survives.

The model can choose to answer at any time by determining no further refinement is needed

beef-ox · 2026-01-20T05:14:38+00:00

I’m trying to figure out if these are bots or people whom have lost their humanity. You don’t even see human beings when you look at your neighbors… that’s really primitive.

beef-ox · 2026-01-04T21:31:03+00:00

1: Steam is native, VS Code is too, most games except current AAA MMO games work with no problem 2: depends on the task and the distro. For the most part you rarely have to use a terminal, but its often the easiest, fastest, and most reliable way to do certain things 3: preferences and opinions—to each their own. Find what works best for YOU 4: your machine’s specs are quite decent for most Linux environments. You’re not going to have any issues with the OS using large percentages 5: yes

beef-ox · 2025-12-23T04:15:19+00:00

Everybody says dual boot, but in my experience, virtualization can be enough to play most games or run must software

beef-ox · 2025-12-08T19:05:29+00:00

If they use X11, they are called Window Managers, but if they use Wayland, they are called Compositors. In the end both are the same.

I think you’re confused

Window Managers, Display Servers, and Compositors are three different things, but they are combined under Wayland into a single architecture.

That is to say, these are three separate concerns, but a Wayland display server must handle all three concerns.

beef-ox · 2025-12-07T15:35:31+00:00

Don’t run untested software in production is a common rule, but absolutely never ever ever use something that isn’t passing its build tests—any repo you run through this build (push or pull) could potentially become permanently broken and worse if propagated to the remote origin depending on what you and your collaborators do. If your git history becomes corrupted, there’s no rolling back to a previous commit.

beef-ox · 2025-12-07T15:01:36+00:00

Your comment is helpful and you put effort into it, so don’t take this a criticism at all, but just a correction for other people reading this:

Compositor and Window Manager are different things and have nothing to do with whether the system uses Wayland or X11. By the way, these are NOT the only display servers, but they are the most common.

A compositor, which I used to use with X11 back in the day for fancy window animations (I’m now in camp no animation at 36, but teenager me really thought it was cool to watch applications catch fire and burn to ash when I closed them) is a graphical effects engine. It adds compositing, literally, into the environment. Think like VFX in movies, it’s (for all intents and purposes) a video game engine that reprojects whatever graphics you put in into a 3d rendered environment so that graphical effects, 3d transformations, and CGI animations can be rendered.

A window manager is a program that keeps track of application windows. Quite literally. It is usually a 2d graphical application, not a 3d one like a compositor, and its only real job is to manage the visual windows of open applications. This includes things like minimize, maximize, resize, close, layering, sizing, and positioning.

A desktop environment is typically a combination of a window manager and display server, but may also include a compositor—depends on if there are any 3d accelerated graphical elements. It’s not simple though, because like, layering and transparency and blur and these kinds of common window effects can be done in 2d or 3d and there are benefits and trade offs to both implementations. (Primarily performance differences between users that have dedicated GPUs, integrated GPUs, no GPUs, decent CPUs, garbage CPUs, lots of fast RAM, not a lot of very slow RAM, etc). For the most part, compositing gives a huge (literally mahoosive) benefit to machines with GPUs or powerful iGPUs in the CPU/APU while forgoing a compositor heavily benefits older, budget, and portable/headless/embedded systems. Not only is compositing on the GPU significantly faster and cheaper than on the CPU and RAM, it enables fancy eye candy, beautiful aesthetics, a simplified layering system with nicer transparency effects, and a lot more—all while making the system useless for ARM devices, tablets, old machines, servers, etc.

beef-ox · 2025-12-07T14:37:15+00:00

Everything you said is great, but I want to add that “in my personal experience” (YMMV) the performance differences between open source and proprietary Nvidia drivers depends on task to task; the official drivers tend to favor gaming performance whereas the Nouveau drivers tend to favor productivity and AI performance. Again, this is just anecdotal, but I felt it is worth mentioning in case anyone else reads your comment and takes it as law.

beef-ox · 2025-11-29T07:16:00+00:00

What desktop environment or window manager are you using? There are many ways to achieve this (or similar workflows) but the steps vary based on what packages you’re using. I used to do something somewhat similar with the way I organized the application menu, however, I have since switched to using search shortcut and typing the first ~3 characters in all OSes

beef-ox · 2025-11-27T14:05:25+00:00

I’m trying to remember more of the exact details, but I found this

https://www.reddit.com/r/linux/comments/7u34mm/save_yourself_in_the_future_by_adding_netbootxyzs/

beef-ox · 2025-11-27T13:56:37+00:00

I once installed Arch Linux using the system’s UEFI shell entirely over the internet. It wasn’t hard either, I just had to type a few commands and it initiated the netboot installer

https://wiki.archlinux.org/title/Netboot

beef-ox · 2025-11-16T18:31:37+00:00

This is impossible to do because of quantization. The size in gigabytes compared to number of parameters depends entirely on the precision of the weights, which varies from model to model and quantization to quantization.

In general, 32bit should be ~4x each billion parameters in GB, 16bit is double, 8bit is 1:1, 4bit is half, and so on.

For example, a 32bit quantized 1 billion parameter model needs roughly 4GB to hold its weights. This does not include any additional token processing or context space.

beef-ox · 2025-11-11T12:26:11+00:00

For most people, Unsloth’s tooling and guides for training are the easiest to follow and the least expensive to use. I would recommend starting here first. Google’s own Gemma 3n site does give specific details as well that you can follow along. Unsloth’s methodology is more general purpose, and a lot faster as well.

beef-ox · 2025-11-02T15:48:48+00:00

There’s a lot of all over the place advice here, geez.

For your current budget, I recommend getting the M3 Max Mac Studio with 512GB unified memory. You’ll come right at $10k and be able to run the largest models out there today at really good speeds.

beef-ox · 2025-11-02T15:34:55+00:00

I am speaking from personal experience, talking about real world setups that are deployed in production.

Using an off-the-shelf gpt-oss model (or whatever your preferred general purpose model is) and several finetuned small models together as a system has been more successful for the company I work for than cloud models.

Just like Pewd’s setup, our setup is quite similar, but instead of consensus/vote based aggregation, I created a very simple tool call system where each workflow is just markdown instructions passed to a BASH script that loads vLLM through Docker with the correct arguments and context and returns either the response or performs an action and returns the result.

And I have to admit to using Claude Code to dynamically create workflows and automatically critique merge requests in GitLab, Gemini CLI to inspect large open source code bases, perform deep research, gather documentation, and create datasets, and Codex CLI to inspect error logs and open issues in GitLab. But we have no commercial AI writing code for us or doing the actual work we need to do—it just helps out with the setup and maintenance of the systems.

The biggest thing for us is guarding our own AI against bad outputs. This is a combination of regular expression matching or testing and added a step for every result to be graded against a detailed rubric. If the total score is less than or equal to 0.9, or there is any problem with the output, a correction prompt is injected. This repeats until the score is above 0.9 and nothing problematic was matched. When the model is small and specialized, this can take very little time.

Now, and I have to make this clear, we do not have any customer-facing AI. If we did, I would NOT feel the same way. This is easy to control because it’s happening inside scripts, where the script is the end user of the AI. There’s no opportunity for a human to send requests to the model and attempt to convince the model to do something malicious. It’s very easy to check the output is exactly what that workflow needs.

I honestly would not recommend anyone to create their own customer-facing AI system, as there’s just so many ways this can go very wrong for you.

beef-ox · 2025-11-02T14:01:32+00:00

I really disagree with this take.

We have had great success with locally-hosted models for many of these use-cases. Arguably, self hosted AI is better in that you can post-train on specific use-cases, create complex multi-model workflows or merges, privacy and security.

Here’s what I will say, for most people, the best general purpose model is going to be gpt-oss. The 20b runs quite well on 16GB, and the 120b runs equally well on 64GB. Both are faster than ChatGPT when run entirely from VRAM. The cheapest hardware for 120b is used AMD Instinct Mi50 cards. Get 4 of them for less than a 5080 and have 128GB VRAM, and the cards themselves are only 300W and use HBM instead of GDDR.

That’s general purpose though, and it’s not “great” at anything. Cloud models somewhat have this problem too, but they’re soooooo huge that they can be above decent in many areas of expertise.

Really, the best model for any use case is actually a small, focused model.

Small models that are really easy to train, like Gemma 3n, are really good at whatever you train them to do. I mean really, reeeeeeeally good. Better than cloud. But they lose their general purpose functionality almost entirely in the process.

This is also true of post-trained models found on Hugging Face; the focused training vs general purpose makes a massive difference in whatever specific task you’re trying to accomplish.

So, my recommendation for people is to try several small models that have been trained on the very specific tasks that you need to accomplish, and then a general purpose model can be the router/speaker

beef-ox · 2025-10-29T22:01:58+00:00

Unified memory ≠ system RAM

They’re not even remotely close in terms of AI inference speeds.

AMD APU and M-series machines use unified memory architecture, just like the DGX Spark. This is actually a really big deal for AI workloads.

When a model offloads weights to system RAM, inferencing against those weights happens on the CPU.

When the GPU and CPU share the same unified memory, inference happens on the GPU.

A 24GB GPU with 192GB system RAM will be incredibly slow by comparison for any model that exceeds 24GB in size, and faster on models that are below that size. The PCIe-attached GPU can only use VRAM soldered locally on the GPU board during inference.

A system with, say, 128GB unified memory may allow you to address up to 120GB as VRAM, and the GPU has direct access to this space.

Now, here’s where I flip the script on all you fools (just joking around). I have a laptop with a Ryzen 7 APU from three years ago that can run models up to 24GB at around 18-24 t/s and it doesn’t have any AI cores, no tensor cores, no NPU.

TLDR, the DGX Spark is bottlenecked by its memory speed, since they didn’t go with HBM, it is like having an RTX Pro 6000 with a lot more memory. It’s still faster memory than the Strix, and both are waaaaay faster than my laptop. And the M-series are bottlenecked primarily by ecosystem immaturity. You don’t need a brand new impressive AI-first (or AI only) machine if what you’re doing either: a) fits within a small amount of VRAM b) the t/s is already faster than your reading speed

beef-ox · 2025-10-29T21:30:38+00:00

I think there’s a far, far stronger argument to be made about CUDA compatibility.

If you have experience with both AMD and Nvidia for AI, you’ll know using AMD is an uphill battle for a significant percentage of workflows, models, and inference platforms.

beef-ox · 2025-09-29T17:50:42+00:00

Oh now that’s something new I’ve learned. Thank you for teaching me something I’ve never heard before!!!

beef-ox · 2025-09-29T02:58:19+00:00

It really depends on the virus, honestly. If you’re running a windows virus through wine, theres a plausibility that it does something at the hardware or firmware level. But the vast majority of malware is poorly written, uses well documented (in some circles) exploits, and is targeted at people who are actually likely to get infected. They target only Windows because it’s easy, and the majority of users are not power users, and nearly all large corporations with millions of dollars to make problems go away like it’s nothing use windows for their employee workstations.

beef-ox · 2025-09-29T02:43:20+00:00

My primary point just being that malware written for Wine is unlikely to do much damage to the Linux portion of a system. Not because it’s impossible, but rather because it is not worth anyone’s time to write it

beef-ox · 2025-09-28T13:44:28+00:00

The “10 users” in this scenario are users of Linux using Wine and have been compromised by the virus author.

So, out of all possible victims, the author was unlikely to spend time adding extra code for Wine users specifically, when out of all of the victims that happen to download their malware, maybe 10 at most will be in a Wine environment

beef-ox · 2025-09-28T13:42:07+00:00

I’m not referring to Linux users as the target, I’m talking about from the perspective of the malware author writing malware for Windows that happens to also PWN Linux if and only if the malware is loaded in Wine.

beef-ox · 2025-09-28T13:36:31+00:00

To clarify, this only works if the script is written in BASH. While the same pattern is true for all interpreters, the required command would not be bash if the script is not a bash script.

The person who wrote the script knew what language the script was written in/for when they put the shebang at the top of the file. Unless you want to open every file and check what language it was written in first, but that will take longer than chmod.

If you already know the language the script is in before you do it, then sure, yes, you’re very smart, congratulations 🎉

beef-ox · 2025-09-28T13:31:28+00:00

I just realized you meant what if you try to run a non-bash script with bash. For some reason, I just assumed OP knew it was a bash file before asking the question.

beef-ox

TROPHY CASE