DEV Community: Andrale

A Qwentessential Breakdown

Andrale — Sat, 04 Jul 2026 09:30:01 +0000

So ur here to learn about Qwen, so break it short, the Qwen family Iz the most recommended family for low ram users due to its brilliant architecture that keeps u up all night

It's honestly a pretty good starting point for a lot of users simply because of how broad a net it casts i recommend for anyone who wants to skip research, all rounder pretty good
The Qwen and her royal incestuous family, we have Qwen2.5 being the main outreach model, but it's garbage it came out it 2025 jan, that's prehistoric
The latest model like Qwen3.5-3.7 are kinda good but they lost the plot of serving low end RAM users they use fancy terms like gated attention which just means how much they can forget to let new info get saved about yours truly, deltanet is the part that handles new info more directly, and lastly the enemy the affairs of the Queen the MoE
The scam, MoE sounds good, sparse attention Yaaay, or is it, sparse attention just means they fire parts of the model, like how u don't use ur stomach to walk,the scam is just because it's firing some "experts" doesn't mean the non used ones are not cold started, they will use some background ram, also most experts would never be used, so u can just get grafted variations,
Always define what u want from Qwen not the other way around, she has a hot and stubborn temper and body 👅, so if u catch her cheating she won't admit it normally unless u get a variation that's a bit more sub
My advice to a beginner with a potato try qwen3.5 hyper quantised, or qwen2.5 to get used of the queen's tantrums, then u can try going crazy, also start at 8k, and get ready to be gaslit more than ur dating life For masos: umm yeah graft it or lora train to reduce hallucinating For deep throats: try using Deepseek R1 distill with qwen instruct and Q4-5 quantisation or try the new IQ quantisation, or even splice it if u feel like Frankenstein
Ask me questions or the French Qwen will haunt you

Charming My Biblically Accurate Mamba

Andrale — Fri, 03 Jul 2026 18:38:49 +0000

So how to find the mamaba for you,

Standard mamba model; honestly pretty great but is it worth the hype? Absolutely have u seen how much transformers get? Narcs
Hybrid models; alr now we're talking honestly mamba-1.58 version is a wet dream if u don't care about precision as much, linear attention (keeps ur tokens and tokens per second [t/s] in check), u can also use zamba2 I feel it's legendary,
Ultra Low ram users like me; best bet is sub-1B if pure ram,and u want good speed,and not want ur device to go bye-bye and kamakaze
Power users who r rich 🤑; the world is ur to take it recommend liquid AI, it's multimodal as well, if u feel extra lengthy and girthy, try learning to graft or auto distill from a larger model like deepseek R1 so this babe actually can do autonomous tasks, the reason is precision, SSMs are notorious for batshit precision, tho mamba3 does solve most of it
Overall good family, very open source, and barely any incest

HDC model for dummies (18+)

Andrale — Fri, 03 Jul 2026 18:05:54 +0000

So well come to my guide for HDC(hyperdimension computing)for beginners and dummies, so ur here to learn HDC good, very maso, love it, 1.first u need python(1.0+)
2.next for libraries it's pytorch + torchdd or holo(If ur feeling extra kinky) which is a rust-python wrapper,

AI example code import torch import torchhd

1. Set the dimensionality

d = 1000 # The "1000D" you mentioned

2. Create a random hypervector (your first "memory")

hv = torchhd.random(1, d) # A tensor with 1,000 random values
print(f"Hypervector shape: {hv.shape}")

3. The fundamental operations

hv2 = torchhd.random(1, d)

BUNDLE (superposition): Think of this as creating a "set" or a "noisy" memory

bundled_hv = torchhd.bundle(hv, hv2)

BIND (association): This creates a new hypervector that encodes a relationship

bound_hv = torchhd.bind(hv, hv2)

4. Similarity (the "search" and "recognition" function)

similarity = torchhd.cosine_similarity(hv, hv2)
print(f"Similarity between two random vectors: {similarity.item():.4f}")

You'll see a number close to 0, as random vectors are nearly orthogonal.

(I recommend claude for free and gemini pro if ur rich 🤑to code for you if ur feeling extra romantic)

I recommend starting at 1k D, cos it's easier to control and won't get you scratching ur head but itching for more
I recommend thinking them of a sheet of graphene(2D) to help u visualise, if ur feeling smart try thinking them of diamond lattice(3D), if u feel even spicier image them as a universe with galaxies as moving nodes cos "distance" is now an abstract concept
Stay hydrated
Have fun 😊
Ask questions, cos it's better if ur responsive and ready to learn -teehee ✨😘

Just another set up guide for a 4GB ram potato

Andrale — Fri, 03 Jul 2026 16:38:12 +0000

So to keep it short, I'll lay out the details for quick readers, it's Bitnet 1.58 bonsai-8B+ bitnet.cpp(or llama.cpp of ur lazy) + tools like persistent memory and auto batching,(or just use ollama and use community plugins) ummm yeah that's it, if ur in for the juice here's more:

Llama.cpp is good honestly works well for Bitnet unless u feel maso u can use Bitnet very similar low learning curve, but try use 512 batching (works for me)if u have a dedicated GPU unlike please use that, it'll get ur bestie,
If ur feeling risky use early speculation like a small 0.5B model but Bitnet is fast enough already(also adds unnecessary ram overhead, or idk lora TTT is a good way? Too many things to do)
Why Bitnet, speed and just raw general IQ is dense AF(gives like 7B accuracy and 45t/s but don't take my word for it,but also so I don't feel bad expect 25t/s)
Should or can u find something better? Absolutely
Maybe ask me questions, I'll answer in a few mins prolly(i hallucinate too)
Any upgrades u can add? In place TTT makes u a mad max model, but lora TTT is going crazy especially if u use the prototype Qlora + inplace TTT, next is like tool calling use TTT or lora to teach it permanently (remember to save if ur using TTT) yeah there's more but honestly this should get u going pretty smoothly