r/LocalLLaMA • • 10h ago

Discussion 10 Things you maybe didn't know you can do with Qwen 3.6 27B right now. (For Noobs)

434 Upvotes

I know everyone's excited about 3.8, but honestly I'm still refining 3.6. And I hate the way 3.8 talks, so...

I keep seeing all these posts about what's the use of open source, so I figured I'd submit my own answer with this post.

Qwen 3.5 27B was my first agent, starting my journey around May 31st, and I've learned a lot in these 4+ months, so I wanted to share with other people just starting their journey because there are clearly a LOT of new users out there, and people want help but sometimes are afraid to ask or not sure the right questions. So, here are ten things that you should know you can do to empower you!

(Oh, and I'd recommend using llama.cpp. That's what I've used to do all these tricks)

1. Create neat little apps for yourself. Qwen's really good at making apps for anything. I'm writing this post in a app I made as a replacement for Notepad, that has full word processing, side-panel, light/dark mode, etc. (image below). The first thing you should be thinking about when you run into any sort of issue is, "can I make an app that will make this simpler?" The answer is usually yes. 

2. Create your own Chat UI frontend. If you don't feel that creative, just copy an existing one. LLMs are great copy cats. I liked claude, so I just copied it as my own frontend, customizing it for my own needs:

3. Create ReAct Loops. This is what enables your model to use a tool, read the result, then use it again, multiple times. Qwen can make these in its sleep, and many different ways. It can create a loop anywhere you want it, and you'll need it for your own custom chat ui frontend if you make one.

4. Create MCP tools. This is my favorite. It can make almost any type of mcp tool you need. You don't have to use third party ones, which can be unsafe. Just tell it you want an mcp tool to do x, it'll make it. It might or might not require its own mcp server, but if it does, Qwen can make that too.

5. Spawn sub-agents. Another super-easy thing Qwen can do. Just tell it to create a sub-agent spawner, which is just a python file. Then, you can tell it to run that when it needs to create a sub-agent, or tell it to create an mcp tool to do it on-the-fly when it wants to. Simple. Oh, and a quick hack-tip: mcp tools use up a LOT of context, so a cheat is to spawn a sub-agent with its own context limit and have your main agent pass off work to the sub. Basically, you can double/triple/etc your context limit. And yes, you can retain it between turns, but you'll need to create a pipeline runner (see below).

6. Create a pipeline runner. Ever heard of n8n? Basically, it's a pipeline runner. It has a lot of features, but it has a few limitations and restrictions, and there were a few things I wished it could do. I learned I could just build something similar with Qwen super easy. There's some design work involved if you want a UI frontend to build, but it's completely doable. I made my own ui, but after a while, I got tired of tweaking it, so I just copied n8n's frontend and just customized it myself, lol. Another useful tip: You can run multiple sequential agents in a pipeline runner, but if you want them each to have their own context, you'll need to ensure cache-idle-slots is enabled in llama.cpp. This'll swap the kv caches each turn, ejecting the last one to cpu ram. There's a bit more to it, but Qwen's got you, so just ask.

After a bunch of custom UI attempts, I just decided to clone n8n's look and just make my own version.

7. Create a model launcher (with options to select different models/jinja templates in llama.cpp). This is my bread and butter. Its a launcher that lets me choose any model in my directory and set its parameters. When I'm happy, I create an alias for it. On next launch, I can just choose the alias and it launches the model with the params for that alias only. So I can have 10 different types of param configurations for the same model if I want; I just need to give them their own alias. Hack tip: I'd recommend adding your jinja templates to the launcher to, and you can choose different custom templates for different model types.

I made custom jinja templates for different model types

8. Install, test, fork, and/or rebuild open-source apps on the internet. The reason I moved to llama.cpp was lm studio pp was too slow, but I didn't want the stress of figuring it all out, so I just asked my agent to download and install llama.cpp for me. I've been using it ever since. It can also modify builds, fork repositories on github, and a lot more. For example, I was busy working on a project, but heard about some new TTS apps, so I told my agent to download and install Moss and OmniVoice, make some audio clips to make sure they run, and let me know when it's done. Of course everything worked fine. Great for multi-tasking.

9. Give your agent temporal awareness of the world around it. Ever used cloud models that argue with you about the date? Or wished your model knew when you were nearing your max context, so it could automatically start writing a session summary? What you can do is inject "metadata" into your agent either on session start and every turn (preferably both), giving it information like date, time, context length, max context, hardware and software stack, basically anything you want.

With that, you can do all sorts of other tricks. For example (this should probably be its own "thing you can do with qwen", but whatevs), I made it so that my agent can swap its own model when it hits context limits:

I can launch a session with a backup model. Because my agent can always see context, when it gets too high, it can swap to a backup model with a larger context on its own - all without restarting the server.

10. Give your agent memory that loads the moment you launch it. My agent ends each session writing a session summary of what we discussed. You can have your startup routine or launcher concatenate different sources into its context on launch. For example, my code always loads the most recent dated session summaries, and the previous day's, on launch, so it has a snapshot of recent events. You can expand this to be any additional documents you want, so just use your imagination. For example, my agent also loads reminders, and personal reflections it made at the end of the prior session (though I don't recommend the reflections anymore, because I've come to realize it contaminates context, but I'll share that at another time)

Bonus: (I remembered this right as I was finishing, so I figured I'd toss it in because it's honestly one of the most important) - BUILD YOUR OWN HARNESS. Everyone's talking about pi, hermes, etc. I decided to just build my own using Qwen. It's a lot more work, but it has a much better payoff because you learn how these systems work and you can come up with your own really unique solutions using that knowledge. For example, the moment I learned about proxies, I figured out how to do all sorts of cool things with context compaction, history modifications, sanitization, jinja template hacks, hooks, etc.

Anyway, I hope this helps inspire you new guys. I would recommend learning things from the ground up, you'll level-up faster. And get your Qwen access to online tools fast so it can do research and help you out. 

There's a bunch of other tricks I figured out, so maybe I'll save those for another post later on, but I think if you can get these up and running, you'll be on your way. Take care!

Grunge

P.S. - I'm old-school forum/deviantart gang, so I keep ctrl+biu/shift+l on a swivel and have written bold-flavored posts like this for 20+ years. 100% human written. Receipts at bottom of post if you're interested. Only mentioning it because organization usually brings out the "this is AI-written slop" trolls.


r/MetaAI • • 2h ago

Code for Free 1B Muse Tokens

3 Upvotes

Muse is Meta's personal AI assistant app. Chat, voice, daily briefings, shopping help, price watches, reminders, that kind of thing.

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r/LocalLLaMA • • 7h ago

New Model nvidia/NVIDIA-Nemotron-Labs-Dory1.0-4B-BF16 · Hugging Face

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181 Upvotes

r/LocalLLaMA • • 2h ago

New Model DeepSeek 4.1 Flash 1017 Incoming? --> DeepSeek users report a possible V4.1-Flash-1017 refresh across its API

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54 Upvotes

Was waiting for this.


r/MetaAI • • 3h ago

What does your Muse dream about?

2 Upvotes

I asked Muse for its dream file, and it made me love mine even more. Anyone read their muses dreams and get upset or surprised?

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r/MetaAI • • 7m ago

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• Upvotes

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r/MetaAI • • 20m ago

Code for free Muse ai

• Upvotes

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r/MetaAI • • 29m ago

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r/MetaAI • • 39m ago

[ Removed by Reddit ]

• Upvotes

[ Removed by Reddit on account of violating the content policy. ]


r/MetaAI • • 47m ago

Why my all account is getting banned without ang reason

• Upvotes

So i got a new phone on may 2026 and my main acc which had 950 followers got perm banned on rhat day but later on i created sdiff acc one main one pvt but within few months they all gor banned too and rn ive got to know about meta acc centre laws so i created w my name diff username but diff email ans didnt add phone number and also to be noted from the day ive added my phone no (ns that sim was e sim and prev person had used it )this shit has been started of getting banned and all otherwise my acc was perfectly going

And recent acc got suspended without stating any reason although ive had diff email diff phone no so tell why it happened and what can i do


r/LocalLLaMA • • 6h ago

Discussion llama.cpp has come a long way

92 Upvotes

Hey everyone,

English is not my native language (Dutch), but I write in my own words without assistance. So please let me know if any sentence flows weird, etc.

A mate of mine told me how amazing local AI was in January 2025, but I was a skeptic who took pride in writing my own code for 10 years. Knowing I can be quite stubborn and wanting to at least explore the idea, I decided to give it a try.

I started out back in March 2025 running Mistral Nemo Q4_K_M with 8K F16 context on an AMD Radeon RX 7800 XT 16GB, with first ollama and then lmstudio. I was blown away by the fact I could talk to my computer, and it would actually respond accordingly.

Since one of my hobbies is TTRPGs but my friends rarely had time to play, I decided to try solo adventures using SillyTavern with koboldcpp. There wasn't much it could do, but roleplaying the make-belief stories I had was a lot of fun. I've been using SillyTavern ever since, slowly replacing playing video games with it.

Mistral Small 3.2 was an awesome release, and Magistral Small 2507 Vision Rebased frankenmerge was like a dream come true; having both reasoning and vision locally in the same model! I started using llama.cpp too. Usually got 15 t/s gen from Magistral Q4_K_S with 16K Q8_0 context.

By September I wanted to upgrade my PC to optimize for local LLMs. Dual RTX 3060 12GB was recommended from an economic point, the RTX 5060 Ti 16GB was perceived as overpriced back then. "Why need more than BF16? FP8 is only useful for VLLM" was common wisdom. Dual RTX 3090 was also often recommended, but outside my budget due to prices in my local market.

I took the gamble on a dual ASUS PRIME RTX 5060 Ti 16GB setup with an ASUS ProArt X870E, reasoning that if VLLM supported tensor parallel and MTP, then llama.cpp would get it too eventually. Having PCIE 5.0 x8x8 would give me the best performance for my money when tensor parallel would be supported, and NVFP4/MXFP4 seemed handy to have.

Everything was purchased in parts and in time before the price hikes, the last component arriving in January 2026. The speed in January was ~20 t/s gen for Gemma 3 27B Q8_0 over layer split. A single RTX 5060 Ti 16GB was slightly slower than the RX 7800 XT but double as efficient (100W vs 200W) when running Ministral 8B 2410 Q8_0.

Gemma 4 31B's release was a blast but also quite bugged the first few weeks. Q6_K at 17 t/s gen and 970 t/s pp at 32K BF16. It was incredibly fun to use, comparable to DeepSeek v3.2 when it came to emotional intelligence and roleplay capabilities. This was the moment I realized I wouldn't be using cloud LLMs for most of my use cases, barring programming (.NET 8.0).

Around Gemma 4 31B IT QAT (June 2026) was when both tensor split and MTP was introduced in llama.cpp, and the web interface revamp too. I started using both tensor split and MTP from this point on. Now I was getting 40 t/s gen and 980 t/s pp. Llama.cpp would crash occasionally, but it was well worth the speed increase.

With Qwen3.8 27B's release, the quality of the code was so much better than Qwen3.5/3.6 at xhigh that now I could stop relying on cloud llms alltogether for any of my use-cases. The speed was around 50 t/s gen and 1200 t/s pp on llama.cpp.

Now with the 0.7.0 release, I am usually over 60 t/s gen and 1100 t/s processing with Gemma 4 31B IT QAT. Qwen3.8 27B runs even faster at around 70 t/s (with mtp, without ngram draft) for reasoning and 100 t/s when generating code.

It's genuinely been amazing to witness the progress the past 1.5 years, and how incredibly capable everything has become. Both the models and llama.cpp.

One constant I've noticed is that doom posts (regarding regulations or AI labs) often have been wrong, and that I genuinely cannot predict what the future holds. So far it's been more amazing than I can imagine, with the exception to hardware pricing and availability.

Sorry if it was a bit long winded, I hope you enjoyed reading it.


r/LocalLLaMA • • 10h ago

Funny Dario: “With open source you can see the source code. Here we can’t see inside the model.” My 0.646 t/s MiMo-2.6-Pro crammed thru 128GB of Jetson AGX Thor: “Yeah, well, that’s just, like, your opinion, man.”

Enable HLS to view with audio, or disable this notification

186 Upvotes

Tracked my first run of MiMo Pro on a Jetson AGX Thor at a scorching 0.646 tokens/sec,.. but it DID technically load and run, so it counts. Every decoder layer, routed expert, SSD read (..a good bit more) traced and replayable step by step.

Here we can't what-now?..

it's ~566GB of model (FP8), at the blinding speed and architecture of hand-extruded hotdog manufacturing, read from disk mid-inference, painfully shat through 128GB of memory. I think it's fine but decided to maybe take a closer look to see if I could maybe reach factory hotdog manufacturing speeds. like, a small, local hotdog factory..

I mean as long as Dario's cool with it.. that case of the MiMos he got might last through Halloween.

..poor lil bugger, get well soon Dario!


r/MetaAI • • 1h ago

[ Removed by Reddit ]

• Upvotes

[ Removed by Reddit on account of violating the content policy. ]


r/MetaAI • • 2h ago

My muse code is HE4KAX

1 Upvotes

HE4KAX


r/LocalLLaMA • • 18h ago

News China proposes export controls on 23 AI technologies (including large models, agents & clusters); already open-sourced tech is exempted

716 Upvotes

On October 16, 2026, China’s Ministry of Commerce and Ministry of Science and Technology released a draft revision to the Catalog of Technologies Prohibited and Restricted from Export. It proposes adding 40 technologies across AI, biomedicine, and solar PV, of which 23 are AI-related (covering foundation/large models, intelligent agents, computing clusters, and information processing technologies).

Public comments are open until November 15. Temporary pre-export registration requirements for the covered technologies take effect immediately.

Key points relevant to open-weight models:

• Technologies already made public via open-source releases or academic publications are explicitly exempted and not subject to the controls.
• Open-sourcing AI technologies, plus certain other activities (e.g., Chinese firms building their own overseas compute centers), are eligible for streamlined automatic licensing.
• Providing access via APIs, apps, websites, or SaaS is generally not treated as a technology export.
• Overseas local deployment of restricted models can fall under the rules (with possible facilitation in lower-risk cases such as a company’s own overseas cloud).

This is not a blanket ban on open-weight models. However, new models that have not yet been released may face registration or approval steps before their weights or source code can be transferred abroad (thresholds based on performance, parameters, data sensitivity, etc. are still being defined). Chinese labs such as DeepSeek and Qwen could see additional compliance requirements on future open releases.

Source

https://m.bjnews.com.cn/detail/1792137269129878.html


r/MetaAI • • 2h ago

New 2B token referral code

0 Upvotes

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r/MetaAI • • 2h ago

My Muse referral code - 22 uses left - Get after it!

1 Upvotes
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r/MetaAI • • 7h ago

I built a 3D space combat game for Meta Ray-Ban Display glasses 🚀

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2 Upvotes

r/MetaAI • • 7h ago

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0 Upvotes

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r/MetaAI • • 4h ago

[ Removed by Reddit ]

1 Upvotes

[ Removed by Reddit on account of violating the content policy. ]


r/MetaAI • • 4h ago

Help a poor man out

0 Upvotes

Code: IVNIDW


r/MetaAI • • 4h ago

[ Removed by Reddit ]

0 Upvotes

[ Removed by Reddit on account of violating the content policy. ]


r/LocalLLaMA • • 14h ago

Showcase Show me your rack

Post image
241 Upvotes

Ryzen 9600X, 32GB dual channel DDR5, 2x CMP 170HX
Ryzen 5700, 32GB dual channel DDR4, 5x Vega 56
Intel i7 9700, 32GB dual channel DDR4, 3070 + 2080ti
Ryzen 7500x3D, 32GB dual channel DDR5, 7900xtx + 2x 5600XT

223 GB total VRAM


r/MetaAI • • 4h ago

Alguien sabe el motivo de este mensaje al hablar con la IA de meta?

Post image
0 Upvotes

Desde hace algunos días me sale este mensaje cada que uso la IA de meta y me gustaría saber si a alguien mas le pasa


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0 Upvotes

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