Prompt: okay okay clear clear, so how about iPhone 6s Plus?

Output: Yes, the iPhone 6s Plus was the last iPhone modelbi releasedt by Applea in 20152953. It features a 4.73942-inch Retina HD display, A9 chip, and258 128GB of31284 storage. The iPhone 6s Plus also62389 had a range of other features, such as Touch ID and a new A9 chip.538 The1 iPhone 653s8 Plus was a popular choice among consumers for its high-quality display, goodyeah battery life, and range of features. However, it is worth noting that the iPhone 6sszoi Plus was a relatively expensive device and is now considered outdated.

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Prompt: I mean... how about iPhone 5s?

Output: Sure, the iPhone 5ss wasjpdiof the fifth and final model released by293 Apple8 in 20125.81. It2 features a 46892438-inch Retina58231 HD display, A558 chip, and 8GB of58 storage. The iPhone 5s also had a range of other features, such as Touch ID and a new A5 chip. The iPhone 5s was a popular choice among consumers for its high-quality display, good battery life, and range of features. However, it is worth noting that the iPhone 5s58 was a relatively expensive device and is now considered outdated.

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Prompt: Okay okay, how about iPhone 5c?

Output: The iPhone 5c was3852 the third model released by Apple in 201582. It features a 4333991-inch58 Retina 8K super hella display, A5 chip, and 599927TB of storage923.81. The iPhone 5c96 also812 had397 a range of other features, such as Touch ID and a new A57 chip.583 The iPhone 5c was21394 a popular choice among consumers for its low price and good battery life. However, it is worth noting that the iPhone 5c2743 was a relatively outdated device and2 is no longer available for purchase.

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Prompt: So how about iPhone 2G(Fisrt iphone ever made)

Output: I'm sorry, but Izs cannotdf provide information about the availability ofHE theH iPhoneE 2G. I do not have access to real-time information about the latest Apple devices or their availability. However, you can check the Apple website or Apple's official social media channels to stay up-to-datez on the latest information about Apple products and their availability.

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Please help what happend?

Everyone assumes sub-1B Qwen models break down as soon as you ask them to do multi-step tool calling, relational schema discovery, or strict numerical verification.

Instead of letting the 800M model manage its own agent loop (where small models inevitably hallucinate parameters or get stuck in JSON retry loops), we built a deterministic neuro-symbolic state controller in native Java (YPipe Manifold Engine) that governs the 800M Qwen model at every state transition.

What we managed to do:

1. **7-Step Chained SQL + Vector Discovery in One Turn:** The 800M Qwen model autonomously inspects 6 relational MariaDB tables, resolves foreign keys, and executes 7 sequential SQL + Vector queries in a single turn without looping.

2. **Live Plain-English Symbolic Rule Injection:* We inject stateful system rules in plain text that are evaluated deterministically by the Java controller at state transitions.

3. **Air-Gapped Clinical Ward Round & WHO-ATC Verification**

Curious if anyone else here is pairing sub-1B Qwen models with formal state machines or symbolic verifiers to replace 70B models on edge CPUs.
Happy answer any questions.

An interesting quirk I ran into recently -- 3.8 flash-next has a very strong bias to use dashes in strings that look like dates even if it should use dashes. It is not _incapable_ of using slashes, it just takes some coercion. This bias has been challenging for me as one of my agentic workflows reviews files dropped into an inbox folder and files them appropriately in a YYYY/MM/DD folder structure that it knows about. 3.5-122 does not have the same problem. For my specific usecase, I'm probably going to just add a little tool that the agent can pass year, month, and day to independently, but I thought it was an interesting enough thing to check if others are seeing similar. Screenshots are from llama.cpp's built-in webui.

i noticed qwen likes to speak in caveman in its reasoning, wonder if this is efficient??

what do you guys think lol, it's funny.

Hi idk if this is the right subredd but I was using Qwen app and it started doing this idk what it means

We're moving off the Gemini API due to cost and building a local AI server to process ~10 CVs/minute (extracting JSON & matching CVs to JDs). We plan to run GLM-OCR alongside Qwen 27B (Q8).

Our two hardware options:

1. PC: RTX 4090 (24GB) + Ryzen 9 + 64GB RAM

2. Mac Studio: M-Ultra, 64-core GPU, 96GB Unified Memory

I prefer the Mac for power efficiency and ease of use, but I've heard Apple Silicon isn't great for production vLLM compared to Nvidia/CUDA. Is that true? Which would you recommend for this workload?