Technology

59772 readers

3191 users here now

This is a most excellent place for technology news and articles.

Our Rules

Follow the lemmy.world rules.
Only tech related content.
Be excellent to each another!
Mod approved content bots can post up to 10 articles per day.
Threads asking for personal tech support may be deleted.
Politics threads may be removed.
No memes allowed as posts, OK to post as comments.
Only approved bots from the list below, to ask if your bot can be added please contact us.
Check for duplicates before posting, duplicates may be removed

Approved Bots

founded 2 years ago

MODERATORS

100

In the space of 1 week, a second open-source Chinese AI model equals the best investors are pouring tens of billions of dollars into. (lemm.ee)

submitted 3 days ago by schizoidman@lemm.ee to c/technology@lemmy.world

54 comments fedilink hide all child comments

cross-posted from: https://futurology.today/post/2910566

Alibaba's Qwen team just released QwQ-32B-Preview, a powerful new open-source AI reasoning model that can reason step-by-step through challenging problems and directly competes with OpenAI's o1 series across benchmarks.

The details:

QwQ features a 32K context window, outperforming o1-mini and competing with o1-preview on key math and reasoning benchmarks.

The model was tested across several of the most challenging math and programming benchmarks, showing major advances in deep reasoning.

QwQ demonstrates ‘deep introspection,’ talking through problems step-by-step and questioning and examining its own answers to reason to a solution.

The Qwen team noted several issues in the Preview model, including getting stuck in reasoning loops, struggling with common sense, and language mixing.

Why it matters: Between QwQ and DeepSeek, open-source reasoning models are here — and Chinese firms are absolutely cooking with new models that nearly match the current top closed leaders. Has OpenAI’s moat dried up, or does the AI leader have something special up its sleeve before the end of the year?

you are viewing a single comment's thread
view the rest of the comments

[–] BetaDoggo_@lemmy.world 2 points 2 days ago

For a 16k context window using q4_k_s quants with llamacpp it requires around 32GB. You can get away with less using smaller context windows and lower accuracy quants but quality will degrade and each chain of thought requires a few thousand tokens so you will lose previous messages quickly.