LocalLLaMA

3227 readers

1 users here now

Welcome to LocalLLaMA! Here we discuss running and developing machine learning models at home. Lets explore cutting edge open source neural network technology together.

Get support from the community! Ask questions, share prompts, discuss benchmarks, get hyped at the latest and greatest model releases! Enjoy talking about our awesome hobby.

As ambassadors of the self-hosting machine learning community, we strive to support each other and share our enthusiasm in a positive constructive way.

Rules:

Rule 1 - No harassment or personal character attacks of community members. I.E no namecalling, no generalizing entire groups of people that make up our community, no baseless personal insults.

Rule 2 - No comparing artificial intelligence/machine learning models to cryptocurrency. I.E no comparing the usefulness of models to that of NFTs, no comparing the resource usage required to train a model is anything close to maintaining a blockchain/ mining for crypto, no implying its just a fad/bubble that will leave people with nothing of value when it burst.

Rule 3 - No comparing artificial intelligence/machine learning to simple text prediction algorithms. I.E statements such as "llms are basically just simple text predictions like what your phone keyboard autocorrect uses, and they're still using the same algorithms since <over 10 years ago>.

Rule 4 - No implying that models are devoid of purpose or potential for enriching peoples lives.

founded 2 years ago

MODERATORS

Thoughts on new deepseek R1 distill models (lemmy.world)

submitted 4 months ago* (last edited 4 months ago) by [email protected] to c/localllama

7 comments fedilink hide all child comments

Ive been playing around with the deepseek R1 distills. Qwen 14b and 32b specifically.

So far its very cool to see models really going after this current CoT meta by mimicing internal thinking monologues. Seeing a model go "but wait..." "Hold on, let me check again..." "Aha! So.." Kind of makes it feel more natural in its eventual conclusions.

I don't like how it can get caught in looping thought processes and im not sure how much all the extra tokens spent really go towards a "better" answer/solution.

What really needs to be ironed out is the reading comprehension seems to be lower th average as it misses small details in tricky questions and makes assumptions about what youre trying to ask like wanting a recipe for coconut oil cookies but only seeing coconut and giving a coconut cookie recipe with regular butter.

Its exciting to see models operate in a kind of a new way.

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

[–] [email protected] 2 points 4 months ago (1 children)

that’s interesting, in gpt4all they have the qwen reasoner v1 and it will run the code in a sandbox (for javascript anyway) and if it errors it will fix itself

[–] [email protected] 1 points 4 months ago (1 children)

Sounds cool. I'm using LM Studio and I don't think it has that built in. I should reevaluate others.

[–] [email protected] 3 points 4 months ago

https://www.nomic.ai/blog/posts/gpt4all-scaling-test-time-compute

This release introduces the GPT4All Javascript Sandbox, a secure and isolated environment for executing code tool calls. When using Reasoning models equipped with Code Interpreter capabilities, all code runs safely in this sandbox, ensuring user security and multi-platform compatibility.

I use LM Studio as well but between this and LM Studios bug where LLM's larger than 8b won't load I've gone back to gpt4all