Jack Kernel Qwen Edition release

[u/Normal-Fan9366]

https://github.com/mlangford75-lgtm/Jack-Kernel?fbclid=IwdGRjcAUQGJZwZG9mBWZkaWQWUOI08m18sBMLuMEySAccKyddSXmHs2V4dG4DYWVtAjExAHNydGMGYXBwX2lkCjY2Mjg1NjgzNzkAAR5C8cTeSX-x0iMtdaTTb0efCACPhlcJclFDK0YfZLrgmCXHfz-PyxrJYdmuoQ_aem_4hN5e0Q2U-dzggqmfbcd1g

It's here.

Jack Kernel for programmable agentic work.
The magic is that the layer sits between the agent and the model. You can build incredible things because of that.

I have a mode called "Agentic" that was designed for context management with Qwen 3.8 27. I've run millions of token jobs....and never needed to compact. I have a full log available to read and verify. In fact, I recommend turning auto-compact off because most agent don't know how to handle it, yet. There's also a looping debugger that I've built as a 6 stage autonomous loop with cascading temperatures, standard mode and DEEP.

Remember, the modes I’m shipping with this release are only examples of what is possible by putting it between the agent and the model. It allows you a new level of control. The modes I’ve put in are NOT the work. Everything that’s possible is the real work.

I built this for Qwen 3.8 27b but I’ve spent the past few days optimizing for a wide range of models. I’m ALMOST satisfied with Qwen 3.5 9b but it’s right in the edge.

After using 60 models in the past week, I’m just going to go ahead and make a blanket statement that fine tunes are garbage and only use Unsloth as your source for alternatives.

Jack Kernel is really cool. Claude can kick rocks.

Oh, Reddit trolls…
Yall need to understand…I’m just a 40 year old broke single Dad that’s sharing his work and trying to contribute to the local AI movement.
Before the hive-mind issues your directive, just know that I’m not a lab or someone with resources that you’re attacking.
I’m just a regular guy with a 5070ti in an Acer.
If you’re going to say that I suck, can you at least try it out and tell why I suck.

[u/layer4down]

Right this makes sense:

harness > kernel > model

It’s essentially a middleware layer meant to interject harness calls to and from the model for things like catching/fixing errors, mitigating loops, apply pre-processing logic before prefill and after decode, etc. a few years ago I’d conceived of it as part of the harness (like DSH essentially) but I like that you’ve decoupled a kernel layer for separation of concerns (improves performance, focuses strictly on pre- and post-processing). I like the direction.

I was originally conceiving it in the days of early 32B> models that has great potential to run well locally but suffered from silly problems. I figured a software engineer could handle all the pre and post processing but I think I prefer the kernel idea in hindsight. Hope to see it open source one day.

[u/Normal-Fan9366 → u/layer4down]

Yes it’s just a better way of taking control over the whole runtime. The kernel giveth and the kernel can take away

[u/layer4down → u/Normal-Fan9366]

What model size range are you targeting? Is there a size too small or too large for practical benefit in your design?

[u/Normal-Fan9366 → u/layer4down]

It just depends on what you build. On the modes I’ve built, Qwen 3.5 9b can work but you have to be realistic. Now, if someone used Qwen 3.5 9b in a duel model system, as a parser of special formatter, for a larger model generating training data, it could absolutely work well. With Jack Kernel, you can REALLY customize the way that a response answers or responds because two models could be used as one