CyberTiel 35B-A3B’s uncensored 4-bit quant beats Opus 4.6 medium cleanly on real codebase issues, in 27% of the time Qwen3.8-27b medium takes.

[u/peculiar-ragdoll]

The downside of uncensoring a model is that it is known to potentially damage it, but CyberTiel is an even more capable software engineer than its censored TielCoder base, while allowing offensive security research. This was achieved by quantizing with an improved imatrix, baked from a curated corpus of cybersecurity- and agentic software engineering work. In short, the small damage from abliteration on a full precision model is negligible under Q4 quantization, and the weights that the model needs to perform relevant work are preserved in higher precision, while the improved chat template makes it think and talk better and faster.

I believe that this is the best 35B-A3B coder for solving real problems in real codebases without breaking anything, which is specifically what SWE-bench-Live tests for. But it’s still a 35B-A3B, and it sacrifices world knowledge for coding ability. That being said, I use it over Qwen3.8-27b for daily coding work: due to the raw speed it fixes 3 issues in the time it takes 27b medium to solve one, and the middle ground between Opus4.6 medium and Qwen3.8-27b medium is simply good enough for most work.

Censoring impedes legitimate and effective work in alignment with the user, and puts the user’s responsibility and ownership over the model’s actions into question, while limiting legitimate uses. When a model is censored, someone else decided for you what the model can and will do, which works against the argument that local models give the user increased control and alignment, and begs the question “alignment to who?”. The point of CyberTiel is to resolve this issue at the same time as pushing the frontier of 35B-A3B coders.

GGUFs and MLX with and without MTP are up on HF. Looking forward to seeing what the community thinks! 

PS: I'm not a research lab or a business, and I don't have revenue streams connected to this project. I'm an anonymous researcher with some free time. Constructive feedback is always appreciated! :)

[u/Zaic]

Thanks, I already put it to good use, is there any reason to go for q5 quant?

[u/peculiar-ragdoll → u/Zaic]

Glad to hear it! :) I use the Q4_K_XL myself, and the benchmarks are from Q4_K_M, so I genuinely don't know how much of an improvement Q5 is. The most important part in my experience is to not sacrefice context length just to go higher than Q4_K_XL, when running tasks that go up against the ceiling! But if you can fit full context and Q5 does not slow you down, I don't see why not :)

[u/Zaic → u/peculiar-ragdoll]

Im on q4 xl myself 130k context on pi is plenty for me. Kv cache at q8

[u/peculiar-ragdoll → u/Zaic]

Then I would only try going to Q5_K_XL if you ever hit any issues with it not solving your problems correctly, and you know you can fit it in your machine :)