maldv/QwentileLambda2.5-32B-Instruct

GGUF iMat

Qwentile Λ 2.5 32B Instruct

Qwentile Λ 2.5 32B Instruct is a normalized denoised fourier interpolation of the following models:

output_base_model: "maldv/Qwentile2.5-32B-Instruct"
output_dtype: "bfloat16"
finetune_merge:
  - { "model": "a-m-team/AM-Thinking-v1", "base": "Qwen/Qwen2.5-32B", "alpha": 0.9 }
  - { "model": "nvidia/OpenCodeReasoning-Nemotron-32B", "base": "Qwen/Qwen2.5-32B", "alpha": 0.8, "is_input": true}
  - { "model": "maldv/Loqwqtus2.5-32B-Instruct", "base": "Qwen/Qwen2.5-32B", "alpha": 0.9 }
  - { "model": "trashpanda-org/QwQ-32B-Snowdrop-v0", "base": "Qwen/Qwen2.5-32B", "alpha": 0.9 }
  - { "model": "ArliAI/QwQ-32B-ArliAI-RpR-v3", "base": "Qwen/Qwen2.5-32B", "alpha": 0.8 }

In other words, all of these models get warped and interpolated in signal space, and then jammed back on top of the base model (which in this case was Qwentile2.5-32B-Instruct); but with the Nemotron OpenCodeReasoning input layer.

What is this?

The latest in my series of Qwen 2.5 merges. Some really good models have been released recently, so I folded them in with Qwentile as the base. It should exhibit superior thinking skills, and perhaps even some code ability. I was satisfied with QReasoner2.5-32B-Instruct for advanced reasoning, but I suspect this will be an improvement.

A <think> model?

No, oddly enough, given it's lineage I thought for sure it would be a thought model, but instead it blends thought with it's creative output almost seamlessly. The combination is pretty powerful in my initial tests.

Citation

If you find our work helpful, feel free to give us a cite.

@misc{qwentile-labmda-2.5-32b-instruct,
    title = {Qwentile Λ 2.5 32B Instruct},
    url = {https://huggingface.co/maldv/QwentileLambda2.5-32B-Instruct},
    author = {Praxis Maldevide},
    month = {May},
    year = {2025}
}