I post-trained Qwen3.6-35B-A3B into my daily-driver local coding/agent model QwiVer3.6-35B-A3B GGUF

Summary

A developer released a post-trained variant of the Qwen3.6 mixture of experts base model under the name QwiVer, published on Hugging Face in GGUF form at several quantization levels. The stated method was a behavior change rather than an architecture change: a low rank adapter trained against the base weights, a selected adapter checkpoint merged back into the frozen base, then conversion and quantization to GGUF, with a check that the original layer, expert routing, context and multi token prediction setup survived the conversion intact.

The goal described is a local coding and agent assistant that follows an existing codebase instead of rewriting it, debugs rather than restarting, calls tools correctly and holds task across long sessions. The author says he prefers it to the stock base on his own workloads, explicitly labels an early smoke test as too small to mean anything, and says formal benchmark runs against the published Qwen3.6 scores are in progress with raw per question results and configuration to be released once they finish.

Why it matters

The set of models reading, summarizing and recommending your content is no longer a short list of hosted assistants. A single person can now take an open weight base, post-train it for their own working style, quantize it, rename it and distribute it, and other people will run that fork locally against their own documents, codebases and browsing. For anyone trying to be found, understood, cited or recommended by AI, that means the audience includes an expanding population of unversioned derivatives that no vendor announces, no dashboard reports and no visibility tool tracks by name, each carrying whatever behavior its post-training pushed it toward. It also relocates part of the discovery problem onto the publisher of the model itself: how this release is documented on its model card, whether the base, the method and the artifacts are named plainly, and whether the benchmark claims arrive with raw results, is what decides whether it gets described accurately when a model or a human is asked what it is. That is the same trust mechanism brands face, applied to an AI artifact instead of a company page, and it is why a release like this is worth watching even by people who will never download it.

Source

• huggingface.co/BlackRiverAI/QwiVer3.6-35B-A3B-GGUF the item itself, published by r/LocalLLaMA, created by RIP26770
• github.com/ggml-org/llama.cpp/releases Public source code or repository
• huggingface.co/docs Original dataset with disclosed methods, first party
• huggingface.co/docs/hub/model-cards Original dataset with disclosed methods, first party
• reddit.com/r/LocalLLaMA/comments/1vpy6yt/i_posttrained_qwen3635ba3b_into_my_dailydriverwhere AIOTruth found it

What was checked

• canonical URL taken from the page itself
• read the item page (200)
• https://github.com/ggml-org/llama.cpp/releases: supports the item, tier 6 (Public source code or repository)
• https://github.com/ggml-org/llama.cpp: does not mention the item, tier 6 (Public source code or repository)
• https://huggingface.co/docs: supports the item, tier 5 (Original dataset with disclosed methods)
• https://huggingface.co/docs/hub/gguf: does not mention the item, tier 5 (Original dataset with disclosed methods)
• https://huggingface.co/docs/inference-providers/index: does not mention the item, tier 5 (Original dataset with disclosed methods)
• https://huggingface.co/docs/hub/model-cards: supports the item, tier 5 (Original dataset with disclosed methods)

Limitations

• Reddit engagement is a discovery signal here, not evidence that the claim is correct.

AIOTruth judgment

The evidence status is Supported, and the reason is disclosure rather than confirmation. The author names the exact base, the training method, the merge and quantization path, the architecture properties he checked afterward, the artifact sizes and the download he considers sensible for most people, and he links the model directly. The checked sources cover the surrounding infrastructure, the llama.cpp release channel he says he runs the benchmarks through and the Hugging Face documentation that governs how a model card should describe itself, plus the discussion thread itself. Usefulness, evidence and originality all scored high because the work is specific, reproducible in outline and not a rename of someone else's weights, and because the author volunteers the limits of his own numbers instead of hiding them, calling a tiny early check meaningless and saying the real runs are still going. Relevance is the one low dimension and it is what pulls the composite down: this is a local model release, adjacent to how brands are found and represented by AI rather than about it. The comparative performance claim is self reported on private workloads, nobody outside has reproduced it, AIOTruth has not tested the model, and the Reddit engagement around the post is a discovery signal only.

Follow the development Open the source

Found something wrong here? AIOTruth corrects material errors openly. Have something we should review? Submit a find.