Small is the point

Uncensored models get the most attention at the top of the parameter range, but the 4B class is where a different argument lives. The base distill’s card says bf16 fits in roughly 8 GB and quantized builds run on laptops and consumer GPUs. Our estimate for serving this checkpoint is about $2.34/hour on a single L40S — an approximate managed price estimate, not a measured throughput figure. At that price, agent loops, local experiments and edge deployments stop being a budget conversation and start being a default. Four and a half billion parameters is enough to carry a tool-calling chat model; it is not enough to carry frontier reasoning.

A 2.4-trillion-parameter teacher, squeezed into 4.5B

Decode the name before anything else. “EmperoAI” is a reference to the empero-ai lineage; the “4B” is the student architecture; “Qwen3.8” is the teacher. Per Empero’s card, Qwen3.8-4B is a full-parameter distillation of Qwen3.8 2.4T A95B into the Qwen3.5-4B architecture — roughly 45,000 curated teacher traces of dense chain-of-thought, quality-filtered, distilled into a student that fits on consumer hardware. Every answer opens with a <think> block. Function calling is native per the Qwen3.5 specification, context is 262,144 tokens natively, and the fine-tune touched every parameter — not an adapter. The card also states the fine-tune is text-only: vision behavior is inherited from the base and was not evaluated.

Empero publishes one head-to-head benchmark table for the distill, measured with lm-evaluation-harness at identical settings against Qwen3.5-4B: gsm8k_cot 0.785 vs 0.850, and MMLU (CoT, 57 subjects) 0.553 vs 0.354. Those are upstream claims for the distill — read them as “MMLU jumped, GSM8K slipped”. They are not numbers for the decensored artifact. Two practical notes from the card matter more than the scores: the linear-attention layers need Gated DeltaNet kernels (flash-linear-attention plus a CUDA-matched causal_conv1d) or they fall back to slow PyTorch ops, and greedy decoding on long generations is a known repetition-loop failure mode for this class — Empero recommends temperature 0.6, top_p 0.95, top_k 20 and generous max_new_tokens, because the reasoning lives inside <think> blocks.

Heretic: abliteration by search, not by hand

Classic abliteration, from Arditi and colleagues’ refusal-direction research, compares activations on harmful versus harmless prompts, estimates a refusal direction, and projects it out of selected weights. The fiddly part is the search: which layer, which direction, how hard to project. Heretic — the tool by Philipp Emanuel Weidmann, AGPL-3.0, roughly 28,000 GitHub stars at the time of writing — replaces that manual search with an optimizer. Its README says it combines an advanced implementation of directional ablation with a TPE-based parameter optimizer powered by Optuna, co-minimizing refusals and KL divergence from the original model. You run heretic <model>, it searches, it exports — no transformer-internals knowledge required.

The README claims Heretic matches hand-tuned abliterations at lower KL: for gemma-3-12b-it it reports 3/100 refusals at KL 0.16, versus mlabonne’s 3/100 at 1.04 and huihui-ai’s 3/100 at 0.45 (PyTorch 2.8, RTX 5090). Those are the tool’s own claims, and its own caveat applies: automated metrics are no substitute for human evaluation. The README also says the community has published well over 5,000 Heretic models, and quotes r/LocalLLaMA users praising Heretic GPT-OSS 20B and a Qwen3-4B Heretic build on 16 GB VRAM — community opinion, cited as such.

The exact run

The insraq card is refreshingly specific: this is a decensored version of empero-ai/Qwen3.8-4B, made with Heretic v1.4.0. The reproduction guide pins the base at commit c83cb7a, the prompt sets (mlabonne’s harmless_alpaca and harmful_behaviors) at their own commits, the RNG seed in config.toml, an Optuna study journal of every trial, and SHA256SUMS for the weight files — heretic --reproduce reproduce.json should rebuild it hash-identical.

The selected trial was 128. The card’s own table: KL divergence 0.0167 versus 0 (by definition) for the base, refusals 6/100 versus 99/100. Publisher claims — but unusually auditable ones. The published abliteration parameters show what the search settled on: direction_index 20.01, attention output-projection weights up to 1.50, MLP down-projection weights up to 1.24. Projection strengths above 1.0 are an over-projection — the edit pushes past merely zeroing the refusal component — concentrated around the upper layers.

What tiny uncensored trades away

There are three stacked losses here, and honesty about them is the point. First the distill: the student already gave up GSM8K ground (0.785 vs 0.850) for its MMLU gain, per Empero. Then the decensor: KL 0.0167 from the base distill is low but not zero, and KL on harmless prompts measures nothing about refusal-relevant behavior or agentic reliability. Third, nothing is independently vetted yet — at the time of writing the repository had zero downloads, zero likes and zero discussions, and the 6/100 figure is the publisher’s own measurement from Heretic’s built-in evaluation.

What you get in exchange is the actual 4B-class deal: refusal-free text, local and private if you want it, native tool calling, 262K context, and a price tag that makes agent loops boring. What you don’t get is frontier reasoning — or any guarantee that a decensored 4B is truthful. Decensored means fewer refusals, not fewer hallucinations.

What is proven here?

The exact artifact, its reproduction log and its weight hashes are inspectable and rerunnable. The refusal counts and KL divergence are the publisher’s claims. Empero’s benchmark table describes the distill, not the decensored artifact. No independent evaluation exists at the time of writing — and a direct Reddit search for community coverage returned 403, so the community picture here is limited to what the Heretic README quotes.

Who made it

Primary sources do not identify a named developer behind the insraq handle, so we credit the handle. The same publisher released a 2B sibling the same morning — insraq/Qwen3.5-2B-EmperoAI-Qwen3.8-Distill-Heretic-Abliterated, tagged “edge” — which reads as a deliberate family aimed at edge devices. The tool is Weidmann’s, the distill is Empero’s, the architecture is Qwen’s. Three layers of authorship, and the weights stay Apache-2.0 through all of them.

How we treat it

Our prepared profile pins the exact revision above and budgets one L40S at an approximate managed price estimate of $2.34/hour. We do not transfer Empero’s benchmarks or the publisher’s refusal counts to our endpoint until the exact artifact is measured. “Abliterated” — or “Heretic-decensored”, pick your branding — means refusal-reduced, not truthful, legal or harmless. The card ships with reproduction instructions and no safety claims; use it in controlled contexts with human review.

The idea, in plain words

A 2.4-trillion-parameter teacher writing homework for a 4-billion student

Knowledge distillation trains a small model on the outputs of a huge one. The student copies how the teacher answers, compressed into a fraction of the size — here, a 4.5B model that fits in ~8 GB and runs on a laptop. You lose some ceiling, but you get a model you can actually run anywhere, uncensored.

Primary sources