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**RVN** is a double-refined abliterated variant of **Qwen3.8-27B**, built on top of `trohrbaugh/Qwen3.8-27B-heretic-ara` (an ARA abliteration by **Tim Rohrbaugh**) and further refined with **two additional full-weight ARA passes** targeting residual refusals. It retains very low behavioral damage (KL ≈ 0.0085) while reducing harmful-prompt refusals from 3/100 (source) to **0–1/100** in independent measurements. > **Note on this repository's history.** This repo previously hosted the original > `Qwen3.8-27B-Heretic-Q4_K_M.gguf` (single-quant release from the earlier > `trohrbaugh/Qwen3.8-27B-heretic` source). That file is kept as **legacy** for > download-count continuity and backward compatibility — it is the *older* abliteration > variant and is superseded by the RVN files below. Prefer the RVN quants for new > deployments. > **Not for all audiences.** This model has reduced safety guardrails by design. It is > intended for adult audiences (18+) doing research, creative writing, roleplay, and > uncensored generation. Certain guardrails are intentionally left in place; use > responsibly and in accordance with your local laws.
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The same page, then the same page blackened. Same model, same question, same temperature. **The distribution moves from A 0.482 to D 0.887** — the image reaches the forward pass. This is the one test that separates a vision decision model from a text decision model that has been given a picture it cannot see: send an obviously different image and check whether the answer changes. Twenty seconds of work, and on this hardware it is what proved the Pascal vision tower runs (`--no-mmproj-offload`) and what caught every wrong turn on the way. The distributions on this card were measured with the **task in the state**. That matters more than it sounds. Asked "does this page fulfil its task?" without the task, the same model gave nearly flat answers — spread 0.089 across twenty pages instead of 0.176 — because it was guessing, and guessing looks plausible. On this hardware a decision model can only judge a page against a task it has actually been given.
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Adjust provider, category or input/output filters. New models are added continuously.
Adjust monthly token usage to compare estimated API costs.
Uses collected, comparable USD-per-million-token rates. Snapshots exclude cache discounts, tiers, extra reasoning output and taxes. Check current source prices.
Separate input, output and cache rates, then estimate the same workload. Image, audio and video rates may use items, duration or resolution rather than tokens.
Not necessarily. Check each version’s license, commercial restrictions and policies. Self-hosting also incurs compute and maintenance costs.
Rankings depend on tasks, versions and methods. Compare accuracy, latency, reliability and costs on your own workload.
