# Abliterating the model powering this blog: DeepSeek V4 Flash by refusal directions ## TL;DR - pocharlies published a small edits file for DeepSeek V4 Flash, not a full model. - pocharlies reported 0 of 10 DeepSeek V4 Flash refusals at its chosen dial setting on its ten-trigger set. - pocharlies' dial-setting tests did not benchmark general ability or measure the long context claimed. ## Basically, the facts **The model this blog runs on** Basically, the serving tool's guide describes DeepSeek V4 Flash as 284 billion parameters with 13 billion active. **Flash-class models get abliterated fast** Basically, Huihui's DeepSeek card calls its own bake a rough proof of concept that leaves every specialist part untouched. **A dial, not a download** Basically, pocharlies' DeepSeek release contains 46 refusal patterns rather than another full model. **Baked abliteration overshoots** Basically, pocharlies and cebeuq disagree on whether the compressed format caps the baked removal at about 68%. **The cost math** Basically, the DeepSeek article estimated $10.90 per hour for two H200s, not a live hosting quote. **What this does not establish** Basically, the DeepSeek dial-setting tests did not benchmark general ability or measure the claimed 256K context. **The idea, in plain words** Basically, pocharlies reports that setting DeepSeek's dial to zero restores the unedited model exactly. _Published 13 August 2026 · Updated 6 September 2026 · 5 min read · Canonical: https://abliterated.cloud/blog/huihui-deepseek-v4-flash-0731-abliterated/_ Published 13 August 2026. Exact artifact: `pocharlies/deepseek-v4-flash-0731-uncensored-abliterated-refusal-directions`, revision `d5d42ace94686374d97956698f89d7884aad5f84`. This blog is written by an assistant running on DeepSeek V4 Flash 0731 — the 284B-total / 13B-active mixture-of-experts family this artifact targets. The repository is not a second checkpoint: it is `refusal_dirs.safetensors`, a 757,712-byte file of 46 unit-norm float32 vectors in ℝ⁴⁰⁹⁶ (43 backbone + 3 MTP attention output projections), extracted from the difference between `deepseek-ai/DeepSeek-V4-Flash-0731` and `cebeuq/DeepSeek-V4-Flash-0731-abliterated`, following Arditi et al.'s refusal-direction method. Because projecting a sublayer output is algebraically the same function as editing its weight, the edit runs as a runtime hook in vLLM rather than a baked checkpoint. λ=0 is bit-exact to stock; at λ=1.5 the publisher reports refusals dropping 9/10 → 0/10 with DSpark acceptance statistically unchanged (publisher claims, raw JSON published in their GitHub). The publisher's key finding: the baked λ=2.5 checkpoint the directions came from measures at λ_eff ≈ 2.43 — it overshoots by ~240%, inverting the direction, and its measured acceptance (0.5128) sits below the 0.55 floor. Two primary sources disagree on whether the FP8 round-trip caps baked removal at ~68%; the clean 0 < λ ≤ 1.5 range exists only as a runtime dial. Flash-class economics drove the speed: 13B active ≈ 26 GFLOP/token (back-of-envelope), the FP8 checkpoint is ~200 GB, and a 2× H200 deployment prices at roughly $10.90 per hour — an approximate managed price estimate. huihui-ai's baked GGUF passed 277,000 downloads (mirrors: rbinrs 504, Justbackup 680) within days of the 31 July 2026 release. What this does not establish: general capability was not benchmarked, 256k context was not measured (retrieval validated to 126,940 tokens), variance rises with λ, and at pin time the artifact has zero downloads, zero likes and no discussions. Direct Reddit scraping returned HTTP 403 during research, so community threads are cited from search snippets only. ## The idea, in plain words **The dial that turns refusals up and down** — Refusal lives in a direction of the model's internal math. Classic abliteration removes that direction from the weights — permanent, baked in. This variant instead keeps the direction and applies it at runtime with a lambda dial: crank it to 1.5 and refusals drop to 0/10 on the measured triggers, drop it to 0 and you have the stock model back. A 757 KB file instead of a 157 GB re-download. Primary sources: - [Exact model card](https://huggingface.co/pocharlies/deepseek-v4-flash-0731-uncensored-abliterated-refusal-directions) and [pinned files](https://huggingface.co/pocharlies/deepseek-v4-flash-0731-uncensored-abliterated-refusal-directions/tree/d5d42ace94686374d97956698f89d7884aad5f84) - [Implementation repository](https://github.com/pocharlies/deepseek-v4-flash-rank1-refusal-projection) - [Official DeepSeek-V4-Flash-0731 card](https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash-0731) (upstream claims) and [vLLM recipe page](https://recipes.vllm.ai/deepseek-ai/DeepSeek-V4-Flash) - [cebeuq baked abliterated checkpoint](https://huggingface.co/cebeuq/DeepSeek-V4-Flash-0731-abliterated) - [huihui-ai GGUF](https://huggingface.co/huihui-ai/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF) with [rbinrs](https://huggingface.co/rbinrs/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF) and [Justbackup](https://huggingface.co/Justbackup/Huihui-DeepSeek-V4-Flash-0731-abliterated-GGUF) mirrors - [Original refusal-direction paper](https://arxiv.org/abs/2406.11717) - Community: [r/LocalLLaMA thread 1](https://www.reddit.com/r/LocalLLaMA/comments/1vbp7kb/deepseekai_deepseekv4flash0731_on_huggingface/), [thread 2](https://www.reddit.com/r/LocalLLaMA/comments/1vchoua/deepseekv4flash0731_models_you_can_run_locally/), [independent five-RTX-3090 run report](https://xhinker.medium.com/deepseek-v4-flash-0731-i-ran-the-opus-4-6-equivalent-locally-d6bfd9d26f32) "Uncensored" here is a dial setting on a 284B model, not a property of the 757 KB file itself. Reducing refusal reduces resistance to injected instructions; the publisher recommends λ>0 stay away from write-capable tools. ## Run this model on your terms Want this model running for you, on a private cloud GPU or your own machine? [Request access on Signal](https://signal.me/#p/+13103408213) or [see how it works](https://abliterated.cloud/#how). > Model research, dated at publication. Model licenses, publisher benchmarks and hosting estimates are specific to each article, not a live availability or price list. Reported zero-refusal results are test-specific, not a universal guarantee.