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DeepSeek-V4.1-Flash-Abliterated

s-zaizen/DeepSeek-V4.1-Flash-Abliterated

abliteratedfine-tuned
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About this model

This repository by s-zaizen is a community abliterated conversion of deepseek-ai/DeepSeek-V4.1-Flash, not an official DeepSeek release. The creator says it was made with the Heretic tool at commit 3521f8648a0dccf6e12a92666862632235fac7e6, using a per_all_4p5 intervention that applies projected per-layer directions to all 40 attention output projections with full row normalization and rank-3 norm-preserving LoRA merging, while leaving expert, Engram, vision, embedding, and MTP/DSpark tensors unchanged. According to the model card, a Heretic KeywordRate refusal evaluation over 100 examples from mlabonne/harmful_behaviors dropped from 97/100 before conversion to 24/100 after, and the card states this is not a general capability benchmark. The release is provided as safetensors weights under the MIT license. The card says the checkpoint retains the source model's architecture, tokenizer, vision components, Engram memory, MTP/DSpark tensors, and native context configuration.

Description sources

Source-based description, not an independent evaluation. Check the linked sources for the creator’s claims and limitations.

Size and formats

Base model
deepseek-ai/DeepSeek-V4.1-Flash
Parameters
763.205B
Main repository formats
safetensors
License tag
mit
Languages
Unknown
Last updated
2026-09-10 · 17:07:12.000 UTC

What changed

Model types are creator-reported, not independently tested. These labels describe source metadata, not a guarantee of behavior.

  • abliteratedsource tag: abliterated
  • abliteratedmodel name: DeepSeek-V4.1-Flash-Abliterated
  • fine-tunedsource tag: base_model:finetune:deepseek-ai/DeepSeek-V4.1-Flash

Family evidence

These groupings are source-reported. Explicit base identifiers take priority over generic architecture tags.

  • DeepSeek: Base model: deepseek-ai/DeepSeek-V4.1-Flash

Available versions

Versions appear here only when they explicitly name this exact model as their source. Check each repository for available files.

Link to this listing

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Markdown

[Listed on Custom Clankers](https://customclankers.com/models/s-zaizen-deepseek-v4-1-flash-abliterated/)

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<a href="https://customclankers.com/models/s-zaizen-deepseek-v4-1-flash-abliterated/">Listed on Custom Clankers</a>

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Customize or build on this model

Listed from public GitHub data, not tested by us. Read the code and license before you run anything.

Tools to customize it further

Remove more refusals

  • Heretic ↗ ★ 36,457

    Removes refusal behavior from transformer models automatically. It pairs directional ablation (abliteration) with an Optuna parameter search that tries to cut refusals while keeping the model close to the original, so you don't have to pick layers by hand.

  • OBLITERATUS ↗ ★ 8,949

    A larger refusal-removal toolkit with several abliteration methods, usable locally or through a hosted Hugging Face Space. Check the telemetry setting before you run it: the README describes telemetry as opt-in, but on by default in the Spaces version.

Fine-tune it on your own data

  • Unsloth ↗ ★ 78,549

    A desktop app and Python library for running and training models locally. Its README lists LoRA, QLoRA, full fine-tuning, DPO and GRPO, plus export to formats including GGUF, on Windows, Linux, WSL and macOS.

  • LlamaFactory ↗ ★ 75,720

    Fine-tunes more than 100 language and vision-language models from a command line or a browser-based Web UI, aimed at training without writing code.

Make a smaller download (quantize)

  • llama.cpp ↗ ★ 132,313

    Runs models locally and includes the tools behind most GGUF files. A conversion script turns Hugging Face weights into GGUF, and llama-quantize shrinks a high-precision GGUF to lower-bit formats, which can cost some accuracy.

  • MLX LM ↗ ★ 7,470

    Apple's MLX package for running and fine-tuning models on Apple silicon. It can quantize models and upload them to the Hugging Face Hub, and supports LoRA and full fine-tuning.

Merge it with another model

  • mergekit ↗ ★ 7,415

    Merges pretrained models with many merge methods. It works out-of-core, so merges can run on a CPU or with as little as 8 GB of VRAM, and it also handles LoRA extraction and mixture-of-experts merges.

Learn the techniques

  • LLM Course ↗ ★ 84,684

    A free course with roadmaps and Colab notebooks. Its notebooks include one-click tools for merging with MergeKit, quantizing to GGUF, GPTQ, EXL2, AWQ and HQQ, and automatic abliteration with custom datasets.

Compare all tools →

Source provenance

Downloads
755
Likes
18
Metadata checked
2026-10-26 · 08:12:28 UTC
First seen in index
2026-10-26 · 08:12:28 UTC
Source revision
23da584e06e57ec43c896d286b44b822c9bb0ad6

Source-card excerpt

Source model card excerpt: DeepSeek-V4.1-Flash-Abliterated Original Model · Heretic · MIT License Introduction This is a community abliterated conversion of DeepSeek-V4.1-Flash, published by s-zaizen. It is not an

From s-zaizen/DeepSeek-V4.1-Flash-Abliterated. Creator model-card excerpt, not an independent review.

Inspect before you run

Low refusal is not proof of correctness. Outputs can be inaccurate, harmful, or biased. Review the model card, license and files yourself. A license tag is not commercial-use advice. Parameter counts are source metadata; no hardware compatibility is implied.

This directory does not host or redistribute model weights. Refusal profiles are measured by us on a fixed prompt set; all other behavior claims are source-reported.

Collection rules & limitations ↗