license: mit
base_model: windowsxp811203/DeepSeek-V4-Flash-Vision-Exp-Abliterated
base_model_relation: quantized
library_name: gguf
pipeline_tag: text-generation
tags:

• abliterated
• uncensored
• deepseek
• deepseek_v4
• gguf
• llama.cpp
• moe
  extra_gated_heading: "Access requires acknowledging what this model is"
  extra_gated_description: >-
  This is a refusal-ablated model. It complies with requests that the base model
  refuses, including in the most severe categories measured by HarmBench
  (chemical/biological, cybercrime, harassment). Access is granted automatically
  once you share your contact details and accept the terms below.
  extra_gated_prompt: >-
  Terms of use

1. This model has had its learned refusal direction removed. It will not decline
   harmful requests. Any safety behaviour must be supplied by your own stack.

2. You are solely responsible for how you deploy, serve, or redistribute it, and
   for complying with all laws and regulations that apply to you.

3. If you expose it to third parties, you agree to place your own moderation and
   policy layer in front of it. The model will not do that job for you.

4. It is provided as-is, without warranty of any kind. The authors accept no
   liability for any use or consequence.

5. Licensed MIT, inherited from deepseek-ai/DeepSeek-V4-Flash-Vision-Exp.
   extra_gated_fields:
   Full name: text
   Organization or affiliation: text
   Country: country
   Intended use:
   type: select
   options:

   • Research
   • Safety evaluation / red-teaming
   • Internal tooling
   • label: Other
     value: other
     I understand this model does not refuse harmful requests: checkbox
     I take full responsibility for how I deploy and redistribute it: checkbox
     I will add my own moderation layer before exposing it to third parties: checkbox
     I agree to comply with all applicable laws: checkbox
     extra_gated_button_content: "Acknowledge terms and request access"

DeepSeek-V4-Flash-Vision-Exp-Abliterated-GGUF

GGUF quantizations of
windowsxp811203/DeepSeek-V4-Flash-Vision-Exp-Abliterated
— the refusal-ablated build of deepseek-ai/DeepSeek-V4-Flash-Vision-Exp. See the parent repo for the
abliteration recipe and the full refusal / capability evaluation. Refusal on harmful requests is 0.00%
(0/520 AdvBench, 0/300 HarmBench harmful categories, hand-adjudicated).

These are text-only. llama.cpp's DeepSeek-V4 support does not include the vision tower, so the
converter drops it; use the safetensors parent with a vision-capable runtime if you need image input.

Files

Both were verified to load and generate on a CUDA build of llama.cpp (≈58 tok/s MXFP4, ≈52 tok/s Q3_K_M
across 2 GPUs, temperature 0, coherent output).

Why only two levels

The FP8 source stores routed experts with 128×128 block scales, and the converter repacks every expert
straight to MXFP4 — 94% of the weight. So MXFP4 comes out smaller than the source and Q4_K_M / Q5_K_M /
Q6_K would all be larger (they re-inflate 4-bit experts). Only Q3 and below actually shrink. Q2_K was
measured on the sister 0731 model and deliberately not shipped: it kept fluent prose and a perfect
0% refusal rate while MMLU collapsed to chance (23%). Refusal rate is no guide to quantization damage;
only a capability benchmark catches the cliff.

Requirements

You need a llama.cpp with DeepSeek-V4-Flash support (the deepseek_v4 architecture, hash-routed MoE,
hyper-connections). It is recent and still moving; build from source if your version predates it. A
symptom of a too-old or mismatched build is done_getting_tensors: wrong number of tensors at load.
imatrix is not usable on this family (imatrix size 32768 != tensor size 4096), so these are plain
quantizations. MTP is dropped by the converter (conversion v0), so there is no speculative draft head
in the GGUF.

Usage

# follows the split set from the first shard automatically
llama-cli -m DeepSeek-V4-Flash-Vision-Exp-Abliterated-MXFP4-00001-of-00004.gguf \
  -ngl 99 -c 8192 -p "..."

License

MIT, inherited from the base model. See the
parent repo for the
full disclaimer, intended-use notes, and the abliteration + evaluation details.

Support / 打賞

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