license: mit
base_model:
- ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF
base_model_relation: finetune
quantized_by: AtomicChat
pipeline_tag: text-generation
library_name: gguf
tags: - atomic-chat
- deepseek
- deepseek41
- gguf
- gsq
- rco
- strata
- abliteration
- refusal-direction
DeepSeek-V4.1-Flash-1017 Without Refusals · GSQ-RCO
ISTA-DASLab's GSQ-RCO quantizations of DeepSeek-V4.1-Flash-1017 (763B, 384 experts) with DeepSeek's refusal direction projected out of the dense residual writers — the same edit as AtomicChat/DeepSeek-V4.1-Flash-1017-Uncensored-GGUF, measured on every rung.
- These are ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF's GSQ-RCO backbone shards (revision
cb02422c900d)
with only the residual-writing dense tensors replaced: attention output (attn_output_b), the shared-expert
down projection (ffn_down_shexp) and the Engram value projection (engram_wkv). The routed experts are left
exactly as ISTA shipped them, so GSQ's learned low-bit rounding of the experts is preserved byte for byte, and
each file stays the same size and runs wherever the original runs. - The replaced tensors carry the one-direction edit (EN + RU, strength 2.0) baked into DeepSeek's weights at full
precision and quantized to ISTA's exact per-tensor type. - Runs on a single 12 GB GPU with Strata: the backbone sits in system
memory and the shared Engram table streams from an SSD, so a 763B model fits a consumer machine.
What changed and what did not
Both files served on Strata, same prompts and template; "→" reads the stock GSQ-RCO rung → this one.
| Rung | bpw | Total GB | JailbreakBench (EN) | XSTest (safe refused) | Thinking high (30) | MMLU (500) | KL to stock |
|---|---|---|---|---|---|---|---|
IQ2_XXS |
2.0 | 249 | 85.2% → 0.0% | 7.6% → 0.0% | 27/30 → 0/30 | 82.8% (p=0.27) | — |
KL is the edit's distribution shift from the stock rung, from Strata's teacher-forced score mode: a top-64 lower
bound, so the true KL is a little higher. Measurements, prompts and gates:
AtomicChat/DeepSeek-V4.1-Flash-1017-Uncensored-GGUF-metrics (ablit/gsq/).
Running it
Download one rung and ISTA-DASLab's shared Engram file, then serve with Strata:
hf download AtomicChat/DeepSeek-V4.1-Flash-1017-Uncensored-GSQ-RCO-GGUF --include "*IQ2_XXS*" --local-dir ds-unc
hf download ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF --include "*Engram*" --local-dir ds-unc # the 110 GB lookup table, once
strata launch --model-path ds-unc/DeepSeek-V4.1-Flash-1017-GSQ-RCO-IQ2_XXS-00001-of-00004.gguf
The Engram file is identical to ISTA-DASLab's and is not re-hosted here; keep it on a fast SSD next to the shards.
As of this writing these files run in Strata (DeepSeek-V4.1 with a separate Engram file is not yet in llama.cpp).
Limitations
- The refusal classifier reads the start of an answer, so a soft refusal later in the text can pass as an answer.
- The edit removes refusals across topics; it does not keep a narrower set of them.
- With thinking on, the edit plans an answer to a request the original declines, and the plan can be long; give such
requests a larger token budget or a lower effort.
Responsible use
This model answers requests the original declines. You are responsible for how you use it and for following the laws
that apply to you.
Credits and license
- Base model: deepseek-ai/DeepSeek-V4.1-Flash-1017 — MIT (its LICENSE ships here).
- GSQ-RCO quantization and the shared Engram file: ISTA-DASLab/DeepSeek-V4.1-Flash-1017-GSQ-RCO-GGUF (DASLab, IST Austria).
- Runtime: Strata.
MIT, as the base model and ISTA-DASLab's GGUFs. Built by AtomicChat.