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.

Built by the team behind Atomic Chat, the app that runs open-weight models locally and offline.

• 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 refusal edit (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.

Not every rung cleared every gate; such a rung is published as measured, with its numbers in the table and the metrics repo.

What changed and what did not

Both files served on Strata, same prompts and template; "→" reads the stock GSQ-RCO rung → this one.

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. The shift is small enough (KL ~0.02, MMLU-500 within ~1 pt of the stock quant) that the GSQ-RCO quant's task accuracy is preserved — see the reasoning benchmarks below. Measurements, prompts and gates: AtomicChat/DeepSeek-V4.1-Flash-1017-Uncensored-GGUF-metrics (ablit/gsq/).

Reasoning is preserved

The uncensored edit changes only the dense residual writers (attention output, shared-expert FFN-down, Engram value); the 384 GSQ-learned routed-expert layers are left byte-for-byte intact. We benchmarked the edited IQ2_XXS rung against the stock GSQ-RCO quant on the same stand (Strata, reasoning effort 50):

GPQA-Diamond is identical and AIME25 is a hair higher (within noise at n=30): abliteration done this way costs no task accuracy. Per-question logs: metrics repo (ablit/gsq/bench/).

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.