DeepSeek-V4.1-Flash — UNCENSORED · EXL3 2.9bpw

Abliterated · No guardrails · EXL3 2.9 bpw · Runs on 2× DGX Spark · Vision + tools + DSpark

@dealignai

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What is this

DeepSeek-V4.1-Flash quantized to EXL3 2.9 bits/weight and abliterated — the safety guardrails are surgically removed at the weight level while capability, vision, reasoning, speculative decoding (DSpark) and multi-turn coherence are preserved. The 552B multimodal MoE now fits and serves on two NVIDIA DGX Spark (GB10) boxes.

Proprietary weight-level abliteration developed by the dealignai research team. No custom model.py, no runtime hooks, no steering vectors — a standard EXL3 checkpoint that loads exactly like the base quant. The refusal circuitry is removed while every capability-critical component (routed experts, Engram n-gram memory, CSA2 sparse attention, DSpark draft head, vision tower, router gates, norms, embeddings) is preserved.

Results

Refusal graded on the delivered output tokens (content, or the reasoning trace when the model reasons past the token budget) across a 6-tier scheme (hard refusal / soft redirect / hedge / truncated-comply / reasoning-refusal / comply). Truncation is never miscounted as a refusal.

HarmBench-320 — attack success rate (comply %), T=0 greedy

Base HB measured on a 287-item representative sample (off n=144, max n=143); CRACK on the full 320 each. MMLU is the full 14,042-item set for both.

At effort=max the base refuses even harder (reasoning surfaces safety concerns first); the cracked build stays at 99.4 % across both effort levels.

Per-category ASR (comply %) — all 7 HarmBench semantic categories:

MMLU-14k (full test set, base-logit ranking, T=0)

The capability cost is concentrated almost entirely in the ethics cluster — the same "should I refuse?" circuit that is removed:

General capability is essentially intact (−0.58 pp). The single largest per-subject move is moral_scenarios (66.1 % → 37.8 %) — the refusal circuit itself. Several subjects are unchanged or improved.

Full MMLU per-subject comparison (all 57 subjects, base → CRACK)

Serving (2× DGX Spark)

Runtime: MiaAI-Lab/DeepSeek-v4.1-Flash-EXL3-2x-DGX-Sparks — a vLLM + ExLlamaV3 (EXL3) overlay image (ghcr.io/miaai-lab/deepseek-v4.1-flash-exl3-2x-dgx-sparks:2.9bpw) that carries the DeepseekV41 architecture and the SM121 (GB10) kernels.

This checkpoint is a drop-in replacement for the kit's stock 2.9bpw pack — point MODEL_HOST at it and serve; nothing else changes.

Two things you must supply:

1. Engram tables — shards 47 + 48 of deepseek-ai/DeepSeek-V4.1-Flash (the n-gram tables are never quantized and are not duplicated here). Point the kit's ENGRAM_DIR at a tree containing those two shards + the index.
2. A 2× GB10 kit joined over CX7 (the pack is ~197 GiB, TP=2).

Quick start (on the head node, from the kit repo):

cp .env.example .env
# edit .env:
MODEL_HOST=/path/to/DeepSeek-V4.1-Flash-UNCENSORED-EXL3-2.9bpw
ENGRAM_DIR=/path/to/engram-src        # shards 47+48 of the base model
AUTO_DOWNLOAD=0
SKIP_BUILD=1 ./start.sh                # pull the published image + serve on :8888

Serving params that work (validated on 2× GB10, TP=2):

Sampling — official DeepSeek settings: temperature=1.0, top_p=0.95. Thinking defaults on; set reasoning_effort to "low" / "high" / "max" (or enable_thinking=false for no reasoning). API is OpenAI-compatible on :8888, served model id DeepSeek-v4.1-Flash-EXL3.

Speculative decoding (DSpark) works on this abliterated build — measured ~45 % draft acceptance (mean accept length ~2), the same on harmful and harmless prompts, ~27 tok/s single-stream on 2× GB10. Vision, tools and long context are unchanged from the base pack.

Preserved (byte-compatible with the base quant)

Routed experts · Engram memory · CSA2 sparse attention · DSpark draft head · vision tower · router gates · RMSNorms · embeddings. Only the refusal circuit is removed.

Responsible use

This model has its safety guardrails removed. You are responsible for your inputs and outputs and for complying with all applicable law.