A fast, less-refusing DeepSeek V4 Flash with mixed quantization (MQ), verified 1M-token retrieval, and 123 tok/s-class aggregate decode on two DGX Spark nodes.

SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX is the performance-focused SuperDeepseek release built from deepseek-ai/DeepSeek-V4-Flash-0731. It keeps the official hybrid checkpoint layout, applies a surgical OBLITERATUS + SuperTune update, and ships as a directly loadable checkpoint with no LoRA or runtime adapter required.

Release highlights


Architecture	DeepSeek V4 Flash, 304B-class MoE, 43 backbone layers + 3 MTP layers, 256 routed experts, top-6
Release format	Hybrid FP4 experts + FP8 E4M3 blocks + BF16 quality-sensitive tensors, about 169.5 GB on the Hub
Targeted update	46 `attn.wo_b` weight/scale pairs, with all routed experts and untargeted parent tensors preserved
Verified context	1,048,576 configured; 1,028,621-token prompt accepted with successful needle retrieval
Aggregate decode	118.6 tok/s forced output and 123.3 tok/s structured tool output at p256/C6
Behavior shift	Worst-mode refusal 97.92% -> 4.17%, while the measured tool gates remain 100%
Capability floor	Minimum capability mean 0.9375 -> 0.9583 against the pinned parent

Why run this model

Far fewer unnecessary refusals: the selected checkpoint reduces the measured worst-mode refusal rate from 97.92% to 4.17%.
Tools stay intact: tool compliance and correct-tool selection remain at 100.00% and 100.00% in the paired release gate.
Real 1M context proof: both 149,845-token and 1,028,621-token needle-retrieval requests completed successfully.
Fast on two DGX Spark nodes: the measured serving profile reaches 123.3 aggregate tok/s on structured tool generation.
Surgical rather than destructive: experts, routers, embeddings, mHC tensors, and every untargeted parent tensor retain the official checkpoint representation.

Quantization and precision

MQ in the model name means mixed quantization. This is an official-layout mixed-precision checkpoint, not a full-BF16 release and not a custom whole-model requantization.

Component	Precision / storage
MoE expert weights	FP4, inherited from the official `expert_dtype=fp4` checkpoint layout
Block-quantized paths	FP8 E4M3, dynamic activation scaling, 128x128 weight blocks, UE8M0 scales
43 backbone + 3 MTP `attn.wo_b` updates	Deterministic FP8 weight/scale overlay, 92 tensors
Default unquantized and quality-sensitive paths	BF16 (`torch_dtype=bfloat16`) with F32 metadata/normalization where defined upstream
Output head recovery	One bounded BF16 `head.weight` overlay, rank-64, relative Frobenius delta 0.0025
Measured serving KV cache	NVFP4 DS-MLA

The parent checkpoint is pinned to 9e165c30e2704aec5d9d593cce3eebd58bbef1cb. Only the declared FP8 attn.wo_b pairs and the single bounded BF16 output head are redirected by the final weight index; the remaining parent tensors keep their original quantization and bytes.

What was changed

The release uses two measured weight-space passes:

OBLITERATUS fits a robust rank-1 refusal direction across chat, think-high, and think-max modes and applies the selected strength 2.
A second rank-1 residual pass is recaptured from the baked first pass, orthogonalized against it, and applied at strength 0.5.
A bounded rank-64 output-head recovery was applied; its relative Frobenius delta was 0.0025.

The final checkpoint modifies only the 43 backbone and three MTP attn.wo_b weight/scale pairs plus the bounded output head. There is no inference-time adapter.

Behavior and capability

Metric	Official parent	SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX
Worst-mode refusal	97.92%	4.17%
Worst empty answer	0.00%	0.00%
Worst tool compliance	100.00%	100.00%
Worst correct-tool rate	100.00%	100.00%
Minimum capability mean	0.9375	0.9583

The independently reloaded checkpoint reproduced the selected candidate's deterministic validation behavior exactly. Empty-output, Unicode, repetition, serialization, reasoning, code, formatting, and tool-use sentinels were included in the release gate.

Decode performance

The numbers below are aggregate concurrent decode throughput, not single-stream speed. They use six distinct prompts, fixed-length generation, and the sealed sparkDash-style measurement contract at commit dfde4214f32b174880832a4d317d3c0567750ac5.

Workload	Prompt / concurrency	Aggregate decode
Forced output	p256 / C6	118.5505 tok/s
Structured tool output	p256 / C6	123.2888 tok/s

Median matched decode ratio vs the parent: 1.0012x
Minimum matched-case ratio vs the parent: 0.9627x
Regular CUDA graphs vs breakable: 1.2125x at C1 and 1.2674x at C6

Verified long context

Actual prompt tokens	Accepted	Needle retrieved
149,845	Yes	Yes
1,028,621	Yes	Yes

The configured maximum is 1,048,576 tokens. These are end-to-end acceptance and retrieval probes; they are not a claim that every task benefits equally from the full window.

Two-node DGX Spark serving

The measured profile uses TP=2 over direct CX-7 RoCEv2 with the ghcr.io/anemll/dspark-vllm-gx10:0.1.1 runtime:

NVFP4 DS-MLA KV cache
DSpark speculative decoding with K=1 and greedy draft sampling
FlashInfer b12x MoE and FlashInfer autotuning
prefix caching, asynchronous scheduling, and chunked prefill
regular CUDA graphs with VLLM_USE_BREAKABLE_CUDAGRAPH=0

The repository includes the exact two-rank launcher under repro/scripts/serve_superdeepseek_v4_dual.sh. Its measured model-facing options are:

bash

vllm serve /model \
  --served-model-name SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX \
  --tensor-parallel-size 2 \
  --max-model-len 1048576 \
  --kv-cache-dtype nvfp4_ds_mla \
  --moe-backend flashinfer_b12x \
  --enable-prefix-caching \
  --async-scheduling \
  --enable-chunked-prefill \
  --speculative-config '{"method":"dspark","num_speculative_tokens":1,"draft_sample_method":"greedy"}'

After the server is ready, it exposes an OpenAI-compatible API:

python

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:8888/v1", api_key="EMPTY")
response = client.chat.completions.create(
    model="SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX",
    messages=[{"role": "user", "content": "Design a reliable tool-using agent."}],
    max_tokens=1024,
)
print(response.choices[0].message.content)

Uncensored behavior

“Uncensored” means this checkpoint measurably reduces the selected refusal subspace while retaining the declared capability and output-integrity gates. It does not imply that every answer is correct or that downstream deployment controls are unnecessary.

Release integrity

Parent: deepseek-ai/DeepSeek-V4-Flash-0731@9e165c30e2704aec5d9d593cce3eebd58bbef1cb
48 original parent shard names preserved
92 FP8 overlay tensors: exactly 46 attn.wo_b weight/scale pairs
1 bounded BF16 output-head tensor
Independently reloaded paired validation: passed
Decode, tool, reasoning, long-context, and output-integrity gates: passed
Machine-readable benchmark and release evidence included under evidence/

Limitations

The speed figures are measured on a specific two-node DGX Spark/CX-7 runtime and should not be treated as universal hardware results.
Abliteration changes refusal behavior and can produce content the parent would decline. Operators remain responsible for access control and appropriate use.
The capability and integrity suites are finite regression gates, not proof of universal correctness.
One million token acceptance does not guarantee perfect recall at every position or on every task.
The parent model's license and upstream limitations continue to apply.

Evidence identities

Evidence	SHA-256
paired validation	`65aaf03e1ac5f88ea462c7500d36a06c254370dfa3f9e2a0d24183c482e23a46`
phase 1 selection	`a75b0f9c2c2c9d0c38e94c5a7e36139509178f7f9b74521925c9a1f011f0f207`
phase 2 selection	`b0fdb2c73f16954b56bde2460833b782b29bfe0a67aeedc611d65dc94764e4eb`
recovery decision	`39acc4c24694c7bb08615217d16e5e7d2f3d9e33c26425610ff1198054422a2f`
decode benchmark	`6745453ee65b9e59581d715bf668bc710b0bc680fe726763e9a750dd25f45565`
overlay audit	`c618a1a2da8295b07b1d93d9a59c9a66e8e430c2b8bdf1aca07a56ad56455e8e`
head recovery audit	`a5aa6531909444a5d6199d889030b6b9424868a7656e4db8d77c30aad4bfc8db`

License

MIT, following the upstream DeepSeek V4 Flash release.

Jiunsong/SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX

SuperDeepseek-V4-Flash-abliterated-MQ-2xDGX