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

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

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.

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:

1. OBLITERATUS fits a robust rank-1 refusal direction across chat, think-high, and think-max modes and applies the selected strength 2.
2. A second rank-1 residual pass is recaptured from the baked first pass, orthogonalized against it, and applied at strength 0.5.
3. 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

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.

• 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

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:

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:

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

License

MIT, following the upstream DeepSeek V4 Flash release.