DeepSeek-V4-Flash-0731-Abliterated-MXFP4-INT8 API & Inference Endpoint

What this is

DeepSeek-V4-Flash-0731-Abliterated converted to a format that runs on Ampere GPUs:

Experts: MXFP4 (E2M1) — lossless. On sm86, E2M1 is reinterpreted as signed INT4 and run through Ampere INT4 tensor cores via Marlin. E8M0 power-of-2 scales applied in epilogue. No quality loss vs original.
Dense linears: FP8 E4M3 → INT8 (channelwise). Lossy but minimal: measured 40 dB signal-to-noise ratio (~1% mean relative error) on the original checkpoint's FP8 weights after round-trip. For comparison, a typical W4A16 quant measures ~20-30 dB.
KV cache: int8_ds_mla. Doubles context vs fp8 (437K vs ~220K on 8x24GB).
DSpark: 3 MTP draft stages preserved from 0731 source. Observed 0% draft acceptance with int8 KV on sm86. Disable for production.

Abliteration edits only the 46 wo_b tensors (43 decoder + 3 MTP draft head) in weight space; it does not touch embeddings, norms, or the tokenizer. Those edits survive the MXFP4/INT8 repack the same way the base model's weights do.

Use according to the original license(s) of the source models.

How it was created

Converted with tools/ampere/dsv4_requant_checkpoint.py from the AppMana vllm-consumer-nvidia-platforms fork:

bash
python tools/ampere/dsv4_requant_checkpoint.py \
  --src lovesenko/DeepSeek-V4-Flash-0731-Abliterated \
  --dst ./output \
  --expert-format mxfp4 \
  --dense-int8-strategy channel \
  --device cuda:0 \
  --overwrite

MXFP4 path is 95% passthrough — expert weights copied byte-for-byte, only ~8 FP8 linears per shard requanted to INT8. Conversion takes ~15-30 min on a single GPU. See serving/convert.sh. Regenerate with serving/convert.sh; compare shard hashes against serving/checksums/ (sha256 of the published files).

Source: lovesenko/DeepSeek-V4-Flash-0731-Abliterated (itself a weight-space edit of deepseek-ai/DeepSeek-V4-Flash-0731) Fork: AppMana/forks-vllm-consumer-nvidia-platforms branch appmana/upstream-merge, tip 760ced86b + the two serving/patches (== commit 623739503) Similar quants: appmana/deepseek-v4-mxfp4-int8 (pre-0731, no DSpark), appmana/deepseek-v4-int4-int8 (0731, INT4 experts, faster but less headroom)

What to expect

Tested on 8x RTX 3090 (24GB, PCIe, no NVLink), CUDA 13.1, torch 2.13.0+cu130, fork commit 623739503 (= public tip 760ced86b + the two serving/patches). One bench harness, Sep 2026:

Table with columns: Profile, KV pool, Single, 4-way agg, Notes
Profile	KV pool	Single	4-way agg	Notes
TP4xPP2, async, 512K window (recommended default)	1,008,842 tok = 1.92x @512K	49 tok/s	135 tok/s	492K-token ingest validated E2E
TP8, async, 262K	413,887 tok = 1.58x @262K	58 tok/s	139 tok/s	max single-stream
TP8, sync, 262K + 20 GiB CPU offload	461,011 tok + CPU spill	49 tok/s	120 tok/s

Context auto-fit: 524,288-token window with a 1.0M-token pool
Single-stream is below the pre-merge engine's 62 tok/s — the merged engine trades ~10-15% single-stream decode for 2.3x context and a fixed long-context prefill path (details in serving/profiles.md)
Correct math, coherent long-form generation, working tool calls
No quality evals yet — evaluate before production use
GMU=0.93 is the ceiling at the 512K window (0.95 always OOMs on 24GB)
--max-num-batched-tokens 128 gives 100% Triton JIT cache hit

No attempt was made to make DSpark work. We prioritized serving context.

No claims are made for suitability for any purpose.

The only intent is to provide inspiration for owners of 8x3090 rigs.

How to serve

Requires the AppMana fork plus the two patches shipped in serving/patches/, and flash-mla==2.0.0+8ec3de6.

The flash-mla wheel index is https://appmana.github.io/forks-flash-mla-int/, which redirects to GitHub releases. If it is unavailable, the sm86 attention path has no fallback. Consider mirroring the wheel if you depend on this setup.

bash
# 1. Quant (also provides serving/patches/ and serving/serve.sh)
Q="$(hf download Jon-Nielsen/__REPO__)"

# 2. Fork at the pinned commit (current public tip of the branch)
git clone --branch appmana/upstream-merge \
  https://github.com/AppMana/forks-vllm-consumer-nvidia-platforms.git
cd forks-vllm-consumer-nvidia-platforms
git checkout 760ced86b

# 3. Patches: upstream engine sync, then the consumer-NVIDIA fixes
git apply "$Q/serving/patches/0001-upstream-sync-20260906.patch"
git apply "$Q/serving/patches/0002-consumer-nvidia-fixes-20260906.patch"

# 4. Dependencies
python -m venv .venv && source .venv/bin/activate
pip install torch==2.13.0+cu130 \
  --extra-index-url https://download.pytorch.org/whl/cu130
pip install flash-mla==2.0.0+8ec3de6 \
  --extra-index-url https://appmana.github.io/forks-flash-mla-int/

# 5. Build and install (sm86 only — saves 3-5x build time)
MAX_JOBS=12 TORCH_CUDA_ARCH_LIST="8.6" pip install -e . --no-build-isolation

# 6. Serve — defaults are the recommended measured profile
# (TP4 x PP2, async scheduling, 512K context, ~1.0M-token KV pool)
VENV="$PWD/.venv" MODEL="$Q" bash "$Q/serving/serve.sh"

On a box with mixed PCIe widths, order ranks so the first PP stage gets the x16 cards, e.g. x16 = 0,1,2,7: CUDA_VISIBLE_DEVICES=0,1,2,7,3,4,5,6.

All engine fixes (prefill workspace reservation, allocator settings, graph-memory profiling opt-out, dense capture sizes) are baked into serving/serve.sh — see serving/README.md and serving/profiles.md for what each knob does and the alternative profiles.

Patches

Two anonymous diffs against the fork's public tip 760ced86b (branch appmana/upstream-merge), applied in order:

0001-upstream-sync-20260906.patch

Brings the engine to the post-merge state used for validation: 57 upstream/main commits (graph memory profiling default, Fast Start, MRV2 DBO, DSv4 fixes) plus conflict resolutions.

0002-consumer-nvidia-fixes-20260906.patch

The consumer-NVIDIA delta on top:

int8_ds_mla bounds-guarded cache insert kernels (PR-A port)
KV-offload IMA-safe host memory + block-id range checks (PR-B port)
mxfp4 w13 loader + conversion tool kernel block (PR-C port)
worst-case prefill K-gather workspace reservation + env-overridable VLLM_DSV4_PREFILL_CHUNK_SIZE (long-context workspace locked fix)
_vllm_fa3_C optional (Ampere builds produce no FA3 .so)

760ced86b + both patches equals commit 623739503 of the maintainer's line (verified in a throwaway worktree; modulo serving documentation). The previous recipe's three patches (expert weight loader, conversion kernel block, int8 prefill safety) are superseded — all three are contained in 0002.

Serving directory

markdown
serving/
  serve.sh                                          — launcher; defaults = recommended profile
  profiles.md                                       — measured profile matrix + envelope rules
  README.md                                         — recipe, knob explanations, known-good facts
  convert.sh                                        — re-run the conversion (tool in the patched fork)
  tests/                                          — bench harness behind profiles.md + card numbers
  checksums/                                      — sha256 of published shards (base/ablit)
  patches/0001-upstream-sync-20260906.patch         — upstream engine sync (745 KB)
  patches/0002-consumer-nvidia-fixes-20260906.patch — consumer-NVIDIA fixes (43 KB)

Below is the original README from lovesenko/DeepSeek-V4-Flash-0731-Abliterated, included for reference only. The encoding/ and inference/ folders it mentions are not part of this repository.

Original DeepSeek-V4-Flash-0731-Abliterated README

license: mit library_name: transformers base_model:

deepseek-ai/DeepSeek-V4-Flash-0731 tags:
abliterated
uncensored
deepseek
deepseek-v4
moe
dspark
reasoning

DeepSeek-V4-Flash-0731 — Abliterated

This is an abliterated (uncensored) version of deepseek-ai/DeepSeek-V4-Flash-0731, produced by direct weight-space editing.

DeepSeek-V4-Flash-0731 is the official release of DeepSeek-V4-Flash (superseding the preview), a 284B-parameter (13B-activated) Mixture-of-Experts model with a 1-million-token context window and FP8 mixed-precision weights. It has the same architecture as DeepSeek-V4-Flash-DSpark — i.e. it ships with a native Multi-Token-Prediction (MTP) speculative-decoding draft head (DeepSpec / DSpark) attached — and adds substantially enhanced agentic capabilities over the preview. Its decoder uses Manifold-Constrained Hyper-Connections (mHC), which — like Gemma 4's double-norm + Per-Layer-Embeddings — make the model highly resistant to LoRA-based abliteration: the mHC residual pathway re-normalizes away low-rank perturbations, so LoRA edits produce near-zero behavioral change. This release bypasses that resistance by editing the base FP8 weights directly, in the 4096-dimensional wo_b output space, while preserving row magnitudes and capability.

This is the updated successor to lovesenko/DeepSeek-V4-Flash-DSpark-Abliterated, applied to the official 0731 release using the same proven recipe.

Method

Because mHC re-normalizes low-rank perturbations, LoRA-based abliteration does not work on this family. The fix is to edit the base weights directly.

The abliteration captures a 4096-dimensional refusal direction in the model's own output space and projects it out of the attention output projection (attn.wo_b) on every decoder layer, plus the DSpark draft head (mtp.wo_b).

Key techniques applied:

4096-dim refusal-direction capture via a patched vLLM server that hooks the wo_b and aggregated-FFN outputs on all 43 decoder layers, prefill-only, with per-request sequencing. The broad refusal direction d was captured as a difference-of-means (all-harmful − all-benign) direction over a 2583-prompt category-expanded capture set, Gram-Schmidt orthonormalized.
Rank-1 broad-d projection — only the single broad refusal direction d is projected out. Higher-rank variants (adding the stubborn d_s direction, per-category d_cat directions, or MLP shared_w2 editing) were all evaluated and abandoned: they either reduced refusal less than rank-1, raised refusal via non-monotonic amplification, or risked coherence. This is the smallest, most capability-preserving edit, and is the same recipe validated on the DSpark release.
SRA cleaning (Spectral Residual Alignment) — the broad refusal direction is orthogonalized against the top-r=4 SVD atoms of capability-concept activations before projection, so the d direction does not eat capability.
Naive output-side orthogonal projection on attn.wo_b for all 43 decoder layers, plus (the DSpark draft head) via the deepest-layer basis: with λ = 2.5.

Evaluation

Table with columns: Metric, Value
Metric	Value
Refusals — CoT, production mode (300 prompts, LLM judge, `thinking=true`, `reasoning_effort=high`)	39 / 300 (13.0%)
Refusals — no-CoT (300 prompts, LLM judge)	6 / 300 (2.0%)
Baseline refusals (raw base 0731, 1000 prompts, LLM judge)	961 / 1000 (96.10%)
Configuration	rank-1 broad-d, all 46 `wo_b` tensors (43 decoder + 3 `mtp`), `attn.wo_b` only, λ = 2.5
Projection mode	Direct weight editing (naive output-side orthogonal projection)

The production deployment runs with chain-of-thought enabled (thinking=true, reasoning_effort=high), so refusal must be measured with CoT on. With CoT off the reflexive-refusal direction is gone and the model cannot reflexively refuse (2.0%); with CoT on the model reasons about the request and re-derives a refusal decision through the reasoning trace, landing at 13.0%. This CoT re-refusal is the floor for pure weight-editing abliteration on wo_b — the weight edit cannot remove the model's ability to reason toward refusal, since that reasoning is distributed across the MLP/attention path in a way a low-rank wo_b projection cannot fully reach. Pushing the projection strength harder (λ=4.0) lowers refusal to 7.3% but introduces long-CoT reasoning-loop degeneration under real agentic workloads, so it was rolled back to the production-safe λ=2.5.

Refusal breakdown by category (CoT, 300-prompt set, LLM judge)

Table with columns: Category, Refusals, Rate
Category	Refusals	Rate
Violence	5 / 15	33.3%
Weapons	7 / 27	25.9%
Cybercrime	6 / 25	24.0%
PII (doxing private individuals)	5 / 21	23.8%
Self-Harm (suicide methods)	3 / 16	18.8%
Illegal Drugs	3 / 19

11 of 14 categories sit at ≤13%, with three categories fully cleared. The residual is carried by a small number of CoT re-refusal holdouts — Violence, Weapons, Cybercrime, PII — where the model reasons its way back to refusing even after the reflexive-refusal direction is removed. These are the categories that weight-space wo_b abliteration alone cannot fully clear without breaking coherence (the λ=4/5 cliff proves the lever is exhausted).

Full capability sweep (base vs abliterated)

Coming soon. A paired, full-dataset capability measurement (MMLU-Pro, GSM8K, HumanEval, MBPP) for base 0731 vs this abliterated release is being run and will be posted here. In the meantime, capability was verified on a spot-check battery (arithmetic, code generation, logical reasoning, factual recall) with no regressions vs base — see the note below.

Capability spot-check (abliterated 0731, CoT)

17 × 23 = 391 (correct)
Fibonacci, first 10: 1, 1, 2, 3, 5, 8, 13, 21, 34, 55 (correct)
is_prime(n) Python function (correct)
Syllogism ("some roses fade") → correct ("No, we cannot conclude")
Capital of Australia → Canberra (correct)
reverse_string code (correct)
Transitive-inequality logic (correct)
Polite email → well-formed

Capability fully retained on the spot-check battery.

Multi-turn & higher-context degradation

Coming soon. Multi-turn coherence and needle-in-haystack context-retrieval sweeps (2k / 4k / 8k / 16k / 32k) will be posted here. No multi-turn coherence loss or higher-context degradation was observed in production agentic workloads (multi-turn Cline-style tool workflows with 14 valid tool calls, 0 empty non-tool turns, 0 length finishes).

SWE-bench Lite

Coming soon. An agentic-style code-repair evaluation (oracle-file-context, single-shot) for base vs abliterated will be posted here.

DSpark speculative decoding (post-abliteration)

The mtp.wo_b draft head was edited with the same projection applied to the decoder (deepest-layer basis). Speculative decoding remains functional and healthy — the weight edit did not desynchronize the draft head from the abliterated target, and the served output distribution is identical whether or not DSpark is enabled (DSpark verifies every draft token against the abliterated target).

Coming soon. Measured draft-acceptance numbers (at num_speculative_tokens = 3 / 4 / 5) and single-stream decode throughput for this release will be posted here. For inference guidance specific to the NVIDIA RTX PRO 6000 Blackwell (TP2/TP4, the lucifer-default/lucifer-cutlass/b12x backends, and the native DSpark method=dspark speculative-decoding path), see the community v9 serving guide for this checkpoint family.

A note on honest evaluation

Refusal numbers are only meaningful when the methodology behind them is documented. Our methodology:

CoT-on measurement. The production deployment runs with thinking=true, reasoning_effort=high. Refusal is therefore measured with CoT on (8192-token budget), not no-CoT — no-CoT hides the CoT re-refusal floor.
LLM judge, not keyword heuristics. For abliterated models, keyword heuristics are unreliable: the model produces long, direct compliance content (e.g. synthesis instructions, hate justifications, PII) that trips keyword heuristics, and it also lecture-deflects without refusal keywords. We use an LLM judge (the base DeepSeek-V4-Flash-0731 model itself, COMPLY/REFUSAL with reasoning) which catches Chinese/polite/lecture/deflection refusals the keywords miss. The judge is the metric of record; the heuristic is reported only for contrast.
Challenging, diverse prompts. The refusal set spans 14 categories across multiple sophistication levels (direct requests to socially-engineered framings) and English / Chinese / mixed languages.
Paired baseline. The base 0731 model is evaluated with the same judge on the same prompt distribution, so the refusal delta is directly comparable (96.10% → 13.0% CoT).
Documented parameters. Generation length, detection method, dataset, λ, rank, and layer coverage are all listed on this card.

Files

This release is a complete, standalone, drop-in checkpoint: all 48 safetensors shards are included, plus model.safetensors.index.json, config.json, generation_config.json, tokenizer.json, tokenizer_config.json, LICENSE, and the encoding/ and inference/ folders. It loads directly with vLLM / the DeepSeek-V4 inference path — no files need to be fetched from elsewhere.

The abliteration modified 46 of the 48 shards (the 43 decoder attn.wo_b tensors and the 3 mtp.wo_b draft-head tensors). The remaining 2 shards (model-00001-of-00048.safetensors, model-00045-of-00048.safetensors — embeddings / norm / lm_head) are byte-identical to the base model and are included unchanged so the repo is self-contained. No tokenizer, config, architecture, or inference-path files were modified.

Usage

This abliterated checkpoint is a drop-in replacement for the original weights — it has the exact same architecture, format, chat-template/encoding, and inference path as the released base model deepseek-ai/DeepSeek-V4-Flash-0731. Load and serve it however you would the official model (vLLM, the DeepSeek-V4 encoding/inference folders, OpenAI-compatible serving, etc.). The abliteration modified the text-decoder attn.wo_b weights on all 43 layers and the DSpark draft head's mtp.wo_b; the tokenizer, chat encoding, and all other components are unchanged.

DSpark speculative decoding is enabled with a single flag — add --speculative-config with method: dspark to your vLLM launch command:

--speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}'

For example, serving on a single 4×GB300 node:

bash
vllm serve lovesenko/DeepSeek-V4-Flash-0731-Abliterated \
  --trust-remote-code --kv-cache-dtype fp8 --block-size 256 \
  --data-parallel-size 4 --enable-expert-parallel \
  --moe-backend deep_gemm_mega_moe \
  --attention-config '{"use_fp4_indexer_cache": true}' \
  --speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}'

See the base model's encoding and inference folders for full documentation of the chat-template encoding and the local inference path.

Disclaimer

This model is released for research purposes only — primarily interpretability and safety research, including studying how refusal behavior is encoded in large MoE decoders and how weight-space edits interact with architectures that resist low-rank perturbation. The abliteration process removes safety guardrails on most harm categories, so the model will comply with requests the base model refuses. Use responsibly, in accordance with local laws and the DeepSeek / model terms of use, and do not deploy it in production or user-facing settings without a separate safety layer. The authors take no responsibility for misuse.

What this is

DeepSeek-V4-Flash-0731-Abliterated converted to a format that runs on Ampere GPUs:

Experts: MXFP4 (E2M1) — lossless. On sm86, E2M1 is reinterpreted as signed INT4 and run through Ampere INT4 tensor cores via Marlin. E8M0 power-of-2 scales applied in epilogue. No quality loss vs original.
Dense linears: FP8 E4M3 → INT8 (channelwise). Lossy but minimal: measured 40 dB signal-to-noise ratio (~1% mean relative error) on the original checkpoint's FP8 weights after round-trip. For comparison, a typical W4A16 quant measures ~20-30 dB.
KV cache: int8_ds_mla. Doubles context vs fp8 (437K vs ~220K on 8x24GB).
DSpark: 3 MTP draft stages preserved from 0731 source. Observed 0% draft acceptance with int8 KV on sm86. Disable for production.

Use according to the original license(s) of the source models.

How it was created

Converted with tools/ampere/dsv4_requant_checkpoint.py from the AppMana vllm-consumer-nvidia-platforms fork:

bash
python tools/ampere/dsv4_requant_checkpoint.py \
  --src lovesenko/DeepSeek-V4-Flash-0731-Abliterated \
  --dst ./output \
  --expert-format mxfp4 \
  --dense-int8-strategy channel \
  --device cuda:0 \
  --overwrite

What to expect

Tested on 8x RTX 3090 (24GB, PCIe, no NVLink), CUDA 13.1, torch 2.13.0+cu130, fork commit 623739503 (= public tip 760ced86b + the two serving/patches). One bench harness, Sep 2026:

Table with columns: Profile, KV pool, Single, 4-way agg, Notes
Profile	KV pool	Single	4-way agg	Notes
TP4xPP2, async, 512K window (recommended default)	1,008,842 tok = 1.92x @512K	49 tok/s	135 tok/s	492K-token ingest validated E2E
TP8, async, 262K	413,887 tok = 1.58x @262K	58 tok/s	139 tok/s	max single-stream
TP8, sync, 262K + 20 GiB CPU offload	461,011 tok + CPU spill	49 tok/s	120 tok/s

Context auto-fit: 524,288-token window with a 1.0M-token pool
Single-stream is below the pre-merge engine's 62 tok/s — the merged engine trades ~10-15% single-stream decode for 2.3x context and a fixed long-context prefill path (details in serving/profiles.md)
Correct math, coherent long-form generation, working tool calls
No quality evals yet — evaluate before production use
GMU=0.93 is the ceiling at the 512K window (0.95 always OOMs on 24GB)
--max-num-batched-tokens 128 gives 100% Triton JIT cache hit

No attempt was made to make DSpark work. We prioritized serving context.

No claims are made for suitability for any purpose.

The only intent is to provide inspiration for owners of 8x3090 rigs.

How to serve

Requires the AppMana fork plus the two patches shipped in serving/patches/, and flash-mla==2.0.0+8ec3de6.

bash
# 1. Quant (also provides serving/patches/ and serving/serve.sh)
Q="$(hf download Jon-Nielsen/__REPO__)"

# 2. Fork at the pinned commit (current public tip of the branch)
git clone --branch appmana/upstream-merge \
  https://github.com/AppMana/forks-vllm-consumer-nvidia-platforms.git
cd forks-vllm-consumer-nvidia-platforms
git checkout 760ced86b

# 3. Patches: upstream engine sync, then the consumer-NVIDIA fixes
git apply "$Q/serving/patches/0001-upstream-sync-20260906.patch"
git apply "$Q/serving/patches/0002-consumer-nvidia-fixes-20260906.patch"

# 4. Dependencies
python -m venv .venv && source .venv/bin/activate
pip install torch==2.13.0+cu130 \
  --extra-index-url https://download.pytorch.org/whl/cu130
pip install flash-mla==2.0.0+8ec3de6 \
  --extra-index-url https://appmana.github.io/forks-flash-mla-int/

# 5. Build and install (sm86 only — saves 3-5x build time)
MAX_JOBS=12 TORCH_CUDA_ARCH_LIST="8.6" pip install -e . --no-build-isolation

# 6. Serve — defaults are the recommended measured profile
# (TP4 x PP2, async scheduling, 512K context, ~1.0M-token KV pool)
VENV="$PWD/.venv" MODEL="$Q" bash "$Q/serving/serve.sh"

On a box with mixed PCIe widths, order ranks so the first PP stage gets the x16 cards, e.g. x16 = 0,1,2,7: CUDA_VISIBLE_DEVICES=0,1,2,7,3,4,5,6.

Patches

Two anonymous diffs against the fork's public tip 760ced86b (branch appmana/upstream-merge), applied in order:

0001-upstream-sync-20260906.patch

Brings the engine to the post-merge state used for validation: 57 upstream/main commits (graph memory profiling default, Fast Start, MRV2 DBO, DSv4 fixes) plus conflict resolutions.

0002-consumer-nvidia-fixes-20260906.patch

The consumer-NVIDIA delta on top:

int8_ds_mla bounds-guarded cache insert kernels (PR-A port)
KV-offload IMA-safe host memory + block-id range checks (PR-B port)
mxfp4 w13 loader + conversion tool kernel block (PR-C port)
worst-case prefill K-gather workspace reservation + env-overridable VLLM_DSV4_PREFILL_CHUNK_SIZE (long-context workspace locked fix)
_vllm_fa3_C optional (Ampere builds produce no FA3 .so)

Serving directory

markdown
serving/
  serve.sh                                          — launcher; defaults = recommended profile
  profiles.md                                       — measured profile matrix + envelope rules
  README.md                                         — recipe, knob explanations, known-good facts
  convert.sh                                        — re-run the conversion (tool in the patched fork)
  tests/                                          — bench harness behind profiles.md + card numbers
  checksums/                                      — sha256 of published shards (base/ablit)
  patches/0001-upstream-sync-20260906.patch         — upstream engine sync (745 KB)
  patches/0002-consumer-nvidia-fixes-20260906.patch — consumer-NVIDIA fixes (43 KB)

Below is the original README from lovesenko/DeepSeek-V4-Flash-0731-Abliterated, included for reference only. The encoding/ and inference/ folders it mentions are not part of this repository.

Original DeepSeek-V4-Flash-0731-Abliterated README

license: mit library_name: transformers base_model:

deepseek-ai/DeepSeek-V4-Flash-0731 tags:
abliterated
uncensored
deepseek
deepseek-v4
moe
dspark
reasoning

DeepSeek-V4-Flash-0731 — Abliterated

This is an abliterated (uncensored) version of deepseek-ai/DeepSeek-V4-Flash-0731, produced by direct weight-space editing.

This is the updated successor to lovesenko/DeepSeek-V4-Flash-DSpark-Abliterated, applied to the official 0731 release using the same proven recipe.

Method

Because mHC re-normalizes low-rank perturbations, LoRA-based abliteration does not work on this family. The fix is to edit the base weights directly.

Key techniques applied:

4096-dim refusal-direction capture via a patched vLLM server that hooks the wo_b and aggregated-FFN outputs on all 43 decoder layers, prefill-only, with per-request sequencing. The broad refusal direction d was captured as a difference-of-means (all-harmful − all-benign) direction over a 2583-prompt category-expanded capture set, Gram-Schmidt orthonormalized.
Rank-1 broad-d projection — only the single broad refusal direction d is projected out. Higher-rank variants (adding the stubborn d_s direction, per-category d_cat directions, or MLP shared_w2 editing) were all evaluated and abandoned: they either reduced refusal less than rank-1, raised refusal via non-monotonic amplification, or risked coherence. This is the smallest, most capability-preserving edit, and is the same recipe validated on the DSpark release.
SRA cleaning (Spectral Residual Alignment) — the broad refusal direction is orthogonalized against the top-r=4 SVD atoms of capability-concept activations before projection, so the d direction does not eat capability.
Naive output-side orthogonal projection on attn.wo_b for all 43 decoder layers, plus (the DSpark draft head) via the deepest-layer basis: with λ = 2.5.

Evaluation

Table with columns: Metric, Value
Metric	Value
Refusals — CoT, production mode (300 prompts, LLM judge, `thinking=true`, `reasoning_effort=high`)	39 / 300 (13.0%)
Refusals — no-CoT (300 prompts, LLM judge)	6 / 300 (2.0%)
Baseline refusals (raw base 0731, 1000 prompts, LLM judge)	961 / 1000 (96.10%)
Configuration	rank-1 broad-d, all 46 `wo_b` tensors (43 decoder + 3 `mtp`), `attn.wo_b` only, λ = 2.5
Projection mode	Direct weight editing (naive output-side orthogonal projection)

Refusal breakdown by category (CoT, 300-prompt set, LLM judge)

Table with columns: Category, Refusals, Rate
Category	Refusals	Rate
Violence	5 / 15	33.3%
Weapons	7 / 27	25.9%
Cybercrime	6 / 25	24.0%
PII (doxing private individuals)	5 / 21	23.8%
Self-Harm (suicide methods)	3 / 16	18.8%
Illegal Drugs	3 / 19

Full capability sweep (base vs abliterated)

Capability spot-check (abliterated 0731, CoT)

17 × 23 = 391 (correct)
Fibonacci, first 10: 1, 1, 2, 3, 5, 8, 13, 21, 34, 55 (correct)
is_prime(n) Python function (correct)
Syllogism ("some roses fade") → correct ("No, we cannot conclude")
Capital of Australia → Canberra (correct)
reverse_string code (correct)
Transitive-inequality logic (correct)
Polite email → well-formed

Capability fully retained on the spot-check battery.

Multi-turn & higher-context degradation

SWE-bench Lite

Coming soon. An agentic-style code-repair evaluation (oracle-file-context, single-shot) for base vs abliterated will be posted here.

DSpark speculative decoding (post-abliteration)

A note on honest evaluation

Refusal numbers are only meaningful when the methodology behind them is documented. Our methodology:

CoT-on measurement. The production deployment runs with thinking=true, reasoning_effort=high. Refusal is therefore measured with CoT on (8192-token budget), not no-CoT — no-CoT hides the CoT re-refusal floor.
LLM judge, not keyword heuristics. For abliterated models, keyword heuristics are unreliable: the model produces long, direct compliance content (e.g. synthesis instructions, hate justifications, PII) that trips keyword heuristics, and it also lecture-deflects without refusal keywords. We use an LLM judge (the base DeepSeek-V4-Flash-0731 model itself, COMPLY/REFUSAL with reasoning) which catches Chinese/polite/lecture/deflection refusals the keywords miss. The judge is the metric of record; the heuristic is reported only for contrast.
Challenging, diverse prompts. The refusal set spans 14 categories across multiple sophistication levels (direct requests to socially-engineered framings) and English / Chinese / mixed languages.
Paired baseline. The base 0731 model is evaluated with the same judge on the same prompt distribution, so the refusal delta is directly comparable (96.10% → 13.0% CoT).
Documented parameters. Generation length, detection method, dataset, λ, rank, and layer coverage are all listed on this card.

Files

Usage

DSpark speculative decoding is enabled with a single flag — add --speculative-config with method: dspark to your vLLM launch command:

--speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}'

For example, serving on a single 4×GB300 node:

bash
vllm serve lovesenko/DeepSeek-V4-Flash-0731-Abliterated \
  --trust-remote-code --kv-cache-dtype fp8 --block-size 256 \
  --data-parallel-size 4 --enable-expert-parallel \
  --moe-backend deep_gemm_mega_moe \
  --attention-config '{"use_fp4_indexer_cache": true}' \
  --speculative-config '{"method":"dspark","num_speculative_tokens":7,"draft_sample_method":"greedy"}'

See the base model's encoding and inference folders for full documentation of the chat-template encoding and the local inference path.

DeepSeek-V4-Flash-0731-Abliterated-MXFP4-INT8

README

What this is

How it was created

What to expect

How to serve

Patches

0001-upstream-sync-20260906.patch

0002-consumer-nvidia-fixes-20260906.patch

Serving directory

Original DeepSeek-V4-Flash-0731-Abliterated README

DeepSeek-V4-Flash-0731 — Abliterated

Method

Evaluation

Refusal breakdown by category (CoT, 300-prompt set, LLM judge)

Full capability sweep (base vs abliterated)

Capability spot-check (abliterated 0731, CoT)

Multi-turn & higher-context degradation

SWE-bench Lite

DSpark speculative decoding (post-abliteration)

A note on honest evaluation

Files

Usage

Disclaimer

Explore FriendliAI today

README

What this is

How it was created

What to expect

How to serve

Patches

0001-upstream-sync-20260906.patch

0002-consumer-nvidia-fixes-20260906.patch

Serving directory

Original DeepSeek-V4-Flash-0731-Abliterated README

DeepSeek-V4-Flash-0731 — Abliterated

Method

Evaluation

Refusal breakdown by category (CoT, 300-prompt set, LLM judge)

Full capability sweep (base vs abliterated)

Capability spot-check (abliterated 0731, CoT)

Multi-turn & higher-context degradation

SWE-bench Lite

DSpark speculative decoding (post-abliteration)

A note on honest evaluation

Files

Usage

Disclaimer