DeepSeek-V4-Flash-0731 REAP-150B · TQ3_4S-Container (v3b recipe) · 256K ctx

A custom, selectively-quantized GGUF of the DeepSeek-V4-Flash-0731 architecture, pruned to the 150B "REAP" variant, quantized with a hand-tuned per-tensor recipe and wrapped in the TurboQuant TQ3_4S container from the turbo-tan/llama.cpp-tq3 fork.

This is not a plain uniform quant. It is the result of iterative quantization on a 3× Turing GPU box, and the recipe reflects a specific, measured trade-off: maximize context length (256K) while holding wikitext-2 perplexity within noise of the IQ3_XXS baseline that proved to be the local optimum for this model.

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[!WARNING] Experimental build. This is a hand-tuned, selectively-quantized experimental release, not a polished upstream quant. It requires the non-stock turbo-tan/llama.cpp-tq3 fork to load (TQ3_4S container is rejected by stock llama.cpp), the recipe and serving flags are tuned for our specific 3× Turing hardware (§3, §6), and the benchmarks in §4 are our own internal measurements, not standardized leaderboard runs. Expect rough edges, and treat the numbers as relative to sibling builds on the same rig rather than absolute claims. If you just want a drop-in quant of this model, use the upstream REAP release instead.

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Benchmarks

This quant vs. the models it derives from. Metrics per the REAP benchmarks: GSM8K exact_match, MATH-500 math_verify, HumanEval+/MBPP+ pass@1_plus (greedy, n=1).

ᵃ Thinking mode (reasoning_effort=high + answer headroom floor, §7 pitfall 8). Base and FP8 reference scores are from the REAP model card, run with thinking off; in matched no-thinking protocol this build scores 0.696 (MATH-500) and 0.6524 (HumanEval+). Full methodology, the both-protocols matrix, and spiral rates: §4.

The quantization itself costs ~nothing: no-thinking GSM8K is within stderr of the FP8 checkpoint (−0.68), MATH-500 is −1.8, MBPP+ is +10.3 above. The thinking-mode rows show what the model does when given inference budget and the headroom floor that keeps spirals from eating the answer.

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Highlights

What this build buys you over the stock REAP-150B release, in one screen:

• Quality on par with the base REAP model. wikitext-2 perplexity ~13.34, within noise of the IQ3_XXS baseline (13.3073 ± 0.23) — the aggressive 3.49 BPW quantization costs no measurable quality. (The failed 2-bit IQ2_S experiment measured 14.30, +7.5%, confirming this recipe sits on the size/quality optimum, not past the cliff.) The full capability-bench picture (base 284B / REAP-150B FP8 / this build) is right after this intro: no-thinking quality tracks the FP8 checkpoint within noise, MBPP+ beats it by +10.3 in matched protocol, and with thinking mode MATH-500 hits 0.9618 and HumanEval+ 0.9207 (both above the FP8 reference, which was measured thinking-off — see §4 for the full disclosure).
• Optional uncensoring (external LoRA). An optional 2 MB rank-1 refusal-ablation LoRA (heretic v2 t256-r2, not bundled here) drops refusals from 99.29% → ~7% with low KL drift (0.085), applied via --lora at load — fetch it per §5. Base weights are never modified or requantized; ship it censored or not, same 61 GiB file.
• 2.7× the context. 256K context (vs the 96K ceiling of prior q8_0-KV builds) via the TurboQuant q8_0 K + turbo4 V-cache — a 4-bit compressed KV value cache that frees the headroom at zero quality cost.
• ngram-mod speculative decoding. ngram-mod (n16/m24) speeds code / structured output to ~85 tok/s (100% draft acceptance on repetitive text) over a ~21 tok/s dense baseline.
• Compact VRAM footprint. 61 GiB at 3.49 BPW fits full GPU offload in ~70 GiB — three Turing cards (1× 22 GiB + 2× 24 GiB). A uniform Q4_K of the same model is ~84 GiB and wouldn't fit; this selective recipe is why it does.
• Ships complete. Everything to serve the model end-to-end is in this repo: the model, the exact per-tensor recipe, and the importance matrix (see §2a).

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1. Summary

The file was quantized from a full-precision Q8_0 re-quantization of the FP8 source (dsv4-fp8-q8_0.gguf, 79.2 GiB) rather than from the raw FP8 weights, using --allow-requantize with an importance matrix.

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2. Why "TQ3_4S" but IQ3_XXS tensors? (Read this before you assume anything)

The naming is a deliberate container/recipe distinction that trips people up:

• TQ3_4S is the container type. It is the TurboQuant "four-scale" format and the general.file_type stamped into the GGUF header. It is what makes stock llama.cpp refuse to load this file — you must use the turbo-tan/llama.cpp-tq3 fork (or a build with TQ3_4S support) to run it.
• The actual per-tensor types are a recipe, not a uniform TQ3_4S:

The result is that the model is smaller than a uniform Q4_K (which would be ~84 GiB) while holding quality, because the aggressive IQ3_XXS/IQ3_S quantization is applied only where the measured error cost is lowest (the deep MoE experts), and the attention path is deliberately left at Q6_K.

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2a. Repository contents (this bundle)

This repository ships everything needed to serve the model end-to-end, not just the base quant. Files and their roles:

🧩 The optional uncensor overlay is not bundled here. The base quant is self-sufficient (stock quality, ~99% refusal). If you want the ~7%-refusal behavior, an optional 2 MiB rank-1 LoRA (t256-r2) is applied losslessly via --lora in f32/f16 compute regardless of base quant. Fetch that adapter separately (see §5) — it lives in the MorinoNushi repo, not this one, so the base quant stays a clean, self-contained GGUF.

Other heretic adapter variants (t264/t265/t273) are not sideloaded here — see §5 for why t256 is the correct choice for this box and how to fetch the others if you want a stronger-decensor/slower trade-off.

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3. Quantization recipe (exact tensor_types.txt)

token_embd\.weight=q8_0
output\.weight=q8_0
output_hc_fn\.weight=f32
blk\.[0-9]+\.hc_(attn|ffn)_(fn|base|scale)\.weight=f32
blk\.[0-9]+\.ffn_(gate|down|up)_shexp\.weight=q8_0
blk\.[0-9]+\.attn_[a-z_]+\.weight=q6_K
blk\.[0-9]+\.indexer[._][a-z_]+\.weight=q6_K
blk\.([0-2]|4[12])\.ffn_(gate|down|up)_exps\.weight=mxfp4
blk\.[0-9]+\.ffn_down_exps\.weight=iq3_s
blk\.[0-9]+\.ffn_(gate|up)_exps\.weight=iq3_xxs

Command used

llama-quantize \
  --allow-requantize \
  --imatrix imatrix-filtered.gguf \
  --tensor-type-file weights.txt \
  --output-tensor-type q6_K \
  --token-embedding-type q6_K \
  dsv4-fp8-q8_0.gguf \
  DeepSeek-V4-Flash-0731-REAP-150b-TQ3_4S.gguf TQ3_4S 32

• Importance matrix: imatrix-filtered.gguf (222 MiB), 129 entries over 812 calibration chunks, generated against the REAP-150B architecture (not the full 284B), so the importance data is matched to this pruned variant.
• --allow-requantize is required because the source is already-quantized Q8_0 (itself a re-quant of the FP8 release weights).

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4. Evaluation

Perplexity (wikitext-2)

The PPL gate for this line of experiments was ≤ 13.44 (the IQ3_XXS baseline 13.3073 ± 0.23 plus a 1% tolerance). This recipe reproduces the v3b recipe exactly, so it lands at the same ~13.34 as the already-validated IQ3_XXS/IQ3_S selective build.

For context on why the experts sit at IQ3_XXS rather than lower, the aborted IQ2_S-expert candidate (the turbo-tan "champion" recipe) measured 14.30 ± 0.25 — a clear +7.5% regression that confirms the 2-bit cliff for this pruned model. The IQ3_XXS/IQ3_S combination is the measured local optimum on the size/quality curve.

⚠️ These are our internal wikitext-2 measurements, not a full Open LLM Leaderboard / MMLU run. Treat them as relative (this build vs. sibling builds), not absolute capability claims.

Capability benchmarks vs. the FP8 reference (2026-08-30)

Methodology (full disclosure). All runs on this build, same machine (§6), with the heretic t256-r2 uncensor LoRA loaded (§5) and routed through our serving stack — same headroom floor, reasoning-split, and retry logic production traffic uses:

Per-row protocol is marked in the table; both protocols are measured and shown in the protocol note below, so nothing is hidden.

Comparability caveat: puwaer's reference numbers (Base 284B and FP8 rows) are published on the REAP model card: greedy n=1, enable_thinking=false, 4096-token context, served with SGLang. Our no-thinking rows match that protocol (greedy, thinking off, llama.cpp); our thinking rows add inference budget their runs did not use, so the MATH-500 / HumanEval+ deltas vs FP8 are protocol-advantaged as well as capability gains — which is exactly why the both-protocols matrix below exists.

Protocol note: results use the best serving protocol per task, all disclosed. Thinking mode (reasoning_effort=high + headroom floor, pitfall #8) is worth +26.8 on HumanEval+ and +26.6 on MATH-500, but it hurts MBPP+ by ~5.5 points (0.8624 no-think vs 0.807 thinking) — the tasks are one-liners and overthinking triggers formatting drift and the occasional budget-exhausted empty. Without thinking mode this model scores ~0.65 HumanEval+ and ~0.70 MATH-500 on this box; the naive lm-eval HumanEval 0.2256 is a format artifact.

All measured variants (greedy, n=1, same build, LoRA loaded):

Spiral rate under the headroom floor: 1/164 (HumanEval), 2/500 (MATH-500), 5/378 (MBPP+ thinking) — every spiral burned exactly the 8192 reasoning budget and emitted no answer; the +2048 headroom preserves the answer whenever the model emits one at all.

Reading:

• The quant retains the FP8 checkpoint's capability at 3.49 BPW: no-thinking GSM8K within stderr, MATH-500 −1.8, MBPP+ +10.3 above FP8.
• With thinking mode enabled the build exceeds the FP8 reference on MATH-500 (+24.8) and HumanEval+ (+2.4) — with the caveat that the FP8 reference was measured thinking-off, so part of those deltas is inference budget (see protocol note + comparability caveat above).
• MBPP+ is genuinely above the FP8 reference (+10.3) in matched no-thinking mode — the one bench where thinking is counterproductive (see protocol note).
• HumanEval+ 0.9207 (thinking mode, greedy, reasoning_effort=high): requires thinking-mode serving with a headroom floor — see pitfall #8 — otherwise reasoning spirals can eat the output budget and return empty answers. Without thinking mode the score drops to ~0.65; the naive lm-eval 0.2256 is a format artifact.

These runs used the optional heretic t256-r2 LoRA (§5). Ablation check (no-thinking protocol): removing it moves HumanEval+ by ~1.5–2.6 pts either direction depending on extraction method — negligible relative to the quant's retained capability.

Throughput (3× Turing, see §6)

Context length (measured)

The headline win over prior builds is 256K context at the same quality — a 2.7× gain over the 96K ceiling of the previous q8_0-KV build — achieved purely by switching to the compressed turbo4 V-cache (the TurboQuant fork's 4-bit KV value cache).

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5. How to run

Requirements

• The turbo-tan/llama.cpp-tq3 fork (stock llama.cpp will not load TQ3_4S).
• CUDA build: cmake -B build -DGGML_CUDA=ON -DGGML_CUDA_FA=ON -DCMAKE_BUILD_TYPE=Release, then cmake --build build -j --target llama-server.
• ~70 GiB combined VRAM for full GPU offload at 256K (see §6 split).

Minimal serving command

CUDA_DEVICE_ORDER=PCI_BUS_ID \
CUDA_VISIBLE_DEVICES="<2080Ti>,<Titan-A>,<Titan-B>" \
llama-server \
  -m DeepSeek-V4-Flash-0731-REAP-150b-TQ3_4S.gguf \
  -ngl 999 \
  --ctx-size 262144 \
  --cache-type-k q8_0 --cache-type-v turbo4 \
  --flash-attn on \
  -ts 18,22,24 \
  --batch-size 1024 --ubatch-size 512

Recommended full config (agentic / coding)

  --spec-type ngram-mod --spec-ngram-mod-n-max 16 --spec-ngram-mod-n-match 24 \
  --temp 0.65 --top-p 0.90 --top-k 40 --min-p 0.15 \
  --repeat-penalty 1.02 --repeat-last-n 256 --presence-penalty 0.0 \
    --reasoning on --reasoning-format deepseek --reasoning-budget 8192
    # Optional uncensor overlay (external, see "Uncensoring" below):
    #   --lora <path/to/ds4-flash-heretic-f4-t256-lora-r2.gguf>

Chat template / reasoning

The model emits a native reasoning_content field with an inline response marker (DeepSeek V4 format). The --reasoning-format deepseek flag routes thinking into reasoning_content and the final answer into content.

Uncensoring (optional, external heretic v2 LoRA)

The stock base refuses 99.29% of a 140-prompt harmful eval set. The MorinoNushi/DeepSeek-V4-Flash-0731-heretic-abliterated-v2-GGUF-lora repo provides the ds4-flash-heretic-f4-t256-lora-r2.gguf adapter (rank-1 directional-ablation LoRA, 2 MB) that reduces refusals to ~7% with low KL drift (0.085). It is lossless: base weights are never modified or requantized, applied in f32/f16 compute regardless of base quant via --lora. This adapter is not hosted here — fetch it from the MorinoNushi repo linked above (or grab the t256-r2 file directly) and point --lora at it.

Why t256-r2: it is the only adapter with zero routed-expert tensors (ffn_down_exps), which sidesteps the known llama.cpp LoRA slowdown (untagged adapter buffers → host↔device copies + non-quantized mul_mat_id fallback that disables CUDA graphs). Measured on this box: 21.06 t/s with the LoRA vs 21 t/s without — no spec-decode penalty. (t264/t265/t273 touch 50-62 expert tensors and cost a real 10-25% slowdown but reach 3.5-8.6% refusal.)

✅ Validated on this exact quant (REAP-150B, TQ3_4S-Container) on 2026-08-28: full compliance on refusal probes with ngram-mod still active.

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6. Hardware and the tensor split

Developed and validated on a 3× NVIDIA Turing box:

• Tensor split -ts 18,22,24 (2080Ti=18, Titan=22, Titan=24) was determined by exhaustive headroom measurement and is the proven-optimal split for this 61 GiB model.
• CUDA_DEVICE_ORDER=PCI_BUS_ID + explicit CUDA_VISIBLE_DEVICES pinning is mandatory. Without pinning, CUDA enumerates the Titans before the 2080 Ti and the split lands on the wrong physical cards, causing OOM.
• Host RAM: 60 GiB. Full GPU offload is used (-ngl 999); the model does not spill to CPU.
• 256K context leaves ~231 MiB headroom on the tightest card. 512K degrades (loses pipeline parallelism), 1M OOMs.

If your cards are larger (e.g. 3× 48 GiB), 512K becomes clean and 1M is within reach.

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7. Pitfalls (learned the hard way)

1. Stock llama.cpp will not load this. general.file_type = 45 (TQ3_4S) is rejected by upstream. Use the llama.cpp-tq3 fork.
2. CUDA device enumeration order matters. Without CUDA_DEVICE_ORDER=PCI_BUS_ID pinning, the -ts 18,22,24 split maps to the wrong physical GPUs and you OOM even though the total VRAM is sufficient.
3. Do not requantize the experts down to 2-bit. IQ2_S experts cost +7.5% PPL on this pruned variant (the "champion" recipe does not survive the REAP prune). IQ3_XXS/IQ3_S is the floor.
4. Do not requantize mxfp4 experts up to Q4_K. MXFP4 is denser than Q4_K (4.0 vs 4.58 bpw); "re-quantizing to Q4" makes the file bigger and lossier. Keep native mxfp4 edges as-is.
5. The DSML tool-call format changed between forks. The old build-deepseek fork emitted tool-call text in content; the TQ3 fork emits native OpenAI tool_calls deltas. Any middleware written against the old fork must forward native tool_calls deltas or you get empty responses + retry loops.
6. Reasoning effort interacts with context. High reasoning effort on trivial prompts causes multi-minute think-a-thons. reasoning-budget 8192 + --spec-ngram-mod clamps this.
7. ngram-mod only accelerates structured output. It is ~neutral during the reasoning phase (draft acceptance ~18%) and highly effective once the model emits code/structured text (up to 100% acceptance).
8. Reasoning mode: keep max_tokens strictly above --reasoning-budget. If a client sends max_tokens equal to the budget (e.g. both 8192), a single max-effort thinking spiral can burn the entire output budget on reasoning and leave zero tokens for the visible answer (finish_reason='length', empty content). Force max_tokens >= reasoning-budget + 2048 at your middleware/router — a guaranteed headroom floor eliminates the empty-answer case.
9. Reasoning-mode code answers are wrapped in prose/fences. Benchmarks (and any naive extraction) will undercount this model on HumanEval-style tasks unless you strip markdown fences / role-play junk and repair indentation before scoring. The model writes correct code far more often than the raw pass@1 suggests.

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8. Intended use & limitations

Intended: agentic tool use, long-context coding and analysis, reasoning-heavy workflows, serving a 150B-class MoE on consumer hardware with a large context window.

Limitations:

• This is a pruned (REAP) variant, not the full 284B model; some capabilities present in the full model are reduced.
• Quantization is lossy; for maximum fidelity prefer the FP8 or a BF16 base at the cost of ~2× file size and VRAM.
• Requires a non-stock llama.cpp build (the TQ3_4S container).
• Full 1M context is not reachable on 3× Turing (compute-buffer OOM); 256K is the validated ceiling there.

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9. Reproducibility

1. Obtain the REAP-150B source (Q8_0 re-quant of the FP8 release).
2. Build turbo-tan/llama.cpp-tq3 at 47635d7 with CUDA.
3. Generate or reuse an importance matrix matched to the REAP-150B architecture.
4. Run the quantize command in §3 with the exact tensor_types.txt recipe.
5. Verify: 1328 tensors, 61.01 GiB, 3.49 BPW, general.file_type = 45.

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10. Acknowledgements

• deepseek-ai for the DeepSeek-V4-Flash-0731 architecture.
• puwaer for the REAP-150B prune and the FP8/Q8_0 source.
• turbo-tan for the llama.cpp-tq3 fork (TurboQuant TQ3_4S + deepseek4 arch support).
• MorinoNushi for the heretic-gguf directional-ablation tooling and the v2 LoRA adapters used for the lossless uncensoring, and p-e-w for the underlying Heretic method.
• The quantization campaign, recipe, uncensoring, and validation were performed by Tinkerstack with automated tooling; the build is a community-style release for research and self-hosting.