162 lines
10 KiB
Markdown
162 lines
10 KiB
Markdown
# Quantization Production — making our own quants, per card and runtime
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**Research page (2026-10-08).** Question: how do WE produce quantizations
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for the RTX 3090, the R9700 (gfx1201), and Strix Halo (gfx1151); what is
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native to each runtime; and the state of the named prior-art families
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(REAP, Heretic, abliteration, HauhauCS K_P "Perfect"). Marked facts:
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**[repo-verified]** = evidenced by a file in this repo; **[verified]** =
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checked first-hand against the cited primary source today; the rest are
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survey claims to re-verify before acting.
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## Runtime-native quant matrix
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| Card | Runtime | Container | Native weight encodings | Production tooling we own |
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|---|---|---|---|---|
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| RTX 3090 (sm_86) | llama.cpp CUDA | GGUF | K-quants (Q4_K_M…Q8_0), I-quants (IQ1_S…IQ4_XS), ML, f16/bf16; any tensor type per-tensor | `llama-quantize` + `ggml-imatrix` (built by `scripts/build-llama-rdna4.sh` targets) **[repo-verified]** |
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| R9700 (gfx1201) | radiance (StillDeadcode) | `.rad` | per-tensor encodings from a recipe: GPTQ w4 (`w4nl64a8h`), bf16, int8, fp8_e4m3, u2, MXFP4/MXFP6 (vLLM-radiance era) | full pipeline: `rad-convert` + calibration tap + `derive-protected-experts.py` + `kld-gate-swift15.sh` **[repo-verified]** — CPU host-backend, the card is irrelevant to production |
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| R9700 (gfx1201) | vLLM (tcclaviger/R4D) | safetensors | native block-FP8, MXFP4/DSFP4, GPTQ/AWQ, compressed-tensors | GPTQModel/AutoRound are hardware-agnostic torch (CPU-scale); AMD decode kernels are the gate **[survey]** |
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| Strix Halo (gfx1151) | halogen-flash-server (peonist, **closed**) | `.hgn` | vendor-defined (w4b era, v2 checkpoint+ngram+vision files) | **NONE public** — no `.hgn` quantizer exists; every `.hgn` on the fleet was made by peonist/Quat3rnion **[repo-verified gap]** |
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| Strix Halo (gfx1151) | llama.cpp / gufo / Kairic / ROCmFPX forks | GGUF | K/I quants (gufo UD-* = imatrix mixed profiles), custom types: `Q4_0_ROCMFP4_STRIX`, `Q8_0_ROCMFPX` (type 103), Kairic IU4 | we built the RUNTIMES (`scripts/build-kairic-edge-gfx1151.sh`, ROCmFPX pin `36e9acd`), never the custom-type quantizers — those files came from agentionai/kingjones **[repo-verified]** |
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Laws that steer everything here:
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- **Production is hardware-agnostic; decode is not.** GPTQ/imatrix/ablation
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surgery runs in torch/CPU anywhere. The card only picks which ENCODED
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FORM it can DECODE — hence the one-table-one-encoding law, the GGUF
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custom-type histogram law ([[GGUF Compatibility]]), and the fact that
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our w4 convert ran 5 h 50 m on the host backend while the GPUs served
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nobody (`scripts/convert-swift15-w4dn8.sh`) **[repo-verified]**.
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- **A custom tensor type locks the file to its fork** (ROCmFPX type 103
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precedent). K_P quants deliberately avoid that trap (below).
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## The named families
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### REAP — Router-weighted Expert Activation Pruning (Cerebras, 2025)
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- arXiv 2510.13999; `github.com/CerebrasResearch/reap` **[verified]**.
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- One-shot MoE surgery: score every expert by router gate-values ×
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activation energy, prune the low-utility tail, renumber+export a
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SMALLER MoE checkpoint (safetensors; GGUF by conversion). Published:
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GLM-4.5-Air −25 % (82B→~A12B class), GLM-4.7-REAP-268B-A32B-FP8;
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near-lossless at 25–50 % compression on generative benchmarks, and
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pruning BEATS expert-merging there **[verified from paper/README]**.
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- Fleet relevance: it is the **inverse of our protected-experts law**.
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`derive-protected-experts.py` ranks experts by calibration-gram energy
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share and keeps the hottest 24/layer at bf16; REAP's saliency is the
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same statistic and deletes the coldest. REAP the Flash-Next MoE (all
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49,152 experts w4, 512/layer) by 25–50 % → resident set ~58–87 GiB →
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the expert pool fits with room for KV/cache, killing the disk tier.
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A REAPed checkpoint re-enters our normal pipeline: stage → calibrate →
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rad-convert → KLD gate. Hardware-agnostic; runs on the host.
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### Heretic (`p-e-w/heretic`) — automated abliteration + layer pruning
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- `github.com/p-e-w/heretic`, AGPLv3 **[verified]**. NOT a quantizer.
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Fully-automatic censorship removal: directional ablation (refusal
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direction from PCA of harmful/harmless residuals), per-layer ablation
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weights, optional LAYER DROPPING (model shrinks), auto-tuned by
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co-minimizing refusals and KL divergence from the original. Runs on
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our 3090 (torch, ~24–48 GB working set for 9–27B classes; long batch
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probes) **[verified README; runtime cost survey]**.
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- Output is a normal HF checkpoint (fp16/bf16) → feeds GGUF, safetensors
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(vLLM), and `.rad` conversion. Order law: **surgery → calibrate →
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quantize**; calibrate GPTQ/imatrix on the SURGICAL output, and keep
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refusal-shaped tokens out of the calibration corpus.
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- Fleet note: our abliterated bases (Quat3rnion v2, HauhauCS
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Aggressive/Balanced, ukisai Swift1.5) are this family's output; the
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Quat3rnion repo lore "the abliteration workflow appends, compaction
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drops dead padding" is exactly Heretic-style layer-editing leaving
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dead blocks (89.8 → 66.7 GiB r2) **[repo-verified lore]**.
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### Ablation / abliteration (directional ablation)
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- Arditi et al. 2024, arXiv 2406.11717: refusal is mediated by a single
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direction; orthogonalizing weights against it removes refusals with a
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rank-1 edit. Open implementations: `p-e-w/heretic` (automated, above),
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Artio/abliteration-toolkits, `EleutherAI/repeng`-style rep-overlap
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variants **[verified (tool existence: heretic; rest survey)]**.
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- Hardware-agnostic fp-editing. On a MoE the edit touches attention/norm
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weights, so it composes with any expert quantization.
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- Fleet status: frank-large runs an abliterated base **by Frank's choice**
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(AGENTS: censored-base refusals were the accepted trade of the 27B era);
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frank-archon's ukisai Swift1.5 is an abliterated finetune of Qwen3.8.
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### HauhauCS QX_K_P "Perfect" quants
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- Our frank-secondary publisher. Method, from the publisher's own cards
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**[verified]**: (1) abliterate + finetune ("Aggressive"/"Balanced"),
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(2) quantize with **imatrix calibration on the ABLITERATED weights**,
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(3) K_P = a **per-model, per-tensor mixed-precision profile** —
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"model-specific analysis to selectively preserve quality where it
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matters most", e.g. Q8_K_P = a census of Q8_K + F32 + F16 tensors
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(FriendliAI's safetensor census of a Q8_K_P shows exactly that
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399/353/99 tensor split). Result: +1–2 quality rungs for +5–15 %
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size. **It is a standard GGUF** — llama-quantize with per-tensor type
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overrides (`--tensor-type`), imatrix on; no fork, no custom types
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(LM Studio just fails to NAME the mixed file, shows "?").
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- Reproducible with stock tooling: `ggml-imatrix` on the target model +
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`llama-quantize --tensor-type <pattern>=<type>` per the model's
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sensitivity census. Both binaries already build from
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`scripts/build-llama-rdna4.sh` **[repo-verified]**.
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- Our own ladder precedent: the fleet's UD-Q8_K_XL (unsloth "Dynamic" =
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same family: imatrix + importance-tiered per-tensor types) and
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agentionai FAST imatrix ROCmFP4-FAST (we ran the imatrix step
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ourselves on the v2 joined-PLE file — the one self-made quant in repo
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history) **[repo-verified]**.
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## Per-card playbooks
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### RTX 3090 — frank-secondary (llama.cpp CUDA)
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1. Surgery on host/3090: abliterate/prune (Heretic) — torch.
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2. `ggml-imatrix` (CUDA build) over a calibration corpus (our
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`/home/dank/corpora` style set; ≥1M tokens; the fleet's KLD corpus
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is a reasonable seed).
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3. `llama-quantize` F16 → target rung; K_P-style `--tensor-type`
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overrides for attention/KV/first-layer upgrades; `--use-repack` for
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IQ rungs.
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4. Validate: perplexity + KLD vs BF16 + the K-quant rank checks.
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Decode-side options we do NOT use: EXL3 (turboderp ExLlamaV3, QTIP
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trellis; CUDA production on the 3090 works, decode is exllama-only —
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new runtime, no OpenAI server), Marlin-GPTQ (vLLM path, not a llama.cpp
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runtime).
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### R9700 — frank-primary (radiance)
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The full self-quant pipeline already exists end to end: fetch BF16 →
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record KLD reference (1.0.13) → capture calibration grams (1.2.0 tap,
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`RADIANCE_CALIB_DIR/TOKENS`) → derive protected experts (energy-share) →
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`convert-swift15-w4dn8.sh` (1.0.13, host backend) → KLD gate (MNBT 2048
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law, pool 40960, mean KL ≤ 0.03, top-1 ≥ 95 %). New model = new recipe
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+ new `.so` arch plugin; every step is scripted. Prior art to fold in:
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REAP the expert set BEFORE step 3; ablate BEFORE calibration.
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Native encodings beyond w4: i8 trunk, fp8 HC/PLE/KV, bf16 protected,
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u2 draft — all recipe-expressible (see the w4dn8 runbook).
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### Strix Halo — frank-large (halogen) and friends
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- `.hgn`: **closed production door.** No public quantizer exists
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(verified absence across peonist-ai repos); the format's lock is the
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engine's lock. We consume vendor artifacts; the repo-side tooling is
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compaction/verification only.
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- Self-quant work on this card therefore runs through **GGUF runtimes**:
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the same imatrix/llama-quantize flow as the 3090 (HIP/Vulkan decode of
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K/I quants, gufo UD-* precedent, `--use-repack` off on ROCm), or the
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custom-type forks (ROCmFP4 STRIX, Kairic IU4) whose PRODUCERS we have
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never been — the quantizers for types 101/103 ship in those forks'
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trees; building one is a fork build, not a flag.
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- Native-FP4 note: RDNA4/Strix get hardware FP4 matmuls (hipBLASLt
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MXFP4/DSFP4), which is the engine-level path (radiance/vLLM), not a
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GGUF decode.
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## Sources
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- REAP: arXiv 2510.13999, github.com/CerebrasResearch/reap,
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cerebras/GLM-4.5-Air-REAP-82B-A12B-FP8, cerebras/GLM-4.7-REAP-268B-A32B-FP8.
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- Heretic: github.com/p-e-w/heretic (AGPLv3).
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- Abliteration: arXiv 2406.11717 (Arditi et al.).
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- K_P: HauhauCS model cards (e.g. `HauhauCS/Gemma-4-E4B-Uncensored-HauhauCS-Aggressive`
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README "What are K_P quants?"), FriendliAI Q8_K_P tensor census,
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fleet payload tree (BF16/Q4_K_M/Q6_K/Q8_0 + mmproj ladder in
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`HauhauCS/Qwen3.5-9B-Uncensored-HauhauCS-Aggressive`).
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- EXL3: github.com/turboderp-org/exllamav3 (QTIP lineage; CUDA).
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- Repo: `scripts/convert-swift15-w4dn8.sh`,
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`scripts/recipes/qwen4exp-swift15-w4dn8-i8-hc8m.recipe`,
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`scripts/kld-gate-swift15.sh`, `scripts/build-llama-rdna4.sh`,
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`scripts/build-kairic-edge-gfx1151.sh`, [[agentionai-fast-imatrix]],
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[[kingjones-strix-rocmfp4]], [[qwen3.8-strix-optimizations]],
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[[frank-primary-radiance-swift15-w4dn8]],
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[[frank-secondary-llama-qwen35-9b-q6k-2x200704]],
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[[frank-large-flash-server-v2-swift15]], [[GGUF Compatibility]].
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