53 lines
4.0 KiB
Markdown
53 lines
4.0 KiB
Markdown
# [2026-08-15] Uncensored gen seat: Qwen3.8-27B-Uncensored deployed; the definitive MTP-graft fix
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**Outcome.** The fleet `gen` seat is now **`JonathanColetti/Qwen3.8-27B-Uncensored`** (Heretic
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abliteration, KL 0.12 vs base, bench Δ −0.5 within noise, refusals 98→12/100), quantized in-house
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to **NVFP4 W4A16** (llm-compressor / compressed-tensors) with a **grafted bf16 MTP head**,
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vision-intact, **262K** ctx, MTP n=3 (**~42% accept, ~68 tok/s**), coherent. Live at ana-ml2 `:8015`
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(project `gen-seat` / container `vllm-gen`), backing all 7 gateway aliases.
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**THE definitive lesson (resolved 3 failed attempts + one premature 50 GB delete).** A grafted bf16
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MTP scored **0% on the quant but 83% at bf16** — for TWO different abliterated models. Root cause was
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NEITHER the abliteration NOR the quant scheme: it was **the grafted `mtp.*` tensors missing from
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`config.json` → `quantization_config.ignore`.** The wrapper-class quant DROPS the MTP before
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llm-compressor sees it, so nothing gets added to `ignore`; vLLM then tries to load the bf16 MTP as
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*quantized* format → "Parameter … not found in params_dict, skip loading" → uninitialized head → 0%.
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**FIX: after grafting, add `re:^mtp.*` to `quantization_config.ignore`** (one line — all unsloth's
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working checkpoint has). MTP jumped 0%→83% (bf16-identical). Full lesson in auto-memory
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`reference_abliteration_mtp_lessons`.
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**The pipeline that works (for the next VL+MTP quant, incl. the W4A8 chase):**
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1. Pull bf16 (kept at `ana-ml2:/tank/aimodels/qwen38-27b-uncensored-bf16`).
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2. Quant via `quant_nvfp4_qwen.py` (darkscarlett dir) = the **wrapper-class** loader
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(`Qwen3_5ForConditionalGeneration`, keeps the vLLM-serveable config); container = `vllm-openai`
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+ `pip install llmcompressor==0.13.0` (drags in a transformers with `qwen3_5`).
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3. **Graft** the author's `model-mtp.safetensors` verbatim into the output + merge the index.
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4. **Reconstruct** `preprocessor_config.json` from `processor_config.json`'s `image_processor`
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sub-dict (the repo omits it → else "Can't load image processor" crash-loop).
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5. **Add `re:^mtp.*` to the output config's `quantization_config.ignore`.** ← the fix.
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6. Serve: `--quantization compressed-tensors --speculative-config '{"method":"qwen3_5_mtp","num_speculative_tokens":3}'`
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`--mamba-cache-dtype float32 --kv-cache-dtype fp8 --reasoning-parser qwen3`.
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**VRAM / full-context budget (measured).** Weights ~27 GB; hybrid attention → **only 16 of 64 layers
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carry KV** → 32 KiB/token → **262K KV = 8.6 GB** (vs ~60–70 GB for a normal dense 27B). Full 262K fits
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GPU0 at **util 0.45** (~43 GB) alongside meromero (~49 GB used, it's a 31B) — pre-flight rejects util
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0.48 (wants 45.6 GB, only 45.5 free). `max-num-seqs 16` keeps cudagraph modest (an ad-hoc serve with
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no cap OOM'd — cudagraph captured to batch-512).
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**Why unsloth's `qwen3.8-27b` (the prior gen model) was faster (97 vs 68 tok/s).** ~half = quant kernel
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(unsloth native NVFP4+FP8 tensor cores vs our W4A16 → Marlin dequant, ~20% even on decode — I'd
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under-stated this); ~half = MTP acceptance (unsloth 55% un-ablated head vs our 42% — inherent to the
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ablation, no quant fixes it). **W4A8 recovers the first ~20% (→~82 tok/s) + prefill; not the MTP half.**
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**modelopt dead-end (for W4A8, avoid).** `nvidia-modelopt[hf]==0.43.0` is too old for qwen3_5's
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transformers: (a) its `NVFP4_DEFAULT_CFG.quant_cfg` is a LIST but 0.43 wants a DICT (pydantic reject);
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(b) it warns transformers 5.15 untested. Use **llm-compressor** for W4A8 instead (custom recipe: NVFP4
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weights + FP8 input_quantizer + calibration on `heretic2-nvfp4-work/production_calib_512.jsonl`).
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**Deleted (premature — the delete I owned).** `windowsxp811203/Qwen3.8-27B-Abliterated` (~79 GB) — I
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declared it desync-dead off a 0% that was actually this ignore bug. Lesson: **test MTP on bf16 first;
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isolate before deleting.**
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Commits: eshpfi `680c30e` (deploy + rename + litellm + README), dotfiles `1d1970f` (CLAUDE.md roster) —
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both UNPUSHED. Related: [[reference_abliteration_mtp_lessons]], [[reference_verify_hf_repo_ids_before_pull]].
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