Mamb2_8B_Recall_SQ3.25

Official WikiText-2 test PPL — Resurface on: 7.77130287.

Published configuration	Resurface adapter	Official WT2 test PPL
Same frozen weights and SQ3.25 state	Off	8.11328914
Same frozen weights and SQ3.25 state	On	7.77130287

147 reset windows · 300,963 next-token targets, including the final partial window. Measured with the published StatePPLQuantV10 32/32/64 codec: carry is requantized after every token, with FP16 computation and FP32 logits. The checkpoint was fixed before test evaluation; no test-driven training or selection.

Packed 3.25-bit recurrent state plus a freshly trained Resurface adapter for the pure nvidia/mamba2-8b-3t-4k language model. Full official WT2 test PPL is 7.77130287, versus 8.11328914 without the adapter, using 27.1797 MiB of persistent state and table storage per sequence at batch one. Historical normal MK is 271/384 (70.57%); it was not rerun for this test update.

SQ3.25 describes recurrent state. The 8B model weights remain FP16. This repository distributes the adapter, calibration table, custom runtime and verification evidence. Download the original NVIDIA base separately. It is not a standalone 8B checkpoint, a 3.25-bit weight model, or a Transformers/PEFT from_pretrained package.

This is the SQ3.25 member of the Mamb2_8B_Recall family. Its adapter was trained fresh on the fixed quantized-state base; it is distinct from the original Mamb2_8B_Recall adapter.

Full official WikiText-2 test PPL — fixed published checkpoint

Configuration	WikiText-2 test PPL
Published SQ3.25 state, without Resurface	8.11328914
Same frozen FP16 base and state + released V11 Resurface	7.77130287

Both complete arms score 300,963 next-token targets in 147 reset windows, including the last window of 1,955 targets. The adapter lowers pooled-token PPL by 4.2151%. The weights, 32/32/64 state table, final adapter and measured runtime are unchanged from v0.1.0-q325-resurface. No training, recalibration, checkpoint/candidate selection, or test-dependent stopping was performed.

Protocol: pinned Salesforce/wikitext, wikitext-2-raw-v1, official test split at revision b08601e04326c79dfdd32d625aee71d232d685c3; documents joined by two newlines and NVIDIA SentencePiece without automatic BOS/EOS. Each window scores at most 2,048 next-token targets with one boundary-token overlap; state is requantized every token. PPL is exp(total NLL / 300963) from FP16 compute and FP32 logits.

Data scope: adapter training used separate official TRAIN data. The test was scored after publication with the fixed released checkpoint and without test-driven selection. Earlier project experiments, including 2.7B and E8/W5, used the official WT2 test text, so this is not an untouched test for the whole project. Source-model pretraining contamination and cross-split duplication have not been audited. No S16 test control or new MK evaluation was run here.

The exact comparison, adapter-free arm, adapted arm, and CPU audit contain all per-window scores/token hashes and the recorded tensor/storage bindings, without raw token IDs. The CPU audit verifies token coverage, pooled NLL/PPL arithmetic, the published runtime and 507-weight/224-adapter bindings, and adapter-removal reset/cache restoration. It does not recompute GPU logits. See the reproduction guide, frozen test protocol, and evidence inventory.

This is a documentation/evaluation overlay. The original release tag, adapter, state table, runtime source and sealed release/ files remain unchanged. Historical validation and MK CONFIRM results remain separate below; they are not WT2-test MK or newly collected test recall measurements.

Historical validation and published MK CONFIRM

Configuration	WikiText-2 validation PPL	Normal MK	N=16	N=64
Original S16, archived reference	7.334322057221965	146/384 (38.02%)	—	—
Fixed Q3.25 state, no adapter	8.186186562837207	32/384 (8.33%)	30/192	2/192
Q3.25 + released Resurface	7.855569605864836	271/384 (70.57%)	181/192	90/192
Q3.25 after adapter removal	8.186186562837207	32/384 (8.33%)	30/192	2/192

PPL covers 130 reset windows of up to 2,048 predicted tokens, with 264,764 targets in the pinned WikiText-2 validation split. MK uses 384 normal prompts and 384 paired target-removed diagnostic prompts. Both Q3.25 configurations match the removed target 0/384 times; this is not an abstention score. The normal-MK denominator excludes those controls.

Against the fixed Q3.25 base, the adapter improves PPL 4.0387% and normal MK 62.2396 percentage points: 242 paired gains and three regressions. The paired bootstrap 95% interval for the MK change is [+57.2917, +67.1875] percentage points. PPL remains 7.11% higher than the archived S16 reference, which was not rerun in this experiment.

All nine predefined quality and integrity gates passed, including the PPL threshold, positive paired recall improvement, exact control replay and unchanged persistent cache. Removing the adapter reproduced all 130 baseline window NLLs and all 768 generated sequences exactly, including reset/cache receipts. An independent CPU audit of the recorded full evidence also passed; that audit does not rerun GPU logits. See full results, raw comparison and full audit.

State format and memory

Each 128-coordinate recurrent-state row stores 32 INT8 coordinates, 32 INT4 coordinates and 64 zero-carry coordinates, plus two FP16 scales. That is 52 bytes per row, or 3.25 bits per coordinate including scales. The custom runtime uses the packed carry for every token, including prompt processing. The selected coordinate table is fixed before adapter training.

Persistent component, batch one	Bytes	Size
SSM cache	23,855,104	22.7500 MiB
Convolution cache	4,587,520	4.3750 MiB
One shared static table	57,344	0.0547 MiB
Cache plus table	28,499,968	27.1797 MiB
Separate FP16 adapter payload	2,308,208	2.3082 MB
Cache, table and adapter	30,808,176	29.3810 MiB
Original FP16 model weights, separate	16,473,999,360	16.4740 GB

The cache/table figure is 76.64% smaller than the archived S16 cache of 116.3750 MiB. Static table and adapter storage can be shared across requests; each is counted once here. These are tensor payloads, excluding temporary computation, allocator reserve and training teacher/optimizer state. They are not total GPU memory requirements. The serialized adapter file is 2,375,743 bytes; its 1,154,104 FP16 parameters occupy 2,308,208 bytes when loaded.

Download and verify

With the Hugging Face CLI installed:

hf download EndlessChasing/Mamb2_8B_Recall_SQ3.25 \
  --local-dir Mamb2_8B_Recall_SQ3.25
cd Mamb2_8B_Recall_SQ3.25
python3 scripts/release_state_resurface_v11.py --verify release

The standard-library verifier checks the exact adapter, table, raw evidence, internal checksums and unchanged measured runtime files. Verification does not require CUDA or download the base. The original 18-file release bundle is preserved under release/; runtime code, scripts and notices are supplied alongside it. This verification checks published evidence rather than running a new quality evaluation.

Download the separate source checkpoint and tokenizer at the pinned revision:

hf download nvidia/mamba2-8b-3t-4k \
  release/mp_rank_00/model_optim_rng.pt \
  mt_nlg_plus_multilingual_ja_zh_the_stack_frac_015_256k.model \
  --revision b915550c63ba9359f88f44d1f6a600d85af27302 \
  --local-dir models/source

The loader verifies the checkpoint and tokenizer SHA-256 values, then casts the official BF16 weights to FP16. Native NVIDIA Megatron BF16 equivalence is not established by these measurements.

Environment and inference

The measured environment was Linux, Python 3.10.12, PyTorch 2.11.0+cu128, Triton 3.6.0, Mamba-SSM 2.3.2.post1, NumPy 1.26.4, datasets 4.8.5 and SentencePiece 0.2.1, on an RTX PRO 6000 Blackwell Server Edition. Install the matching CUDA/PyTorch native Mamba and causal-convolution dependencies first, then install this package from the downloaded root:

python -m pip install --no-deps -e .
python scripts/infer_state_resurface_v11.py \
  --bundle release \
  --source-dir models/source \
  --prompt 'The capital of France is' \
  --max-new-tokens 32

The entrypoint checks recorded backend versions, external source hashes, precision flags and the fixed 16-warp RMSNorm configuration. An incompatible backend fails before model loading. pyproject.toml contains broad dependency ranges, not an exact environment lock; other GPU architectures are not validated. See the release guide.

This wrapper provides greedy batch-one generation with prompts up to 4,096 tokens. Its separate GPU smoke reproduced one archived 263-token MK prompt and all 12 continuation token IDs, with the same cache bytes. That check validates the wrapper on that case; it does not repeat or extend the full PPL/MK evaluation. The example prompt above is a usage example, not a reported quality measurement. Native InferenceParams, variable-length batching and a serving integration are not provided.

To check the package through the inference entrypoint without torch or CUDA:

python3 scripts/infer_state_resurface_v11.py --bundle release --verify-only

Training and limitations

The original 8,236,999,680 base parameters remain frozen. After fixing the selected state table, a fresh post-D Resurface-style adapter completed 1,536 successful TRAIN updates, with five numerical overflow retries. Only the final TRAIN-derived FP16 export was evaluated; validation/CONFIRM did not select a checkpoint. Training/export forward parity was checked at 128 and 512 tokens, with separately audited gradient fixtures.

The validation corpus and CONFIRM prompt families have historical exposure. The official test text also appeared in earlier project experiments, as scoped in the fixed-checkpoint test section above. The results establish performance on this specified protocol. They do not establish unseen-template recall, full-4K recall quality, other-language performance or broad language-model generalization. The wrapper's 4K prompt limit is an interface constraint, not a demonstrated 4K recall result. The historical validation source identity/version/gradient guards did not constitute a complete post-run bytewise base audit. The new fixed-checkpoint test records and checks all 507 actual FP16 weight hashes before/after both arms; its CPU audit binds those recorded hashes and does not recompute GPU logits. Adapter/table transfer to other checkpoints requires separate validation.

The tagged repository contains the frozen protocol, full training/evaluation reproduction guide and retained evidence. Base weights and training corpora are downloaded separately.

Provenance and licenses

This release corresponds to GitHub v0.1.0-q325-resurface, commit be037e1a2635ad7b540e0a1d2e551febf647c346.

• Adapter SHA-256: 339334b3431027504fb8da4ce3167d63c8c2f309920cfb0076bde1f72a115cb7.
• Selected calibration SHA-256: a77bbd86ae609d8ef7cf90f10d0904cf95b4df8e059c9ea6cb2fd12886436fb8.
• Full independent audit SHA-256: ad2b3f14a075b0d60993f3579e99fc98bfbac8aa527f6a0c8d684cc9493e7d72.

New project code and the fresh adapter are distributed under GPL-3.0. The original NVIDIA model, Mamba implementation and StateQuant reference have their separate Apache-2.0 licenses. The root GPL license does not relicense those upstream assets or WikiText text. The base checkpoint is not included. See LICENSE and third-party notices for source attribution, StateQuant provenance and WikiText licensing details.

Method references: StateQuant and Resurface. The preserved StateQuant reference comes from the user-supplied archive identified in the notices; current availability of that external repository is not required to use this packaged runtime.