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Release audited repaired FP4 G16 native-S16 Resurface model
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Verify and run Mamb2_8B_FP4_Recall

The complete bundle is sufficient for inference. The original NVIDIA checkpoint is not required. The Python CLI uses the included custom source directly and loads the packed weights into resident FP16; it has no Transformers AutoModel.from_pretrained entry point or packed-resident FP4 kernel.

Download a complete pinned bundle

Hugging Face 

hf download EndlessChasing/Mamb2_8B_FP4_Recall \
  --revision v0.1.0-fp4g16-repaired-s16-resurface \
  --local-dir Mamb2_8B_FP4_Recall
cd Mamb2_8B_FP4_Recall/release

GitHub Release

Download all 119 weight assets and the runtime archive into a fresh directory:

gh release download v0.1.0-fp4g16-repaired-s16-resurface \
  --repo EndlessChasing/mamb2_8B_FP4_Recall \
  --pattern '*-runtime.tar.gz'
tar -xzf mamba2-8b-fp4g16-repaired-s16-resurface-v1-runtime.tar.gz
gh release download v0.1.0-fp4g16-repaired-s16-resurface \
  --repo EndlessChasing/mamb2_8B_FP4_Recall \
  --pattern '*.safetensors' \
  --dir mamba2-8b-fp4g16-repaired-s16-resurface-v1/weights
cd mamba2-8b-fp4g16-repaired-s16-resurface-v1

The runtime archive includes weight metadata, adapter, tokenizer, custom source, license texts and compact evidence. A source-only Git checkout does not contain the 119 large weight files. The HF snapshot's release/ is the same complete bundle content as the assembled GitHub download.

CPU-only complete-file verification

From the complete bundle root:

python scripts/infer_fp4_repaired_s16_resurface_v1.py --bundle . --verify-only

This uses Python's standard library, checks the declared inventory and file SHA256 identities, and does not load Torch or initialize CUDA. It verifies the files; it does not execute the model or remeasure PPL/MK.

Compatible CUDA environment

The measured reference uses Linux, Python3.10.12, PyTorch2.11.0+cu128, CUDA12.8, Triton3.6.0, mamba-ssm2.3.2.post1, NumPy1.26.4, datasets4.8.5 and sentencepiece0.2.1, on NVIDIA RTX PRO6000 Blackwell Server Edition. Create the environment beside the bundle, preserving its strict inventory. The CLI loads its included source directly; installing the bundle itself with pip is unnecessary.

With Python3.10 and a matching CUDA build toolchain:

python3.10 -m venv ../fp4-recall-env
source ../fp4-recall-env/bin/activate
python -m pip install --upgrade pip setuptools wheel
python -m pip install 'torch==2.11.0' --index-url https://download.pytorch.org/whl/cu128
python -m pip install 'triton==3.6.0' 'numpy==1.26.4' \
  'sentencepiece==0.2.1' 'einops==0.8.2' 'safetensors==0.8.0' \
  'datasets==4.8.5' 'huggingface-hub==0.36.2' 'packaging==26.3' 'ninja==1.13.2'
python -m pip install --no-build-isolation \
  'mamba-ssm @ git+https://github.com/state-spaces/mamba.git@e9594ce1c732d97440f0332fdc43170a2294dbfa'

Mamba may compile an extension for the installed PyTorch/CUDA ABI. The runtime checks bound package versions, external kernel source hashes and the singleton warp16 RMSNorm policy, with TF32 off. Version strings alone do not guarantee exact numerical replay on another stack. The complete base expands to 16,473,999,360 bytes of FP16 weight tensors; request buffers and activations need additional CUDA memory.

One published recall sample and generation 

python scripts/infer_fp4_repaired_s16_resurface_v1.py --bundle . --smoke-check
python scripts/infer_fp4_repaired_s16_resurface_v1.py --bundle . \
  --prompt 'The capital of France is' --max-new-tokens 64
python scripts/infer_fp4_repaired_s16_resurface_v1.py --bundle . \
  --without-adapter --prompt 'The capital of France is' --max-new-tokens 64

--smoke-check replays the first published normal Resurface case and checks its prompt-token identity, generated-token sequence and actual native FP16 cache receipt. It covers one sample, not the full147-window PPL or768-case MK run. --without-adapter uses the same repaired base without the added adapter.

Optional full paired quality replay

The frozen evaluator and all its bound source files are included. From the bundle root, copy the exact small training report and adapter to a fresh sibling directory; leave the complete verified bundle unchanged:

mkdir ../fp4-recall-replay-formal
cp evidence/training_report.json ../fp4-recall-replay-formal/report.json
cp adapter_fp16.pt ../fp4-recall-replay-formal/adapter_fp16.pt
python scripts/run_fp4_repaired_s16_resurface_v1.py \
  --packed-dir weights --source-dir tokenizer \
  --repair-selection evidence/repair_selection.json \
  --repair-test-comparison evidence/repair_test_comparison.json \
  --training-report ../fp4-recall-replay-formal/report.json \
  --training-audit evidence/training_audit.json \
  --out-dir ../fp4-recall-replay-evaluation

This downloads the pinned WikiText test split and evaluates all three arms: baseline, final Resurface and adapter removed. It measures147 reset windows / 300,963 targets and 768 CONFIRM cases, then checks exact restoration and the predeclared gates. Its saved token file and outputs are written outside the bundle. Use a fresh output directory and the recorded backend. A new run is a new measurement; the reported values are bound to the published evidence.

The shipped CPU audit receipts describe checks already completed on GPU-host and Mac copies. Full training-checkpoint/archive audit reproduction additionally needs the four progress checkpoint/optimizer archives, which are research artifacts excluded from the model download. The shipped reports and hashes do not replay those tensor archives or GPU logits.

Read measured results, license scope and third-party notices for the accounting and provenance.