# Credits: DeepSeek-V4.1-Flash-1017 TrellisMX K4

- **Compute: Lambda.** The encode and every measurement on this card ran on an 8x NVIDIA B200 node that
  [Lambda](https://lambda.ai) donated for this work. Our sincere thanks to Lambda
  ([@LambdaAPI](https://x.com/LambdaAPI), https://x.com/LambdaAPI) for supporting independent open-model research: this
  build would not exist without that compute.
- **Base model.** DeepSeek-V4.1-Flash-1017 by DeepSeek (`deepseek-ai/DeepSeek-V4.1-Flash-1017`), MIT License (`LICENSE`).
- **Runtime: Local Inference Lab and Luke Alonso.** The SM120 runtime this checkpoint targets is built on the vLLM image
  by Local Inference Lab and Luke Alonso (`ghcr.io/local-inference-lab/vllm`, https://github.com/local-inference-lab), a
  fork of vLLM (Apache-2.0, vLLM contributors), and the open-source projects that image bundles, each under its own
  license: FlashInfer, LMCache, InstantTensor, xgrammar and fastokens.
- **Kernels: b12x by Luke Alonso.** The MoE, attention and GEMM kernels of that runtime come from b12x
  (https://github.com/lukealonso/b12x, mirrored at https://github.com/local-inference-lab/b12x) by Luke Alonso, with
  Martin Vit and other contributors (Apache-2.0).
- **CSF encoder: Local Inference Lab.** The lossless compressed scale format (CSF) the expert scales are stored in, and
  its encoder and decoder, are Local Inference Lab's work, shipped in Local Inference Lab's FlashInfer.
- **TrellisMX: QTIP and EXL3.** TrellisMX, Brandon M. Music's trellis-coded format, was inspired by QTIP (Albert Tseng,
  Qingyao Sun, David Hou and Christopher De Sa, "QTIP: Quantization with Trellises and Incoherence Processing",
  NeurIPS 2024) and by ExLlamaV3 and its EXL3 format by turboderp and contributors
  (https://github.com/turboderp-org/exllamav3, MIT License).
- **Codec changes: QSRT and Luke Alonso.** Decoding the trellis directly into FP8 (E4M3) tensor-core operands was
  inspired by Luke Alonso's KQuant/QSRT work and the b12x W4A8 trellis path, which decodes the QSRT trellis to E4M3.
- **Methods.** The encoder's Hessian-based error feedback follows GPTQ (Elias Frantar, Saleh Ashkboos, Torsten Hoefler
  and Dan Alistarh, ICLR 2023). The incoherence processing and rotations follow QuIP (Jerry Chee, Yaohui Cai, Volodymyr
  Kuleshov and Christopher De Sa, NeurIPS 2023), QuIP# (Albert Tseng, Jerry Chee, Qingyao Sun, Volodymyr Kuleshov and
  Christopher De Sa, ICML 2024), QuaRot (Saleh Ashkboos et al., NeurIPS 2024) and SpinQuant (Zechun Liu et al., 2024).
  The UE8M0 block scales follow the OCP Microscaling (MX) Formats Specification (Bita Darvish Rouhani et al., 2023).
- **Evaluation data.** The evaluation windows behind the KLD figures were built from public datasets, each under the
  license on its own dataset card: NVIDIA's Nemotron-SFT-Agentic-v2, Nemotron-SFT-Math-v4, Nemotron-SFT-Science-v2
  (CC-BY-SA-4.0), Nemotron-SFT-Competitive-Programming-v2, Nemotron-SFT-Instruction-Following-Chat-v3,
  Nemotron-SFT-Multilingual-v2, Nemotron-SFT-Long-Context-Transduction-v1 (OpenMDW-1.1), Nemotron-SFT-Safety-v2,
  Nemotron-Pretraining-Legal-v1 (CC-BY-4.0) and Nemotron-Pretraining-Dataset-sample (Nemotron-CC text; NVIDIA Open Data
  License Agreement); and Nebius's SWE-agent-trajectories (CC-BY-4.0). Each window's sources are listed in
  `evaluation/eval-windows.json`.