Publish canonical NVFP4 MXFP8 CSF QAD master checkpoint

Standard-size safetensors shards with canonical supporting files. Preserve trained normalization storage and the selected mixed-precision vision recipes.

.gitattributes

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+model.safetensors.index.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

CITATION.cff

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-cff-version: 1.2.0
-message: "If you use GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD, please cite it as below. The landing page of any project, model, dataset, application, service, or distribution that contains, is derived from, or runs it must begin with the Attribution Notice required by the Local Inference Lab License, Version 1.0 (see the LICENSE file)."
-type: software
-title: "GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD"
-abstract: "NVFP4 (QAD) and MXFP8 checkpoint of GLM-5.3-Flash by Local Inference Lab, Inc., a non-profit organization, in the NVFP4-CSF container with losslessly compressed routed-expert scales."
-authors:
-  - name: "Local Inference Lab, Inc."
-    address: "311 West Main Street"
-    city: "Grayson"
-    region: "Kentucky"
-    post-code: "41143"
-    country: "US"
-url: "https://huggingface.co/local-inference-lab/GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD"
-repository-artifact: "https://huggingface.co/local-inference-lab/GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD"
-license-url: "https://huggingface.co/local-inference-lab/GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD/blob/main/LICENSE"
-date-released: "2026-10-03"
-keywords:
-  - "Local Inference Lab"
-  - "LicenseRef-LIL-1.0"
-  - "csf"

README.md

@@ -1,126 +1,56 @@
 ---
-base_model: local-inference-lab/GLM-5.3-Flash-NVFP4
+base_model: local-inference-lab/GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD
 base_model_relation: quantized
 license: other
-pipeline_tag: text-generation
-language:
-  - en
-  - zh
+license_name: lil-license-1.0
+license_link: LICENSE
+pipeline_tag: image-text-to-text
 tags:
-- quantized
 - nvfp4
 - mxfp8
 - modelopt
 - quantization-aware-distillation
 - lossless-compression
-- lil-license-1.0
-license_name: lil-license-1.0
-license_link: LICENSE
-lil_license:
-  spdx: LicenseRef-LIL-1.0
-  name: Local Inference Lab License, Version 1.0
-  licensor: Local Inference Lab, Inc.
-  work_name: GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD
-  original_location: https://huggingface.co/local-inference-lab/GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD
-  canary: Ні пуху, ні пера
-  canary_id: LIL-CANARY-F795-6543-0661-0749
 ---
 
 # GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD
 
-GLM-5.3-Flash with every precision choice stored in the checkpoint, so serving
-needs no load-time conversion:
-
-```
-Main model:
-  Routed expert projections - NVFP4, quantization-aware distillation (QAD)
-  Attention projections     - MXFP8 (402: KDA + MLA/indexer, Spark selection)
-  Shared experts            - MXFP8 (129, Spark selection)
-
-MTP (layer 45):
-  Routed expert projections - NVFP4 (W4A16 group)
-  Attention / shared        - MXFP8 (Spark selection)
-
-Routed-expert E4M3 block scales - lossless CSF compression
-```
-
-## Provenance
-
-- Routed experts: `local-inference-lab/GLM-5.3-Flash-NVFP4` revision `mtp-bf16`
-  (`cfd47bd7680e68408924df09b179d5bed25b2ae9`), the QAD checkpoint, unchanged.
-- MXFP8: the 531 projections of `local-inference-lab/GLM-5.3-Flash-NVFP4-Spark`'s
-  MXFP8 groups, quantized from the QAD checkpoint's BF16 weights with vLLM's
-  `mxfp8_e4m3_quantize` (E4M3 values, UE8M0 scale per 32) - the same values vLLM's
-  online MXFP8 produces.
-- MTP routed experts: quantized from the QAD BF16 MTP experts with vLLM's online
-  NVFP4 MoE quantizer (`_quantize_moe_weight_to_nvfp4`).
-- CSF: codec `byte-window4-fixed-stream-u24-exceptions/1`, 36,288 scale matrices,
-  19.03 GB of scales stored in 9.83 GB. `verification.json` records exact
-  reconstruction of every source shard (SHA-256 of reconstructed and compressed
-  files).
-
-## Layout
-
-The `lil-nvfp4-csf-checkpoint/1` container:
-
-- `tensors/` - 44 safetensors shards; routed-expert scales are CSF-compressed
-- `metadata/` - config (ModelOpt `MIXED_PRECISION`, 574 quantized layers),
-  tokenizer, chat template, generation config
-- `config.json` - a copy of `metadata/config.json` at the top of the repository,
-  where the Hub counts downloads; runtimes read `metadata/`
-- `manifest.json`, `build-contract.json`, `receipts/`, `verification.json`
-- `LICENSE`, `NOTICE`, `LICENSES/` (upstream license texts), `CITATION.cff`, `REUSE.toml`, and the SHA-256 of every file in `SHA256SUMS` and `lil-manifest.json`
+This model is based on GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD by Local Inference Lab, Inc., a non-profit organization, available at https://huggingface.co/local-inference-lab/GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD. GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD is licensed under the Local Inference Lab License, Version 1.0.
 
-## Serving
+Multimodal checkpoint with QAD decoder weights, losslessly compressed routed-expert block scales, MXFP8 attention, NVFP4 MTP routed experts, and mixed-precision vision weights.
 
-Use a runtime with the NVFP4-CSF decoder (Karmic Kraken beta images: vLLM
-`--quantization nvfp4_csf --load-format nvfp4_csf`). The served model directory
-holds the files of `metadata/`, with `config.json`'s `quantization_config`
-replaced by:
+| Component | Stored precision |
+| --- | --- |
+| Decoder routed experts | QAD NVFP4; E2M1 values, E4M3 block scales, FP32 global/input scales |
+| Decoder attention | MXFP8 projection selection from the pinned source |
+| MTP routed experts | NVFP4, BF16 activations; block scales stored directly |
+| Vision attention linears | MXFP8 E4M3 values; UE8M0 scales per 32 weights; 48 matrices |
+| Vision MLP and merger linears | NVFP4 E2M1 values; E4M3 scales per 16 weights and FP32 global scale; 76 matrices; BF16 activations |
+| Other vision tensors | BF16; 223 tensors, including normalization, biases, position embeddings, and convolutions |
+| Normalization parameters | 277 BF16 tensors; original bytes preserved |
 
-```json
-{
-  "quant_method": "nvfp4_csf",
-  "format_version": 1,
-  "checkpoint_root": "/path/to/this/snapshot",
-  "source_quantization_config": { "...": "metadata/config.json quantization_config" }
-}
-```
+Vision uses weight-only post-training conversion for NVFP4 linears and dynamic MXFP8 attention activations. The vision weights occupy 390,629,680 bytes (372.53 MiB), saving 702.50 MiB of tensor storage versus their BF16 source. These numbers exclude runtime workspaces, activations, and KV cache.
 
-For exact arithmetic on GLM-5.3-Flash's checksum-style prompts we run the routed
-experts as W4A16 with FP32 router-weight combine
-(`VLLM_B12X_MOE_FP4_FORCE_A16=1`, `B12X_W4A16_FP32_TOPK_WEIGHTS=1`).
+The pinned input is [`local-inference-lab/GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD`](https://huggingface.co/local-inference-lab/GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD/tree/fd660d51d1fc3caae26a4bf31b7451475bbb9bdc) at revision `fd660d51d1fc3caae26a4bf31b7451475bbb9bdc`. All tensors outside the converted vision linear weights retain their source values and dtypes. CSF reconstructs the original scale bytes exactly.
 
-Measured on 2x RTX PRO 6000 Blackwell Max-Q (TP2, DCP2, MTP with 3 speculative
-tokens): model memory 83.2 GiB per GPU; decode about 170 tok/s at concurrency 1
-and 490-540 tok/s at concurrency 8 (CSF decoder version dependent); prefill
-about 7.6K tok/s at 32K context. Needle-checksum (500 identical 8K prompts at
-temperature 0, concurrency 8, no prefix-cache reuse): 0.5% near misses, against
-0.25% for the same QAD experts with BF16 attention and shared experts (the
-difference is within run-to-run noise).
+## Storage contract
 
-## Upstream license
+The checkpoint contains 36 safetensors shards targeting 5 GB each, under `tensors/`. `model.safetensors.index.json` maps stored tensors to these files.
 
-GLM-5.3-Flash ([`zai-org/GLM-5.3-Flash-BF16`](https://huggingface.co/zai-org/GLM-5.3-Flash-BF16)) is licensed by Z.AI under the MIT License. Its license text is in `LICENSES/MIT.txt`, and `NOTICE` names the upstream materials. This checkpoint is licensed under the Local Inference Lab License, Version 1.0 (`LICENSE`); see [License and attribution](#license-and-attribution) below.
+Scale encoding: `weight_scale_encoding: "csf"`. Codec: `byte-window4-fixed-stream-u24-exceptions/1`. The 36,288 compressed matrices are exclusively main-decoder routed-expert E4M3 block scales. Each logical scale tensor is represented by a uint8 `.nvfp4_csf_fixed` stream and uint32 `.nvfp4_csf_exceptions`. MTP, vision, PLE, MXFP8, global, and activation scales remain uncompressed.
 
-<!-- lil-license-section:begin (managed by lilkit) -->
-## License and attribution
+## Runtime requirements
 
-GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD is licensed under the [Local Inference Lab License, Version 1.0](https://huggingface.co/local-inference-lab/GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD/blob/main/LICENSE), which reproduces the terms and conditions of the Apache License, Version 2.0, and adds conditions that restrict them. It is not the Apache License, and these files are not open source. Copyright (c) 2026 Local Inference Lab, Inc., a non-profit organization, 311 West Main Street, Grayson, Kentucky 41143. Revisions published up to and including revision 9ca06e24effde995982c439091722e9e76db82bc were released under the MIT License, and copies of those revisions remain under it. The Local Inference Lab License, Version 1.0 applies to later revisions.
+`config.json` uses the standard `quant_method: "modelopt"` and `quant_algo: "MIXED_PRECISION"`. Decoder expert recipes declare `quant_algo: "NVFP4"`, `group_size: 16`, and `weight_scale_encoding: "csf"`. MTP and vision recipes retain their own precision settings without CSF encoding.
 
-- **No reuploads.** Do not upload, mirror, or redistribute these files, or any Substantially Similar Copy of them (including renamed, re-sharded, re-packaged, metadata-stripped, converted, or dequantized copies). Link to <https://huggingface.co/local-inference-lab/GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD> instead.
-- **Attribution at the top.** Every README, model card, or other landing page for a project, model, dataset, application, service, or distribution that contains, is derived from, or runs GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD must begin with this Attribution Notice as its first paragraph (a single title line may come before it):
+vLLM uses its standard safetensors loader and `modelopt_mixed` quantization. The quantization layer receives compressed scale tensors through normal weight-loading hooks and prepares TP-local scales for b12x. No checkpoint-root setting or CSF-specific load format is needed.
 
-> This model is based on [GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD](https://huggingface.co/local-inference-lab/GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD) by Local Inference Lab, Inc., a non-profit organization, available at <https://huggingface.co/local-inference-lab/GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD>. GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD is licensed under the Local Inference Lab License, Version 1.0.
+GLM runtimes that force the vision tower to BF16 must enable its explicit mixed-precision recipes, including the attention QKV projections.
 
-  Plain-text form:
+## Validation
 
-```text
-This model is based on GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD by Local Inference Lab, Inc., a non-profit organization, available at https://huggingface.co/local-inference-lab/GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD. GLM-5.3-Flash-NVFP4-MXFP8-CSF-QAD is licensed under the Local Inference Lab License, Version 1.0.
-```
+Every stored tensor and shard passed SHA-256 verification. Retained tensors match the pinned input byte-for-byte. Every compressed scale passed independent lossless reconstruction, and reconstructed shard hashes match the reference serialization. Vision NVFP4 packing and rounding were checked against an independent E2M1 reference. GPU inference and multimodal quality evaluation have not been run for this checkpoint.
 
-- **Keep the marks.** Do not remove the license metadata, the Identifying Marks ("Ні пуху, ні пера", LIL-CANARY-F795-6543-0661-0749), or the copyright notices embedded in these files.
-- **Breach.** A breach of the no-reupload or attribution terms ends the license immediately (Section 5.1), and Local Inference Lab, Inc. may ask hosting services to remove the material (Section 5.2).
+## License
 
-File integrity: `SHA256SUMS` and `lil-manifest.json` list the SHA-256 of every file.
-<!-- lil-license-section:end -->
+`LICENSE`, `NOTICE`, and `LICENSES/` retain the source distribution's license and attribution materials, including the upstream model terms.

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SHA256SUMS

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metadata/README.md

@@ -1,95 +0,0 @@
----
-base_model: zai-org/GLM-5.3-Flash-BF16
-base_model_relation: quantized
-library_name: transformers
-pipeline_tag: text-generation
-license: mit
-language:
-  - en
-  - zh
-tags:
-  - quantized
-  - nvfp4
-  - mxfp8
-  - modelopt
-  - distillation
-  - quantization-aware-distillation
-  - conversational
-  - multimodal
----
-
-> **Branch `mtp-bf16`.** Identical to `main` (QAD) except the MTP routed experts (`model.language_model.layers.45.mlp.experts`, 288 experts): they are stored in BF16, copied from [zai-org/GLM-5.3-Flash-BF16](https://huggingface.co/zai-org/GLM-5.3-Flash-BF16) `a5b45eb41df6`, instead of MXFP8. QAD did not train the MTP layer, so these are the original unquantized weights; the MXFP8 group for layer 45 is removed from the quantization config. Every other file and tensor is unchanged.
-
-## Model Description
-
-local-inference-lab/GLM-5.3-Flash-NVFP4 is a quantization-aware distilled version of
-[zai-org/GLM-5.3-Flash-BF16](https://huggingface.co/zai-org/GLM-5.3-Flash-BF16).
-The student is trained against the original BF16 teacher with NVFP4 expert
-weights in its forward pass, learning to compensate for quantization error
-rather than relying on post-training quantization alone.
-
-The architecture is unchanged: 45 decoder layers, including three dense
-layers and 42 MoE layers with 288 routed experts each, eight active per
-token, and one shared expert. Attention combines linear attention and
-DeepSeek Sparse Attention. The release occupies approximately 199 GB on disk.
-
-### What's quantized
-
-| Component | Stored format | Distillation |
-| --- | --- | --- |
-| Routed decoder experts: gate, up and down projections | NVFP4 | Trained |
-| Dense MLPs in layers 0–2 and shared experts | BF16 | Trained |
-| Router projection matrices | BF16 | Trained |
-| Decoder and final RMSNorm weights | BF16 | Trained |
-| Routing correction biases | FP32 | Updated during distillation |
-| Attention, residual-stream mixing, token embeddings and LM head | Original BF16/FP32 | Frozen |
-| Vision encoder | BF16 | Unchanged; outside text distillation |
-| Auxiliary MTP routed experts | MXFP8 | Unchanged; outside text distillation |
-
-NVFP4 stores 4-bit E2M1 values with FP8 E4M3 block scales per 16 elements
-and FP32 global scales. Gate/up projections share a global scale within
-each expert; down projections have their own. The scales are recomputed
-from the trained weights during quantization.
-
-The multi-token prediction (MTP) module is retained from the mixed-precision
-base checkpoint. Its routed experts use MXFP8 E4M3 values with power-of-two
-block scales per 32 elements; its other parameters retain their original
-precision. The vision encoder and MTP module were not distilled.
-
-### Quantization-aware distillation
-
-The BF16 teacher generates chat responses and supplies probability and
-hidden-state targets for the student.
-
-During training, the student's routed-expert weights are quantized to NVFP4
-and reconstructed for each forward pass. Gradients update the underlying
-BF16 weights so their low-precision representations better match the
-teacher's next-token probabilities and final hidden states.
-
-The student uses its own expert routing during training. Routers, shared
-experts, dense MLPs and normalization weights are also adapted, allowing
-the unquantized components to compensate alongside the NVFP4 experts.
-
-### Training data
-
-The distillation corpus contains approximately 200M prompt-and-response
-tokens. Assistant responses come from the BF16 GLM teacher using its native chat template.
-
-### Activation calibration
-
-Distillation uses BF16 activations. For NVFP4 activation quantization at
-inference, expert-specific input scales are calibrated separately on the
-200M-token chat corpus.
-
-Calibration uses natural routing and covers all 12,096 layer/expert pairs
-without fallback scales. Gate/up share their expert's input range; down
-uses the post-activation range. The serving configuration also retains
-dynamic MXFP8 activation quantization for MTP routed experts.
-
-### Requirements
-
-Use a runtime supporting the `glm5_next` architecture and this mixed
-NVFP4/MXFP8 ModelOpt layout, including per-expert activation scales.
-The tokenizer, upstream chat template, generation settings, vision assets
-and MTP tensors are included. KV cache and runtime buffers require memory
-in addition to the model weights.

metadata/amax_checkpoint.json

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-{
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metadata/build-receipt.json

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metadata/quatrain-materialization.json

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