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VERIFY-REPORT.json

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+ },
+ "status": "PASS"
+}

carrier/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2026 Z.AI Co., Ltd
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

carrier/README.md

@@ -0,0 +1,68 @@
+---
+language:
+- en
+- zh
+library_name: transformers
+license: mit
+pipeline_tag: text-generation
+---
+
+# GLM-5.3-Flash-BF16
+
+<div align="center">
+<img src=https://raw.githubusercontent.com/zai-org/GLM-5/refs/heads/main/resources/logo.svg width="15%"/>
+</div>
+<p align="center">
+    👋 Join our <a href="https://raw.githubusercontent.com/zai-org/GLM-5/refs/heads/main/resources/wechat.png" target="_blank">WeChat</a> or <a href="https://discord.gg/QR7SARHRxK" target="_blank">Discord</a> community.
+    <br>
+    📖 Check out the GLM-5.3-Flash <a href="https://z.ai/blog/glm-5.3-flash" target="_blank">blog</a> and GLM-5 <a href="https://arxiv.org/abs/2602.15763" target="_blank">Technical report</a>.
+    <br>
+    📍 Use GLM-5.3-Flash API services on <a href="https://docs.z.ai/guides/llm/glm-5.3-flash">Z.ai API Platform. </a>
+</p>
+
+## Introduction
+
+We introduce GLM-5.3-Flash, the first natively multimodal model in the GLM-5 series. With 320B total parameters and just 18B active parameters, it outperforms GLM-5.2 across benchmarks and real-world workloads at one-tenth the price, while approaching Claude Opus 4.8 on coding and agentic benchmarks.
+
+GLM-5.3-Flash starts from a newly trained base model, with its architecture and training recipe redesigned around capability and efficiency. For the first time in the GLM series, we introduce a hybrid architecture combining sparse and linear attention, sharply reducing long-context serving costs while preserving precise long-context capabilities. The model also adopts Manifold-Constrained Hyper-Connections (mHC) to further improve scaling efficiency. Together with our latest 30T-token multimodal pre-training corpus, these changes enable GLM-5.3-Flash to deliver more intelligence with less compute.
+
+![bench_53](https://raw.githubusercontent.com/zai-org/GLM-5/refs/heads/main/resources/bench_53.png)
+
+
+## Serve GLM-5.3-Flash Locally
+
+GLM-5.3-Flash supports deployment with the following frameworks. Feel free to try them out:
+
+- [SGLang](https://github.com/sgl-project/sglang) — see [cookbook](https://cookbook.sglang.io/autoregressive/GLM/GLM-5.3-Flash)
+- [vLLM](https://github.com/vllm-project/vllm) — see [recipes](https://recipes.vllm.ai/zai-org/GLM-5.3-Flash)
+- [TokenSpeed](https://github.com/lightseekorg/tokenspeed) — see [here](https://lightseek.org/tokenspeed/recipes/models#glm-5-3-flash)
+- [KTransformers](https://github.com/kvcache-ai/ktransformers) — see [tutorial](https://github.com/kvcache-ai/ktransformers/blob/main/doc/en/kt-kernel/GLM-5.3-Flash-Tutorial.md)
+
+
+## Footnotes
+
+* **HLE w/ tools (full set)**: We use sampling parameters of `temperature=1.0` and `top_p=0.95` for evaluation, with a maximum generation length of `163,840` tokens. The evaluation is conducted with a maximum context length of `300,000` tokens, using a context management strategy. We use GPT-5.6-luna (medium) as the judge model.
+* **NL2Repo**: We evaluated NL2Repo with temperature=1.0, top_p=1.0, and max_new_tokens=64k under 1M context. To prevent hacking, we use rule-based and a LLM-based judgement to prevent malicious behaviors (e.g., unauthorized pip or curl operations).
+* **DeepSWE**: We run DeepSWE using the mini-swe-agent harness with `temperature=0.95`, `top_p=1.0`, `timeout=6h` and 400K context.
+* **Terminal-Bench 2.1**: We evaluate in Claude Code 2.1.207 with temperature=1.0, top_p=1, max_new_tokens=65536 with 6h timeout.
+* **Agent’s Last Exam**: 
+* **Toolathlon Verified**: We obtain all results via the official evaluation service and report pass@1 averaged over 3 independent runs.
+* **AutomationBench**: We evaluate on AutomationBench **v1.0.6**, incorporating the fix for the `null`-type handling issue introduced in [PR #13](https://github.com/zapier/AutomationBench/pull/13).
+* **GDPval-AA v2**: Models are evaluated by Artificial Analysis.
+* **BabyVision**: We use temperature=1.0, top_p=0.95, and a maximum context length of 164K tokens. We resize the input images such that their shorter side is at least 1.5K pixels, consistent with other baselines.
+
+## Citation
+
+If you find GLM-5.3-Flash useful in your research, please cite our technical report:
+
+```bibtex
+@misc{glm5team2026glm5vibecodingagentic,
+      title={GLM-5: from Vibe Coding to Agentic Engineering},
+      author={GLM-5-Team and : and Aohan Zeng and Xin Lv and Zhenyu Hou and Zhengxiao Du and Qinkai Zheng and Bin Chen and Da Yin and Chendi Ge and Chenghua Huang and Chengxing Xie and Chenzheng Zhu and Congfeng Yin and Cunxiang Wang and Gengzheng Pan and Hao Zeng and Haoke Zhang and Haoran Wang and Huilong Chen and Jiajie Zhang and Jian Jiao and Jiaqi Guo and Jingsen Wang and Jingzhao Du and Jinzhu Wu and Kedong Wang and Lei Li and Lin Fan and Lucen Zhong and Mingdao Liu and Mingming Zhao and Pengfan Du and Qian Dong and Rui Lu and Shuang-Li and Shulin Cao and Song Liu and Ting Jiang and Xiaodong Chen and Xiaohan Zhang and Xuancheng Huang and Xuezhen Dong and Yabo Xu and Yao Wei and Yifan An and Yilin Niu and Yitong Zhu and Yuanhao Wen and Yukuo Cen and Yushi Bai and Zhongpei Qiao and Zihan Wang and Zikang Wang and Zilin Zhu and Ziqiang Liu and Zixuan Li and Bojie Wang and Bosi Wen and Can Huang and Changpeng Cai and Chao Yu and Chen Li and Chengwei Hu and Chenhui Zhang and Dan Zhang and Daoyan Lin and Dayong Yang and Di Wang and Ding Ai and Erle Zhu and Fangzhou Yi and Feiyu Chen and Guohong Wen and Hailong Sun and Haisha Zhao and Haiyi Hu and Hanchen Zhang and Hanrui Liu and Hanyu Zhang and Hao Peng and Hao Tai and Haobo Zhang and He Liu and Hongwei Wang and Hongxi Yan and Hongyu Ge and Huan Liu and Huanpeng Chu and Jia'ni Zhao and Jiachen Wang and Jiajing Zhao and Jiamin Ren and Jiapeng Wang and Jiaxin Zhang and Jiayi Gui and Jiayue Zhao and Jijie Li and Jing An and Jing Li and Jingwei Yuan and Jinhua Du and Jinxin Liu and Junkai Zhi and Junwen Duan and Kaiyue Zhou and Kangjian Wei and Ke Wang and Keyun Luo and Laiqiang Zhang and Leigang Sha and Liang Xu and Lindong Wu and Lintao Ding and Lu Chen and Minghao Li and Nianyi Lin and Pan Ta and Qiang Zou and Rongjun Song and Ruiqi Yang and Shangqing Tu and Shangtong Yang and Shaoxiang Wu and Shengyan Zhang and Shijie Li and Shuang Li and Shuyi Fan and Wei Qin and Wei Tian and Weining Zhang and Wenbo Yu and Wenjie Liang and Xiang Kuang and Xiangmeng Cheng and Xiangyang Li and Xiaoquan Yan and Xiaowei Hu and Xiaoying Ling and Xing Fan and Xingye Xia and Xinyuan Zhang and Xinze Zhang and Xirui Pan and Xu Zou and Xunkai Zhang and Yadi Liu and Yandong Wu and Yanfu Li and Yidong Wang and Yifan Zhu and Yijun Tan and Yilin Zhou and Yiming Pan and Ying Zhang and Yinpei Su and Yipeng Geng and Yong Yan and Yonglin Tan and Yuean Bi and Yuhan Shen and Yuhao Yang and Yujiang Li and Yunan Liu and Yunqing Wang and Yuntao Li and Yurong Wu and Yutao Zhang and Yuxi Duan and Yuxuan Zhang and Zezhen Liu and Zhengtao Jiang and Zhenhe Yan and Zheyu Zhang and Zhixiang Wei and Zhuo Chen and Zhuoer Feng and Zijun Yao and Ziwei Chai and Ziyuan Wang and Zuzhou Zhang and Bin Xu and Minlie Huang and Hongning Wang and Juanzi Li and Yuxiao Dong and Jie Tang},
+      year={2026},
+      eprint={2602.15763},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2602.15763},
+}
+```

carrier/amax.safetensors

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+size 9779904

carrier/amax_checkpoint.json

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+{
+  "batch_num": 192,
+  "calib_config": "/home/luke/projects/quantization/configs/calib_glm5_3_flash.toml",
+  "calib_config_sha256": "6538f422242df4de8ab268672f119ae03a6b14efe1086a325ac8ad2ea4013976",
+  "calib_method": "max",
+  "dataset_batch_counts": [
+    32,
+    25,
+    32,
+    205
+  ],
+  "datasets": [
+    {
+      "batch_size": 128,
+      "limit": 4096,
+      "max_len": 1024,
+      "path": "data/text/agentic_coding_calib_v3.jsonl"
+    },
+    {
+      "batch_size": 4,
+      "limit": 100,
+      "max_len": 2048,
+      "multimodal": true,
+      "path": "data/multimodal/coco_grounded_image_calib.jsonl"
+    },
+    {
+      "batch_size": 128,
+      "limit": 4096,
+      "max_len": 1024,
+      "path": "data/text/diverse_calib.jsonl"
+    },
+    {
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+      "limit": 819,
+      "max_len": 8192,
+      "path": "data/text/deep_calib.jsonl"
+    }
+  ],
+  "model": "glm5_3_flash",
+  "model_id": "/data/cache/huggingface/hub/models--zai-org--GLM-5.3-Flash-BF16/snapshots/a6c167b62691b2bac901344b65cb651a70f53e43",
+  "plan_sha256": "9b09e568033fd131d5f096be071d3c5a54258f5c42cf8f4985449c835c1e22b1",
+  "total_batches": 294
+}

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+size 9780048

carrier/chat_template.jinja

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+[gMASK]<sop>
+{%- set effective_reasoning_effort = reasoning_effort if reasoning_effort is defined and reasoning_effort in ['low', 'high'] else 'max' -%}
+{%- if effective_reasoning_effort is not none -%}<|system|>Reasoning Effort: {{ effective_reasoning_effort | capitalize }}{%- endif -%}
+{%- set clear_thinking = clear_thinking if clear_thinking is defined else false -%}
+{%- if tools -%}
+{%- macro tool_to_json(tool) -%}
+    {%- set ns_tool = namespace(first=true) -%}
+    {{ '{' -}}
+    {%- for k, v in tool.items() -%}
+        {%- if k != 'defer_loading' and k != 'strict' -%}
+            {%- if not ns_tool.first -%}{{- ', ' -}}{%- endif -%}
+            {%- set ns_tool.first = false -%}
+            "{{ k }}": {{ v | tojson(ensure_ascii=False) }}
+        {%- endif -%}
+    {%- endfor -%}
+    {{- '}' -}}
+{%- endmacro -%}
+{%- macro tool_references_to_response(refs) -%}
+    {{- '<tool_response><tools>\n' -}}
+    {%- for tr in refs -%}
+        {%- for tool in tools -%}
+            {%- if 'function' in tool -%}
+                {%- set tool = tool['function'] -%}
+            {%- endif -%}
+            {%- if tool.name == tr.name -%}
+                {{- tool_to_json(tool) + '\n' -}}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- endfor -%}
+    {{- '</tools></tool_response>' -}}
+{%- endmacro -%}
+<|system|>
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{% for tool in tools %}
+{%- if 'function' in tool -%}
+    {%- set tool = tool['function'] -%}
+{%- endif -%}
+{% if tool.defer_loading is not defined or not tool.defer_loading %}
+{{ tool_to_json(tool) }}
+{% endif %}
+{% endfor %}
+</tools>
+
+For each function call, output the function name and arguments within the following XML format:
+<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>{%- endif -%}
+{%- macro emit_image() -%}<|begin_of_image|><|image|><|end_of_image|>{%- endmacro -%}
+{%- macro emit_video() -%}<|begin_of_video|><|video|><|end_of_video|>{%- endmacro -%}
+{%- macro emit_audio() -%}<|begin_of_audio|><|end_of_audio|>{%- endmacro -%}
+{%- macro visible_text(content) -%}
+    {%- if content is string -%}
+        {{- content -}}
+    {%- elif content is iterable and content is not mapping -%}
+        {%- for item in content -%}
+            {%- if item is mapping and item.type == 'text' -%}
+                {{- item.text -}}
+            {%- elif item is string -%}
+                {{- item -}}
+            {%- elif item is mapping and item.type in ['image', 'image_url'] -%}
+                {{- emit_image() -}}
+            {%- elif item is mapping and item.type in ['video', 'video_url'] -%}
+                {{- emit_video() -}}
+            {%- elif item is mapping and item.type in ['audio', 'audio_url', 'input_audio'] -%}
+                {{- emit_audio() -}}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{- content }}
+    {%- endif -%}
+{%- endmacro -%}
+{%- macro tool_response(text) -%}
+{{- '<tool_response>' + text + '</tool_response>' -}}
+{%- endmacro -%}
+{%- macro render_tool_response(m) -%}
+{%- if m.content is string -%}
+    {{- tool_response(m.content) -}}
+{%- elif m.content and m.content is not mapping and m.content.0.type == "tool_reference" -%}
+    {{- tool_references_to_response(m.content) -}}
+{%- elif is_list_of_outputs(m) -%}
+    {%- for tr in m.content -%}
+        {%- if tr.output is iterable and tr.output is not string and tr.output is not mapping and tr.output and tr.output.0.type == "tool_reference" -%}
+            {{- tool_references_to_response(tr.output) -}}
+        {%- else -%}
+            {{- tool_response(visible_text(tr.output)) -}}
+        {%- endif -%}
+    {%- endfor -%}
+{%- else -%}
+    {{- tool_response(visible_text(m.content)) -}}
+{%- endif -%}
+{%- endmacro -%}
+{%- macro id_of(obj) -%}
+    {%- if obj.tool_call_id -%}
+        {{- obj.tool_call_id -}}
+    {%- elif obj.id -%}
+        {{- obj.id -}}
+    {%- endif -%}
+{%- endmacro -%}
+{%- macro is_list_of_outputs(m) -%}
+    {%- if m.content and m.content.0.output is defined -%}1{%- endif -%}
+{%- endmacro -%}
+{%- macro has_dup_tool_result_id(lo, hi, target) -%}
+    {%- set ns_cnt = namespace(n=0) -%}
+    {%- for k in range(lo, hi + 1) -%}
+        {%- set m = messages[k] -%}
+        {%- if is_list_of_outputs(m) -%}
+            {%- for entry in m.content -%}
+                {%- if id_of(entry) == target -%}
+                    {%- set ns_cnt.n = ns_cnt.n + 1 -%}
+                {%- endif -%}
+            {%- endfor -%}
+        {%- elif id_of(m) == target -%}
+            {%- set ns_cnt.n = ns_cnt.n + 1 -%}
+        {%- endif -%}
+        {%- if ns_cnt.n > 1 -%}{%- break -%}{%- endif -%}
+    {%- endfor -%}
+    {%- if ns_cnt.n > 1 -%}1{%- endif -%}
+{%- endmacro -%}
+{%- macro tc_id_exists(tcs, target) -%}
+    {%- set ns_f = namespace(found=false) -%}
+    {%- for tc in tcs -%}
+        {%- if id_of(tc) == target -%}
+            {%- set ns_f.found = true -%}
+            {%- break -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {%- if ns_f.found -%}1{%- endif -%}
+{%- endmacro -%}
+{%- set ns = namespace(last_user_index=-1) -%}
+{%- for m in messages %}
+    {%- if m.role == 'user' %}
+        {%- set ns.last_user_index = loop.index0 -%}
+    {%- endif %}
+{%- endfor %}
+{%- for m in messages -%}
+{%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }}
+{%- elif m.role == 'assistant' -%}
+<|assistant|>
+{%- set content = visible_text(m.content) %}
+{%- if m.reasoning_content is string %}
+    {%- set reasoning_content = m.reasoning_content %}
+{%- elif '</think>' in content %}
+    {%- set reasoning_content = content.split('</think>')[0].split('<think>')[-1] %}
+    {%- set content = content.split('</think>')[-1] %}
+{%- endif %}
+{%- if (not clear_thinking or loop.index0 > ns.last_user_index) and reasoning_content is defined -%}
+{{ '<think>' + reasoning_content +  '</think>'}}
+{%- else -%}
+{{ '<think></think>' }}
+{%- endif -%}
+{%- if content.strip() -%}
+{{ content.strip() }}
+{%- endif -%}
+{% if m.tool_calls %}
+{% for tc in m.tool_calls %}
+{%- if tc.function %}
+    {%- set tc = tc.function %}
+{%- endif %}
+{{- '<tool_call>' + tc.name -}}
+{% set _args = tc.arguments %}{% for k, v in _args.items() %}<arg_key>{{ k }}</arg_key><arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value>{% endfor %}</tool_call>{% endfor %}
+{% endif %}
+{%- elif m.role == 'tool' -%}
+{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+    {{- '<|observation|>' -}}
+    {%- set block_start = loop.index0 -%}
+    {%- set ns_blk = namespace(end=block_start) -%}
+    {%- for j in range(block_start, messages|length) -%}
+        {%- if messages[j].role == 'tool' -%}
+            {%- set ns_blk.end = j -%}
+        {%- else -%}
+            {%- break -%}
+        {%- endif -%}
+    {%- endfor -%}
+    {%- set ns_a = namespace(tool_calls=none) -%}
+    {%- if block_start > 0 and messages[block_start - 1].role == 'assistant' and messages[block_start - 1].tool_calls -%}
+        {%- set ns_a.tool_calls = messages[block_start - 1].tool_calls -%}
+    {%- endif -%}
+    {%- set ns_chk = namespace(can_sort=true) -%}
+    {%- if not ns_a.tool_calls -%}
+        {%- set ns_chk.can_sort = false -%}
+    {%- else -%}
+        {%- for k in range(block_start, ns_blk.end + 1) -%}
+            {%- set m = messages[k] -%}
+            {%- if is_list_of_outputs(m) -%}
+                {%- for entry in m.content -%}
+                    {%- set eid = id_of(entry) -%}
+                    {%- if not eid -%}
+                        {%- set ns_chk.can_sort = false -%}
+                    {%- elif has_dup_tool_result_id(block_start, ns_blk.end, eid) -%}
+                        {%- set ns_chk.can_sort = false -%}
+                    {%- elif not tc_id_exists(ns_a.tool_calls, eid) -%}
+                        {%- set ns_chk.can_sort = false -%}
+                    {%- endif -%}
+                {%- endfor -%}
+            {%- else -%}
+                {%- set tk_id = id_of(m) -%}
+                {%- if not tk_id -%}
+                    {%- set ns_chk.can_sort = false -%}
+                {%- elif has_dup_tool_result_id(block_start, ns_blk.end, tk_id) -%}
+                    {%- set ns_chk.can_sort = false -%}
+                {%- elif not tc_id_exists(ns_a.tool_calls, tk_id) -%}
+                    {%- set ns_chk.can_sort = false -%}
+                {%- endif -%}
+            {%- endif -%}
+        {%- endfor -%}
+        {%- for i in range(ns_a.tool_calls | length) -%}
+            {%- set tc_id = id_of(ns_a.tool_calls[i]) -%}
+            {%- if not tc_id -%}
+                {%- set ns_chk.can_sort = false -%}
+            {%- endif -%}
+            {%- for j in range(i + 1, ns_a.tool_calls | length) -%}
+                {%- if id_of(ns_a.tool_calls[j]) == tc_id -%}
+                    {%- set ns_chk.can_sort = false -%}
+                {%- endif -%}
+            {%- endfor -%}
+    {%- endfor -%}
+    {%- endif -%}
+    {%- if ns_chk.can_sort -%}
+        {%- for tc in ns_a.tool_calls -%}
+            {%- set tc_id = id_of(tc) -%}
+            {%- for k in range(block_start, ns_blk.end + 1) -%}
+                {%- set m = messages[k] -%}
+                {%- if is_list_of_outputs(m) -%}
+                    {%- for entry in m.content -%}
+                        {%- set eid = id_of(entry) -%}
+                        {%- if eid == tc_id -%}
+                            {%- if entry.output is iterable and entry.output is not string and entry.output is not mapping and entry.output and entry.output.0.type == "tool_reference" -%}
+                                {{- tool_references_to_response(entry.output) -}}
+                            {%- else -%}
+                                {{- tool_response(visible_text(entry.output)) -}}
+                            {%- endif -%}
+                        {%- endif -%}
+    {%- endfor -%}
+{%- else -%}
+                    {%- set tk_id = id_of(m) -%}
+                    {%- if tk_id == tc_id -%}
+                        {{- render_tool_response(m) -}}
+                    {%- endif -%}
+                {%- endif -%}
+            {%- endfor -%}
+        {%- endfor -%}
+    {%- else -%}
+        {%- for k in range(block_start, ns_blk.end + 1) -%}
+            {{- render_tool_response(messages[k]) -}}
+        {%- endfor -%}
+    {%- endif -%}
+{% endif -%}
+{%- elif m.role == 'system' -%}
+<|system|>{{ visible_text(m.content) }}
+{%- endif -%}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    <|assistant|>{{- '<think>' -}}
+{%- endif -%}

carrier/config.json

@@ -0,0 +1,607 @@
+{
+  "_name_or_path": "",
+  "architectures": [
+    "Glm5NextForConditionalGeneration"
+  ],
+  "chunk_size_feed_forward": 0,
+  "dtype": "bfloat16",
+  "id2label": {
+    "0": "LABEL_0",
+    "1": "LABEL_1"
+  },
+  "image_end_token_id": 154831,
+  "image_start_token_id": 154830,
+  "image_token_id": 154854,
+  "is_encoder_decoder": false,
+  "label2id": {
+    "LABEL_0": 0,
+    "LABEL_1": 1
+  },
+  "model_type": "glm5_next",
+  "output_attentions": false,
+  "output_hidden_states": false,
+  "problem_type": null,
+  "quantization_config": {
+    "config_groups": {
+      "group_mxfp8_mtp_routed_experts": {
+        "input_activations": {
+          "dynamic": true,
+          "group_size": 32,
+          "num_bits": 8,
+          "type": "float"
+        },
+        "targets": [
+          "model.language_model.layers.45.mlp.experts"
+        ],
+        "weights": {
+          "dynamic": false,
+          "group_size": 32,
+          "num_bits": 8,
+          "type": "float",
+          "weight_block_size": [
+            1,
+            32
+          ]
+        }
+      },
+      "group_nvfp4_routed_experts": {
+        "input_activations": {
+          "dynamic": false,
+          "group_size": 16,
+          "num_bits": 4,
+          "type": "float"
+        },
+        "targets": [
+          "model.language_model.layers.3.mlp.experts",
+          "model.language_model.layers.4.mlp.experts",
+          "model.language_model.layers.5.mlp.experts",
+          "model.language_model.layers.6.mlp.experts",
+          "model.language_model.layers.7.mlp.experts",
+          "model.language_model.layers.8.mlp.experts",
+          "model.language_model.layers.9.mlp.experts",
+          "model.language_model.layers.10.mlp.experts",
+          "model.language_model.layers.11.mlp.experts",
+          "model.language_model.layers.12.mlp.experts",
+          "model.language_model.layers.13.mlp.experts",
+          "model.language_model.layers.14.mlp.experts",
+          "model.language_model.layers.15.mlp.experts",
+          "model.language_model.layers.16.mlp.experts",
+          "model.language_model.layers.17.mlp.experts",
+          "model.language_model.layers.18.mlp.experts",
+          "model.language_model.layers.19.mlp.experts",
+          "model.language_model.layers.20.mlp.experts",
+          "model.language_model.layers.21.mlp.experts",
+          "model.language_model.layers.22.mlp.experts",
+          "model.language_model.layers.23.mlp.experts",
+          "model.language_model.layers.24.mlp.experts",
+          "model.language_model.layers.25.mlp.experts",
+          "model.language_model.layers.26.mlp.experts",
+          "model.language_model.layers.27.mlp.experts",
+          "model.language_model.layers.28.mlp.experts",
+          "model.language_model.layers.29.mlp.experts",
+          "model.language_model.layers.30.mlp.experts",
+          "model.language_model.layers.31.mlp.experts",
+          "model.language_model.layers.32.mlp.experts",
+          "model.language_model.layers.33.mlp.experts",
+          "model.language_model.layers.34.mlp.experts",
+          "model.language_model.layers.35.mlp.experts",
+          "model.language_model.layers.36.mlp.experts",
+          "model.language_model.layers.37.mlp.experts",
+          "model.language_model.layers.38.mlp.experts",
+          "model.language_model.layers.39.mlp.experts",
+          "model.language_model.layers.40.mlp.experts",
+          "model.language_model.layers.41.mlp.experts",
+          "model.language_model.layers.42.mlp.experts",
+          "model.language_model.layers.43.mlp.experts",
+          "model.language_model.layers.44.mlp.experts"
+        ],
+        "weights": {
+          "dynamic": false,
+          "group_size": 16,
+          "num_bits": 4,
+          "type": "float"
+        }
+      }
+    },
+    "ignore": [],
+    "producer": {
+      "name": "modelopt",
+      "version": "0.39.0.dev290+gf9d9a71de.d20260407"
+    },
+    "quant_algo": "MIXED_PRECISION",
+    "quant_method": "modelopt",
+    "quantized_layers": {
+      "model.language_model.layers.10.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.11.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.12.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.13.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.14.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.15.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.16.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.17.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.18.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.19.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.20.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.21.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.22.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.23.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.24.mlp.experts": {
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+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.25.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.26.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.27.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.28.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.29.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.3.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.30.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.31.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.32.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.33.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.34.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.35.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.36.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.37.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.38.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.39.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.4.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.40.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.41.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.42.mlp.experts": {
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+        "quant_algo": "NVFP4"
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+        "quant_algo": "MXFP8"
+      },
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+      },
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+      },
+      "model.language_model.layers.7.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      },
+      "model.language_model.layers.8.mlp.experts": {
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+      },
+      "model.language_model.layers.9.mlp.experts": {
+        "group_size": 16,
+        "quant_algo": "NVFP4"
+      }
+    }
+  },
+  "return_dict": true,
+  "text_config": {
+    "_name_or_path": "",
+    "attention_bias": false,
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+      154829
+    ],
+    "first_k_dense_replace": 3,
+    "hc_eps": 1e-06,
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+    "head_dim": 0,
+    "hidden_act": "silu",
+    "hidden_size": 4096,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "index_head_dim": 128,
+    "index_kpool": 4,
+    "index_kpool_always_select_tail": true,
+    "index_kpool_compress": true,
+    "index_n_heads": 32,
+    "index_share_for_mtp_iteration": true,
+    "index_topk": 2048,
+    "indexer_rope_interleave": true,
+    "indexer_types": [
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+    "is_encoder_decoder": false,
+    "kv_lora_rank": 512,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_types": [
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+      "linear_attention",
+      "linear_attention",
+      "deepseek_sparse_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "deepseek_sparse_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "deepseek_sparse_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "deepseek_sparse_attention",
+      "linear_attention",
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+      "linear_attention",
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+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
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+      "linear_attention",
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+      "linear_attention",
+      "deepseek_sparse_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
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+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "deepseek_sparse_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "deepseek_sparse_attention",
+      "linear_attention",
+      "linear_attention",
+      "linear_attention",
+      "deepseek_sparse_attention",
+      "linear_attention"
+    ],
+    "linear_attn_config": {
+      "full_attn_layers": [
+        3,
+        7,
+        11,
+        15,
+        19,
+        23,
+        27,
+        31,
+        35,
+        39,
+        43
+      ],
+      "gate_lower_bound": -5.0,
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+      "kda_layers": [
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+    "max_position_embeddings": 1048576,
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+    "moe_router_dtype": "float32",
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+    "n_routed_experts": 288,
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+    "num_experts_per_tok": 8,
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+    "num_key_value_heads": 64,
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+    "output_attentions": false,
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+    "output_router_logits": false,
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+    "q_lora_rank": 1536,
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+    "qk_nope_head_dim": 256,
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+    "return_dict": true,
+    "rms_norm_eps": 1e-05,
+    "routed_scaling_factor": 2.5,
+    "router_aux_loss_coef": 0.001,
+    "scoring_func": "sigmoid",
+    "swiglu_limit": 10.0,
+    "tie_word_embeddings": false,
+    "topk_group": 1,
+    "topk_method": "noaux_tc",
+    "use_cache": true,
+    "v_head_dim": 256,
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+  },
+  "tie_word_embeddings": false,
+  "transformers_version": "5.16.1",
+  "video_end_token_id": 154833,
+  "video_start_token_id": 154832,
+  "video_token_id": 154855,
+  "vision_config": {
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+    "architectures": null,
+    "attention_bias": true,
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+    "initializer_range": 0.02,
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+    "is_encoder_decoder": false,
+    "label2id": {
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+    "num_heads": 16,
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+    "patch_size": 14,
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+    "rms_norm_eps": 1e-05,
+    "spatial_merge_size": 2,
+    "swiglu_limit": 10.0,
+    "temporal_patch_size": 2
+  }
+}

carrier/generation_config.json

@@ -0,0 +1,79 @@
+{
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+    "max_new_tokens": null,
+    "min_length": null,
+    "min_new_tokens": null,
+    "early_stopping": null,
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+    "stop_strings": null,
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+    "num_beams": null,
+    "use_mtp": null,
+    "use_cache": true,
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+    "bad_words_ids": null,
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+    "remove_invalid_values": null,
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+    "num_return_sequences": null,
+    "output_attentions": false,
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+    "output_scores": null,
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+        154827,
+        154829
+    ],
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+    "decoder_start_token_id": null,
+    "is_assistant": null,
+    "num_assistant_tokens": null,
+    "num_assistant_tokens_schedule": null,
+    "assistant_confidence_threshold": null,
+    "prompt_lookup_num_tokens": null,
+    "max_matching_ngram_size": null,
+    "assistant_early_exit": null,
+    "assistant_lookbehind": null,
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+    "disable_compile": null,
+    "continuous_batching_config": null,
+    "low_memory": null,
+    "penalty_alpha": null,
+    "dola_layers": null,
+    "diversity_penalty": null,
+    "num_beam_groups": null,
+    "constraints": null,
+    "force_words_ids": null,
+    "prefill_chunk_size": null,
+    "_from_model_config": true,
+    "transformers_version": "5.16.1"
+}

carrier/hf_quant_config.json

@@ -0,0 +1,264 @@
+{
+  "config_groups": {
+    "group_mxfp8_mtp_routed_experts": {
+      "input_activations": {
+        "dynamic": true,
+        "group_size": 32,
+        "num_bits": 8,
+        "type": "float"
+      },
+      "targets": [
+        "model.language_model.layers.45.mlp.experts"
+      ],
+      "weights": {
+        "dynamic": false,
+        "group_size": 32,
+        "num_bits": 8,
+        "type": "float",
+        "weight_block_size": [
+          1,
+          32
+        ]
+      }
+    },
+    "group_nvfp4_routed_experts": {
+      "input_activations": {
+        "dynamic": false,
+        "group_size": 16,
+        "num_bits": 4,
+        "type": "float"
+      },
+      "targets": [
+        "model.language_model.layers.3.mlp.experts",
+        "model.language_model.layers.4.mlp.experts",
+        "model.language_model.layers.5.mlp.experts",
+        "model.language_model.layers.6.mlp.experts",
+        "model.language_model.layers.7.mlp.experts",
+        "model.language_model.layers.8.mlp.experts",
+        "model.language_model.layers.9.mlp.experts",
+        "model.language_model.layers.10.mlp.experts",
+        "model.language_model.layers.11.mlp.experts",
+        "model.language_model.layers.12.mlp.experts",
+        "model.language_model.layers.13.mlp.experts",
+        "model.language_model.layers.14.mlp.experts",
+        "model.language_model.layers.15.mlp.experts",
+        "model.language_model.layers.16.mlp.experts",
+        "model.language_model.layers.17.mlp.experts",
+        "model.language_model.layers.18.mlp.experts",
+        "model.language_model.layers.19.mlp.experts",
+        "model.language_model.layers.20.mlp.experts",
+        "model.language_model.layers.21.mlp.experts",
+        "model.language_model.layers.22.mlp.experts",
+        "model.language_model.layers.23.mlp.experts",
+        "model.language_model.layers.24.mlp.experts",
+        "model.language_model.layers.25.mlp.experts",
+        "model.language_model.layers.26.mlp.experts",
+        "model.language_model.layers.27.mlp.experts",
+        "model.language_model.layers.28.mlp.experts",
+        "model.language_model.layers.29.mlp.experts",
+        "model.language_model.layers.30.mlp.experts",
+        "model.language_model.layers.31.mlp.experts",
+        "model.language_model.layers.32.mlp.experts",
+        "model.language_model.layers.33.mlp.experts",
+        "model.language_model.layers.34.mlp.experts",
+        "model.language_model.layers.35.mlp.experts",
+        "model.language_model.layers.36.mlp.experts",
+        "model.language_model.layers.37.mlp.experts",
+        "model.language_model.layers.38.mlp.experts",
+        "model.language_model.layers.39.mlp.experts",
+        "model.language_model.layers.40.mlp.experts",
+        "model.language_model.layers.41.mlp.experts",
+        "model.language_model.layers.42.mlp.experts",
+        "model.language_model.layers.43.mlp.experts",
+        "model.language_model.layers.44.mlp.experts"
+      ],
+      "weights": {
+        "dynamic": false,
+        "group_size": 16,
+        "num_bits": 4,
+        "type": "float"
+      }
+    }
+  },
+  "ignore": [],
+  "producer": {
+    "name": "modelopt",
+    "version": "0.39.0.dev290+gf9d9a71de.d20260407"
+  },
+  "quant_algo": "MIXED_PRECISION",
+  "quant_method": "modelopt",
+  "quantized_layers": {
+    "model.language_model.layers.10.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
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+      "quant_algo": "NVFP4"
+    },
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+      "quant_algo": "NVFP4"
+    },
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+      "quant_algo": "NVFP4"
+    },
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+      "quant_algo": "NVFP4"
+    },
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+      "quant_algo": "NVFP4"
+    },
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+      "quant_algo": "NVFP4"
+    },
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+      "quant_algo": "NVFP4"
+    },
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+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
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+      "quant_algo": "NVFP4"
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+    },
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+      "quant_algo": "NVFP4"
+    },
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+      "quant_algo": "NVFP4"
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+      "quant_algo": "NVFP4"
+    },
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+      "quant_algo": "NVFP4"
+    },
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+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
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+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.32.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.33.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.34.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.35.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.36.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.37.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.38.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.39.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.4.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.40.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.41.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.42.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.43.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.44.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.45.mlp.experts": {
+      "group_size": 32,
+      "quant_algo": "MXFP8"
+    },
+    "model.language_model.layers.5.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.6.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.7.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.8.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    },
+    "model.language_model.layers.9.mlp.experts": {
+      "group_size": 16,
+      "quant_algo": "NVFP4"
+    }
+  }
+}

carrier/model-hf-nonexpert-00001-of-00004.safetensors

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carrier/model-hf-nonexpert-00002-of-00004.safetensors

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carrier/processor_config.json

@@ -0,0 +1,44 @@
+{
+  "image_processor": {
+    "do_rescale": true,
+    "patch_expand_factor": 1,
+    "merge_size": 2,
+    "image_mean": [
+      0.48145466,
+      0.4578275,
+      0.40821073
+    ],
+    "image_std": [
+      0.26862954,
+      0.26130258,
+      0.27577711
+    ],
+    "temporal_patch_size": 2,
+    "patch_size": 14,
+    "min_image_tokens": 16,
+    "max_image_tokens": 8000,
+    "image_processor_type": "Glm5NextImageProcessor"
+  },
+  "video_processor": {
+    "do_rescale": true,
+    "video_processor_type": "Glm5NextVideoProcessor",
+    "patch_expand_factor": 1,
+    "merge_size": 2,
+    "image_mean": [
+      0.48145466,
+      0.4578275,
+      0.40821073
+    ],
+    "image_std": [
+      0.26862954,
+      0.26130258,
+      0.27577711
+    ],
+    "temporal_patch_size": 2,
+    "patch_size": 14,
+    "min_image_tokens": 16,
+    "max_image_tokens": 240000,
+    "fps": 2
+  },
+  "processor_class": "Glm5NextProcessor"
+}

carrier/tokenizer_config.json

@@ -0,0 +1,33 @@
+{
+  "backend": "tokenizers",
+  "clean_up_tokenization_spaces": false,
+  "do_lower_case": false,
+  "eos_token": "<|endoftext|>",
+  "extra_special_tokens": [
+    "<|endoftext|>",
+    "[MASK]",
+    "[gMASK]",
+    "[sMASK]",
+    "<sop>",
+    "<eop>",
+    "<|system|>",
+    "<|user|>",
+    "<|assistant|>",
+    "<|observation|>",
+    "<|begin_of_image|>",
+    "<|end_of_image|>",
+    "<|begin_of_video|>",
+    "<|end_of_video|>",
+    "<|begin_of_audio|>",
+    "<|end_of_audio|>",
+    "<|begin_of_transcription|>",
+    "<|end_of_transcription|>"
+  ],
+  "is_local": true,
+  "model_max_length": 1048576,
+  "model_specific_special_tokens": {},
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "remove_space": false,
+  "tokenizer_class": "TokenizersBackend"
+}

design/transform.json

@@ -0,0 +1 @@
+{"activation":"silu-cap10","boundary":"coupled-h512-h128-suh-svh-v1","cast_order":"luke-qsrt-coupled-reference-v1","fc1_interleave":"slot0-atom32-slot1-atom32-v1","h128":"normalized-sylvester-128-v1","h512":"normalized-sylvester-512-v1","quantized_down_order":"silu-cap10-fp32-sign-h128-fp32-down-suh-fp32-h128-fp32-e4m3-ue8m0-k32","quantized_input_order":"bf16-cvt-rn-fp16-h512-fp32-suh-fp32-h128-fp32-e4m3-ue8m0-k32","sign_draw":0,"sign_generator":"qsrt-coupled-signs-v1","sign_post_axis":2,"sign_pre_axis":1,"tp_slice":"contiguous-atom32-v1","transform_id":"normalized-h512-outer-h128-inner-sign-draw0-v1"}

evidence/encode_layer_v2.py

@@ -0,0 +1,210 @@
+#!/usr/bin/env python3
+"""Codec-v2 encode of one GLM-5.3-Flash routed layer (or an expert range of it). Same runtime format as r27.
+
+Changes versus the r27 encoder (glm53_nvfp4/quantize_p8_coupled_rate_layer.py), and nothing else:
+  1. Hessian: EVERY routed token of the expert in the 64 fit windows (route^2-weighted, exactly as
+     output_aware.route_weighted_hessian), mixed trace-normalised 50/50 (beta) with an all-token Hessian
+     (every 4th token of the 64 fit windows pushed through the expert, unweighted).  r27: first 256 routed tokens.
+  2. GPTQ damping: percdamp = 0.3 x mean diagonal.  r27: 0.01.
+  3. UE8M0 block scales: nearest power of two of the continuous least-squares block scale found with two trellis
+     passes, then the unchanged r27 loop (3 passes, 2 power-of-two refits).  r27: scalar nearest-level rule.
+Transform (coupled H512/H128, sign draw 0), EXL3 suh/svh (read from the r27 sidecars: byte-identical), the
+procedural MCG alpha-2 E4M3 law, trellis packing and UE8M0 packing are exactly r27's; the r27 functions are
+imported read-only. Closure check per projection: the packed payload must decode to the encoder reconstruction.
+"""
+from __future__ import annotations
+
+import argparse
+import hashlib
+import json
+import os
+import time
+from pathlib import Path
+
+import numpy as np
+import torch
+from safetensors import safe_open
+from safetensors.torch import save_file
+
+from glm53_nvfp4 import trellis_mxf as tm
+from glm53_nvfp4 import trellis_nvfp4 as tn
+from glm53_nvfp4.capture import LayerCapture
+from glm53_nvfp4.p8_coupled_scale import (
+    COUPLED_SIGN_DRAW,
+    CoupledScaleSet,
+    coupled_input_carrier,
+    coupled_middle_carrier,
+    encode_coupled_scale_weights,
+)
+from glm53_nvfp4.shard_index import IndexedCheckpoint
+
+CAPTURE = Path(os.environ["CV2_CAPTURE"])
+ROLES = Path(os.environ["CV2_ROLES"])
+SOURCE = Path(os.environ["CV2_BF16"])
+R27 = Path(os.environ["CV2_R27"])
+PROJ = ("gate_proj", "up_proj", "down_proj")
+
+
+def r27_scales(layer: int) -> CoupledScaleSet:
+    """EXL3 suh/svh exactly as stored in the r27 rank sidecars (verified byte-identical to the EXL3 checkpoint)."""
+    suh = svh = None
+    parts = []
+    for r in range(4):
+        with safe_open(str(R27 / "sidecars" / f"p8-layer-{layer:03d}-tp4-rank-{r}.safetensors"), framework="pt",
+                       device="cpu") as s:
+            a, b, inter = s.get_tensor("gate_up_suh_fp16"), s.get_tensor("down_svh_fp16"), s.get_tensor(
+                "intermediate_scales_fp16")
+        if suh is None:
+            suh, svh = a, b
+        elif not (torch.equal(suh, a) and torch.equal(svh, b)):
+            raise RuntimeError("shared suh/svh differ across ranks")
+        n = inter.shape[1] // 3  # coupled_scale_order gate_svh-up_svh-down_suh
+        parts.append((inter[:, :n], inter[:, n:2 * n], inter[:, 2 * n:]))
+    scales = CoupledScaleSet(
+        gate_up_suh=suh.contiguous(), down_svh=svh.contiguous(),
+        gate_svh=torch.cat([p[0] for p in parts], 1).contiguous(),
+        up_svh=torch.cat([p[1] for p in parts], 1).contiguous(),
+        down_suh=torch.cat([p[2] for p in parts], 1).contiguous(),
+        source_path=R27, source_sha256="", source_metadata={}, tensor_hashes={})
+    scales.validate()
+    return scales
+
+
+def routed_rows(cap, windows, expert):
+    rows, weights = [], []
+    for w in windows:
+        ids = np.asarray(cap.topk_ids[w * 2048:(w + 1) * 2048])
+        tok, slot = np.nonzero(ids == expert)
+        if tok.size:
+            rows.append(w * 2048 + tok.astype(np.int64))
+            weights.append(np.asarray(cap.topk_weights[w * 2048:(w + 1) * 2048])[tok, slot].astype(np.float32))
+    rows = np.concatenate(rows)
+    hidden = torch.from_numpy(np.array(cap.hidden_words[rows], copy=True)).view(torch.bfloat16)
+    return hidden, torch.from_numpy(np.concatenate(weights))
+
+
+def hessian_chunks(x_cpu, route_cpu, fn, device, chunk=4096):
+    """route_weighted_hessian (route^2, normalised) accumulated in chunks."""
+    total = None
+    wsum = float(route_cpu.float().square().sum())
+    for s in range(0, x_cpu.shape[0], chunk):
+        x = fn(x_cpu[s:s + chunk].to(device))
+        r = route_cpu[s:s + chunk].to(device).float()
+        xw = x.float() * (r.square() / wsum).sqrt()[:, None]
+        part = xw.t() @ xw
+        total = part if total is None else total + part
+    return total
+
+
+def mix(h_routed, h_all, beta):
+    if beta == 0:
+        return h_routed
+    return ((1 - beta) * h_routed / torch.diagonal(h_routed).mean()
+            + beta * h_all / torch.diagonal(h_all).mean())
+
+
+def refit_cont(weight, rec, scales):
+    blocks = weight.float().reshape(weight.shape[0], -1, 32)
+    q = rec.reshape_as(blocks) / scales[..., None]
+    num = (blocks.double() * q.double()).sum(-1)
+    den = q.double().square().sum(-1)
+    return torch.where(den > 0, (num / den.clamp_min(1e-30)).float(), scales).clamp_min(2.0 ** -126)
+
+
+def encode_v2(weight, hessian, bits, percdamp):
+    lut = tm.state_lut(bits, alphabet="e4m3", law="mcg", compander_scale=2.0, device=weight.device)
+    c_rms = float(lut.view(torch.float8_e4m3fn).float().square().mean().sqrt())
+    kw = dict(bits=bits, block_size=32, tailbite_context=128, percdamp=percdamp, column_block=128)
+    scales = (weight.float().reshape(weight.shape[0], -1, 32).square().mean(-1).sqrt() / c_rms).clamp_min(2.0 ** -126)
+    for _ in range(2):
+        rec, _ = tm._encode_mxf_with_gptq_feedback(weight, hessian, scales, lut, **kw)
+        scales = refit_cont(weight, rec, scales)
+    scales = torch.pow(2.0, torch.round(torch.log2(scales)))
+    for it in range(3):
+        rec, states = tm._encode_mxf_with_gptq_feedback(weight, hessian, scales, lut, **kw)
+        if it < 2:
+            scales = tm._refit_power2_scales(weight, rec.reshape(weight.shape[0], -1, 32) / scales[..., None], scales)
+    trellis = tn.pack_trellis_edges(states, bits)
+    codes = tm.pack_ue8m0(scales)
+    decoded = tm.decode_trellis_mxf(trellis, lut, codes, bits=bits, block_size=32, rows=weight.shape[0],
+                                    width=weight.shape[1], device=weight.device)
+    if not torch.equal(decoded, rec):
+        raise RuntimeError("codec closure failed: packed payload does not decode to the encoder reconstruction")
+    return rec, trellis.cpu().contiguous(), codes.cpu().contiguous()
+
+
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--layer", type=int, required=True)
+    ap.add_argument("--expert-start", type=int, default=0)
+    ap.add_argument("--expert-end", type=int, default=288)
+    ap.add_argument("--bits", type=int, required=True, choices=(3, 4, 5))
+    ap.add_argument("--beta", type=float, default=0.5)
+    ap.add_argument("--percdamp", type=float, default=0.3)
+    ap.add_argument("--out", type=Path, required=True)
+    args = ap.parse_args()
+    if args.out.exists():
+        raise FileExistsError(args.out)
+    device = torch.device("cuda:0")
+    torch.cuda.set_device(device)
+    scales_all = r27_scales(args.layer)
+    cap = LayerCapture(CAPTURE, args.layer, ROLES, max_samples=256, sample_offset=0, data_role="fit",
+                       sampling_strategy="domain-balanced")
+    windows = list(cap.window_indices)
+    assert len(windows) == 64
+    every = np.asarray([w * 2048 + t for w in windows for t in range(0, 2048, 4)], dtype=np.int64)
+    hid_every = torch.from_numpy(np.array(cap.hidden_words[every], copy=True)).view(torch.bfloat16)
+    ones_every = torch.ones(hid_every.shape[0])
+    source = IndexedCheckpoint(SOURCE, SOURCE / "model.safetensors.index.json")
+    tensors, metrics, h_all_in = {}, [], None
+    started = time.time()
+    for expert in range(args.expert_start, args.expert_end):
+        t0 = time.time()
+        base = source.expert_prefix(args.layer, expert)
+        orig = {p: source.get(f"{base}{p}.weight").to(device).float() for p in PROJ}
+        sc = CoupledScaleSet(
+            gate_up_suh=scales_all.gate_up_suh.to(device), gate_svh=scales_all.gate_svh[expert:expert + 1].to(device),
+            up_svh=scales_all.up_svh[expert:expert + 1].to(device),
+            down_suh=scales_all.down_suh[expert:expert + 1].to(device), down_svh=scales_all.down_svh.to(device),
+            source_path=scales_all.source_path, source_sha256="", source_metadata={}, tensor_hashes={})
+        tw = encode_coupled_scale_weights(orig["gate_proj"], orig["up_proj"], orig["down_proj"], sc, expert=0,
+                                          intermediate_draw=COUPLED_SIGN_DRAW)
+        carrier = lambda h: coupled_input_carrier(h, sc.gate_up_suh, quantize=True)  # noqa: E731
+        hid_r, rt_r = routed_rows(cap, windows, expert)
+        if h_all_in is None and args.beta > 0:
+            h_all_in = hessian_chunks(hid_every, ones_every, carrier, device)
+        h_in = mix(hessian_chunks(hid_r, rt_r, carrier, device), h_all_in, args.beta)
+        g_rec, g_tr, g_sc = encode_v2(tw[0], h_in, args.bits, args.percdamp)
+        u_rec, u_tr, u_sc = encode_v2(tw[1], h_in, args.bits, args.percdamp)
+        mid = lambda h: coupled_middle_carrier(carrier(h), g_rec, u_rec, sc, expert=0,  # noqa: E731
+                                               intermediate_draw=COUPLED_SIGN_DRAW, quantize=True)
+        h_dn = hessian_chunks(hid_r, rt_r, mid, device)
+        if args.beta > 0:
+            h_dn = mix(h_dn, hessian_chunks(hid_every, ones_every, mid, device), args.beta)
+        d_rec, d_tr, d_sc = encode_v2(tw[2], h_dn, args.bits, args.percdamp)
+        row = {"expert": expert, "routed_tokens": int(hid_r.shape[0]), "seconds": round(time.time() - t0, 2)}
+        for name, rec, tr, codes, target in (("gate", g_rec, g_tr, g_sc, tw[0]), ("up", u_rec, u_tr, u_sc, tw[1]),
+                                             ("down", d_rec, d_tr, d_sc, tw[2])):
+            tensors[f"{expert}.{name}.trellis"] = tr
+            tensors[f"{expert}.{name}.scale_ue8m0"] = codes
+            row[f"{name}_tw_nmse"] = float((rec - target).double().square().sum() / target.double().square().sum())
+        metrics.append(row)
+        print(json.dumps(row), flush=True)
+        del orig, tw, h_in, h_dn, g_rec, u_rec, d_rec, hid_r, rt_r
+        torch.cuda.empty_cache()
+    meta = {"schema": "trellismx-codec-v2-chunk.v1", "layer": str(args.layer), "bits": str(args.bits),
+            "expert_range": f"{args.expert_start}:{args.expert_end}", "beta": str(args.beta),
+            "percdamp": str(args.percdamp), "encoder": Path(__file__).name,
+            "encoder_sha256": hashlib.sha256(Path(__file__).read_bytes()).hexdigest(),
+            "calibration": "fit role, 64 windows: all routed tokens + every 4th token (all-token Hessian)"}
+    args.out.parent.mkdir(parents=True, exist_ok=True)
+    tmp = args.out.with_suffix(".partial")
+    save_file(tensors, str(tmp), metadata=meta)
+    tmp.rename(args.out)
+    args.out.with_suffix(".metrics.json").write_text(json.dumps({"meta": meta, "rows": metrics,
+                                                                 "seconds": time.time() - started}, indent=1))
+    print(json.dumps({"done": str(args.out), "seconds": round(time.time() - started, 1)}), flush=True)
+
+
+if __name__ == "__main__":
+    main()

evidence/kld_core.py

@@ -0,0 +1,117 @@
+"""Per-token KL divergence kernel.
+
+Direction: KL(teacher || student), in nats:
+
+    d_i = sum_v p_i(v) * (ln p_i(v) - ln q_i(v))
+
+Inputs are FP32 logits from both sides. Both sides are masked to the real
+tokenizer vocabulary (dropping padded ``lm_head`` columns) before the softmax.
+Log-softmax and the per-token vocabulary sum are computed in float64 — the
+protocol's FP32-log-softmax/FP64-accumulate floor, exceeded on the log-softmax
+side so the kernel itself contributes < 1e-12 relative error (validated in
+``tests/test_kld.py`` against an FP64 SciPy reference).
+
+Alignment contract: teacher row r and student row r are the model outputs
+after consuming tokens[0..r], predicting tokens[r+1]. Never shift.
+"""
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+import numpy as np
+import torch
+
+
+@dataclass
+class WindowScores:
+    """Per-scored-position diagnostics for one window."""
+
+    kld: np.ndarray  # float64, KL(teacher||student) per position
+    teacher_entropy: np.ndarray  # float64, H(p)
+    teacher_top1: np.ndarray  # int64 token id
+    student_top1: np.ndarray  # int64 token id
+    teacher_top1_p: np.ndarray  # float64
+    student_top1_p: np.ndarray  # float64
+    realized_token: np.ndarray  # int64, tokens[r+1]
+    teacher_logp_realized: np.ndarray  # float64
+    student_logp_realized: np.ndarray  # float64
+    delta_p_realized: np.ndarray  # float64, q(x) - p(x)
+
+
+def score_window(
+    teacher_logits: np.ndarray,
+    student_logits: np.ndarray,
+    token_ids: np.ndarray,
+    vocab_limit: int,
+    chunk_rows: int = 256,
+) -> WindowScores:
+    """Compute per-token KLD and diagnostics for one aligned window."""
+    if teacher_logits.ndim != 2 or student_logits.ndim != 2:
+        raise ValueError("logits must be rank-2 (positions, vocab)")
+    rows, teacher_vocab = teacher_logits.shape
+    s_rows, student_vocab = student_logits.shape
+    if s_rows != rows:
+        raise ValueError(f"row mismatch: teacher {rows} vs student {s_rows}")
+    if token_ids.ndim != 1 or token_ids.size != rows + 1:
+        raise ValueError(
+            f"token_ids must have {rows + 1} entries for {rows} scored positions"
+        )
+    if vocab_limit <= 0:
+        raise ValueError("vocab_limit must be positive")
+    if student_vocab < vocab_limit:
+        raise ValueError(
+            f"student vocab {student_vocab} narrower than tokenizer vocab "
+            f"{vocab_limit}; refusing (exact KLD impossible)"
+        )
+    if teacher_vocab < vocab_limit:
+        raise ValueError(
+            f"teacher stored vocab {teacher_vocab} narrower than tokenizer "
+            f"vocab {vocab_limit}; refusing"
+        )
+    realized = np.asarray(token_ids[1:], dtype=np.int64)
+    if realized.min() < 0 or realized.max() >= vocab_limit:
+        raise ValueError("realized token id outside tokenizer vocabulary")
+
+    out = WindowScores(
+        kld=np.empty(rows, dtype=np.float64),
+        teacher_entropy=np.empty(rows, dtype=np.float64),
+        teacher_top1=np.empty(rows, dtype=np.int64),
+        student_top1=np.empty(rows, dtype=np.int64),
+        teacher_top1_p=np.empty(rows, dtype=np.float64),
+        student_top1_p=np.empty(rows, dtype=np.float64),
+        realized_token=realized,
+        teacher_logp_realized=np.empty(rows, dtype=np.float64),
+        student_logp_realized=np.empty(rows, dtype=np.float64),
+        delta_p_realized=np.empty(rows, dtype=np.float64),
+    )
+    for start in range(0, rows, chunk_rows):
+        stop = min(start + chunk_rows, rows)
+        t = torch.from_numpy(
+            np.ascontiguousarray(teacher_logits[start:stop, :vocab_limit])
+        ).to(torch.float64)
+        s = torch.from_numpy(
+            np.ascontiguousarray(student_logits[start:stop, :vocab_limit])
+        ).to(torch.float64)
+        t_logp = torch.log_softmax(t, dim=-1)
+        s_logp = torch.log_softmax(s, dim=-1)
+        p = torch.exp(t_logp)
+        kld = torch.sum(p * (t_logp - s_logp), dim=-1)
+        entropy = -torch.sum(p * t_logp, dim=-1)
+        t_top = torch.argmax(t, dim=-1)
+        s_top = torch.argmax(s, dim=-1)
+        idx = torch.arange(stop - start)
+        real = torch.from_numpy(realized[start:stop])
+        out.kld[start:stop] = kld.numpy()
+        out.teacher_entropy[start:stop] = entropy.numpy()
+        out.teacher_top1[start:stop] = t_top.numpy()
+        out.student_top1[start:stop] = s_top.numpy()
+        out.teacher_top1_p[start:stop] = torch.exp(t_logp[idx, t_top]).numpy()
+        out.student_top1_p[start:stop] = torch.exp(s_logp[idx, s_top]).numpy()
+        t_lp_real = t_logp[idx, real]
+        s_lp_real = s_logp[idx, real]
+        out.teacher_logp_realized[start:stop] = t_lp_real.numpy()
+        out.student_logp_realized[start:stop] = s_lp_real.numpy()
+        out.delta_p_realized[start:stop] = (
+            torch.exp(s_lp_real) - torch.exp(t_lp_real)
+        ).numpy()
+    return out

evidence/native-carrier-receipt.json

@@ -0,0 +1,145 @@
+{
+  "repo": "local-inference-lab/GLM-5.3-Flash-NVFP4",
+  "revision": "520de24eabf507659eaef7c70f14fd584527facc",
+  "source_verification": "PASS",
+  "index_header_coverage": "PASS",
+  "indexed_tensors": 39634,
+  "omitted_replaced_routed_tensors": 108864,
+  "bytes": 26863056046,
+  "files": [
+    {
+      "path": "model-hf-nonexpert-00001-of-00004.safetensors",
+      "bytes": 5356357736,
+      "source_sha256": "9f7ede71b2213a6962ea3801015e84edd185ddf00e1b8a0aa9ea6288c28014bf",
+      "sha256": "9f7ede71b2213a6962ea3801015e84edd185ddf00e1b8a0aa9ea6288c28014bf"
+    },
+    {
+      "path": "model-hf-nonexpert-00002-of-00004.safetensors",
+      "bytes": 5318341144,
+      "source_sha256": "b26a6aeebb246e95fcb5d54f0b6f8dd66fdfeb17fe54652ae835af1b7d64f6ef",
+      "sha256": "b26a6aeebb246e95fcb5d54f0b6f8dd66fdfeb17fe54652ae835af1b7d64f6ef"
+    },
+    {
+      "path": "model-hf-nonexpert-00003-of-00004.safetensors",
+      "bytes": 5343414728,
+      "source_sha256": "0d56d129e4bd3d732968e7c5dbf3fc11e3e23566619842832325c44306cc68a5",
+      "sha256": "0d56d129e4bd3d732968e7c5dbf3fc11e3e23566619842832325c44306cc68a5"
+    },
+    {
+      "path": "model-hf-nonexpert-00004-of-00004.safetensors",
+      "bytes": 2951938800,
+      "source_sha256": "43f38b4fe13a7002a5cf2841083caaac5baa65ebf72e7fb4d9c58bf9bff0280a",
+      "sha256": "43f38b4fe13a7002a5cf2841083caaac5baa65ebf72e7fb4d9c58bf9bff0280a"
+    },
+    {
+      "path": "model-mtp-00001-of-00004.safetensors",
+      "bytes": 67133912,
+      "source_sha256": "872508b605ecf1a423bd83d5ef1601d9ff4c4f635c235882839dc5ac8fc5a7be",
+      "sha256": "872508b605ecf1a423bd83d5ef1601d9ff4c4f635c235882839dc5ac8fc5a7be"
+    },
+    {
+      "path": "model-mtp-00002-of-00004.safetensors",
+      "bytes": 2768332568,
+      "source_sha256": "c19aef14bf823752d94cf45f34331ced6ead9d4c140195d4877132330d10cc37",
+      "sha256": "c19aef14bf823752d94cf45f34331ced6ead9d4c140195d4877132330d10cc37"
+    },
+    {
+      "path": "model-mtp-00003-of-00004.safetensors",
+      "bytes": 2768332552,
+      "source_sha256": "7be2f838840af87bc3f503ce9eba1d1ae509272e347704922ccec22e7aca4875",
+      "sha256": "7be2f838840af87bc3f503ce9eba1d1ae509272e347704922ccec22e7aca4875"
+    },
+    {
+      "path": "model-mtp-00004-of-00004.safetensors",
+      "bytes": 2240372552,
+      "source_sha256": "f4dc6b851f6acce39f339e91a8f4ff3763ba7ada3f352f8c28bd3b81d0ba5400",
+      "sha256": "f4dc6b851f6acce39f339e91a8f4ff3763ba7ada3f352f8c28bd3b81d0ba5400"
+    },
+    {
+      "path": "model-inputscales.safetensors",
+      "bytes": 4726664,
+      "source_sha256": "4255779f031450572af8548c610fd9abfe7df89704985d18595c21788593cd05",
+      "sha256": "4255779f031450572af8548c610fd9abfe7df89704985d18595c21788593cd05"
+    },
+    {
+      "path": "model.safetensors.index.json",
+      "bytes": 4280186,
+      "source_sha256": "0d1d9e6b226e76520e182de10d4e7194cc885c5cb1bf885bb90de1916ce312cb",
+      "sha256": "e48b053a820c385ce25b75a778f5e51f00712906b6ab0d72ace241f26f22f67c"
+    },
+    {
+      "path": "config.json",
+      "bytes": 15761,
+      "source_sha256": "676382abd1e90a6c85f0c8f33d45441ecd45fd514fd7b63ce5610e732d8e4996",
+      "sha256": "676382abd1e90a6c85f0c8f33d45441ecd45fd514fd7b63ce5610e732d8e4996"
+    },
+    {
+      "path": "generation_config.json",
+      "bytes": 2233,
+      "source_sha256": "80475d7d0e56cf2729f1e0e60bfec01bda743f3829bc5c42e14f54fb6d52e612",
+      "sha256": "80475d7d0e56cf2729f1e0e60bfec01bda743f3829bc5c42e14f54fb6d52e612"
+    },
+    {
+      "path": "hf_quant_config.json",
+      "bytes": 8052,
+      "source_sha256": "9c084477c9fe496929a15dde8fb795d13b143e32911e613fe7209121b5606ee9",
+      "sha256": "9c084477c9fe496929a15dde8fb795d13b143e32911e613fe7209121b5606ee9"
+    },
+    {
+      "path": "processor_config.json",
+      "bytes": 909,
+      "source_sha256": "aae38374c94b08cc9b0547c6e64f05b951bd9735cea571c6988f5ed552bed3ed",
+      "sha256": "aae38374c94b08cc9b0547c6e64f05b951bd9735cea571c6988f5ed552bed3ed"
+    },
+    {
+      "path": "tokenizer.json",
+      "bytes": 20217442,
+      "source_sha256": "19e773648cb4e65de8660ea6365e10acca112d42a854923df93db4a6f333a82d",
+      "sha256": "19e773648cb4e65de8660ea6365e10acca112d42a854923df93db4a6f333a82d"
+    },
+    {
+      "path": "tokenizer_config.json",
+      "bytes": 761,
+      "source_sha256": "98b1271574f41abf89427ae2dda030d94dc9478f0edc5a8bd240db213c6fd5fc",
+      "sha256": "98b1271574f41abf89427ae2dda030d94dc9478f0edc5a8bd240db213c6fd5fc"
+    },
+    {
+      "path": "chat_template.jinja",
+      "bytes": 10644,
+      "source_sha256": "34d5ee66b12fa6446cdae131c352b8f68cd85369e0e6fda115583805fada3891",
+      "sha256": "34d5ee66b12fa6446cdae131c352b8f68cd85369e0e6fda115583805fada3891"
+    },
+    {
+      "path": "LICENSE",
+      "bytes": 1070,
+      "source_sha256": "30b85b6b9659f2e78aa259f8faf5d920a68dee7c9ced3fa6dba1f19f2bc4fca1",
+      "sha256": "30b85b6b9659f2e78aa259f8faf5d920a68dee7c9ced3fa6dba1f19f2bc4fca1"
+    },
+    {
+      "path": "README.md",
+      "bytes": 7243,
+      "source_sha256": "3f0894b80aefb75c2afbaa0d4fb0f0f0993f14cb9700c6a5077ea6c201637b7b",
+      "sha256": "3f0894b80aefb75c2afbaa0d4fb0f0f0993f14cb9700c6a5077ea6c201637b7b"
+    },
+    {
+      "path": "amax.safetensors",
+      "bytes": 9779904,
+      "source_sha256": "db629a0e7cc2b77ea8a4ec560d326ae94c2af561f4fdbd39d8f611818da43d2b",
+      "sha256": "db629a0e7cc2b77ea8a4ec560d326ae94c2af561f4fdbd39d8f611818da43d2b"
+    },
+    {
+      "path": "amax_checkpoint.safetensors",
+      "bytes": 9780048,
+      "source_sha256": "d3ef0aa55826c7f2a29c65a1d8b32368a26a527cf36f27c1665bd0bec5d34fa1",
+      "sha256": "d3ef0aa55826c7f2a29c65a1d8b32368a26a527cf36f27c1665bd0bec5d34fa1"
+    },
+    {
+      "path": "amax_checkpoint.json",
+      "bytes": 1137,
+      "source_sha256": "e2ab014bc3354ad679c4057c5890e786a45dd6517b61d049a652ac76ec9cba6c",
+      "sha256": "e2ab014bc3354ad679c4057c5890e786a45dd6517b61d049a652ac76ec9cba6c"
+    }
+  ],
+  "layout": "carrier/ under checkpoint root; MODEL_ROOT points to carrier/",
+  "native_execution_tested": false
+}

evidence/prepare-native-carrier.py

@@ -0,0 +1,84 @@
+"""Fetch unchanged native carrier assets and omit replaced routed NVFP4 shards."""
+import hashlib
+import json
+import re
+import struct
+from pathlib import Path
+
+from huggingface_hub import HfApi, snapshot_download
+
+repo = "local-inference-lab/GLM-5.3-Flash-NVFP4"
+revision = "520de24eabf507659eaef7c70f14fd584527facc"
+out = Path("/root/cv2data/out")
+dest = out / "native-carrier"
+names = [
+    *(f"model-hf-nonexpert-{i:05d}-of-00004.safetensors" for i in range(1, 5)),
+    *(f"model-mtp-{i:05d}-of-00004.safetensors" for i in range(1, 5)),
+    "model-inputscales.safetensors", "model.safetensors.index.json",
+    "config.json", "generation_config.json", "hf_quant_config.json",
+    "processor_config.json", "tokenizer.json", "tokenizer_config.json",
+    "chat_template.jinja", "LICENSE", "README.md",
+    "amax.safetensors", "amax_checkpoint.safetensors", "amax_checkpoint.json",
+]
+info = HfApi().model_info(repo, revision=revision, files_metadata=True)
+assert info.sha == revision
+remote = {f.rfilename: f for f in info.siblings}
+assert set(names) <= set(remote)
+snapshot_download(repo, revision=revision, local_dir=dest, allow_patterns=names, max_workers=8)
+
+records = []
+for name in names:
+    f = dest / name
+    entry = remote[name]
+    assert f.stat().st_size == entry.size, name
+    with f.open("rb") as stream:
+        digest = hashlib.file_digest(stream, "sha256").hexdigest()
+    if entry.lfs:
+        assert digest == entry.lfs.sha256, name
+    else:
+        data = f.read_bytes()
+        assert hashlib.sha1(b"blob " + str(len(data)).encode() + b"\0" + data).hexdigest() == entry.blob_id, name
+    records.append({"path": name, "bytes": entry.size, "source_sha256": digest})
+
+index = json.loads((dest / "model.safetensors.index.json").read_text())
+original_map = index["weight_map"]
+selected = {name for name in names if name.startswith("model-") and name.endswith(".safetensors")}
+omitted = {name: file for name, file in original_map.items() if file not in selected}
+routed = re.compile(r"model\.language_model\.layers\.(?:[3-9]|[1-3][0-9]|4[0-4])\.mlp\.experts\.")
+assert omitted and all(routed.match(name) for name in omitted), "Non-routed weights would be lost"
+kept = {name: file for name, file in original_map.items() if file in selected}
+seen = {}
+tensor_bytes = 0
+for name in sorted(selected):
+    with (dest / name).open("rb") as stream:
+        header_size = struct.unpack("<Q", stream.read(8))[0]
+        header = json.loads(stream.read(header_size))
+    for key, tensor in header.items():
+        if key == "__metadata__":
+            continue
+        assert key not in seen
+        seen[key] = name
+        tensor_bytes += tensor["data_offsets"][1] - tensor["data_offsets"][0]
+assert seen == kept, "Selected index and carrier tensor headers differ"
+# Keep the carrier config intact: the runtime selects TrellisMX through the
+# overlay environment variable and replaces the routed NVFP4 allocations.
+index["weight_map"] = kept
+index.setdefault("metadata", {})["total_size"] = tensor_bytes
+(dest / "model.safetensors.index.json").write_text(json.dumps(index, indent=2) + "\n")
+for record in records:
+    f = dest / record["path"]
+    record["bytes"] = f.stat().st_size
+    record["sha256"] = record["source_sha256"]
+    if record["path"] == "model.safetensors.index.json":
+        record["sha256"] = hashlib.sha256(f.read_bytes()).hexdigest()
+receipt = {
+    "repo": repo, "revision": revision, "source_verification": "PASS",
+    "index_header_coverage": "PASS", "indexed_tensors": len(kept),
+    "omitted_replaced_routed_tensors": len(omitted),
+    "bytes": sum(record["bytes"] for record in records), "files": records,
+    "layout": "carrier/ under checkpoint root; MODEL_ROOT points to carrier/",
+    "native_execution_tested": False,
+}
+(out / "native-carrier-receipt.json").write_text(json.dumps(receipt, indent=2) + "\n")
+(out / "status/native-carrier.ready").write_text(revision + "\n")
+print(json.dumps({k: v for k, v in receipt.items() if k != "files"}), flush=True)

evidence/run-kld-reference.py

@@ -0,0 +1,282 @@
+"""Full-model B200 reference KLD; explicitly distinct from native SM120 true-decode KLD."""
+from __future__ import annotations
+
+import argparse
+import dataclasses
+import hashlib
+import json
+import os
+import re
+import sys
+import time
+import types
+from concurrent.futures import ThreadPoolExecutor
+from pathlib import Path
+
+import numpy as np
+import torch
+import torch.nn.functional as F
+from accelerate import dispatch_model, init_empty_weights
+from accelerate.utils import set_module_tensor_to_device
+from safetensors import safe_open
+from transformers import AutoConfig, Glm5NextForConditionalGeneration
+
+import encode_layer_v2 as encoder
+from glm53_nvfp4 import p8_coupled_scale as coupled
+from glm53_nvfp4.p8_layer_rate_damage import RankLayerStreams, decode_expert, state_codebook
+from kld_core import score_window
+
+
+def sha(path):
+    h = hashlib.sha256()
+    with Path(path).open("rb") as f:
+        for part in iter(lambda: f.read(64 << 20), b""):
+            h.update(part)
+    return h.hexdigest()
+
+
+def qdq_rp2(values, global_scale, policy):
+    if global_scale != 1.0 or policy != "amax":
+        raise ValueError("Only the fixed RP2 amax carrier is supported")
+    blocks = values.float().reshape(*values.shape[:-1], values.shape[-1] // 32, 32)
+
+    def term(x):
+        maximum = x.abs().amax(-1, keepdim=True)
+        exponent = torch.ceil(torch.log2((maximum / 448).clamp_min(2.0 ** -127))).clamp(-127, 128)
+        scale = torch.exp2(exponent)
+        return (x / scale).clamp(-448, 448).to(torch.float8_e4m3fn).float() * scale
+
+    hi = term(blocks)
+    return (hi + term(blocks - hi)).reshape_as(values)
+
+
+def experts_forward(self, hidden_states, top_k_index, top_k_weights):
+    final = torch.zeros_like(hidden_states, dtype=torch.float32)
+    # Every expert shares this row-local input transform. Evaluate it once per layer;
+    # selecting rows afterward preserves the reference's casts and arithmetic.
+    fc1_input = coupled.coupled_input_carrier(
+        hidden_states, self._cv2_scales.gate_up_suh, quantize=True)
+    # These are exact decoded physical weights; scales and rotations stay in the forward path.
+    for expert in torch.unique(top_k_index).tolist():
+        token_idx, route_idx = torch.where(top_k_index == expert)
+        gate, up = self.gate_up_proj[expert].chunk(2, dim=0)
+        down_input = coupled.coupled_middle_carrier(
+            fc1_input[token_idx], gate, up, self._cv2_scales,
+            expert=expert, intermediate_draw=coupled.COUPLED_SIGN_DRAW, quantize=True)
+        down = F.linear(down_input.float(), self.down_proj[expert].float()).to(torch.float16).float()
+        route = coupled.block_hadamard(down, block_size=coupled.HADAMARD_INPUT)
+        route = route * self._cv2_scales.down_svh.float()
+        result = coupled.block_hadamard(route, block_size=coupled.HADAMARD_RESIDUAL)
+        final.index_add_(0, token_idx, result * top_k_weights[token_idx, route_idx, None].float())
+    return final.to(hidden_states.dtype)
+
+
+def tr3_experts_forward(self, hidden_states, top_k_index, top_k_weights):
+    final = torch.zeros_like(hidden_states, dtype=torch.float32)
+    for expert in torch.unique(top_k_index).tolist():
+        token_idx, route_idx = torch.where(top_k_index == expert)
+        gate, up = self.gate_up_proj[expert].chunk(2, dim=0)
+        result = coupled.source_expert_reference(hidden_states[token_idx], gate, up, self.down_proj[expert])
+        final.index_add_(0, token_idx, result * top_k_weights[token_idx, route_idx, None].float())
+    return final.to(hidden_states.dtype)
+
+
+def layer_device(layer):
+    # Balance the 42 routed layers 11/11/10/10, leaving decoding/forward headroom.
+    return 0 if layer < 14 else 1 if layer < 25 else 2 if layer < 35 else 3
+
+
+@torch.inference_mode()
+def load_model(source, checkpoint, mode):
+    config = AutoConfig.from_pretrained(source, local_files_only=True)
+    config._attn_implementation = "sdpa"
+    config.text_config._attn_implementation = "sdpa"
+    old_dtype = torch.get_default_dtype()
+    torch.set_default_dtype(torch.bfloat16)
+    try:
+        with init_empty_weights():
+            model = Glm5NextForConditionalGeneration(config)
+    finally:
+        torch.set_default_dtype(old_dtype)
+    wm = json.loads((source / "model.safetensors.index.json").read_text())["weight_map"]
+    # A root entry makes Accelerate move every descendant to GPU0 before the
+    # per-layer hooks attach. Map complete sibling blocks explicitly instead.
+    device_map = {"model.language_model.embed_tokens": 0,
+                  "model.language_model.norm": 3,
+                  "model.language_model.hc_head": 3, "lm_head": 3}
+    for name, _ in model.model.named_children():
+        if name != "language_model":
+            device_map[f"model.{name}"] = 0
+    for layer in range(45):
+        device_map[f"model.language_model.layers.{layer}"] = layer_device(layer)
+
+    def target_device(name):
+        match = re.search(r"language_model\.layers\.(\d+)\.", name)
+        if match:
+            return layer_device(int(match.group(1)))
+        return 3 if name.startswith(("lm_head.", "model.language_model.norm.", "model.language_model.hc_head.")) else 0
+
+    def source_names(name):
+        # Exact GLM5Next renames/concatenation from Transformers 5.17 conversion_mapping.py.
+        if ".self_attn.conv1d.weight" in name:
+            return [name.replace("self_attn.conv1d.weight", f"self_attn.{axis}_conv1d.weight")
+                    for axis in ("q", "k", "v")]
+        name = name.replace("self_attn.forget_gate.", "self_attn.")
+        for hop in ("attn", "ffn"):
+            for part in ("fn", "base", "scale"):
+                name = name.replace(f"{hop}_hc.{part}", f"hc_{hop}_{part}")
+        return [name]
+
+    # Only non-routed tensors are read from BF16. Routed storage is filled directly from the checkpoint.
+    missing = []
+    for name, tensor in list(model.named_parameters()) + list(model.named_buffers()):
+        if ".mlp.experts." in name:
+            continue
+        names = source_names(name)
+        if any(key not in wm for key in names):
+            if tensor.is_meta:
+                missing.append(name)
+            continue
+        values = []
+        for key in names:
+            with safe_open(str(source / wm[key]), framework="pt", device="cpu") as f:
+                values.append(f.get_tensor(key))
+        value = torch.cat(values, dim=0) if len(values) > 1 else values[0]
+        if tuple(value.shape) != tuple(tensor.shape):
+            raise ValueError(f"Non-expert shape mismatch: {name}: {value.shape} vs {tensor.shape}")
+        set_module_tensor_to_device(model, name, target_device(name), value=value, dtype=value.dtype)
+    if missing:
+        raise ValueError(f"Missing source parameters: {missing}")
+
+    tr3_map = None
+    if mode == "tr3":
+        tr3_map = json.loads((checkpoint / "model.safetensors.index.json").read_text())["weight_map"]
+        sys.path.insert(0, str(Path(os.environ["GLM53_KQUANT_ROOT"])))
+        from kquant.exl3_reference import decode_exl3_weight
+        from glm53_nvfp4 import trellis_nvfp4 as tn
+
+    @torch.inference_mode()
+    def load_layers(gpu):
+        torch.cuda.set_device(gpu)
+        device = torch.device(f"cuda:{gpu}")
+        for layer in range(3, 45):
+            if layer_device(layer) != gpu:
+                continue
+            start = time.time()
+            prefix = f"model.language_model.layers.{layer}.mlp.experts"
+            module = model.get_submodule(prefix)
+            for name in ("gate_up_proj", "down_proj"):
+                shape = getattr(module, name).shape
+                value = torch.empty(shape, dtype=torch.float16 if mode == "tr3" else torch.bfloat16, device=device)
+                set_module_tensor_to_device(model, f"{prefix}.{name}", device, value=value, dtype=value.dtype)
+            if mode == "tr3":
+                mcg = tn.procedural_state_values("mcg").float().to(device)
+                def get(name):
+                    with safe_open(str(checkpoint / tr3_map[name]), framework="pt", device="cpu") as f:
+                        return f.get_tensor(name).to(device)
+                for expert in range(288):
+                    for projection in ("gate", "up", "down"):
+                        base = f"model.language_model.layers.{layer}.mlp.experts.{expert}.{projection}_proj"
+                        trellis = get(base + ".trellis")
+                        bits = trellis.shape[-1] // 16
+                        assert bits == 4
+                        states = tn.reconstruct_trellis_states(tn.unpack_trellis_edges(trellis, bits), bits)
+                        weight = decode_exl3_weight(states, get(base + ".suh").float(), get(base + ".svh").float(),
+                                                    codebook_values=mcg, bits=bits).t()
+                        target = (module.down_proj[expert] if projection == "down" else
+                                  module.gate_up_proj[expert, :2048] if projection == "gate" else
+                                  module.gate_up_proj[expert, 2048:])
+                        target.copy_(weight)
+                module.forward = types.MethodType(tr3_experts_forward, module)
+                torch.cuda.synchronize(device)
+                print(json.dumps({"decoded_tr3_layer": layer, "gpu": gpu, "seconds": round(time.time() - start, 2)}), flush=True)
+                continue
+            streams = RankLayerStreams([checkpoint / "sidecars" / f"p8-layer-{layer:03d}-tp4-rank-{r}.safetensors"
+                                        for r in range(4)])
+            lut = state_codebook(streams.bits, device)
+            for expert in range(288):
+                gate, up, down = decode_expert(streams.expert(expert), streams.bits, lut, device)
+                module.gate_up_proj[expert, :2048].copy_(gate)
+                module.gate_up_proj[expert, 2048:].copy_(up)
+                module.down_proj[expert].copy_(down)
+            scales = encoder.r27_scales(layer)
+            module._cv2_scales = dataclasses.replace(scales, **{
+                field.name: getattr(scales, field.name).to(device)
+                for field in dataclasses.fields(scales)
+                if isinstance(getattr(scales, field.name), torch.Tensor)})
+            module.forward = types.MethodType(experts_forward, module)
+            torch.cuda.synchronize(device)
+            print(json.dumps({"decoded_layer": layer, "gpu": gpu, "seconds": round(time.time() - start, 2)}), flush=True)
+    with ThreadPoolExecutor(4) as pool:
+        list(pool.map(load_layers, range(4)))
+    if any(p.is_meta for p in model.parameters()):
+        raise RuntimeError("Unloaded model parameter")
+    if mode == "p8":
+        coupled._qdq_e4m3_k32 = qdq_rp2
+    model = dispatch_model(model, device_map=device_map, force_hooks=True)
+    model.eval()
+    return model
+
+
+@torch.inference_mode()
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--checkpoint", type=Path, required=True)
+    ap.add_argument("--out", type=Path, required=True)
+    ap.add_argument("--mode", choices=("p8", "tr3"), default="p8")
+    args = ap.parse_args()
+    source = Path(os.environ["CV2_BF16"])
+    root = Path(os.environ["CV2_ROOT"])
+    teacher = Path(os.environ["CV2_DATA"]) / "kld-teacher"
+    manifest = json.loads((root / "ops/verified-inputs.json").read_text())
+    args.out.mkdir(parents=True, exist_ok=True)
+    torch.set_num_threads(16)
+    torch.backends.cuda.matmul.allow_tf32 = False
+    model = load_model(source, args.checkpoint, args.mode)
+    rows = []
+    for window in manifest["windows"]:
+        wid = window["id"]
+        summary_path = args.out / f"{wid}.json"
+        if summary_path.exists():
+            rows.append(json.loads(summary_path.read_text()))
+            continue
+        started = time.time()
+        token_path = teacher / f"calibration/panel-v1/arrays/{wid}.tokens.npy"
+        teacher_path = teacher / window["teacher_path"]
+        assert sha(token_path) == window["input_sha256"], wid
+        assert sha(teacher_path) == window["teacher_sha256"], wid
+        tokens = np.load(token_path, allow_pickle=False).reshape(-1).astype(np.int64)
+        assert tokens.shape == (2048,)
+        assert hashlib.sha256(tokens.astype("<i8").tobytes()).hexdigest() == window["token_values_sha256"]
+        inputs = torch.from_numpy(tokens[None]).to("cuda:0")
+        logits = model(input_ids=inputs, use_cache=False).logits[0, :2047, :154880].float().cpu().numpy()
+        with safe_open(str(teacher_path), framework="pt", device="cpu") as f:
+            tensors = [f.get_tensor(key) for key in f.keys() if f.get_slice(key).get_shape() == [2047, 154880]]
+        assert len(tensors) == 1, wid
+        scores = score_window(tensors[0].numpy(), logits, tokens, 154880, chunk_rows=128)
+        np.savez(args.out / f"{wid}.npz", kld=scores.kld)
+        row = {"window": wid, "domain": window["domain"], "mean_kld_rows1_2046": float(scores.kld[1:].mean()),
+               "teacher_sha256": window["teacher_sha256"], "token_values_sha256": window["token_values_sha256"],
+               "seconds": round(time.time() - started, 2)}
+        summary_path.write_text(json.dumps(row, indent=2) + "\n")
+        rows.append(row)
+        print(json.dumps(row), flush=True)
+    report = {"checkpoint": str(args.checkpoint), "windows": len(rows),
+              "mean_kld": float(np.mean([r["mean_kld_rows1_2046"] for r in rows])),
+              "backend": ("Transformers 5.17 GLM5Next BF16 backbone; " +
+                          ("decoded coupled P8 with two-term E4M3 reference carriers" if args.mode == "p8" else
+                           "TR3 FP16 MCG decoded with H128 and suh/svh, FP16 weight storage, FP32 reference expert math")),
+              "mode": args.mode,
+              "execution": "B200 full-window causal forward, no KV quantization; rows 1..2046 scored",
+              "native_sm120_true_decode_equivalence": False,
+              "historical_tr3_fp8_true_decode_kld": 0.030099944949203105,
+              "historical_r27_rp2_fp8_true_decode_kld": 0.03260408965478527,
+              "comparison_limit": "Historical scores use a different backbone, KV and execution path; this result alone does not establish a production TR3 win or runtime speed.",
+              "checkpoint_manifest_sha256": sha(args.checkpoint / ("trellismx-manifest.json" if args.mode == "p8"
+                                                                    else "model.safetensors.index.json"))}
+    (args.out / "REPORT.json").write_text(json.dumps(report, indent=2) + "\n")
+    print(json.dumps(report), flush=True)
+
+
+if __name__ == "__main__":
+    main()

evidence/verified-inputs.json

@@ -0,0 +1,1680 @@
+{
+ "roles_path": "/media/brandonmusic/klcstore/bmxfp4-glm53/roles/roles-codec-conditional-fit32-v1.json",
+ "roles_sha256": "b5d7e4524eb98ddfbd230a5d9a44de0dc5dbeb796c03e859898b5838e4463d14",
+ "teacher_root": "/media/brandonmusic/klcstore/bmxfp4-glm53/teacher",
+ "teacher_revision": "7c378d5f17dba158c4c803eff27c346dd0615660",
+ "teacher_model_revision": "a6c167b62691b2bac901344b65cb651a70f53e43",
+ "metric_sha256": "752af6740595791f300cf47f15ef81f8ca85245d5282fc600ef26ee57eae41e6",
+ "prediction_rows": 2047,
+ "true_decode_rows": 2046,
+ "mask": "rows1..2046 inclusive; exclude row0",
+ "elapsed": 40.282702684402466,
+ "windows": [
+  {
+   "domain": "axis1_general",
+   "id": "conditional-fit-0056",
+   "input_sha256": "21d952a5587f2b985a3db4853d446eabc0d05b13964f441e5d30c4d13e92ad6d",
+   "prediction_positions": 2047,
+   "source_partition": "previously-used-conditional-fit",
+   "teacher_bytes_verified": true,
+   "teacher_path": "logits/full-panel/conditional-fit/conditional-fit-0056.safetensors",
+   "teacher_sha256": "55a70a330f39c5f23294c23d270a1ddc53031def99a42206e2706d4d0bb4466b",
+   "teacher_source_role": "conditional-fit",
+   "token_path": "/home/brandonmusic/KLC_SANDBOXES/glm53-flash-kld-eval/data/teacher/calibration/panel-v1/arrays/conditional-fit-0056.tokens.npy",
+   "token_values_sha256": "219d8cd8873c6cf41f52782297cc5cfa9c4ed9666f2bc30d84cee9caa68ea744"
+  },
+  {
+   "domain": "axis1_general",
+   "id": "conditional-fit-0008",
+   "input_sha256": "c6d580427dea063ddfd06cb599c1369fb9efbf84532d47f55ae72a5cd76ffddb",
+   "prediction_positions": 2047,
+   "source_partition": "previously-used-conditional-fit",
+   "teacher_bytes_verified": true,
+   "teacher_path": "logits/full-panel/conditional-fit/conditional-fit-0008.safetensors",
+   "teacher_sha256": "040641247b2e060035d89c2e7345e029d7c142983585e7c5d63dc818486f650d",
+   "teacher_source_role": "conditional-fit",
+   "token_path": "/home/brandonmusic/KLC_SANDBOXES/glm53-flash-kld-eval/data/teacher/calibration/panel-v1/arrays/conditional-fit-0008.tokens.npy",
+   "token_values_sha256": "581d72e3e78b15c02d9e73f6bc742260cb57cdcd74337f4419c8f90cbc7c9c11"
+  },
+  {
+   "domain": "axis1_general",
+   "id": "conditional-fit-0100",
+   "input_sha256": "918a8bebb0c12c817b7cb4be4b726bf8b39dc8e51a0a3a7ecdc224125898fbbd",
+   "prediction_positions": 2047,
+   "source_partition": "previously-used-conditional-fit",
+   "teacher_bytes_verified": true,
+   "teacher_path": "logits/full-panel/conditional-fit/conditional-fit-0100.safetensors",
+   "teacher_sha256": "f62991383b3ef5e8db0ed71442b17c428972e58da1c868c6aeb0f3fa143c865b",
+   "teacher_source_role": "conditional-fit",
+   "token_path": "/home/brandonmusic/KLC_SANDBOXES/glm53-flash-kld-eval/data/teacher/calibration/panel-v1/arrays/conditional-fit-0100.tokens.npy",
+   "token_values_sha256": "7f008ec99068f373c6e15dd059ee977fb70e6dc832f46e009f0f299d48d19892"
+  },
+  {
+   "domain": "axis1_general",
+   "id": "conditional-fit-0094",
+   "input_sha256": "6be0a582894a1f7ed2bdb55bdf1ed7f515ef867681e9874362fcbd6af73e69a5",
+   "prediction_positions": 2047,
+   "source_partition": "previously-used-conditional-fit",
+   "teacher_bytes_verified": true,
+   "teacher_path": "logits/full-panel/conditional-fit/conditional-fit-0094.safetensors",
+   "teacher_sha256": "656911b8fb34d7f42e984dd21311caf9ef56db0b7618b414cad4089562d93cba",
+   "teacher_source_role": "conditional-fit",
+   "token_path": "/home/brandonmusic/KLC_SANDBOXES/glm53-flash-kld-eval/data/teacher/calibration/panel-v1/arrays/conditional-fit-0094.tokens.npy",
+   "token_values_sha256": "6b6a9b21c49a9984cc8c5ea90e0bda6db5761d039c7cdd335dc65e30444e017b"
+  },
+  {
+   "domain": "axis2_legal",
+   "id": "conditional-fit-0083",
+   "input_sha256": "537f95642b564e9fcd51877acc3a1382b406a4c9e2f165bef6a6bb0aa99addd2",
+   "prediction_positions": 2047,
+   "source_partition": "previously-used-conditional-fit",
+   "teacher_bytes_verified": true,
+   "teacher_path": "logits/full-panel/conditional-fit/conditional-fit-0083.safetensors",
+   "teacher_sha256": "66a7870a1f13c8b5d647c1681fca005bb0f55a6e502e94f282da673a6bed3f1b",
+   "teacher_source_role": "conditional-fit",
+   "token_path": "/home/brandonmusic/KLC_SANDBOXES/glm53-flash-kld-eval/data/teacher/calibration/panel-v1/arrays/conditional-fit-0083.tokens.npy",
+   "token_values_sha256": "3df9d2872815e061ff8d32a9a19da0d11d80ac67070cccd28579b24ec3079552"
+  },
+  {
+   "domain": "axis2_legal",
+   "id": "conditional-fit-0021",
+   "input_sha256": "7e12a220d5d1c3fe51d60e3f57a11e5448524cc893ab70d6b490b9d3e6e36ed9",
+   "prediction_positions": 2047,
+   "source_partition": "previously-used-conditional-fit",
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