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  1. .gitattributes +1 -0
  2. .materialization/shards/model-00001-of-00120.safetensors.json +0 -0
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  36. LICENSE +21 -0
  37. MANIFEST.json +1 -0
  38. README.md +29 -0
  39. SHA256SUMS +328 -0
  40. chat_template.jinja +210 -0
  41. config.json +300 -0
  42. exl3-mcg-storage-abi.json +1 -0
  43. generation_config.json +12 -0
  44. materialization-receipt.json +1 -0
  45. processor_config.json +44 -0
  46. provenance/source-model-revision.json +1 -0
  47. quantization/recipe.json +1 -0
  48. receipts/checkpoint.json +1 -0
  49. runtime/src/quant_pipeline/scoring/blend.py +69 -0
  50. tokenizer_config.json +33 -0
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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LICENSE ADDED
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+ MIT License
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+ Copyright (c) 2026 Z.AI Co., Ltd
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+ Permission is hereby granted, free of charge, to any person obtaining a copy
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+ of this software and associated documentation files (the "Software"), to deal
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+ in the Software without restriction, including without limitation the rights
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+ copies of the Software, and to permit persons to whom the Software is
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+ furnished to do so, subject to the following conditions:
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+ The above copyright notice and this permission notice shall be included in all
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+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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README.md ADDED
@@ -0,0 +1,29 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # GLM-5.3-Flash-EXL3-4bpw
2
+
3
+ Source: `zai-org/GLM-5.3-Flash-BF16@a6c167b62691b2bac901344b65cb651a70f53e43`. All routed experts including MTP45 are uniform four-bit EXL3/TR3 MCG; non-routed tensors retain their official native dtype. The custom TP2 runtime is qualified by actual-runtime BF16-teacher KLD plus byte-identical rank outputs and multi-token generation; its stricter decoded raw-logit parity diagnostic remains failed.
4
+
5
+ Five-cold-run mean teacher-to-student KLD: `0.024554564250` over 51,175 sealed causal positions per run. Actual TP2 runtime qualification-window KLD: `0.022750847878` over 2,047 positions (both gates: mean KLD < 0.06). This checkpoint requires the included custom Transformers TP2 adapter and is not a stock vLLM/ExLlamaV3 compatibility claim.
6
+
7
+ ## Five cold KLD runs
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+
9
+ | Run | Mean teacher-to-student KLD | Positions | Report receipt | Capture receipt |
10
+ |---:|---:|---:|---|---|
11
+ | 1 | 0.024554564249958 | 51,175 | `ef6a8dedc20f11e582658f94923da3e66c2b6cea4ff62d936abb790e376e2461` | `013759025d8414f8811fa140250e2c79097c1082926edd4ae2cfc6751722fc8d` |
12
+ | 2 | 0.024554564249958 | 51,175 | `b7d1cac829f6b21471da4ea724aac479f9db250d4286edd412e099fa747f8257` | `eae08903737bde9f31bf6f8632d2de7b6539f4b1efd8113c5f81461d92aaf671` |
13
+ | 3 | 0.024554564249958 | 51,175 | `663629ccd2bda08a4c299d767b7e6e6d622a81ad6830ad1acf08d0eb8ca1a196` | `000896721ea7116322eb31d8e75718985d29240fabd6a921627bb02c03516bec` |
14
+ | 4 | 0.024554564249958 | 51,175 | `cdb2d8ee4ce795f695f335f0bb3ce7bd135dcf6df4f48c6e3862b40cd1340586` | `7ece4defa651c3693bffd624ad7d07ff85c0dceb7674a7752ec136dea6370c3f` |
15
+ | 5 | 0.024554564249958 | 51,175 | `ac4d6d94aef27b09ca9b2dd513516e793cf5f4afe3d1f2b008a3fb4ed64ae243` | `5b59145332206b4c0fb82f791e2c09be8fadb16d18e6e58818b78e919294cb65` |
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+
17
+ All five accepted executions used the same sealed 25-window panel, so each has 51,175 causal prediction positions. They produced the same tokenwise-KLD SHA-256 and a population standard deviation of zero. The first attempt at the fifth capture received an external SIGTERM before it wrote any logits; it retained only its plan and reader identity and is excluded. The table's run 5 is the clean `run5b` retry, with a distinct cold-execution backend/capture receipt and the same measured KLD as runs 1-4.
18
+
19
+ The direct packed TP2 serving result (`0.022750847878`) is a separate one-window runtime qualification measurement, not a replacement for the five full-panel runs. The raw decoded-logit absolute-error diagnostic remains failed and is disclosed in the receipts; qualification is based on teacher-to-runtime KLD, rank-identical output, complete packed-tensor census, and multi-token generation.
20
+
21
+ Code and the five-run receipts: [brandonmmusic-max/glm-5.3-flash-exl3-4bpw](https://github.com/brandonmmusic-max/glm-5.3-flash-exl3-4bpw).
22
+
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+ ## Minimal TP2 launch
24
+
25
+ Use Transformers 5.16.1, clone ExLlamaV3 at commit `c5d9c657966ffeeaa9353f0cc899f18629da4a13`, compile its CUDA extension, then run:
26
+
27
+ ```bash
28
+ PYTHONPATH=runtime/src torchrun --standalone --nproc-per-node=2 runtime/scripts/run_glm53_custom_tp_runtime.py --model . --exllamav3-source /path/to/exllamav3 --prompt 'Hello'
29
+ ```
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+ 7c83b2785d338dcaff8a5fe3b87b2957dea6ea8c1c31ddb02465b797f4c4647e runtime/scripts/run_glm53_custom_tp_runtime.py
257
+ c1d7fb3aaa98d2baa24064ad19c91336ecbb89b35e446500e06bff9a1b61045e runtime/src/quant_pipeline/__init__.py
258
+ 01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b runtime/src/quant_pipeline/allocation/__init__.py
259
+ 09fa93212c4400f5dba4f47be95a22a52d97c923a0889e2c104e4f54581e0a2e runtime/src/quant_pipeline/allocation/global_dp.py
260
+ b9e11264f116c3ee55d30f4c35080d7e266851b4325742b8e4a58f7e328d5ea7 runtime/src/quant_pipeline/calibration/__init__.py
261
+ 038a40c84ee23ab46e8d7e7aefa92fcfe4008c9d0c7204c006481f9c1ce50ba8 runtime/src/quant_pipeline/calibration/fitter.py
262
+ ccb49f16c91b37dcbd235af18de422a7b54773d32f11a3a958172b984fb6c161 runtime/src/quant_pipeline/calibration/glm53_capture.py
263
+ c91df3cc86363a325a2334291ba6171cf2d5e9cab2d29bb7b09268c0795c2d05 runtime/src/quant_pipeline/calibration/glm53_mtp_capture.py
264
+ 86a6f7dc01961f6117a880c4dc8ce394af6a31ce821aa32d73ffcf091e2d3a73 runtime/src/quant_pipeline/calibration/qwen_capture.py
265
+ 84f3bc4042b36f9002ab49084ad5629c627ab4e2415651d416b9f96c4e1ca0b4 runtime/src/quant_pipeline/calibration/route_mass.py
266
+ 87cab3cc4dce85458d61e93baac28dde9735335ba2c9b84178675b40d313a08c runtime/src/quant_pipeline/calibration/windows.py
267
+ 6757125714ce3d4e6eb60cb73329d9bb6f64cb20ddf86306b4176a7159c0339f runtime/src/quant_pipeline/campaign/__init__.py
268
+ de572b6e6b88f00c183e26889fceaf1e94baed151cf3a372a3a5cfc8fbc663de runtime/src/quant_pipeline/campaign/glm53_direct_k4.py
269
+ fc5b456893454b2fa0dc234e3837a7894f48cda3cd96bcabfa22071e54f7552d runtime/src/quant_pipeline/campaign/glm53_mcg_preparation.py
270
+ 03bd9f5720a9c19e0dc5026fa78068fbc5fc433056a8a43b3e5e5427ff01627c runtime/src/quant_pipeline/campaign/glm53_mtp_k4.py
271
+ 5b196e0892638f76d1a0a6d8cfacff8f7dcff28d10e1ae0c5636532275b34f9e runtime/src/quant_pipeline/campaign/glm53_prepared_backend.py
272
+ f93faa0909667e3a71aab46fcc0fde52a7c724d7803cf68e32404ee0f65e29ec runtime/src/quant_pipeline/campaign/glm53_provenance.py
273
+ a60fbee62ef2faf45b56157c8624a522deb72f6a325450d58c8ab395c4ba7ba5 runtime/src/quant_pipeline/campaign/glm53_uniform_k4.py
274
+ 85d0c3644e380c2ba9017ca199a8caa77b719f4a7df6103e08ba8d3a9db13500 runtime/src/quant_pipeline/campaign/glm_contract.py
275
+ 1e2d5fed6d8ef3b6f354aa572646df37ba03065d9102cb60dfd07b2a84a5fedc runtime/src/quant_pipeline/campaign/qwen_adapter.py
276
+ 70c0a088047164a98d830271e724a5e2ad36647ffaaccdd80111dc47aaf29854 runtime/src/quant_pipeline/campaign/qwen_attribution.py
277
+ e8fd5f74f704b2b486bfe85093198c3b049125faafbe4e8f97ca14cea7babc5c runtime/src/quant_pipeline/campaign/qwen_services.py
278
+ a70323b46d86988bd27b5186d4d0103d1b0d0b755e636def7ffd4c7e2cbf2d5e runtime/src/quant_pipeline/campaign/qwen_work_units.py
279
+ 20e96288b145594e43428a91965f85979ffc7163688d286cd6867ee20dc07ba8 runtime/src/quant_pipeline/campaign/runner.py
280
+ 4c74cc45cdefef59f07265d5996ea4123ba26fe6e69dbc5f54091c0082f47a8b runtime/src/quant_pipeline/candidates/__init__.py
281
+ 96fca4dbe7d85be45052d18c18ed461ad2c2303467c2cbe4b60b425e99813f36 runtime/src/quant_pipeline/candidates/factory_allocation.py
282
+ c5b012837a683106ea21e4e52d5f7853e14eb414f9cc9f37047ebf94e6f9a0c1 runtime/src/quant_pipeline/candidates/factory_calibration.py
283
+ 2d6a1825c23b8149e208db26f302e4c2bf1339e3ec6f51e0784913f15c62b212 runtime/src/quant_pipeline/candidates/factory_union.py
284
+ f313d706df03efb5bd7fd06dd1a88ef749170ada6546240bfb02bced07d47162 runtime/src/quant_pipeline/candidates/ledger.py
285
+ e6450a45cffacc49da22bfdc1eeac70d4a00b884e5525af88a9293b4d46874d6 runtime/src/quant_pipeline/candidates/payload_store.py
286
+ 30366e8c26f27b770fabc6325b833bea97f32087a7e9b48bf0605f5f36ba5a9a runtime/src/quant_pipeline/capacity/__init__.py
287
+ f45ef249c48131005580714888397840400f81e3c6f0caf023382fef1a8ab882 runtime/src/quant_pipeline/capacity/mla.py
288
+ 97f1d9bb510eb7be6aeaac52e627ab645de01f3ff2788045087ce0e876ed9cdc runtime/src/quant_pipeline/checkpoint/__init__.py
289
+ 74cf73cf90a2c8d42e071ab10da343b3e4ae3666c848703287b53706d91adbcb runtime/src/quant_pipeline/checkpoint/btx_qwen.py
290
+ 65fd985494625d2b1e0541ae9106d57a085063f22df1cce2bf0307b63970e47b runtime/src/quant_pipeline/checkpoint/exact_payload.py
291
+ a9aad8da1d228afaf2edd2cd84a6657ac5b53628554e89918484aca20d602d22 runtime/src/quant_pipeline/checkpoint/glm53_mcg_materializer.py
292
+ 19466f3fd531a03f7c43fabafe2ca395981d47cdd1ed818630a5fb80213817b6 runtime/src/quant_pipeline/checkpoint/official_btx.py
293
+ 36f84586d89ae7816172929471a35dc81ec5beff63c47a68cca862b61a1dc4dc runtime/src/quant_pipeline/checkpoint/packed_payload.py
294
+ 4a00fffddd2993998af7c3c660efbfdc4ea0abff96a17d60db969f193b3a60cb runtime/src/quant_pipeline/checkpoint/reference_pack.py
295
+ d75754a774f21054d221b76ca82863da1fc343d409107eb925cf64ec67456fb9 runtime/src/quant_pipeline/cli.py
296
+ 01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b runtime/src/quant_pipeline/codecs/__init__.py
297
+ 13263a584125d0600a580eb63ba7397d4abcb70d8a0be4a3fb328c9ec53741cd runtime/src/quant_pipeline/codecs/exl3_mcg.py
298
+ 5615adfb8e39407a18b6ad6e4942985e14483eae7b4604bf6b85111441e1e652 runtime/src/quant_pipeline/codecs/protocols.py
299
+ a1910785f0bab4f498563f135984f55c68735869203fd8d076ffcd9ced67c5ed runtime/src/quant_pipeline/codecs/uniform.py
300
+ 01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b runtime/src/quant_pipeline/core/__init__.py
301
+ b50023ac37b2c1bf78a1497e243ad8dab11330a45b12c2fd1d99af17470e2087 runtime/src/quant_pipeline/core/artifacts.py
302
+ a85ccafbfb64a7bb4dafd254966fcc5864056b6ed60dd68acee6b6a91a8092ed runtime/src/quant_pipeline/evaluation/__init__.py
303
+ 22d0bb24bd350cfe45f701a5f6e57bd0e6f17666ae8b7538abfc54b1d4845a3d runtime/src/quant_pipeline/evaluation/glm53_logits.py
304
+ 994b211f75411a5b8a0f9242559adfa062c28284e5cf8b215df8dedef8e0583d runtime/src/quant_pipeline/evaluation/glm53_packed_k4_reader.py
305
+ dbe98e31e77e6feb60c95b8f1eaa2825328b28515be6add492fc88e5fa6b9567 runtime/src/quant_pipeline/evaluation/kld_window.py
306
+ 01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b runtime/src/quant_pipeline/models/__init__.py
307
+ 72f151f612f4ed5d4de7f1dd4b2a0f63c3a787ad7d6a0a966cfbc3524cc20525 runtime/src/quant_pipeline/models/hf_capture.py
308
+ c2f8344e2fe62de4a4b95b9e27866877005e268894ea52ca6218cb0ca16a7300 runtime/src/quant_pipeline/models/inventory.py
309
+ 8611f5f6678017e1e32f934ae659106eeed5ac376236450156616a0d56366a96 runtime/src/quant_pipeline/normalization/__init__.py
310
+ eb3dd0ea0a59e16085ffe116d692af6de84f0d6bb8a5482a69290a175c44ea57 runtime/src/quant_pipeline/normalization/absolute_v31.py
311
+ d257d5f5e04ed7c2ec6341050c12015d0038428381d807505966139ba00276bf runtime/src/quant_pipeline/normalization/artifact_v31.py
312
+ d883764ff24fd2de0e7809bde20e3f33d0c8aa1122929b37e4b843912a85a1d5 runtime/src/quant_pipeline/normalization/prior_search.py
313
+ 573ded7f185316ecaf5a581f05b102b56d546e20bc1fd5a4dc73feae9a951da2 runtime/src/quant_pipeline/normalization/streaming_v31.py
314
+ fdf134e41cc7ab16d894259271eab7149131fa7ffdac7676f73c18b6edf6dac3 runtime/src/quant_pipeline/publication/__init__.py
315
+ bc561815be7a34e3f482aa372cc7e6600c3b368813e3fcfac42c400f8b36ac23 runtime/src/quant_pipeline/publication/glm53_hf.py
316
+ 49f9917cb9b88fbeab1d6b88ab75b9d830a4a563d8f9f89490a2e9efc5bfecc2 runtime/src/quant_pipeline/publication/glm53_k4_postmtp.py
317
+ 0bc92706bc208a322b806de849e57b8ac7c39d3c90ed86934de6e69414b0ac07 runtime/src/quant_pipeline/results/__init__.py
318
+ 44fb83ded80dca0e115d9285b2143053339bbc4231f9fd112c739898637bc696 runtime/src/quant_pipeline/results/ledger.py
319
+ c44558f4859c2111808ab02d654214ce02a1a8d7559030cb7d684700d76a50c4 runtime/src/quant_pipeline/runtime/__init__.py
320
+ 11617266954541f4c7337bf90501ec6f540ae3a346c486d90bdbe3380d625e67 runtime/src/quant_pipeline/runtime/glm53_tp2_exl3.py
321
+ 01ba4719c80b6fe911b091a7c05124b64eeece964e09c058ef8f9805daca546b runtime/src/quant_pipeline/scoring/__init__.py
322
+ ff386c0463d135b1b6e3c67f5daa0c16847b206253727edd6a453386ae1d4be3 runtime/src/quant_pipeline/scoring/attribution.py
323
+ 8beb3e84f9f11263bcc2b69bc122cdf09133289cdc992a6d75cef7c4137c5859 runtime/src/quant_pipeline/scoring/blend.py
324
+ a43789dc10a504c8f3b13a9e760e323e443e95c551579a54b6fa1c210ffe0d3b runtime/src/quant_pipeline/scoring/kld.py
325
+ fc2787667aef44bf1705b1ef4bb7bae984782c92ac69ff314ab8bf60f471f80f runtime/src/quant_pipeline/scoring/qwen_experts.py
326
+ 9aed1d7b7aa54f1d65ad01a61d1b50de20fb3c6ec90644a25e49e317628707fd runtime/src/quant_pipeline/spec.py
327
+ 19e773648cb4e65de8660ea6365e10acca112d42a854923df93db4a6f333a82d tokenizer.json
328
+ 98b1271574f41abf89427ae2dda030d94dc9478f0edc5a8bd240db213c6fd5fc tokenizer_config.json
chat_template.jinja ADDED
@@ -0,0 +1,210 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ [gMASK]<sop>
2
+ {%- set effective_reasoning_effort = reasoning_effort if reasoning_effort is defined and reasoning_effort in ['low', 'high'] else 'max' -%}
3
+ {%- if effective_reasoning_effort is not none -%}<|system|>Reasoning Effort: {{ effective_reasoning_effort | capitalize }}{%- endif -%}
4
+ {%- if tools -%}
5
+ {%- macro tool_to_json(tool) -%}
6
+ {%- set ns_tool = namespace(first=true) -%}
7
+ {{ '{' -}}
8
+ {%- for k, v in tool.items() -%}
9
+ {%- if k != 'defer_loading' and k != 'strict' -%}
10
+ {%- if not ns_tool.first -%}{{- ', ' -}}{%- endif -%}
11
+ {%- set ns_tool.first = false -%}
12
+ "{{ k }}": {{ v | tojson(ensure_ascii=False) }}
13
+ {%- endif -%}
14
+ {%- endfor -%}
15
+ {{- '}' -}}
16
+ {%- endmacro -%}
17
+ {%- macro tool_references_to_response(refs) -%}
18
+ {{- '<tool_response><tools>\n' -}}
19
+ {%- for tr in refs -%}
20
+ {%- for tool in tools -%}
21
+ {%- if 'function' in tool -%}
22
+ {%- set tool = tool['function'] -%}
23
+ {%- endif -%}
24
+ {%- if tool.name == tr.name -%}
25
+ {{- tool_to_json(tool) + '\n' -}}
26
+ {%- endif -%}
27
+ {%- endfor -%}
28
+ {%- endfor -%}
29
+ {{- '</tools></tool_response>' -}}
30
+ {%- endmacro -%}
31
+ <|system|>
32
+ # Tools
33
+
34
+ You may call one or more functions to assist with the user query.
35
+
36
+ You are provided with function signatures within <tools></tools> XML tags:
37
+ <tools>
38
+ {% for tool in tools %}
39
+ {%- if 'function' in tool -%}
40
+ {%- set tool = tool['function'] -%}
41
+ {%- endif -%}
42
+ {% if tool.defer_loading is not defined or not tool.defer_loading %}
43
+ {{ tool_to_json(tool) }}
44
+ {% endif %}
45
+ {% endfor %}
46
+ </tools>
47
+
48
+ For each function call, output the function name and arguments within the following XML format:
49
+ <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 -%}
50
+ {%- macro visible_text(content) -%}
51
+ {%- if content is string -%}
52
+ {{- content }}
53
+ {%- elif content is iterable and content is not mapping -%}
54
+ {%- for item in content -%}
55
+ {%- if item is mapping and item.type == 'text' -%}
56
+ {{- item.text }}
57
+ {%- elif item is string -%}
58
+ {{- item }}
59
+ {%- elif item is mapping and item.type in ['image', 'image_url', 'video', 'video_url', 'audio', 'audio_url', 'input_audio'] -%}
60
+ {%- set media_type = item.type | replace('_url', '') | replace('input_', '') -%}
61
+ {{- "<reminder>You are unable to process this " ~ media_type ~ " because you don't have multi-modal input ability. Try different methods.</reminder>" }}
62
+ {%- endif -%}
63
+ {%- endfor -%}
64
+ {%- else -%}
65
+ {{- content }}
66
+ {%- endif -%}
67
+ {%- endmacro -%}
68
+ {%- macro tool_response(text) -%}
69
+ {{- '<tool_response>' + text + '</tool_response>' -}}
70
+ {%- endmacro -%}
71
+ {%- macro render_tool_response(m) -%}
72
+ {%- if m.content is string -%}
73
+ {{- tool_response(m.content) -}}
74
+ {%- elif m.content and m.content is not mapping and m.content.0.type == "tool_reference" -%}
75
+ {{- tool_references_to_response(m.content) -}}
76
+ {%- elif is_list_of_outputs(m) -%}
77
+ {%- for tr in m.content -%}
78
+ {%- 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" -%}
79
+ {{- tool_references_to_response(tr.output) -}}
80
+ {%- else -%}
81
+ {{- tool_response(visible_text(tr.output)) -}}
82
+ {%- endif -%}
83
+ {%- endfor -%}
84
+ {%- else -%}
85
+ {{- tool_response(visible_text(m.content)) -}}
86
+ {%- endif -%}
87
+ {%- endmacro -%}
88
+ {%- macro id_of(obj) -%}
89
+ {%- if obj.tool_call_id -%}
90
+ {{- obj.tool_call_id -}}
91
+ {%- elif obj.id -%}
92
+ {{- obj.id -}}
93
+ {%- endif -%}
94
+ {%- endmacro -%}
95
+ {%- macro is_list_of_outputs(m) -%}
96
+ {%- if m.content and m.content.0.output is defined -%}1{%- endif -%}
97
+ {%- endmacro -%}
98
+ {%- set ns = namespace(last_user_index=-1) -%}
99
+ {%- for m in messages %}
100
+ {%- if m.role == 'user' %}
101
+ {%- set ns.last_user_index = loop.index0 -%}
102
+ {%- endif %}
103
+ {%- endfor %}
104
+ {%- for m in messages -%}
105
+ {%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }}
106
+ {%- elif m.role == 'assistant' -%}
107
+ <|assistant|>
108
+ {%- set content = visible_text(m.content) %}
109
+ {%- if m.reasoning_content is string %}
110
+ {%- set reasoning_content = m.reasoning_content %}
111
+ {%- elif '</think>' in content %}
112
+ {%- set reasoning_content = content.split('</think>')[0].split('<think>')[-1] %}
113
+ {%- set content = content.split('</think>')[-1] %}
114
+ {%- endif %}
115
+ {%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content is defined -%}
116
+ {{ '<think>' + reasoning_content + '</think>'}}
117
+ {%- else -%}
118
+ {{ '<think></think>' }}
119
+ {%- endif -%}
120
+ {%- if content.strip() -%}
121
+ {{ content.strip() }}
122
+ {%- endif -%}
123
+ {% if m.tool_calls %}
124
+ {% for tc in m.tool_calls %}
125
+ {%- if tc.function %}
126
+ {%- set tc = tc.function %}
127
+ {%- endif %}
128
+ {{- '<tool_call>' + tc.name -}}
129
+ {% 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 %}
130
+ {% endif %}
131
+ {%- elif m.role == 'tool' -%}
132
+ {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
133
+ {{- '<|observation|>' -}}
134
+ {%- set block_start = loop.index0 -%}
135
+ {%- set ns_blk = namespace(end=block_start) -%}
136
+ {%- for j in range(block_start, messages|length) -%}
137
+ {%- if messages[j].role == 'tool' -%}
138
+ {%- set ns_blk.end = j -%}
139
+ {%- else -%}
140
+ {%- break -%}
141
+ {%- endif -%}
142
+ {%- endfor -%}
143
+ {%- set ns_a = namespace(tool_calls=none) -%}
144
+ {%- if block_start > 0 and messages[block_start - 1].role == 'assistant' and messages[block_start - 1].tool_calls -%}
145
+ {%- set ns_a.tool_calls = messages[block_start - 1].tool_calls -%}
146
+ {%- endif -%}
147
+ {%- set ns_chk = namespace(can_sort=true) -%}
148
+ {%- if not ns_a.tool_calls -%}
149
+ {%- set ns_chk.can_sort = false -%}
150
+ {%- else -%}
151
+ {%- for k in range(block_start, ns_blk.end + 1) -%}
152
+ {%- set m = messages[k] -%}
153
+ {%- if is_list_of_outputs(m) -%}
154
+ {%- for entry in m.content -%}
155
+ {%- set eid = id_of(entry) -%}
156
+ {%- if not eid -%}
157
+ {%- set ns_chk.can_sort = false -%}
158
+ {%- endif -%}
159
+ {%- endfor -%}
160
+ {%- else -%}
161
+ {%- set tk_id = id_of(m) -%}
162
+ {%- if not tk_id -%}
163
+ {%- set ns_chk.can_sort = false -%}
164
+ {%- endif -%}
165
+ {%- endif -%}
166
+ {%- endfor -%}
167
+ {%- for tc in ns_a.tool_calls -%}
168
+ {%- set tc_id = id_of(tc) -%}
169
+ {%- if not tc_id -%}
170
+ {%- set ns_chk.can_sort = false -%}
171
+ {%- endif -%}
172
+ {%- endfor -%}
173
+ {%- endif -%}
174
+ {%- if ns_chk.can_sort -%}
175
+ {%- for tc in ns_a.tool_calls -%}
176
+ {%- set tc_id = id_of(tc) -%}
177
+ {%- for k in range(block_start, ns_blk.end + 1) -%}
178
+ {%- set m = messages[k] -%}
179
+ {%- if is_list_of_outputs(m) -%}
180
+ {%- for entry in m.content -%}
181
+ {%- set eid = id_of(entry) -%}
182
+ {%- if eid == tc_id -%}
183
+ {%- 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" -%}
184
+ {{- tool_references_to_response(entry.output) -}}
185
+ {%- else -%}
186
+ {{- tool_response(visible_text(entry.output)) -}}
187
+ {%- endif -%}
188
+ {%- endif -%}
189
+ {%- endfor -%}
190
+ {%- else -%}
191
+ {%- set tk_id = id_of(m) -%}
192
+ {%- if tk_id == tc_id -%}
193
+ {{- render_tool_response(m) -}}
194
+ {%- endif -%}
195
+ {%- endif -%}
196
+ {%- endfor -%}
197
+ {%- endfor -%}
198
+ {%- else -%}
199
+ {%- for k in range(block_start, ns_blk.end + 1) -%}
200
+ {{- render_tool_response(messages[k]) -}}
201
+ {%- endfor -%}
202
+ {%- endif -%}
203
+ {% endif -%}
204
+ {%- elif m.role == 'system' -%}
205
+ <|system|>{{ visible_text(m.content) }}
206
+ {%- endif -%}
207
+ {%- endfor -%}
208
+ {%- if add_generation_prompt -%}
209
+ <|assistant|>{{- '<think>' -}}
210
+ {%- endif -%}
config.json ADDED
@@ -0,0 +1,300 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "architectures": [
3
+ "Glm5NextForConditionalGeneration"
4
+ ],
5
+ "image_end_token_id": 154831,
6
+ "image_start_token_id": 154830,
7
+ "image_token_id": 154854,
8
+ "model_type": "glm5_next",
9
+ "quantization_config": {
10
+ "bits": 4,
11
+ "codebook": "mcg",
12
+ "head_bits": 16,
13
+ "non_routed_dtype_policy": "official_source_native",
14
+ "quant_method": "exl3",
15
+ "scope": "glm53_routed_experts_only",
16
+ "serving_reader_qualified": false,
17
+ "version": "0.0.43"
18
+ },
19
+ "text_config": {
20
+ "attention_bias": false,
21
+ "attention_dropout": 0.0,
22
+ "dtype": "bfloat16",
23
+ "eos_token_id": [
24
+ 154820,
25
+ 154827,
26
+ 154829
27
+ ],
28
+ "first_k_dense_replace": 3,
29
+ "hc_eps": 1e-06,
30
+ "hc_mult": 4,
31
+ "hc_sinkhorn_iters": 20,
32
+ "head_dim": 0,
33
+ "hidden_act": "silu",
34
+ "hidden_size": 4096,
35
+ "index_head_dim": 128,
36
+ "index_kpool": 4,
37
+ "index_kpool_always_select_tail": true,
38
+ "index_kpool_compress": true,
39
+ "index_n_heads": 32,
40
+ "index_share_for_mtp_iteration": true,
41
+ "index_topk": 2048,
42
+ "indexer_rope_interleave": true,
43
+ "indexer_types": [
44
+ "full",
45
+ "full",
46
+ "full",
47
+ "full",
48
+ "full",
49
+ "full",
50
+ "full",
51
+ "full",
52
+ "full",
53
+ "full",
54
+ "full",
55
+ "full",
56
+ "full",
57
+ "full",
58
+ "full",
59
+ "full",
60
+ "full",
61
+ "full",
62
+ "full",
63
+ "full",
64
+ "full",
65
+ "full",
66
+ "full",
67
+ "full",
68
+ "full",
69
+ "full",
70
+ "full",
71
+ "full",
72
+ "full",
73
+ "full",
74
+ "full",
75
+ "full",
76
+ "full",
77
+ "full",
78
+ "full",
79
+ "full",
80
+ "full",
81
+ "full",
82
+ "full",
83
+ "full",
84
+ "full",
85
+ "full",
86
+ "full",
87
+ "full",
88
+ "full"
89
+ ],
90
+ "initializer_range": 0.02,
91
+ "intermediate_size": 12288,
92
+ "kv_lora_rank": 512,
93
+ "layer_types": [
94
+ "linear_attention",
95
+ "linear_attention",
96
+ "linear_attention",
97
+ "deepseek_sparse_attention",
98
+ "linear_attention",
99
+ "linear_attention",
100
+ "linear_attention",
101
+ "deepseek_sparse_attention",
102
+ "linear_attention",
103
+ "linear_attention",
104
+ "linear_attention",
105
+ "deepseek_sparse_attention",
106
+ "linear_attention",
107
+ "linear_attention",
108
+ "linear_attention",
109
+ "deepseek_sparse_attention",
110
+ "linear_attention",
111
+ "linear_attention",
112
+ "linear_attention",
113
+ "deepseek_sparse_attention",
114
+ "linear_attention",
115
+ "linear_attention",
116
+ "linear_attention",
117
+ "deepseek_sparse_attention",
118
+ "linear_attention",
119
+ "linear_attention",
120
+ "linear_attention",
121
+ "deepseek_sparse_attention",
122
+ "linear_attention",
123
+ "linear_attention",
124
+ "linear_attention",
125
+ "deepseek_sparse_attention",
126
+ "linear_attention",
127
+ "linear_attention",
128
+ "linear_attention",
129
+ "deepseek_sparse_attention",
130
+ "linear_attention",
131
+ "linear_attention",
132
+ "linear_attention",
133
+ "deepseek_sparse_attention",
134
+ "linear_attention",
135
+ "linear_attention",
136
+ "linear_attention",
137
+ "deepseek_sparse_attention",
138
+ "linear_attention"
139
+ ],
140
+ "linear_attn_config": {
141
+ "full_attn_layers": [
142
+ 3,
143
+ 7,
144
+ 11,
145
+ 15,
146
+ 19,
147
+ 23,
148
+ 27,
149
+ 31,
150
+ 35,
151
+ 39,
152
+ 43
153
+ ],
154
+ "gate_lower_bound": -5.0,
155
+ "head_dim": 128,
156
+ "kda_layers": [
157
+ 0,
158
+ 1,
159
+ 2,
160
+ 4,
161
+ 5,
162
+ 6,
163
+ 8,
164
+ 9,
165
+ 10,
166
+ 12,
167
+ 13,
168
+ 14,
169
+ 16,
170
+ 17,
171
+ 18,
172
+ 20,
173
+ 21,
174
+ 22,
175
+ 24,
176
+ 25,
177
+ 26,
178
+ 28,
179
+ 29,
180
+ 30,
181
+ 32,
182
+ 33,
183
+ 34,
184
+ 36,
185
+ 37,
186
+ 38,
187
+ 40,
188
+ 41,
189
+ 42,
190
+ 44
191
+ ],
192
+ "num_heads": 64,
193
+ "short_conv_kernel_size": 4
194
+ },
195
+ "max_position_embeddings": 1048576,
196
+ "mhc": true,
197
+ "mla_use_nope": true,
198
+ "mlp_layer_types": [
199
+ "dense",
200
+ "dense",
201
+ "dense",
202
+ "sparse",
203
+ "sparse",
204
+ "sparse",
205
+ "sparse",
206
+ "sparse",
207
+ "sparse",
208
+ "sparse",
209
+ "sparse",
210
+ "sparse",
211
+ "sparse",
212
+ "sparse",
213
+ "sparse",
214
+ "sparse",
215
+ "sparse",
216
+ "sparse",
217
+ "sparse",
218
+ "sparse",
219
+ "sparse",
220
+ "sparse",
221
+ "sparse",
222
+ "sparse",
223
+ "sparse",
224
+ "sparse",
225
+ "sparse",
226
+ "sparse",
227
+ "sparse",
228
+ "sparse",
229
+ "sparse",
230
+ "sparse",
231
+ "sparse",
232
+ "sparse",
233
+ "sparse",
234
+ "sparse",
235
+ "sparse",
236
+ "sparse",
237
+ "sparse",
238
+ "sparse",
239
+ "sparse",
240
+ "sparse",
241
+ "sparse",
242
+ "sparse",
243
+ "sparse"
244
+ ],
245
+ "model_type": "glm5_next_text",
246
+ "moe_intermediate_size": 2048,
247
+ "moe_router_dtype": "float32",
248
+ "n_group": 1,
249
+ "n_routed_experts": 288,
250
+ "n_shared_experts": 1,
251
+ "norm_topk_prob": true,
252
+ "num_attention_heads": 64,
253
+ "num_experts_per_tok": 8,
254
+ "num_hidden_layers": 45,
255
+ "num_key_value_heads": 64,
256
+ "num_nextn_predict_layers": 1,
257
+ "output_router_logits": false,
258
+ "pad_token_id": 154820,
259
+ "q_lora_rank": 1536,
260
+ "qk_head_dim": 256,
261
+ "qk_nope_head_dim": 256,
262
+ "qk_rope_head_dim": 0,
263
+ "rms_norm_eps": 1e-05,
264
+ "routed_scaling_factor": 2.5,
265
+ "router_aux_loss_coef": 0.001,
266
+ "scoring_func": "sigmoid",
267
+ "swiglu_limit": 10.0,
268
+ "tie_word_embeddings": false,
269
+ "topk_group": 1,
270
+ "topk_method": "noaux_tc",
271
+ "use_cache": true,
272
+ "v_head_dim": 256,
273
+ "vocab_size": 154880
274
+ },
275
+ "tie_word_embeddings": false,
276
+ "transformers_version": "5.16.0",
277
+ "video_end_token_id": 154833,
278
+ "video_start_token_id": 154832,
279
+ "video_token_id": 154855,
280
+ "vision_config": {
281
+ "attention_bias": true,
282
+ "attention_dropout": 0.0,
283
+ "depth": 24,
284
+ "hidden_act": "silu",
285
+ "hidden_size": 1024,
286
+ "image_size": 448,
287
+ "in_channels": 3,
288
+ "initializer_range": 0.02,
289
+ "intermediate_size": 4096,
290
+ "model_type": "glm5_next_vision",
291
+ "num_heads": 16,
292
+ "out_hidden_size": 4096,
293
+ "patch_size": 14,
294
+ "projection_intermediate_size": 10240,
295
+ "rms_norm_eps": 1e-05,
296
+ "spatial_merge_size": 2,
297
+ "swiglu_limit": 10.0,
298
+ "temporal_patch_size": 2
299
+ }
300
+ }
exl3-mcg-storage-abi.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"bits":4,"codec_family":"exl3-mcg","exllamav3":{"git_commit":"c5d9c657966ffeeaa9353f0cc899f18629da4a13","linear_storage_group":[["su","suh"],["sv","svh"],"trellis"],"module_key_rule":"official_weight_name_without_.weight","version":"0.0.43","written_suffixes":["trellis","suh","svh","mcg"]},"mcg_multiplier_hex":"0xCBAC1FED","output_tensor_count":150226,"output_tensor_names_sha256":"fef5367167148f0498c01da23584a68ab687dc9665668fc6f8c2f87e725b46eb","packed_reader_abi_sha256":"1990dbffd78f0866a8e75011c8276a55b784d2fe1886d1e6271d2173ed4f5e3d","plan_sha256":"a359003aea48137bdec97a0de50b5c9a31475a25d636ee7ca830e315a755f667","qualified_tp_sizes":[],"reason":"ExLlamaV3 v0.0.43 has no audited GLM-5.3 TP model load/inference receipt","receipt_sha256":"61a1becf7dfe3a9a0ab1579ed2b8157031cb74ae50d83762baedd93ce46b30c0","schema":"quant-pipeline.glm53-exl3-mcg-storage-abi.v1","serving_reader_qualified":false,"storage_checkpoint_verified":true}
generation_config.json ADDED
@@ -0,0 +1,12 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "_from_model_config": true,
3
+ "eos_token_id": [
4
+ 154820,
5
+ 154827,
6
+ 154829
7
+ ],
8
+ "pad_token_id": 154820,
9
+ "temperature": 1.0,
10
+ "top_p": 0.95,
11
+ "transformers_version": "5.16.0"
12
+ }
materialization-receipt.json ADDED
@@ -0,0 +1 @@
 
 
1
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processor_config.json ADDED
@@ -0,0 +1,44 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "image_processor": {
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+ "do_rescale": true,
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+ "patch_expand_factor": 1,
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+ "merge_size": 2,
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+ "image_mean": [
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+ 0.48145466,
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+ 0.4578275,
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+ 0.40821073
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+ ],
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+ "image_std": [
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+ 0.26862954,
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+ 0.26130258,
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+ 0.27577711
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+ ],
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+ "temporal_patch_size": 2,
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+ "patch_size": 14,
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+ "min_image_tokens": 16,
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+ "max_image_tokens": 8000,
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+ "image_processor_type": "Glm5NextImageProcessor"
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+ },
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+ "video_processor": {
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+ "do_rescale": true,
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+ "video_processor_type": "Glm5NextVideoProcessor",
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+ "patch_expand_factor": 1,
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+ "merge_size": 2,
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+ "image_mean": [
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+ 0.48145466,
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+ 0.4578275,
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+ 0.40821073
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+ ],
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+ "image_std": [
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+ 0.26862954,
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+ 0.26130258,
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+ 0.27577711
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+ ],
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+ "temporal_patch_size": 2,
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+ "patch_size": 14,
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+ "min_image_tokens": 16,
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+ "max_image_tokens": 240000,
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+ "fps": 2
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+ },
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+ "processor_class": "Glm5NextProcessor"
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+ }
provenance/source-model-revision.json ADDED
@@ -0,0 +1 @@
 
 
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+ {"example_only":false,"inventory_sha256":"f56e9d6250e2d108f8307322f033e53c0ff26d5b2688ebf12b891c26439fea44","model_id":"zai-org/GLM-5.3-Flash-BF16","receipt_sha256":"bba6e387dd056f69c144dd0fc6c61ff44cf0d447bed3ffe88091e11390a626dd","revision":"a6c167b62691b2bac901344b65cb651a70f53e43","schema":"quant-pipeline.glm53-source-model.v1","weight_dtype":"bfloat16"}
quantization/recipe.json ADDED
@@ -0,0 +1 @@
 
 
1
+ {"candidate_rate_grid_invoked":false,"codec":"EXL3/TR3 MCG","example_only":false,"global_allocator_invoked":false,"nonrouted_policy":"native","profile":"k4","routed_expert_bits":4,"schema":"quant-pipeline.glm53-uniform-quant-recipe.v1","source_model_id":"zai-org/GLM-5.3-Flash-BF16","source_revision":"a6c167b62691b2bac901344b65cb651a70f53e43","target_tensor_parallel":2,"tensor_policy":"uniform-routed-experts"}
receipts/checkpoint.json ADDED
@@ -0,0 +1 @@
 
 
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+ {"example_only":false,"five_run_kld_receipt_sha256":"df3d2388967f3ff578bd5c92e11875c4198d0337c25aa98eeb328271c0034ba3","materialization_receipt_sha256":"092be1ffa8db66bf02d4c370d0433a57aa48d4a6e5ce89723ef6a3bb7ca32643","native_copy_receipt_sha256":"fede3330cb1c8635df9174435a9de492f2d6a7416e258910d40978954107bb51","packed_kld_receipt_sha256":"ce48cf6d3adc69da40d06422789f98a61c400bb9cda726c64e0851df792bedd6","profile":"k4","qualified":true,"receipt_sha256":"1120467d01f99cd890c055ed74460a955e32c174b5ece7fadb495eb5d142158d","schema":"quant-pipeline.glm53-k4-publication-checkpoint-gate.v1","source_revision":"a6c167b62691b2bac901344b65cb651a70f53e43","tp2_runtime_receipt_sha256":"a4631352b8fe705e9c6106ea51bb08325be6019c186e7d148306c1df7aab3951"}
runtime/src/quant_pipeline/scoring/blend.py ADDED
@@ -0,0 +1,69 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ from __future__ import annotations
2
+
3
+ from collections.abc import Callable, Sequence
4
+
5
+
6
+ class BlendedModule:
7
+ """Factory for a torch module that blends BF16 and candidate outputs."""
8
+
9
+ @staticmethod
10
+ def wrap(source, candidate, alpha):
11
+ import torch
12
+
13
+ class _Blend(torch.nn.Module):
14
+ def __init__(self):
15
+ super().__init__()
16
+ self.source = source
17
+ self.candidate = candidate
18
+ self.alpha = alpha
19
+
20
+ def forward(self, *args, **kwargs):
21
+ source_output = self.source(*args, **kwargs)
22
+ candidate_output = self.candidate(*args, **kwargs)
23
+ if not isinstance(source_output, torch.Tensor) or not isinstance(candidate_output, torch.Tensor):
24
+ raise TypeError("blended modules must return a tensor")
25
+ blend = self.alpha.to(device=source_output.device, dtype=source_output.dtype)
26
+ return source_output + blend * (candidate_output - source_output)
27
+
28
+ return _Blend()
29
+
30
+
31
+ def module_path_attribution(
32
+ model,
33
+ module_pairs: Sequence[tuple[object, str, object]],
34
+ loss_for_batch: Callable[[object], object],
35
+ batches: Sequence[object],
36
+ path_nodes: int,
37
+ ):
38
+ """Run simultaneous layer/module Aumann-Shapley attribution.
39
+
40
+ module_pairs contains (parent_module, attribute_name, candidate_module).
41
+ The source module is restored even on failure. Parameters should be frozen;
42
+ only one scalar alpha per unit receives gradients.
43
+ """
44
+ import numpy as np
45
+ import torch
46
+
47
+ nodes, quadrature = np.polynomial.legendre.leggauss(path_nodes)
48
+ nodes = (nodes + 1.0) / 2.0
49
+ quadrature = quadrature / 2.0
50
+ alphas = [torch.nn.Parameter(torch.tensor(0.0, device=next(model.parameters()).device)) for _ in module_pairs]
51
+ sources = []
52
+ for (parent, name, candidate), alpha in zip(module_pairs, alphas, strict=True):
53
+ source = getattr(parent, name)
54
+ sources.append(source)
55
+ setattr(parent, name, BlendedModule.wrap(source, candidate, alpha))
56
+ attribution = torch.zeros(len(alphas), dtype=torch.float64)
57
+ try:
58
+ for node, weight in zip(nodes, quadrature, strict=True):
59
+ for alpha in alphas:
60
+ alpha.data.fill_(float(node))
61
+ alpha.grad = None
62
+ for batch in batches:
63
+ loss = loss_for_batch(batch)
64
+ gradients = torch.autograd.grad(loss, alphas)
65
+ attribution += float(weight) * torch.tensor([float(g.detach().cpu()) for g in gradients], dtype=torch.float64) / len(batches)
66
+ finally:
67
+ for (parent, name, _), source in zip(module_pairs, sources, strict=True):
68
+ setattr(parent, name, source)
69
+ return attribution.numpy()
tokenizer_config.json ADDED
@@ -0,0 +1,33 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ {
2
+ "backend": "tokenizers",
3
+ "clean_up_tokenization_spaces": false,
4
+ "do_lower_case": false,
5
+ "eos_token": "<|endoftext|>",
6
+ "extra_special_tokens": [
7
+ "<|endoftext|>",
8
+ "[MASK]",
9
+ "[gMASK]",
10
+ "[sMASK]",
11
+ "<sop>",
12
+ "<eop>",
13
+ "<|system|>",
14
+ "<|user|>",
15
+ "<|assistant|>",
16
+ "<|observation|>",
17
+ "<|begin_of_image|>",
18
+ "<|end_of_image|>",
19
+ "<|begin_of_video|>",
20
+ "<|end_of_video|>",
21
+ "<|begin_of_audio|>",
22
+ "<|end_of_audio|>",
23
+ "<|begin_of_transcription|>",
24
+ "<|end_of_transcription|>"
25
+ ],
26
+ "is_local": true,
27
+ "model_max_length": 1048576,
28
+ "model_specific_special_tokens": {},
29
+ "pad_token": "<|endoftext|>",
30
+ "padding_side": "left",
31
+ "remove_space": false,
32
+ "tokenizer_class": "TokenizersBackend"
33
+ }