Publish Qwen3.6-35B-A3B-UD-Q4_K_XL full-v1 package (batch 1/30)

Publish full-v1 Mesh package artifacts for Qwen3.6-35B-A3B-UD-Q4_K_XL (unsloth/Qwen3.6-35B-A3B-GGUF:UD-Q4_K_XL)

Upload batch 1/30.

.gitattributes

@@ -58,3 +58,5 @@ variants/Q4_K_XL/experts/expert-020.gguf filter=lfs diff=lfs merge=lfs -text
 variants/Q4_K_XL/experts/expert-021.gguf filter=lfs diff=lfs merge=lfs -text
 variants/Q4_K_XL/experts/expert-022.gguf filter=lfs diff=lfs merge=lfs -text
 variants/Q4_K_XL/experts/expert-023.gguf filter=lfs diff=lfs merge=lfs -text
+variants/Q4_K_XL/experts/expert-024.gguf filter=lfs diff=lfs merge=lfs -text
+variants/Q4_K_XL/experts/expert-025.gguf filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,51 @@
+---
+license: apache-2.0
+base_model:
+- unsloth/Qwen3.6-35B-A3B-GGUF
+pipeline_tag: image-text-to-text
+library_name: mesh-llm
+tags:
+- distributed-inference
+- gguf
+- mesh-llm
+- moe
+- qwen
+- qwen3_5_moe
+- topology-independent
+- unsloth
+---
+
+# Mesh-LLM MoE Package
+
+This repository stores Mesh-LLM topology-independent MoE package artifacts derived from `unsloth/Qwen3.6-35B-A3B-GGUF`.
+It is published as `meshllm/qwen3.6-35b-a3b-gguf-moe` and is meant to be consumed by `mesh-llm serve`.
+
+## Source
+
+- repo: `unsloth/Qwen3.6-35B-A3B-GGUF`
+- revision: `9280dd353ab587157920d5bd391ada414d84e552`
+
+## What This Repository Contains
+
+- `meshllm.json` describes the upstream source repo and all published variants in this package repository.
+- `variants/<variant>/manifest.json` is the runtime entrypoint used to materialize MoE shards.
+- `variants/<variant>/ranking.csv` and `variants/<variant>/analysis.json` contain the analyzer output for that variant.
+- `variants/<variant>/trunk.gguf` plus `variants/<variant>/experts/` hold the topology-independent component artifacts.
+
+## Available Variants
+
+### `Q4_K_XL`
+
+- Mesh model ref: `unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_XL`
+- Distribution id: `Qwen3.6-35B-A3B-UD-Q4_K_XL`
+- Manifest: `variants/Q4_K_XL/manifest.json`
+- Serve with:
+
+```bash
+mesh-llm serve 'unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_XL'
+```
+
+## Notes
+
+- This is a derived Mesh-LLM package repository, not the original upstream model repository.
+- `mesh-llm` will prefer published package artifacts from this repository when a matching catalog entry exists.

meshllm.json

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+{
+  "schema_version": 1,
+  "source": {
+    "repo": "unsloth/Qwen3.6-35B-A3B-GGUF",
+    "revision": "9280dd353ab587157920d5bd391ada414d84e552"
+  },
+  "variants": {
+    "Q4_K_XL": {
+      "distribution_id": "Qwen3.6-35B-A3B-UD-Q4_K_XL",
+      "manifest": "variants/Q4_K_XL/manifest.json"
+    }
+  }
+}

variants/Q4_K_XL/analysis.json

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+      "path": "experts/expert-180.gguf",
+      "sha256": "sha256:a9421305dac8130720560da4829295a6fbf9f9bdb52fef82e26a2618f9397cfd",
+      "expert_id": 180
+    },
+    {
+      "path": "experts/expert-181.gguf",
+      "sha256": "sha256:c85f56ac4aca0c0977783b95a48fc35d72224152c5e88c89dea905190db4fa5e",
+      "expert_id": 181
+    },
+    {
+      "path": "experts/expert-182.gguf",
+      "sha256": "sha256:b8806428e5ad3490fe1a7c0e57130a2bcc9262cd3a3e29d6a57da27139ccebf0",
+      "expert_id": 182
+    },
+    {
+      "path": "experts/expert-183.gguf",
+      "sha256": "sha256:e86f46152b6c03b8c70fef3b7c2e83dbff22e1174fb81b04c4845c218f7e0f82",
+      "expert_id": 183
+    },
+    {
+      "path": "experts/expert-184.gguf",
+      "sha256": "sha256:17ec33fab87c34446511f2b55dae96643f39b96fe68efcf8b8679da5a9af3a45",
+      "expert_id": 184
+    },
+    {
+      "path": "experts/expert-185.gguf",
+      "sha256": "sha256:e7e40d1cf490eee7583d7ace7818af2621f3a098393e7211d02c5cc608c979c7",
+      "expert_id": 185
+    },
+    {
+      "path": "experts/expert-186.gguf",
+      "sha256": "sha256:1751f4fc8c84b39cbd41c6f5ec8069b1cc229869e83de8948e6ff592224627b2",
+      "expert_id": 186
+    },
+    {
+      "path": "experts/expert-187.gguf",
+      "sha256": "sha256:dd9dd0d34a5b040b7dec5a4b3ed454246c6cb9ec124afcd8777d363f7bb0f8d6",
+      "expert_id": 187
+    },
+    {
+      "path": "experts/expert-188.gguf",
+      "sha256": "sha256:fa8c276abaa1f7e304a0bf63f7e2ecb630a0b4f262ea3b0a4949292b7432f2ca",
+      "expert_id": 188
+    },
+    {
+      "path": "experts/expert-189.gguf",
+      "sha256": "sha256:029b737214dac83984a4553ecc777322e39c7919418a5f08be496352a3d01b1a",
+      "expert_id": 189
+    },
+    {
+      "path": "experts/expert-190.gguf",
+      "sha256": "sha256:466ed3623e0f4357a16011aca395657ab1705ce367991d32c26a94cb78d1ae58",
+      "expert_id": 190
+    },
+    {
+      "path": "experts/expert-191.gguf",
+      "sha256": "sha256:4fb08f003d43e901c40143c3e4f84cade08b4ee2e5fabbc279d84693b48c9b72",
+      "expert_id": 191
+    },
+    {
+      "path": "experts/expert-192.gguf",
+      "sha256": "sha256:ecffaab499ae06f298a18276b32e0697e7a8d5c8249c538485e46eb057a03284",
+      "expert_id": 192
+    },
+    {
+      "path": "experts/expert-193.gguf",
+      "sha256": "sha256:925488a17285c7de486ef1da886159b6bf3d6e0ba12d8bacce60033cca397f20",
+      "expert_id": 193
+    },
+    {
+      "path": "experts/expert-194.gguf",
+      "sha256": "sha256:b8a95364bc2b4bf8a89b0a197d3da0dcd4925b59915a0c8a23e4c2cd5da45d65",
+      "expert_id": 194
+    },
+    {
+      "path": "experts/expert-195.gguf",
+      "sha256": "sha256:11e3f796f563d404cea36fe98a14d41f96075d153bb3008d95b2c88922173c75",
+      "expert_id": 195
+    },
+    {
+      "path": "experts/expert-196.gguf",
+      "sha256": "sha256:4ad19364aaed4fbadac678d853b18b9e1bf5744cac998ce3579890fded7ffc9c",
+      "expert_id": 196
+    },
+    {
+      "path": "experts/expert-197.gguf",
+      "sha256": "sha256:dc5343d82a35cca10f94bf6d07e797bf34b06079f5bc27b07d3316c31c38da1d",
+      "expert_id": 197
+    },
+    {
+      "path": "experts/expert-198.gguf",
+      "sha256": "sha256:53ef817177e50ce3d0e282a119bd2dd1b985cae92cb9477ee7a55ad93ca4f90f",
+      "expert_id": 198
+    },
+    {
+      "path": "experts/expert-199.gguf",
+      "sha256": "sha256:7d307b464186b30c6fcbc1c141b723b2ce0fbddc4b5826b98d713dc44e4d63cf",
+      "expert_id": 199
+    },
+    {
+      "path": "experts/expert-200.gguf",
+      "sha256": "sha256:4a9a706f922166312d37b6726e9ea5f79b698b2f05e8d1da0906640f2931be9b",
+      "expert_id": 200
+    },
+    {
+      "path": "experts/expert-201.gguf",
+      "sha256": "sha256:ecc095389dc0832c0d8b7610af81cee8e1beca8e1fb36c2bd9ab4cb5fdeeba30",
+      "expert_id": 201
+    },
+    {
+      "path": "experts/expert-202.gguf",
+      "sha256": "sha256:989ddee2d92f2f73e677dc7b5c95388914c9cf8ee7dcc9dce1020184d8450f47",
+      "expert_id": 202
+    },
+    {
+      "path": "experts/expert-203.gguf",
+      "sha256": "sha256:ba0b0ca412516471ae594c47f718f19a53e3889e969ec311f0e60ae21324ab8c",
+      "expert_id": 203
+    },
+    {
+      "path": "experts/expert-204.gguf",
+      "sha256": "sha256:f932f7ef294b022c9e96c170606f7ee91e5047f6b50f049a8c6569cb7bbb0341",
+      "expert_id": 204
+    },
+    {
+      "path": "experts/expert-205.gguf",
+      "sha256": "sha256:9a8f37b3f01e5ad2ed6ebeb21a4bab25789c1f384e1a59de830dc35557d6d9dc",
+      "expert_id": 205
+    },
+    {
+      "path": "experts/expert-206.gguf",
+      "sha256": "sha256:6365be61811a3d0745a920f10123d45b7b7669df0cdffa0e4421d2422119b444",
+      "expert_id": 206
+    },
+    {
+      "path": "experts/expert-207.gguf",
+      "sha256": "sha256:6e1474bae08e54982c5e7430fb60c5535dd2948d8fde9666464547e07cc976ec",
+      "expert_id": 207
+    },
+    {
+      "path": "experts/expert-208.gguf",
+      "sha256": "sha256:653b0c73a8e15c63d680b18efcf290c2c02c60344c426d791b9390d9943b2b65",
+      "expert_id": 208
+    },
+    {
+      "path": "experts/expert-209.gguf",
+      "sha256": "sha256:b326eec3eede1c4d0707fbe5ed9b1b6b5a5e09278d3a2e81674b61822f7d49d7",
+      "expert_id": 209
+    },
+    {
+      "path": "experts/expert-210.gguf",
+      "sha256": "sha256:6c98564616e0859756d62f0c931521f239862ca8ad32a48a3186c4c138adc1ef",
+      "expert_id": 210
+    },
+    {
+      "path": "experts/expert-211.gguf",
+      "sha256": "sha256:57211650bc0a3cb96ea615868ba770af20033a15d416720421f2ba4311c61d40",
+      "expert_id": 211
+    },
+    {
+      "path": "experts/expert-212.gguf",
+      "sha256": "sha256:b512271789d82a0b18c0bb328a87b03263389b2219ca9f4ce37ca566ca48f257",
+      "expert_id": 212
+    },
+    {
+      "path": "experts/expert-213.gguf",
+      "sha256": "sha256:25d99b540532b92a58d48511a3ac6cb23ce606b6c311bd3c0a722f33c6931996",
+      "expert_id": 213
+    },
+    {
+      "path": "experts/expert-214.gguf",
+      "sha256": "sha256:035a673993c9ccfe32e8ee123e3545a67e7b6883e0668590b910cd76cc00a854",
+      "expert_id": 214
+    },
+    {
+      "path": "experts/expert-215.gguf",
+      "sha256": "sha256:5d26ed14b1358724c2c0420becc196cf95dcc935718cab29990e5994e9fed17c",
+      "expert_id": 215
+    },
+    {
+      "path": "experts/expert-216.gguf",
+      "sha256": "sha256:edaef55a7863d88e80e9e6e4e10141f46c0b9c29b88c33f5f386412806c94af9",
+      "expert_id": 216
+    },
+    {
+      "path": "experts/expert-217.gguf",
+      "sha256": "sha256:350bbe67a1955b5e244d0aa868470c37f249453911540560183fd2a12da35c80",
+      "expert_id": 217
+    },
+    {
+      "path": "experts/expert-218.gguf",
+      "sha256": "sha256:50cc6aa307a52ba7d9e157425407ca97e0582d48b69e0fd6b93ad2ceb187ad20",
+      "expert_id": 218
+    },
+    {
+      "path": "experts/expert-219.gguf",
+      "sha256": "sha256:f79945124a75e465974e1606baff6c1d9a877a735ea064a431ef64869d228c39",
+      "expert_id": 219
+    },
+    {
+      "path": "experts/expert-220.gguf",
+      "sha256": "sha256:cd285f1854e93a6eff7c2ebf894ba2c407ac4ac6fa5efce3ee8e2de2d0fa5f89",
+      "expert_id": 220
+    },
+    {
+      "path": "experts/expert-221.gguf",
+      "sha256": "sha256:09296acfdafd1c7d254aa3d9ef6448545665b2283aaadc021561f40a207c82ac",
+      "expert_id": 221
+    },
+    {
+      "path": "experts/expert-222.gguf",
+      "sha256": "sha256:64f3ef87c266121d01d92446c6f8665ca16b31d8517ea256eb22e9a5025a5e2d",
+      "expert_id": 222
+    },
+    {
+      "path": "experts/expert-223.gguf",
+      "sha256": "sha256:99cbf7801333653e21823b0b4ee9216be191ee4f5f1a8dc913d570940aabd44f",
+      "expert_id": 223
+    },
+    {
+      "path": "experts/expert-224.gguf",
+      "sha256": "sha256:f91f76ae8f5a241fbb5c00919405aa61437cdbe668e91c4c59986aa0518fbaae",
+      "expert_id": 224
+    },
+    {
+      "path": "experts/expert-225.gguf",
+      "sha256": "sha256:556f570b62676521e78cfbb085fa9272ed26d54b98dfc215b75f98d89ac5a0c2",
+      "expert_id": 225
+    },
+    {
+      "path": "experts/expert-226.gguf",
+      "sha256": "sha256:2fe4a25df5bd58ceffd8af12c7e8191374b26f03e37549d687098d2b6230bdae",
+      "expert_id": 226
+    },
+    {
+      "path": "experts/expert-227.gguf",
+      "sha256": "sha256:9b02905ece1de47a921049b26bc6b4fbad78307da337bcc22dc15241d9c57b73",
+      "expert_id": 227
+    },
+    {
+      "path": "experts/expert-228.gguf",
+      "sha256": "sha256:4741b18365d9d186042d87aab4a80619560bd4fdf74b8d908f3e60e77106f108",
+      "expert_id": 228
+    },
+    {
+      "path": "experts/expert-229.gguf",
+      "sha256": "sha256:8d9d61a8ca2ea76d3f7991500300431569c02029db70975bc3a8795b957ddbbf",
+      "expert_id": 229
+    },
+    {
+      "path": "experts/expert-230.gguf",
+      "sha256": "sha256:d49d76d5100af4a746553f176d4812c790ec2187037c3741aebb040472676c12",
+      "expert_id": 230
+    },
+    {
+      "path": "experts/expert-231.gguf",
+      "sha256": "sha256:970a2bb09bf1511f32281e71d9da89fb844958ed47c8a64e5fc613acffe8d5df",
+      "expert_id": 231
+    },
+    {
+      "path": "experts/expert-232.gguf",
+      "sha256": "sha256:e3cabf92c43c13c75c7b1c034d4d84facf399d8a097237150f7a9c6f68cb50b8",
+      "expert_id": 232
+    },
+    {
+      "path": "experts/expert-233.gguf",
+      "sha256": "sha256:85c630738fe531d2570cef84e0fe7f7c003eccb8c7405e10447a2cba686ff1a5",
+      "expert_id": 233
+    },
+    {
+      "path": "experts/expert-234.gguf",
+      "sha256": "sha256:4ab0c523d537c5df8d1f9314156b0f73b27da1e55bc58e8514f7a9bc4081bb1a",
+      "expert_id": 234
+    },
+    {
+      "path": "experts/expert-235.gguf",
+      "sha256": "sha256:0bd6611b2e29ce94958bc2f8ed469a2a99a89f9458a36a9ad020801315f93826",
+      "expert_id": 235
+    },
+    {
+      "path": "experts/expert-236.gguf",
+      "sha256": "sha256:46bbe3a45b4f0489f9ba4f1c0b8964f3b196a79978b14009f50f2f474a66f5b2",
+      "expert_id": 236
+    },
+    {
+      "path": "experts/expert-237.gguf",
+      "sha256": "sha256:bd404221cf23645579471a62f968ae90f96b0ddb7a900335d7339eb629c6fda6",
+      "expert_id": 237
+    },
+    {
+      "path": "experts/expert-238.gguf",
+      "sha256": "sha256:89c579cbff048cede5bcd75588c3d9e52cf1395f3cebd43d8691f0547db9fbef",
+      "expert_id": 238
+    },
+    {
+      "path": "experts/expert-239.gguf",
+      "sha256": "sha256:1eea6227599a9b5a1fba3af1aba19b48f827ae83b70c6dd29df1d0910082dbc9",
+      "expert_id": 239
+    },
+    {
+      "path": "experts/expert-240.gguf",
+      "sha256": "sha256:13c00288f65dc5b23f8c3ff21fd3b8445371ddab9c64f871c3dd34d60b1407db",
+      "expert_id": 240
+    },
+    {
+      "path": "experts/expert-241.gguf",
+      "sha256": "sha256:89049300d9a03047a3fbaa2fa2bb9113337aa63a75254c9ee08c410a8bf37943",
+      "expert_id": 241
+    },
+    {
+      "path": "experts/expert-242.gguf",
+      "sha256": "sha256:b2767d9772fa27af2deb0143cd326c16b10d2ef61620b6e1ac1c867cadf3641d",
+      "expert_id": 242
+    },
+    {
+      "path": "experts/expert-243.gguf",
+      "sha256": "sha256:132fffad3a6708f1d169e78183e3578044b07f96edd97258415a9d2ae04c225e",
+      "expert_id": 243
+    },
+    {
+      "path": "experts/expert-244.gguf",
+      "sha256": "sha256:6e39083421a4ca6ac5bfd450c98460d3099ad8857a78f82af8f3da774056cb37",
+      "expert_id": 244
+    },
+    {
+      "path": "experts/expert-245.gguf",
+      "sha256": "sha256:34f4c784262faacc4ad334b54b587a57222ec426315648b7e8aa53622d6c95ad",
+      "expert_id": 245
+    },
+    {
+      "path": "experts/expert-246.gguf",
+      "sha256": "sha256:05847744920574b537e1d04e4b81a4519c16d3aec8d598f3d7aeeb57a3b4e064",
+      "expert_id": 246
+    },
+    {
+      "path": "experts/expert-247.gguf",
+      "sha256": "sha256:c7e45ace7ae5c6c65c2bd3f66e4c4b0cfd294c13812d6ee25823e69822f832a5",
+      "expert_id": 247
+    },
+    {
+      "path": "experts/expert-248.gguf",
+      "sha256": "sha256:89a1859713e96f37f3b3e9dc272fb2091ff16514727d9d135ec6d571e6abcf7a",
+      "expert_id": 248
+    },
+    {
+      "path": "experts/expert-249.gguf",
+      "sha256": "sha256:2acc01996220071e21e39ae0936f0f237d1a7b7393f275b00dd40cd0c56814cc",
+      "expert_id": 249
+    },
+    {
+      "path": "experts/expert-250.gguf",
+      "sha256": "sha256:c83a55990597af4c63caeaca907df5fdcf1a95c3b19934c2b9e0b8b38273002d",
+      "expert_id": 250
+    },
+    {
+      "path": "experts/expert-251.gguf",
+      "sha256": "sha256:fd411a199fb0f66264c770f288291321ce2d4e12784799d2cd201130029f4a22",
+      "expert_id": 251
+    },
+    {
+      "path": "experts/expert-252.gguf",
+      "sha256": "sha256:ad6ac9c730c379da4d0dccc888eac25d002e9d2ece8aae366611f752fe6b7286",
+      "expert_id": 252
+    },
+    {
+      "path": "experts/expert-253.gguf",
+      "sha256": "sha256:dd07ba6c2ffe784435dc75ec652f93a6ee36616c3294486f2345de679d689d91",
+      "expert_id": 253
+    },
+    {
+      "path": "experts/expert-254.gguf",
+      "sha256": "sha256:3008c59c58b0b7c829af28a75bd6231a88a6cb72ee16a56b3d9c6cdd4960f5a5",
+      "expert_id": 254
+    },
+    {
+      "path": "experts/expert-255.gguf",
+      "sha256": "sha256:6b6d49a10a6d8c10bde98daa8db96bc23fcd8f721055ba88d15593b1067409e1",
+      "expert_id": 255
+    }
+  ]
+}

variants/Q4_K_XL/ranking.csv

@@ -0,0 +1,265 @@
+# MoE expert ranking by gate mass
+# Model: /Users/jdumay/.cache/huggingface/hub/models--unsloth--Qwen3.6-35B-A3B-GGUF/snapshots/9280dd353ab587157920d5bd391ada414d84e552/Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf
+# Experts: 256 (top-8)
+# Prompts: 10 x 32 tokens
+# Layers logged: all
+# Total token-layer observations: 3304225
+#
+# Format: expert_id,gate_mass,mass_pct,selection_count
+# Sorted by gate_mass descending (hottest first)
+0,12897.1,32.0625,3277738
+243,153.063,0.380518,1539
+89,150.49,0.37412,1298
+60,149.057,0.370558,1645
+224,146.013,0.362991,1489
+64,142.587,0.354475,1426
+95,140.235,0.348627,1550
+189,140.116,0.348331,1045
+229,136.135,0.338433,1522
+36,136.132,0.338425,1348
+125,134.277,0.333815,1138
+108,133.868,0.332798,1271
+254,133.33,0.33146,1197
+167,133.256,0.331275,1316
+43,132.86,0.330292,1652
+88,132.632,0.329726,1366
+137,132.245,0.328764,1186
+165,131.32,0.326463,1255
+160,129.608,0.322208,1217
+46,129.056,0.320835,1160
+35,128.984,0.320657,1510
+134,128.404,0.319215,1327
+65,127.282,0.316426,1397
+103,126.612,0.314761,1334
+191,126.241,0.313836,1438
+182,126.235,0.313821,1221
+72,125.683,0.31245,1384
+56,125.096,0.31099,1116
+204,125.043,0.31086,1170
+203,125.007,0.31077,1321
+158,125.007,0.310769,1112
+20,124.022,0.308322,1580
+248,123.246,0.306392,1154
+220,122.568,0.304705,1275
+61,122.235,0.303879,1274
+163,121.789,0.30277,1178
+221,121.181,0.301257,1050
+206,121.022,0.300863,1151
+105,120.96,0.300708,769
+201,120.941,0.30066,976
+47,120.627,0.29988,1263
+151,119.378,0.296776,1568
+127,119.272,0.296513,1033
+99,119.161,0.296236,1090
+208,119.019,0.295884,924
+219,118.918,0.295632,1047
+171,118.732,0.29517,1007
+111,118.732,0.29517,1115
+185,118.714,0.295126,1265
+251,118.705,0.295103,1041
+121,118.695,0.295078,978
+116,118.639,0.294939,1415
+87,118.281,0.294049,1160
+209,117.858,0.292998,1164
+217,117.032,0.290944,921
+107,116.513,0.289654,953
+42,116.391,0.289351,976
+239,115.628,0.287454,937
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variants/Q4_K_XL/run.log

@@ -0,0 +1,333 @@
+$ /Users/jdumay/.codex/worktrees/dddc/mesh-llm/llama.cpp/build/bin/llama-moe-analyze -m /Users/jdumay/.cache/huggingface/hub/models--unsloth--Qwen3.6-35B-A3B-GGUF/snapshots/9280dd353ab587157920d5bd391ada414d84e552/Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf --all-layers --export-ranking /Users/jdumay/.cache/mesh-llm/packages/unsloth/Qwen3.6-35B-A3B-GGUF/9280dd353ab587157920d5bd391ada414d84e552/variants/Q4_K_XL/ranking.csv -n 32 -c 4096 -ngl 0
+
+[stdout]
+
+[stderr]
+ggml_metal_device_init: tensor API disabled for pre-M5 and pre-A19 devices
+ggml_metal_library_init: using embedded metal library
+ggml_metal_library_init: loaded in 0.013 sec
+ggml_metal_rsets_init: creating a residency set collection (keep_alive = 180 s)
+ggml_metal_device_init: GPU name:   MTL0
+ggml_metal_device_init: GPU family: MTLGPUFamilyApple7  (1007)
+ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003)
+ggml_metal_device_init: GPU family: MTLGPUFamilyMetal4  (5002)
+ggml_metal_device_init: simdgroup reduction   = true
+ggml_metal_device_init: simdgroup matrix mul. = true
+ggml_metal_device_init: has unified memory    = true
+ggml_metal_device_init: has bfloat            = true
+ggml_metal_device_init: has tensor            = false
+ggml_metal_device_init: use residency sets    = true
+ggml_metal_device_init: use shared buffers    = true
+ggml_metal_device_init: recommendedMaxWorkingSetSize  = 115448.73 MB
+common_init_result: fitting params to device memory, for bugs during this step try to reproduce them with -fit off, or provide --verbose logs if the bug only occurs with -fit on
+llama_params_fit_impl: projected to use 16 MiB of device memory vs. 110100 MiB of free device memory
+llama_params_fit_impl: will leave 110084 >= 1024 MiB of free device memory, no changes needed
+llama_params_fit: successfully fit params to free device memory
+llama_params_fit: fitting params to free memory took 0.41 seconds
+llama_model_load_from_file_impl: using device MTL0 (Apple M1 Ultra) (unknown id) - 110100 MiB free
+llama_model_loader: loaded meta data with 54 key-value pairs and 733 tensors from /Users/jdumay/.cache/huggingface/hub/models--unsloth--Qwen3.6-35B-A3B-GGUF/snapshots/9280dd353ab587157920d5bd391ada414d84e552/Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf (version GGUF V3 (latest))
+llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
+llama_model_loader: - kv   0:                       general.architecture str              = qwen35moe
+llama_model_loader: - kv   1:                               general.type str              = model
+llama_model_loader: - kv   2:                     general.sampling.top_k i32              = 20
+llama_model_loader: - kv   3:                     general.sampling.top_p f32              = 0.950000
+llama_model_loader: - kv   4:                      general.sampling.temp f32              = 1.000000
+llama_model_loader: - kv   5:                               general.name str              = Qwen3.6-35B-A3B
+llama_model_loader: - kv   6:                           general.basename str              = Qwen3.6-35B-A3B
+llama_model_loader: - kv   7:                       general.quantized_by str              = Unsloth
+llama_model_loader: - kv   8:                         general.size_label str              = 35B-A3B
+llama_model_loader: - kv   9:                            general.license str              = apache-2.0
+llama_model_loader: - kv  10:                       general.license.link str              = https://huggingface.co/Qwen/Qwen3.6-3...
+llama_model_loader: - kv  11:                           general.repo_url str              = https://huggingface.co/unsloth
+llama_model_loader: - kv  12:                   general.base_model.count u32              = 1
+llama_model_loader: - kv  13:                  general.base_model.0.name str              = Qwen3.6 35B A3B
+llama_model_loader: - kv  14:          general.base_model.0.organization str              = Qwen
+llama_model_loader: - kv  15:              general.base_model.0.repo_url str              = https://huggingface.co/Qwen/Qwen3.6-3...
+llama_model_loader: - kv  16:                               general.tags arr[str,3]       = ["qwen3_5_moe", "qwen", "image-text-t...
+llama_model_loader: - kv  17:                      qwen35moe.block_count u32              = 40
+llama_model_loader: - kv  18:                   qwen35moe.context_length u32              = 262144
+llama_model_loader: - kv  19:                 qwen35moe.embedding_length u32              = 2048
+llama_model_loader: - kv  20:             qwen35moe.attention.head_count u32              = 16
+llama_model_loader: - kv  21:          qwen35moe.attention.head_count_kv u32              = 2
+llama_model_loader: - kv  22:          qwen35moe.rope.dimension_sections arr[i32,4]       = [11, 11, 10, 0]
+llama_model_loader: - kv  23:                   qwen35moe.rope.freq_base f32              = 10000000.000000
+llama_model_loader: - kv  24: qwen35moe.attention.layer_norm_rms_epsilon f32              = 0.000001
+llama_model_loader: - kv  25:                     qwen35moe.expert_count u32              = 256
+llama_model_loader: - kv  26:                qwen35moe.expert_used_count u32              = 8
+llama_model_loader: - kv  27:             qwen35moe.attention.key_length u32              = 256
+llama_model_loader: - kv  28:           qwen35moe.attention.value_length u32              = 256
+llama_model_loader: - kv  29:       qwen35moe.expert_feed_forward_length u32              = 512
+llama_model_loader: - kv  30: qwen35moe.expert_shared_feed_forward_length u32              = 512
+llama_model_loader: - kv  31:                  qwen35moe.ssm.conv_kernel u32              = 4
+llama_model_loader: - kv  32:                   qwen35moe.ssm.state_size u32              = 128
+llama_model_loader: - kv  33:                  qwen35moe.ssm.group_count u32              = 16
+llama_model_loader: - kv  34:               qwen35moe.ssm.time_step_rank u32              = 32
+llama_model_loader: - kv  35:                   qwen35moe.ssm.inner_size u32              = 4096
+llama_model_loader: - kv  36:          qwen35moe.full_attention_interval u32              = 4
+llama_model_loader: - kv  37:             qwen35moe.rope.dimension_count u32              = 64
+llama_model_loader: - kv  38:                       tokenizer.ggml.model str              = gpt2
+llama_model_loader: - kv  39:                         tokenizer.ggml.pre str              = qwen35
+llama_model_loader: - kv  40:                      tokenizer.ggml.tokens arr[str,248320]  = ["!", "\"", "#", "$", "%", "&", "'", ...
+llama_model_loader: - kv  41:                  tokenizer.ggml.token_type arr[i32,248320]  = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
+llama_model_loader: - kv  42:                      tokenizer.ggml.merges arr[str,247587]  = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
+llama_model_loader: - kv  43:                tokenizer.ggml.eos_token_id u32              = 248046
+llama_model_loader: - kv  44:            tokenizer.ggml.padding_token_id u32              = 248055
+llama_model_loader: - kv  45:                tokenizer.ggml.bos_token_id u32              = 248044
+llama_model_loader: - kv  46:               tokenizer.ggml.add_bos_token bool             = false
+llama_model_loader: - kv  47:                    tokenizer.chat_template str              = {%- set image_count = namespace(value...
+llama_model_loader: - kv  48:               general.quantization_version u32              = 2
+llama_model_loader: - kv  49:                          general.file_type u32              = 15
+llama_model_loader: - kv  50:                      quantize.imatrix.file str              = Qwen3.6-35B-A3B-GGUF/imatrix_unsloth....
+llama_model_loader: - kv  51:                   quantize.imatrix.dataset str              = unsloth_calibration_Qwen3.6-35B-A3B.txt
+llama_model_loader: - kv  52:             quantize.imatrix.entries_count u32              = 510
+llama_model_loader: - kv  53:              quantize.imatrix.chunks_count u32              = 76
+llama_model_loader: - type  f32:  361 tensors
+llama_model_loader: - type q8_0:  252 tensors
+llama_model_loader: - type q4_K:   78 tensors
+llama_model_loader: - type q5_K:   38 tensors
+llama_model_loader: - type q6_K:    4 tensors
+print_info: file format = GGUF V3 (latest)
+print_info: file type   = Q4_K - Medium
+print_info: file size   = 20.81 GiB (5.16 BPW) 
+load: 0 unused tokens
+load: printing all EOG tokens:
+load:   - 248044 ('<|endoftext|>')
+load:   - 248046 ('<|im_end|>')
+load:   - 248063 ('<|fim_pad|>')
+load:   - 248064 ('<|repo_name|>')
+load:   - 248065 ('<|file_sep|>')
+load: special tokens cache size = 33
+load: token to piece cache size = 1.7581 MB
+print_info: arch                  = qwen35moe
+print_info: vocab_only            = 0
+print_info: no_alloc              = 0
+print_info: n_ctx_train           = 262144
+print_info: n_embd                = 2048
+print_info: n_embd_inp            = 2048
+print_info: n_layer               = 40
+print_info: n_head                = 16
+print_info: n_head_kv             = 2
+print_info: n_rot                 = 64
+print_info: n_swa                 = 0
+print_info: is_swa_any            = 0
+print_info: n_embd_head_k         = 256
+print_info: n_embd_head_v         = 256
+print_info: n_gqa                 = 8
+print_info: n_embd_k_gqa          = 512
+print_info: n_embd_v_gqa          = 512
+print_info: f_norm_eps            = 0.0e+00
+print_info: f_norm_rms_eps        = 1.0e-06
+print_info: f_clamp_kqv           = 0.0e+00
+print_info: f_max_alibi_bias      = 0.0e+00
+print_info: f_logit_scale         = 0.0e+00
+print_info: f_attn_scale          = 0.0e+00
+print_info: n_ff                  = 0
+print_info: n_expert              = 256
+print_info: n_expert_used         = 8
+print_info: n_expert_groups       = 0
+print_info: n_group_used          = 0
+print_info: causal attn           = 1
+print_info: pooling type          = -1
+print_info: rope type             = 40
+print_info: rope scaling          = linear
+print_info: freq_base_train       = 10000000.0
+print_info: freq_scale_train      = 1
+print_info: n_ctx_orig_yarn       = 262144
+print_info: rope_yarn_log_mul     = 0.0000
+print_info: rope_finetuned        = unknown
+print_info: mrope sections        = [11, 11, 10, 0]
+print_info: ssm_d_conv            = 4
+print_info: ssm_d_inner           = 4096
+print_info: ssm_d_state           = 128
+print_info: ssm_dt_rank           = 32
+print_info: ssm_n_group           = 16
+print_info: ssm_dt_b_c_rms        = 0
+print_info: model type            = 35B.A3B
+print_info: model params          = 34.66 B
+print_info: general.name          = Qwen3.6-35B-A3B
+print_info: vocab type            = BPE
+print_info: n_vocab               = 248320
+print_info: n_merges              = 247587
+print_info: BOS token             = 248044 '<|endoftext|>'
+print_info: EOS token             = 248046 '<|im_end|>'
+print_info: EOT token             = 248046 '<|im_end|>'
+print_info: PAD token             = 248055 '<|vision_pad|>'
+print_info: LF token              = 198 'Ċ'
+print_info: FIM PRE token         = 248060 '<|fim_prefix|>'
+print_info: FIM SUF token         = 248062 '<|fim_suffix|>'
+print_info: FIM MID token         = 248061 '<|fim_middle|>'
+print_info: FIM PAD token         = 248063 '<|fim_pad|>'
+print_info: FIM REP token         = 248064 '<|repo_name|>'
+print_info: FIM SEP token         = 248065 '<|file_sep|>'
+print_info: EOG token             = 248044 '<|endoftext|>'
+print_info: EOG token             = 248046 '<|im_end|>'
+print_info: EOG token             = 248063 '<|fim_pad|>'
+print_info: EOG token             = 248064 '<|repo_name|>'
+print_info: EOG token             = 248065 '<|file_sep|>'
+print_info: max token length      = 256
+load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
+load_tensors: offloading 0 repeating layers to GPU
+load_tensors: offloaded 0/41 layers to GPU
+load_tensors:   CPU_Mapped model buffer size = 20798.80 MiB
+load_tensors:   CPU_REPACK model buffer size = 20699.06 MiB
+.................................................................................................
+common_init_result: added <|endoftext|> logit bias = -inf
+common_init_result: added <|im_end|> logit bias = -inf
+common_init_result: added <|fim_pad|> logit bias = -inf
+common_init_result: added <|repo_name|> logit bias = -inf
+common_init_result: added <|file_sep|> logit bias = -inf
+llama_context: constructing llama_context
+llama_context: n_seq_max     = 1
+llama_context: n_ctx         = 4096
+llama_context: n_ctx_seq     = 4096
+llama_context: n_batch       = 2048
+llama_context: n_ubatch      = 512
+llama_context: causal_attn   = 1
+llama_context: flash_attn    = auto
+llama_context: kv_unified    = false
+llama_context: freq_base     = 10000000.0
+llama_context: freq_scale    = 1
+llama_context: n_ctx_seq (4096) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
+ggml_metal_init: allocating
+ggml_metal_init: found device: Apple M1 Ultra
+ggml_metal_init: picking default device: Apple M1 Ultra
+ggml_metal_init: use fusion         = true
+ggml_metal_init: use concurrency    = true
+ggml_metal_init: use graph optimize = true
+llama_context:        CPU  output buffer size =     0.95 MiB
+llama_kv_cache:        CPU KV buffer size =    80.00 MiB
+llama_kv_cache: size =   80.00 MiB (  4096 cells,  10 layers,  1/1 seqs), K (f16):   40.00 MiB, V (f16):   40.00 MiB
+llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
+llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
+llama_memory_recurrent:        CPU RS buffer size =    62.81 MiB
+llama_memory_recurrent: size =   62.81 MiB (     1 cells,  40 layers,  1 seqs), R (f32):    2.81 MiB, S (f32):   60.00 MiB
+sched_reserve: reserving ...
+sched_reserve: Flash Attention was auto, set to enabled
+sched_reserve: resolving fused Gated Delta Net support:
+sched_reserve: fused Gated Delta Net (autoregressive) enabled
+sched_reserve: fused Gated Delta Net (chunked) enabled
+sched_reserve:       MTL0 compute buffer size =    16.01 MiB
+sched_reserve:        CPU compute buffer size =   493.00 MiB
+sched_reserve: graph nodes  = 3729
+sched_reserve: graph splits = 281 (with bs=512), 1 (with bs=1)
+sched_reserve: reserve took 8.30 ms, sched copies = 1
+
+=== MoE Expert Routing Analysis ===
+Model experts: 256, used per token: 8
+Logging ALL MoE layers (--all-layers)
+Will export expert ranking to: /Users/jdumay/.cache/mesh-llm/packages/unsloth/Qwen3.6-35B-A3B-GGUF/9280dd353ab587157920d5bd391ada414d84e552/variants/Q4_K_XL/ranking.csv
+Running 10 prompts, generating 32 tokens each
+Logging first 9999 MoE layers per eval
+
+Prompt 1/10: <|im_start|>user
+Write a Python function to find the nth Fib...
+  collected 2640 layer snapshots (total: 2640)
+Prompt 2/10: <|im_start|>user
+Write a Rust function that reads a CSV file...
+  collected 2640 layer snapshots (total: 5280)
+Prompt 3/10: <|im_start|>user
+Explain how a B-tree index works in a datab...
+  collected 2640 layer snapshots (total: 7920)
+Prompt 4/10: <|im_start|>user
+If all roses are flowers and some flowers f...
+  collected 2640 layer snapshots (total: 10560)
+Prompt 5/10: <|im_start|>user
+A train travels 120km in 2 hours. It then s...
+  collected 2640 layer snapshots (total: 13200)
+Prompt 6/10: <|im_start|>user
+Hello! What's the best way to learn a new l...
+  collected 2640 layer snapshots (total: 15840)
+Prompt 7/10: <|im_start|>user
+Tell me a joke about programmers.<|im_end|>...
+  collected 1840 layer snapshots (total: 17680)
+Prompt 8/10: <|im_start|>user
+Summarize the key differences between TCP a...
+  collected 2640 layer snapshots (total: 20320)
+Prompt 9/10: <|im_start|>user
+Translate 'The weather is beautiful today' ...
+  collected 2640 layer snapshots (total: 22960)
+Prompt 10/10: <|im_start|>user
+List 5 healthy breakfast options with brief...
+  collected 2640 layer snapshots (total: 25600)
+
+=== Expert Popularity (gate mass, summed across all tokens & logged layers) ===
+Total tokens × layers: 3304225
+
+Top 20 experts by gate mass:
+  Expert    Mass          Mass%     Selected  
+  0         12897.1294    32.06     3277738   
+  243       153.0632      0.38      1539      
+  89        150.4899      0.37      1298      
+  60        149.0570      0.37      1645      
+  224       146.0130      0.36      1489      
+  64        142.5874      0.35      1426      
+  95        140.2351      0.35      1550      
+  189       140.1161      0.35      1045      
+  229       136.1348      0.34      1522      
+  36        136.1316      0.34      1348      
+  125       134.2771      0.33      1138      
+  108       133.8681      0.33      1271      
+  254       133.3297      0.33      1197      
+  167       133.2555      0.33      1316      
+  43        132.8598      0.33      1652      
+  88        132.6322      0.33      1366      
+  137       132.2451      0.33      1186      
+  165       131.3198      0.33      1255      
+  160       129.6081      0.32      1217      
+  46        129.0560      0.32      1160      
+
+Concentration:
+  Top   4 experts: 33.2% of total gate mass
+  Top   8 experts: 34.6% of total gate mass
+  Top  16 experts: 37.3% of total gate mass
+  Top  32 experts: 42.3% of total gate mass
+  Top  64 experts: 51.8% of total gate mass
+
+Exported expert ranking to: /Users/jdumay/.cache/mesh-llm/packages/unsloth/Qwen3.6-35B-A3B-GGUF/9280dd353ab587157920d5bd391ada414d84e552/variants/Q4_K_XL/ranking.csv
+Use with moe-split: --group-map <file generated from this ranking>
+
+
+=== Group Masking Analysis (best-group capture ratio) ===
+For each group count, what fraction of the unrestricted top-8 mass
+is captured by the best single group?
+
+  Groups    Replicas  Exp/Grp     Mean        P25         P50         P5        
+  2         0         128         0.999       1.000       1.000       1.000     
+  2         1         128         0.999       1.000       1.000       1.000     
+  2         2         128         0.999       1.000       1.000       1.000     
+  2         4         128         0.999       1.000       1.000       1.000     
+  2         8         128         0.999       1.000       1.000       1.000     
+
+  4         0         64          0.997       1.000       1.000       1.000     
+  4         1         64          0.997       1.000       1.000       1.000     
+  4         2         64          0.998       1.000       1.000       1.000     
+  4         4         64          0.998       1.000       1.000       1.000     
+  4         8         64          0.998       1.000       1.000       1.000     
+
+  8         0         32          0.997       1.000       1.000       1.000     
+  8         1         32          0.997       1.000       1.000       1.000     
+  8         2         32          0.997       1.000       1.000       1.000     
+  8         4         32          0.997       1.000       1.000       1.000     
+  8         8         32          0.997       1.000       1.000       1.000     
+
+=== Interpretation ===
+Mean close to 1.0 = masking barely hurts (best group captures most of top-k mass)
+Mean < 0.7 = significant quality risk from group restriction
+P5 close to 1.0 = even worst-case tokens are OK
+P5 < 0.5 = some tokens will be badly served by any single group
+
+=== Phase 1b: Masked Generation Quality (logprob comparison) ===
+Testing 4 groups (64 experts/group) vs baseline (all 256 experts)
+Using first 5 prompts, generating 32 tokens each
+
+  Group 0 (experts 0-63 + 2 hot replicas): avg logprob delta = -0.3751
+  Group 1 (experts 64-127 + 2 hot replicas): avg logprob delta = -0.1213
+  Group 2 (experts 128-191 + 2 hot replicas): avg logprob delta = -0.1904
+  Group 3 (experts 192-255 + 2 hot replicas): avg logprob delta = -0.3067
+
+=== Interpretation (logprob delta) ===
+Delta near 0.0 = masking barely affects generation quality
+Delta < -0.1 = noticeable quality loss
+Delta < -0.5 = significant degradation
+
+ggml_metal_free: deallocating