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qwen3.6-35b-a3b-gguf-moe

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Publish Qwen3.6-35B-A3B-UD-Q4_K_XL full-v1 package (batch 1/34)

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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 1/34.

Files changed (6) hide show
  1. README.md +34 -0
  2. meshllm.json +13 -0
  3. variants/Q4_K_XL/analysis.json +80 -0
  4. variants/Q4_K_XL/manifest.json +1294 -0
  5. variants/Q4_K_XL/ranking.csv +265 -0
  6. variants/Q4_K_XL/run.log +333 -0
README.md ADDED
@@ -0,0 +1,34 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Mesh-LLM MoE Package
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+
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+ This repository stores Mesh-LLM topology-independent MoE package artifacts derived from `unsloth/Qwen3.6-35B-A3B-GGUF`.
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+ It is published as `meshllm/qwen3.6-35b-a3b-gguf-moe` and is meant to be consumed by `mesh-llm serve`.
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+
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+ ## Source
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+
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+ - repo: `unsloth/Qwen3.6-35B-A3B-GGUF`
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+ - revision: `9280dd353ab587157920d5bd391ada414d84e552`
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+
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+ ## What This Repository Contains
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+
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+ - `meshllm.json` describes the upstream source repo and all published variants in this package repository.
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+ - `variants/<variant>/manifest.json` is the runtime entrypoint used to materialize MoE shards.
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+ - `variants/<variant>/ranking.csv` and `variants/<variant>/analysis.json` contain the analyzer output for that variant.
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+ - `variants/<variant>/trunk.gguf` plus `variants/<variant>/experts/` hold the topology-independent component artifacts.
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+
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+ ## Available Variants
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+
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+ ### `Q4_K_XL`
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+
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+ - Mesh model ref: `unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_XL`
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+ - Distribution id: `Qwen3.6-35B-A3B-UD-Q4_K_XL`
24
+ - Manifest: `variants/Q4_K_XL/manifest.json`
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+ - Serve with:
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+
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+ ```bash
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+ mesh-llm serve 'unsloth/Qwen3.6-35B-A3B-GGUF:Q4_K_XL'
29
+ ```
30
+
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+ ## Notes
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+
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+ - This is a derived Mesh-LLM package repository, not the original upstream model repository.
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+ - `mesh-llm` will prefer published package artifacts from this repository when a matching catalog entry exists.
meshllm.json ADDED
@@ -0,0 +1,13 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "schema_version": 1,
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+ "source": {
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+ "repo": "unsloth/Qwen3.6-35B-A3B-GGUF",
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+ "revision": "9280dd353ab587157920d5bd391ada414d84e552"
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+ },
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+ "variants": {
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+ "Q4_K_XL": {
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+ "distribution_id": "Qwen3.6-35B-A3B-UD-Q4_K_XL",
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+ "manifest": "variants/Q4_K_XL/manifest.json"
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+ }
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+ }
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+ }
variants/Q4_K_XL/analysis.json ADDED
@@ -0,0 +1,80 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ {
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+ "analyzer_id": "full-v1",
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+ "artifacts": {
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+ "manifest": "manifest.json",
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+ "ranking": "ranking.csv"
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+ },
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+ "created_at": "2026-04-18T22:45:41+00:00",
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+ "memory": {
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+ "base_resident_bytes": 2678180352,
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+ "expert_bytes": {
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+ "bytes_per_expert": 76840960,
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+ "kind": "uniform"
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+ },
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+ "expert_tensor_bytes_total": 19671285760,
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+ "full_model_bytes": 22360456160,
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+ "shard_file_overhead_bytes": 10990048
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+ },
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+ "parameters": {
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+ "all_layers": true,
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+ "context_size": 4096,
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+ "prompt_count": null,
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+ "prompt_set": null,
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+ "token_count": 32
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+ },
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+ "planner": {
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+ "recommended_overlap": 1
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+ },
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+ "ranking": {
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+ "mass_checkpoints": [
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+ "mass_fraction": 0.320624258670914,
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+ },
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+ {
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+ {
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+ "mass_fraction": 1.0,
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+ "top_n": 256
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+ }
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+ ],
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+ "rows": 256,
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+ "sha256": "sha256:5f514ec9a2a890798f6692b2606005fb84175092ef25555f8b5e1d0c625349b9"
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+ },
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+ "schema_version": 1,
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+ "summary": {
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+ "n_expert": 256,
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+ "n_expert_used": 8
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+ },
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+ "tool": {
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+ "name": "llama-moe-analyze",
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+ "version": "mesh-llm-fork"
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+ }
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+ }
variants/Q4_K_XL/manifest.json ADDED
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variants/Q4_K_XL/ranking.csv ADDED
@@ -0,0 +1,265 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ # MoE expert ranking by gate mass
2
+ # Model: /Users/jdumay/.cache/huggingface/hub/models--unsloth--Qwen3.6-35B-A3B-GGUF/snapshots/9280dd353ab587157920d5bd391ada414d84e552/Qwen3.6-35B-A3B-UD-Q4_K_XL.gguf
3
+ # Experts: 256 (top-8)
4
+ # Prompts: 10 x 32 tokens
5
+ # Layers logged: all
6
+ # Total token-layer observations: 3304225
7
+ #
8
+ # Format: expert_id,gate_mass,mass_pct,selection_count
9
+ # Sorted by gate_mass descending (hottest first)
10
+ 0,12897.1,32.0625,3277738
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variants/Q4_K_XL/run.log ADDED
@@ -0,0 +1,333 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ $ /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
2
+
3
+ [stdout]
4
+
5
+ [stderr]
6
+ ggml_metal_device_init: tensor API disabled for pre-M5 and pre-A19 devices
7
+ ggml_metal_library_init: using embedded metal library
8
+ ggml_metal_library_init: loaded in 0.029 sec
9
+ ggml_metal_rsets_init: creating a residency set collection (keep_alive = 180 s)
10
+ ggml_metal_device_init: GPU name: MTL0
11
+ ggml_metal_device_init: GPU family: MTLGPUFamilyApple7 (1007)
12
+ ggml_metal_device_init: GPU family: MTLGPUFamilyCommon3 (3003)
13
+ ggml_metal_device_init: GPU family: MTLGPUFamilyMetal4 (5002)
14
+ ggml_metal_device_init: simdgroup reduction = true
15
+ ggml_metal_device_init: simdgroup matrix mul. = true
16
+ ggml_metal_device_init: has unified memory = true
17
+ ggml_metal_device_init: has bfloat = true
18
+ ggml_metal_device_init: has tensor = false
19
+ ggml_metal_device_init: use residency sets = true
20
+ ggml_metal_device_init: use shared buffers = true
21
+ ggml_metal_device_init: recommendedMaxWorkingSetSize = 115448.73 MB
22
+ 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
23
+ llama_params_fit_impl: projected to use 16 MiB of device memory vs. 110100 MiB of free device memory
24
+ llama_params_fit_impl: will leave 110084 >= 1024 MiB of free device memory, no changes needed
25
+ llama_params_fit: successfully fit params to free device memory
26
+ llama_params_fit: fitting params to free memory took 0.37 seconds
27
+ llama_model_load_from_file_impl: using device MTL0 (Apple M1 Ultra) (unknown id) - 110100 MiB free
28
+ 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))
29
+ llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
30
+ llama_model_loader: - kv 0: general.architecture str = qwen35moe
31
+ llama_model_loader: - kv 1: general.type str = model
32
+ llama_model_loader: - kv 2: general.sampling.top_k i32 = 20
33
+ llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
34
+ llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
35
+ llama_model_loader: - kv 5: general.name str = Qwen3.6-35B-A3B
36
+ llama_model_loader: - kv 6: general.basename str = Qwen3.6-35B-A3B
37
+ llama_model_loader: - kv 7: general.quantized_by str = Unsloth
38
+ llama_model_loader: - kv 8: general.size_label str = 35B-A3B
39
+ llama_model_loader: - kv 9: general.license str = apache-2.0
40
+ llama_model_loader: - kv 10: general.license.link str = https://huggingface.co/Qwen/Qwen3.6-3...
41
+ llama_model_loader: - kv 11: general.repo_url str = https://huggingface.co/unsloth
42
+ llama_model_loader: - kv 12: general.base_model.count u32 = 1
43
+ llama_model_loader: - kv 13: general.base_model.0.name str = Qwen3.6 35B A3B
44
+ llama_model_loader: - kv 14: general.base_model.0.organization str = Qwen
45
+ llama_model_loader: - kv 15: general.base_model.0.repo_url str = https://huggingface.co/Qwen/Qwen3.6-3...
46
+ llama_model_loader: - kv 16: general.tags arr[str,3] = ["qwen3_5_moe", "qwen", "image-text-t...
47
+ llama_model_loader: - kv 17: qwen35moe.block_count u32 = 40
48
+ llama_model_loader: - kv 18: qwen35moe.context_length u32 = 262144
49
+ llama_model_loader: - kv 19: qwen35moe.embedding_length u32 = 2048
50
+ llama_model_loader: - kv 20: qwen35moe.attention.head_count u32 = 16
51
+ llama_model_loader: - kv 21: qwen35moe.attention.head_count_kv u32 = 2
52
+ llama_model_loader: - kv 22: qwen35moe.rope.dimension_sections arr[i32,4] = [11, 11, 10, 0]
53
+ llama_model_loader: - kv 23: qwen35moe.rope.freq_base f32 = 10000000.000000
54
+ llama_model_loader: - kv 24: qwen35moe.attention.layer_norm_rms_epsilon f32 = 0.000001
55
+ llama_model_loader: - kv 25: qwen35moe.expert_count u32 = 256
56
+ llama_model_loader: - kv 26: qwen35moe.expert_used_count u32 = 8
57
+ llama_model_loader: - kv 27: qwen35moe.attention.key_length u32 = 256
58
+ llama_model_loader: - kv 28: qwen35moe.attention.value_length u32 = 256
59
+ llama_model_loader: - kv 29: qwen35moe.expert_feed_forward_length u32 = 512
60
+ llama_model_loader: - kv 30: qwen35moe.expert_shared_feed_forward_length u32 = 512
61
+ llama_model_loader: - kv 31: qwen35moe.ssm.conv_kernel u32 = 4
62
+ llama_model_loader: - kv 32: qwen35moe.ssm.state_size u32 = 128
63
+ llama_model_loader: - kv 33: qwen35moe.ssm.group_count u32 = 16
64
+ llama_model_loader: - kv 34: qwen35moe.ssm.time_step_rank u32 = 32
65
+ llama_model_loader: - kv 35: qwen35moe.ssm.inner_size u32 = 4096
66
+ llama_model_loader: - kv 36: qwen35moe.full_attention_interval u32 = 4
67
+ llama_model_loader: - kv 37: qwen35moe.rope.dimension_count u32 = 64
68
+ llama_model_loader: - kv 38: tokenizer.ggml.model str = gpt2
69
+ llama_model_loader: - kv 39: tokenizer.ggml.pre str = qwen35
70
+ llama_model_loader: - kv 40: tokenizer.ggml.tokens arr[str,248320] = ["!", "\"", "#", "$", "%", "&", "'", ...
71
+ llama_model_loader: - kv 41: tokenizer.ggml.token_type arr[i32,248320] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
72
+ llama_model_loader: - kv 42: tokenizer.ggml.merges arr[str,247587] = ["Ġ Ġ", "ĠĠ ĠĠ", "i n", "Ġ t",...
73
+ llama_model_loader: - kv 43: tokenizer.ggml.eos_token_id u32 = 248046
74
+ llama_model_loader: - kv 44: tokenizer.ggml.padding_token_id u32 = 248055
75
+ llama_model_loader: - kv 45: tokenizer.ggml.bos_token_id u32 = 248044
76
+ llama_model_loader: - kv 46: tokenizer.ggml.add_bos_token bool = false
77
+ llama_model_loader: - kv 47: tokenizer.chat_template str = {%- set image_count = namespace(value...
78
+ llama_model_loader: - kv 48: general.quantization_version u32 = 2
79
+ llama_model_loader: - kv 49: general.file_type u32 = 15
80
+ llama_model_loader: - kv 50: quantize.imatrix.file str = Qwen3.6-35B-A3B-GGUF/imatrix_unsloth....
81
+ llama_model_loader: - kv 51: quantize.imatrix.dataset str = unsloth_calibration_Qwen3.6-35B-A3B.txt
82
+ llama_model_loader: - kv 52: quantize.imatrix.entries_count u32 = 510
83
+ llama_model_loader: - kv 53: quantize.imatrix.chunks_count u32 = 76
84
+ llama_model_loader: - type f32: 361 tensors
85
+ llama_model_loader: - type q8_0: 252 tensors
86
+ llama_model_loader: - type q4_K: 78 tensors
87
+ llama_model_loader: - type q5_K: 38 tensors
88
+ llama_model_loader: - type q6_K: 4 tensors
89
+ print_info: file format = GGUF V3 (latest)
90
+ print_info: file type = Q4_K - Medium
91
+ print_info: file size = 20.81 GiB (5.16 BPW)
92
+ load: 0 unused tokens
93
+ load: printing all EOG tokens:
94
+ load: - 248044 ('<|endoftext|>')
95
+ load: - 248046 ('<|im_end|>')
96
+ load: - 248063 ('<|fim_pad|>')
97
+ load: - 248064 ('<|repo_name|>')
98
+ load: - 248065 ('<|file_sep|>')
99
+ load: special tokens cache size = 33
100
+ load: token to piece cache size = 1.7581 MB
101
+ print_info: arch = qwen35moe
102
+ print_info: vocab_only = 0
103
+ print_info: no_alloc = 0
104
+ print_info: n_ctx_train = 262144
105
+ print_info: n_embd = 2048
106
+ print_info: n_embd_inp = 2048
107
+ print_info: n_layer = 40
108
+ print_info: n_head = 16
109
+ print_info: n_head_kv = 2
110
+ print_info: n_rot = 64
111
+ print_info: n_swa = 0
112
+ print_info: is_swa_any = 0
113
+ print_info: n_embd_head_k = 256
114
+ print_info: n_embd_head_v = 256
115
+ print_info: n_gqa = 8
116
+ print_info: n_embd_k_gqa = 512
117
+ print_info: n_embd_v_gqa = 512
118
+ print_info: f_norm_eps = 0.0e+00
119
+ print_info: f_norm_rms_eps = 1.0e-06
120
+ print_info: f_clamp_kqv = 0.0e+00
121
+ print_info: f_max_alibi_bias = 0.0e+00
122
+ print_info: f_logit_scale = 0.0e+00
123
+ print_info: f_attn_scale = 0.0e+00
124
+ print_info: n_ff = 0
125
+ print_info: n_expert = 256
126
+ print_info: n_expert_used = 8
127
+ print_info: n_expert_groups = 0
128
+ print_info: n_group_used = 0
129
+ print_info: causal attn = 1
130
+ print_info: pooling type = -1
131
+ print_info: rope type = 40
132
+ print_info: rope scaling = linear
133
+ print_info: freq_base_train = 10000000.0
134
+ print_info: freq_scale_train = 1
135
+ print_info: n_ctx_orig_yarn = 262144
136
+ print_info: rope_yarn_log_mul = 0.0000
137
+ print_info: rope_finetuned = unknown
138
+ print_info: mrope sections = [11, 11, 10, 0]
139
+ print_info: ssm_d_conv = 4
140
+ print_info: ssm_d_inner = 4096
141
+ print_info: ssm_d_state = 128
142
+ print_info: ssm_dt_rank = 32
143
+ print_info: ssm_n_group = 16
144
+ print_info: ssm_dt_b_c_rms = 0
145
+ print_info: model type = 35B.A3B
146
+ print_info: model params = 34.66 B
147
+ print_info: general.name = Qwen3.6-35B-A3B
148
+ print_info: vocab type = BPE
149
+ print_info: n_vocab = 248320
150
+ print_info: n_merges = 247587
151
+ print_info: BOS token = 248044 '<|endoftext|>'
152
+ print_info: EOS token = 248046 '<|im_end|>'
153
+ print_info: EOT token = 248046 '<|im_end|>'
154
+ print_info: PAD token = 248055 '<|vision_pad|>'
155
+ print_info: LF token = 198 'Ċ'
156
+ print_info: FIM PRE token = 248060 '<|fim_prefix|>'
157
+ print_info: FIM SUF token = 248062 '<|fim_suffix|>'
158
+ print_info: FIM MID token = 248061 '<|fim_middle|>'
159
+ print_info: FIM PAD token = 248063 '<|fim_pad|>'
160
+ print_info: FIM REP token = 248064 '<|repo_name|>'
161
+ print_info: FIM SEP token = 248065 '<|file_sep|>'
162
+ print_info: EOG token = 248044 '<|endoftext|>'
163
+ print_info: EOG token = 248046 '<|im_end|>'
164
+ print_info: EOG token = 248063 '<|fim_pad|>'
165
+ print_info: EOG token = 248064 '<|repo_name|>'
166
+ print_info: EOG token = 248065 '<|file_sep|>'
167
+ print_info: max token length = 256
168
+ load_tensors: loading model tensors, this can take a while... (mmap = true, direct_io = false)
169
+ load_tensors: offloading 0 repeating layers to GPU
170
+ load_tensors: offloaded 0/41 layers to GPU
171
+ load_tensors: CPU_Mapped model buffer size = 20798.80 MiB
172
+ load_tensors: CPU_REPACK model buffer size = 20699.06 MiB
173
+ .................................................................................................
174
+ common_init_result: added <|endoftext|> logit bias = -inf
175
+ common_init_result: added <|im_end|> logit bias = -inf
176
+ common_init_result: added <|fim_pad|> logit bias = -inf
177
+ common_init_result: added <|repo_name|> logit bias = -inf
178
+ common_init_result: added <|file_sep|> logit bias = -inf
179
+ llama_context: constructing llama_context
180
+ llama_context: n_seq_max = 1
181
+ llama_context: n_ctx = 4096
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+ llama_context: n_ctx_seq = 4096
183
+ llama_context: n_batch = 2048
184
+ llama_context: n_ubatch = 512
185
+ llama_context: causal_attn = 1
186
+ llama_context: flash_attn = auto
187
+ llama_context: kv_unified = false
188
+ llama_context: freq_base = 10000000.0
189
+ llama_context: freq_scale = 1
190
+ llama_context: n_ctx_seq (4096) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
191
+ ggml_metal_init: allocating
192
+ ggml_metal_init: found device: Apple M1 Ultra
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+ ggml_metal_init: picking default device: Apple M1 Ultra
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+ ggml_metal_init: use fusion = true
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+ ggml_metal_init: use concurrency = true
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+ ggml_metal_init: use graph optimize = true
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+ llama_context: CPU output buffer size = 0.95 MiB
198
+ llama_kv_cache: CPU KV buffer size = 80.00 MiB
199
+ llama_kv_cache: size = 80.00 MiB ( 4096 cells, 10 layers, 1/1 seqs), K (f16): 40.00 MiB, V (f16): 40.00 MiB
200
+ llama_kv_cache: attn_rot_k = 0, n_embd_head_k_all = 256
201
+ llama_kv_cache: attn_rot_v = 0, n_embd_head_k_all = 256
202
+ llama_memory_recurrent: CPU RS buffer size = 62.81 MiB
203
+ llama_memory_recurrent: size = 62.81 MiB ( 1 cells, 40 layers, 1 seqs), R (f32): 2.81 MiB, S (f32): 60.00 MiB
204
+ sched_reserve: reserving ...
205
+ sched_reserve: Flash Attention was auto, set to enabled
206
+ sched_reserve: resolving fused Gated Delta Net support:
207
+ sched_reserve: fused Gated Delta Net (autoregressive) enabled
208
+ sched_reserve: fused Gated Delta Net (chunked) enabled
209
+ sched_reserve: MTL0 compute buffer size = 16.01 MiB
210
+ sched_reserve: CPU compute buffer size = 493.00 MiB
211
+ sched_reserve: graph nodes = 3729
212
+ sched_reserve: graph splits = 281 (with bs=512), 1 (with bs=1)
213
+ sched_reserve: reserve took 8.09 ms, sched copies = 1
214
+
215
+ === MoE Expert Routing Analysis ===
216
+ Model experts: 256, used per token: 8
217
+ Logging ALL MoE layers (--all-layers)
218
+ Will export expert ranking to: /Users/jdumay/.cache/mesh-llm/packages/unsloth/Qwen3.6-35B-A3B-GGUF/9280dd353ab587157920d5bd391ada414d84e552/variants/Q4_K_XL/ranking.csv
219
+ Running 10 prompts, generating 32 tokens each
220
+ Logging first 9999 MoE layers per eval
221
+
222
+ Prompt 1/10: <|im_start|>user
223
+ Write a Python function to find the nth Fib...
224
+ collected 2640 layer snapshots (total: 2640)
225
+ Prompt 2/10: <|im_start|>user
226
+ Write a Rust function that reads a CSV file...
227
+ collected 2640 layer snapshots (total: 5280)
228
+ Prompt 3/10: <|im_start|>user
229
+ Explain how a B-tree index works in a datab...
230
+ collected 2640 layer snapshots (total: 7920)
231
+ Prompt 4/10: <|im_start|>user
232
+ If all roses are flowers and some flowers f...
233
+ collected 2640 layer snapshots (total: 10560)
234
+ Prompt 5/10: <|im_start|>user
235
+ A train travels 120km in 2 hours. It then s...
236
+ collected 2640 layer snapshots (total: 13200)
237
+ Prompt 6/10: <|im_start|>user
238
+ Hello! What's the best way to learn a new l...
239
+ collected 2640 layer snapshots (total: 15840)
240
+ Prompt 7/10: <|im_start|>user
241
+ Tell me a joke about programmers.<|im_end|>...
242
+ collected 1840 layer snapshots (total: 17680)
243
+ Prompt 8/10: <|im_start|>user
244
+ Summarize the key differences between TCP a...
245
+ collected 2640 layer snapshots (total: 20320)
246
+ Prompt 9/10: <|im_start|>user
247
+ Translate 'The weather is beautiful today' ...
248
+ collected 2640 layer snapshots (total: 22960)
249
+ Prompt 10/10: <|im_start|>user
250
+ List 5 healthy breakfast options with brief...
251
+ collected 2640 layer snapshots (total: 25600)
252
+
253
+ === Expert Popularity (gate mass, summed across all tokens & logged layers) ===
254
+ Total tokens × layers: 3304225
255
+
256
+ Top 20 experts by gate mass:
257
+ Expert Mass Mass% Selected
258
+ 0 12897.1294 32.06 3277738
259
+ 243 153.0632 0.38 1539
260
+ 89 150.4899 0.37 1298
261
+ 60 149.0570 0.37 1645
262
+ 224 146.0130 0.36 1489
263
+ 64 142.5874 0.35 1426
264
+ 95 140.2351 0.35 1550
265
+ 189 140.1161 0.35 1045
266
+ 229 136.1348 0.34 1522
267
+ 36 136.1316 0.34 1348
268
+ 125 134.2771 0.33 1138
269
+ 108 133.8681 0.33 1271
270
+ 254 133.3297 0.33 1197
271
+ 167 133.2555 0.33 1316
272
+ 43 132.8598 0.33 1652
273
+ 88 132.6322 0.33 1366
274
+ 137 132.2451 0.33 1186
275
+ 165 131.3198 0.33 1255
276
+ 160 129.6081 0.32 1217
277
+ 46 129.0560 0.32 1160
278
+
279
+ Concentration:
280
+ Top 4 experts: 33.2% of total gate mass
281
+ Top 8 experts: 34.6% of total gate mass
282
+ Top 16 experts: 37.3% of total gate mass
283
+ Top 32 experts: 42.3% of total gate mass
284
+ Top 64 experts: 51.8% of total gate mass
285
+
286
+ Exported expert ranking to: /Users/jdumay/.cache/mesh-llm/packages/unsloth/Qwen3.6-35B-A3B-GGUF/9280dd353ab587157920d5bd391ada414d84e552/variants/Q4_K_XL/ranking.csv
287
+ Use with moe-split: --group-map <file generated from this ranking>
288
+
289
+
290
+ === Group Masking Analysis (best-group capture ratio) ===
291
+ For each group count, what fraction of the unrestricted top-8 mass
292
+ is captured by the best single group?
293
+
294
+ Groups Replicas Exp/Grp Mean P25 P50 P5
295
+ 2 0 128 0.999 1.000 1.000 1.000
296
+ 2 1 128 0.999 1.000 1.000 1.000
297
+ 2 2 128 0.999 1.000 1.000 1.000
298
+ 2 4 128 0.999 1.000 1.000 1.000
299
+ 2 8 128 0.999 1.000 1.000 1.000
300
+
301
+ 4 0 64 0.997 1.000 1.000 1.000
302
+ 4 1 64 0.997 1.000 1.000 1.000
303
+ 4 2 64 0.998 1.000 1.000 1.000
304
+ 4 4 64 0.998 1.000 1.000 1.000
305
+ 4 8 64 0.998 1.000 1.000 1.000
306
+
307
+ 8 0 32 0.997 1.000 1.000 1.000
308
+ 8 1 32 0.997 1.000 1.000 1.000
309
+ 8 2 32 0.997 1.000 1.000 1.000
310
+ 8 4 32 0.997 1.000 1.000 1.000
311
+ 8 8 32 0.997 1.000 1.000 1.000
312
+
313
+ === Interpretation ===
314
+ Mean close to 1.0 = masking barely hurts (best group captures most of top-k mass)
315
+ Mean < 0.7 = significant quality risk from group restriction
316
+ P5 close to 1.0 = even worst-case tokens are OK
317
+ P5 < 0.5 = some tokens will be badly served by any single group
318
+
319
+ === Phase 1b: Masked Generation Quality (logprob comparison) ===
320
+ Testing 4 groups (64 experts/group) vs baseline (all 256 experts)
321
+ Using first 5 prompts, generating 32 tokens each
322
+
323
+ Group 0 (experts 0-63 + 2 hot replicas): avg logprob delta = -0.3751
324
+ Group 1 (experts 64-127 + 2 hot replicas): avg logprob delta = -0.1213
325
+ Group 2 (experts 128-191 + 2 hot replicas): avg logprob delta = -0.1904
326
+ Group 3 (experts 192-255 + 2 hot replicas): avg logprob delta = -0.3067
327
+
328
+ === Interpretation (logprob delta) ===
329
+ Delta near 0.0 = masking barely affects generation quality
330
+ Delta < -0.1 = noticeable quality loss
331
+ Delta < -0.5 = significant degradation
332
+
333
+ ggml_metal_free: deallocating