Mortdecai
6bce2d4c3b
google/diffusiongemma-26B-A4B-it (released 2026-06-10) — Google's first open-weight text-diffusion LLM. Does NOT run in Ollama (unknown arch 'diffusion-gemma'); built llama-diffusion-cli from ggml-org/llama.cpp PR #24423 and smoke-tested Q4_K_M on steel141's 3090 Ti. - New reference doc with specs, build recipe, throughput, and gotchas - CORPUS_ollama_variants.md: "not an Ollama variant" callout - README index line for the reference doc - scripts/diffusiongemma-smoketest/ harness + raw result logs Findings: ~106 tok/s effective / ~2030 tok/s in-step-parallel; correct code + coherent reasoning; <|channel>thought CoT eats the 256-tok canvas so strict short formats need block budgeting. nvidia-smi index != CUDA index on steel141 (select 3090 Ti by UUID). Experimental research artifact, not homelab-deployable until diffusion arch merges to llama.cpp mainline. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
44 lines
8.2 KiB
Plaintext
44 lines
8.2 KiB
Plaintext
0.00.778.413 W load: control-looking token: 212 '</s>' was not control-type; this is probably a bug in the model. its type will be overridden
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0.00.778.868 W load: control-looking token: 50 '<|tool_response>' was not control-type; this is probably a bug in the model. its type will be overridden
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0.00.803.531 W load: special_eog_ids contains '<|tool_response>', removing '</s>' token from EOG list
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0.04.672.736 I diffusion: -n 512 -> 2 blocks, n_ubatch=2560 n_batch=2560 n_ctx=2560 (canvas_length=256)
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0.04.672.739 I diffusion: --fit has no effect here; context is sized from -n and the canvas. Set -ngl / --n-cpu-moe to control device memory.
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0.04.673.245 W llama_context: n_ctx_seq (2560) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
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0.04.677.664 W sched_reserve: layer 5 is assigned to device CUDA0 but the Flash Attention tensor is assigned to device CPU (usually due to missing support)
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0.04.677.667 W sched_reserve: Flash Attention was auto, set to disabled
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0.06.828.575 I diffusion_params: steps=128 schedule=0 algorithm=4 temperature=0.800 eps=0.001000 mask_token=4
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0.06.828.825 I diffusion_eb: max_steps=48 t=[0.400,0.800] entropy_bound=0.1000 stability=1 confidence=0.0050 kv_cache=on gpu_sampling=on sample_reduce=on
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0.06.829.987 W init: embeddings required but some input tokens were not marked as outputs -> overriding
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0.07.297.536 I
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diffusion step: 0/48 [ ] 0%0.07.401.754 I
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diffusion step: 1/48 [= ] 2%0.07.506.767 I
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diffusion step: 2/48 [== ] 4%0.07.612.593 I
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diffusion step: 3/48 [=== ] 6%0.07.720.060 I
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diffusion step: 4/48 [==== ] 8%0.07.827.278 I
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diffusion step: 5/48 [===== ] 10%0.07.933.928 I
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diffusion step: 6/48 [====== ] 12%0.08.042.088 I
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diffusion step: 7/48 [======= ] 14%0.08.150.433 I
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diffusion step: 8/48 [======== ] 16%0.08.259.575 I
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diffusion step: 9/48 [========= ] 18%0.08.367.825 I
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diffusion step: 10/48 [========== ] 20%0.08.475.147 I
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diffusion step: 11/48 [=========== ] 22%0.08.584.038 I
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diffusion step: 12/48 [============ ] 25%0.08.693.905 I
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diffusion step: 13/48 [============= ] 27%0.08.803.010 I
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diffusion step: 14/48 [============== ] 29%0.08.912.275 I
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diffusion step: 15/48 [=============== ] 31%0.09.021.574 I
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diffusion step: 16/48 [================ ] 33%0.09.130.837 I
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diffusion step: 17/48 [================= ] 35%0.09.240.344 I
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diffusion step: 18/48 [================== ] 37%0.09.350.482 I
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diffusion step: 19/48 [=================== ] 39%0.09.458.691 I
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diffusion step: 20/48 [==================== ] 41%0.09.458.811 W init: embeddings required but some input tokens were not marked as outputs -> overriding
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0.09.908.398 I
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diffusion step: 0/48 [ ] 0%0.10.012.788 I
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diffusion step: 1/48 [= ] 2%0.10.116.823 I
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diffusion step: 2/48 [== ] 4%0.10.221.634 I
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diffusion step: 3/48 [=== ] 6%0.10.327.638 I
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diffusion step: 4/48 [==== ] 8%0.10.436.834 I
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diffusion step: 5/48 [===== ] 10%0.10.544.714 I
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diffusion step: 6/48 [====== ] 12%0.10.652.512 I
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diffusion step: 7/48 [======= ] 14%0.10.762.174 I
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diffusion step: 8/48 [======== ] 16%0.10.871.902 I
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diffusion step: 9/48 [========= ] 18%0.10.984.597 I
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diffusion step: 10/48 [========== ] 20%0.11.095.808 I
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diffusion step: 11/48 [=========== ] 22%0.11.207.852 I
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diffusion step: 12/48 [============ ] 25%0.11.320.651 I
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diffusion step: 13/48 [============= ] 27%0.11.434.508 I
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diffusion step: 14/48 [============== ] 29%0.11.547.781 I
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diffusion step: 15/48 [=============== ] 31%0.11.660.326 I
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diffusion step: 16/48 [================ ] 33%0.11.773.495 I
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diffusion step: 17/48 [================= ] 35%0.11.886.310 I
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diffusion step: 18/48 [================== ] 37%0.12.002.123 I
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diffusion step: 19/48 [=================== ] 39%0.12.115.707 I
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diffusion step: 20/48 [==================== ] 41%0.12.229.939 I
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diffusion step: 21/48 [===================== ] 43%0.12.343.164 I
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diffusion step: 22/48 [====================== ] 45%0.12.458.359 I
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diffusion step: 23/48 [======================= ] 47%0.12.572.563 I
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diffusion step: 24/48 [========================= ] 50%0.12.685.770 I
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diffusion step: 25/48 [========================== ] 52%0.12.799.935 I
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diffusion step: 26/48 [=========================== ] 54%0.12.912.457 I
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diffusion step: 27/48 [============================ ] 56%0.13.028.569 I
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diffusion step: 28/48 [============================= ] 58%0.13.144.189 I
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diffusion step: 29/48 [============================== ] 60%
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<|channel>thought
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* Topic: Primary colors of light.
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* Constraint 1: Exactly five colors.
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* Constraint 2: One per line.
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* Constraint 3: Nothing else (no intro, no outro).
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* The standard primary colors of light (additive) are Red, Green, and Blue (3 colors).
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* Wait, the user is asking for *exactly five*.
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* Is there a system with five primary colors of light?
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* Standard RGB = Red, Green, Blue (3).
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* CMY (subtractive) = Cyan, Magenta, Yellow (3).
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* RYB (artistic) = Red, Yellow, Blue (3).
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* Some color space models or specific display technologies might use more primaries.
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* However, strictly speaking, "primary colors of light" refers to RGB.
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* If I must list *five*, I might need to look at a specific model or include the primaries and sub-primary/secondary colors, or perhaps the user is testing the model.
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* Actually, usually, when a prompt asks for "exactly X" of something that has "Y", it's often a trick or a request for a specific interpretation.
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* Is there a 5-primary system?
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* Some high-gamut displays use Red, Green, Blue, Cyan, Amber (RGB-CA).
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* In some contexts of color theory, people might list more combinations.
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* Let's look at the prompt again: "exactly five primary colors of light".
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* If I list only 3, I fail the "exactly five" constraint.
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* If I list 5, I might be factually incorrect regarding the standard "primary colors of light".
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* However, in the context of additive and subtractive primaries or specific spectral models, there might be five.
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* Let's check if there's a common "5 primary colors" list. Not really.
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* Maybe the user means Red, Green, Blue, Cyan, Magenta? No, those aren't all primaries.
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* Wait, perhaps the user is referring to the primary colors of light in a specific context?
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* Actually, if I provide 5 colors, I should satisfy the
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total time: 6314.54ms, time per step: 123.81ms (51 steps over 2 blocks, entropy-bound)
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throughput: 81.1 tok/s (512 tok in 6314.54ms), in-step parallel 2068 tok/s (256-tok canvas x 25.5 steps/block)
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WALL_SECONDS=13.802324462 RC=0
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