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>
45 lines
3.6 KiB
Plaintext
45 lines
3.6 KiB
Plaintext
0.00.836.466 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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0.00.837.141 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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0.00.873.644 W load: special_eog_ids contains '<|tool_response>', removing '</s>' token from EOG list
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0.04.996.883 I diffusion: -n 160 -> 1 blocks, n_ubatch=2304 n_batch=2304 n_ctx=2304 (canvas_length=256)
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0.04.996.886 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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0.04.997.441 W llama_context: n_ctx_seq (2304) < n_ctx_train (262144) -- the full capacity of the model will not be utilized
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0.05.002.413 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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0.05.002.416 W sched_reserve: Flash Attention was auto, set to disabled
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0.07.343.745 I diffusion_params: steps=128 schedule=0 algorithm=4 temperature=0.800 eps=0.001000 mask_token=4
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0.07.344.014 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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0.07.345.228 W init: embeddings required but some input tokens were not marked as outputs -> overriding
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0.07.822.959 I
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diffusion step: 0/48 [ ] 0%0.07.925.523 I
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diffusion step: 1/48 [= ] 2%0.08.028.933 I
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diffusion step: 2/48 [== ] 4%0.08.132.168 I
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diffusion step: 3/48 [=== ] 6%0.08.238.755 I
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diffusion step: 4/48 [==== ] 8%0.08.343.911 I
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diffusion step: 5/48 [===== ] 10%0.08.450.845 I
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diffusion step: 6/48 [====== ] 12%0.08.558.796 I
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diffusion step: 7/48 [======= ] 14%0.08.665.123 I
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diffusion step: 8/48 [======== ] 16%0.08.771.930 I
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diffusion step: 9/48 [========= ] 18%0.08.881.776 I
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diffusion step: 10/48 [========== ] 20%0.08.988.729 I
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diffusion step: 11/48 [=========== ] 22%0.09.096.269 I
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diffusion step: 12/48 [============ ] 25%
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<|channel>thought
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* Task: Write a Python function `is_prime(n)`.
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* Requirement: Include a docstring.
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* Constraint: Output *only* the code, no explanation.
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* A prime number is a natural number greater than 1 that has no positive divisors other than 1 and itself.
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* If $n \le 1$, not prime.
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* If $n = 2$, prime.
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* If $n$ is even and $>2$, not prime.
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* Check divisors from 3 to $\sqrt{n}$.
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```python
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def is_prime(n):
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"""
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Determines if a number n is prime.
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Args:
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n (int): The number to check.
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Returns:
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bool: True if n is prime, False otherwise.
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"""
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if n <= 1:
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return False
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if n == 2:
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return True
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if n % 2 == 0:
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return False
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for i in range(3, int(n**0.5) + 1,
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total time: 1751.40ms, time per step: 134.72ms (13 steps over 1 blocks, entropy-bound)
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throughput: 146.2 tok/s (256 tok in 1751.40ms), in-step parallel 1900 tok/s (256-tok canvas x 13.0 steps/block)
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WALL_SECONDS=9.693752862 RC=0
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