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
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
0.00.873.644 W load: special_eog_ids contains '<|tool_response>', removing '</s>' token from EOG list
0.04.996.883 I diffusion: -n 160 -> 1 blocks, n_ubatch=2304 n_batch=2304 n_ctx=2304 (canvas_length=256)
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.
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
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)
0.05.002.416 W sched_reserve: Flash Attention was auto, set to disabled
0.07.343.745 I diffusion_params: steps=128 schedule=0 algorithm=4 temperature=0.800 eps=0.001000 mask_token=4
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
0.07.345.228 W init: embeddings required but some input tokens were not marked as outputs -> overriding
0.07.822.959 I 
diffusion step: 0/48 [                                                  ] 0%0.07.925.523 I 
diffusion step: 1/48 [=                                                 ] 2%0.08.028.933 I 
diffusion step: 2/48 [==                                                ] 4%0.08.132.168 I 
diffusion step: 3/48 [===                                               ] 6%0.08.238.755 I 
diffusion step: 4/48 [====                                              ] 8%0.08.343.911 I 
diffusion step: 5/48 [=====                                             ] 10%0.08.450.845 I 
diffusion step: 6/48 [======                                            ] 12%0.08.558.796 I 
diffusion step: 7/48 [=======                                           ] 14%0.08.665.123 I 
diffusion step: 8/48 [========                                          ] 16%0.08.771.930 I 
diffusion step: 9/48 [=========                                         ] 18%0.08.881.776 I 
diffusion step: 10/48 [==========                                        ] 20%0.08.988.729 I 
diffusion step: 11/48 [===========                                       ] 22%0.09.096.269 I 
diffusion step: 12/48 [============                                      ] 25%
<|channel>thought
*   Task: Write a Python function `is_prime(n)`.
    *   Requirement: Include a docstring.
    *   Constraint: Output *only* the code, no explanation.

    *   A prime number is a natural number greater than 1 that has no positive divisors other than 1 and itself.
    *   If $n \le 1$, not prime.
    *   If $n = 2$, prime.
    *   If $n$ is even and $>2$, not prime.
    *   Check divisors from 3 to $\sqrt{n}$.

    ```python
    def is_prime(n):
        """
        Determines if a number n is prime.

        Args:
            n (int): The number to check.

        Returns:
            bool: True if n is prime, False otherwise.
        """
        if n <= 1:
            return False
        if n == 2:
            return True
        if n % 2 == 0:
            return False
        for i in range(3, int(n**0.5) + 1, 
total time: 1751.40ms, time per step: 134.72ms (13 steps over 1 blocks, entropy-bound)
throughput: 146.2 tok/s (256 tok in 1751.40ms), in-step parallel 1900 tok/s (256-tok canvas x 13.0 steps/block)
WALL_SECONDS=9.693752862 RC=0