`specialized_infer.py` returns `401 Client Error` about octopus-v4 HOT 3 CLOSED

$ python specialized_infer.py
`flash-attention` package not found, consider installing for better performance: No module named 'flash_attn'.
Current `flash-attenton` does not support `window_size`. Either upgrade or use `attn_implementation='eager'`.
Loading checkpoint shards: 100% 2/2 [00:02<00:00,  1.45s/it]
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.

============= Below is Octopus-V4 response ==============

You are not running the flash-attention implementation, expect numerical differences.
2024-06-04 00:10:05.319401: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
2024-06-04 00:10:05.372195: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-06-04 00:10:05.372249: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-06-04 00:10:05.374141: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-06-04 00:10:05.382628: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.
To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.
2024-06-04 00:10:06.476382: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
<nexa_4> ('Determine the derivative of the function f(x) = x^3 at the point where x equals 2, and interpret the result within the context of rate of change and tangent slope.')<nexa_end>
Elapsed time: 7.09s
Functional Token: <nexa_4>
Format Argument: Determine the derivative of the function f(x) = x^3 at the point where x equals 2, and interpret the result within the context of rate of change and tangent slope.

============= Below is specialized LLM response ==============

config.json: 100% 623/623 [00:00<00:00, 5.82MB/s]
pytorch_model.bin.index.json: 100% 23.9k/23.9k [00:00<00:00, 57.6MB/s]
Downloading shards:   0% 0/2 [00:00<?, ?it/s]
pytorch_model-00001-of-00002.bin:   0% 0.00/9.94G [00:00<?, ?B/s]
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pytorch_model-00001-of-00002.bin: 100% 9.94G/9.94G [00:34<00:00, 290MB/s]
Downloading shards:  50% 1/2 [00:34<00:34, 34.50s/it]
pytorch_model-00002-of-00002.bin:   0% 0.00/4.54G [00:00<?, ?B/s]
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pytorch_model-00002-of-00002.bin: 100% 4.54G/4.54G [00:15<00:00, 290MB/s]
Downloading shards: 100% 2/2 [00:50<00:00, 25.15s/it]
Loading checkpoint shards: 100% 2/2 [00:03<00:00,  1.98s/it]
generation_config.json: 100% 120/120 [00:00<00:00, 1.15MB/s]
WARNING:root:Some parameters are on the meta device device because they were offloaded to the cpu.
tokenizer_config.json: 100% 1.69k/1.69k [00:00<00:00, 16.3MB/s]
tokenizer.model: 100% 493k/493k [00:00<00:00, 198MB/s]
added_tokens.json: 100% 90.0/90.0 [00:00<00:00, 711kB/s]
special_tokens_map.json: 100% 101/101 [00:00<00:00, 948kB/s]
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.
Setting `pad_token_id` to `eos_token_id`:32000 for open-end generation.


To find the derivative of the function f(x) = x^3 at the point where x equals 2, we will use the power rule of differentiation. The power rule states that if a function is in the form f(x) = x^n, then the derivative of the function is f'(x) = n * x^(n-1).

In this case, n = 3, so the derivative of f(x) = x^3 is f'(x) = 3 * x^2.

Now, we need to evaluate the derivative at x = 2:

f'(2) = 3 * (2)^2 = 3 * 4 = 12

So, the derivative of f(x) = x^3 at the point where x equals 2 is f'(2) = 12.

Interpreting the result within the context of rate of change and tangent slope:

The derivative of a function represents the rate of change of the function with respect to the independent variable. In this case, the rate of change of f(x) = x^3 with respect to x at x = 2 is 12.

The tangent slope at the point (2, f(2)) is also equal to the derivative f'(2) = 12. This means that the tangent line to the curve y = x^3 at the point (2, 8) has a slope of 12.

In conclusion, the derivative of f(x) = x^3 at the point where x equals 2 is f'(2) = 12, which represents the rate of change of the function and the slope of the tangent line at that point.