curl http://localhost:8001/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Llama-2-7b-chat-hf/",
"prompt": "To continue from where I left off, the revised non-deterministic matrix I described earlier can be used to model a perceptron learning to classify input data into two categories.\n\nFor example, suppose we have the following input data:\n\n| X1 | X2 | Output |\n| --- | --- | --- |\n| 0 | 0 | 0 |\n| 0 | 1 | 1 |\n| 1 | 0 | 1 |\n| 1 | 1 | 0 |\n\nWe can use the revised non-deterministic matrix to determine the truth-value that corresponds to each input data point.\n\nFor the first input data point (X1 = 0, X2 = 0), the revised non-deterministic matrix returns a set containing only 0, which means that the perceptron is not activated. This is consistent with the output of 0 for this input data point.\n\nFor the second input data point (X1 = 0, X2 = 1), the revised non-deterministic matrix returns a set containing 1 and 2, which means that the perceptron is activated in both scenario 1 and scenario 2. In this case, we can choose either scenario 1 or scenario 2, since both are consistent with the output of 1 for this input data point.\n\nFor the third input data point (X1 = 1, X2 = 0), the revised non-deterministic matrix returns a set containing 1 and 2, which means that the perceptron is activated in both scenario 1 and scenario 2. In this case, we can choose either scenario 1 or scenario 2, since both are consistent with the output of 1 for this input data point.\n\nFinally, for the fourth input data point (X1 = 1, X2 = 1), the revised non-deterministic matrix returns a set containing only 0, which means that the perceptron is not activated. This is consistent with the output of 0 for this input data point.\n\nBy using this revised non-deterministic matrix, we can model the learning process of a perceptron, where the truth-values represent different scenarios of activation and the connectives allow us to combine the activation scenarios for different input features. The revised non-deterministic matrix allows us to model the non-deterministic behavior of the perceptron, where different activation scenarios may be possible for a given input data point. This is important for understanding how the perceptron is able to learn and classify input data into two categories.",
"max_tokens": 7,
"temperature": 0,
"stream": true
}'

curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Llama-2-7b-chat-hf/",
"prompt": "To continue from where I left off, the revised non-deterministic matrix I described earlier can be used to model a perceptron learning to classify input data into two categories.\n\nFor example, suppose we have the following input data:\n\n| X1 | X2 | Output |\n| --- | --- | --- |\n| 0 | 0 | 0 |\n| 0 | 1 | 1 |\n| 1 | 0 | 1 |\n| 1 | 1 | 0 |\n\nWe can use the revised non-deterministic matrix to determine the truth-value that corresponds to each input data point.\n\nFor the first input data point (X1 = 0, X2 = 0), the revised non-deterministic matrix returns a set containing only 0, which means that the perceptron is not activated. This is consistent with the output of 0 for this input data point.\n\nFor the second input data point (X1 = 0, X2 = 1), the revised non-deterministic matrix returns a set containing 1 and 2, which means that the perceptron is activated in both scenario 1 and scenario 2. In this case, we can choose either scenario 1 or scenario 2, since both are consistent with the output of 1 for this input data point.\n\nFor the third input data point (X1 = 1, X2 = 0), the revised non-deterministic matrix returns a set containing 1 and 2, which means that the perceptron is activated in both scenario 1 and scenario 2. In this case, we can choose either scenario 1 or scenario 2, since both are consistent with the output of 1 for this input data point.\n\nFinally, for the fourth input data point (X1 = 1, X2 = 1), the revised non-deterministic matrix returns a set containing only 0, which means that the perceptron is not activated. This is consistent with the output of 0 for this input data point.\n\nBy using this revised non-deterministic matrix, we can model the learning process of a perceptron, where the truth-values represent different scenarios of activation and the connectives allow us to combine the activation scenarios for different input features. The revised non-deterministic matrix allows us to model the non-deterministic behavior of the perceptron, where different activation scenarios may be possible for a given input data point. This is important for understanding how the perceptron is able to learn and classify input data into two categories.",
"max_tokens": 7,
"temperature": 0,
"stream": true
}'



curl http://localhost:8001/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Llama-2-7b-chat-hf/",
"prompt": "1+1=2, yes or no?",
"max_tokens": 100,
"temperature": 0,
"stream": true
}'

curl http://localhost:8000/v1/completions \
-H "Content-Type: application/json" \
-d '{
"model": "Llama-2-7b-chat-hf/",
"prompt": "1+1=2, yes or no?",
"max_tokens": 100,
"temperature": 0,
"stream": true
}'
