Text Generation
Transformers
Safetensors
English
llama
conversational
text-generation-inference
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Update README.md

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@@ -5,6 +5,58 @@ datasets:
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  - TokenBender/code_instructions_122k_alpaca_style
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  metrics:
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  - accuracy
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  pipeline_tag: text-generation
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  base_model: codellama/CodeLlama-13b-Instruct-hf
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  ---
 
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  - TokenBender/code_instructions_122k_alpaca_style
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  metrics:
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  - accuracy
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+ language:
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+ - en
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+ widget:
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+ - text: |
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+ ### Instruction: write a neural network complete code in Keras
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+ ### Input: Use cifar dataset
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+ ### Response:
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+
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+ output:
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+ text: >-
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+ from keras.datasets import cifar10
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+ import numpy as np
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+ from keras.models import Sequential
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+ from keras.layers import Dense, Dropout, Activation, Flatten
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+ from keras.layers import Conv2D, MaxPooling2D
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+ from keras.utils import to_categorical
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+ from keras.optimizers import Adam
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+
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+ # Load the CIFAR-10 dataset
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+ (X_train, y_train), (X_test, y_test) = cifar10.load_data()
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+
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+ # Normalize pixel values to be between 0 and 1
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+ X_train = X_train.astype('float32') / 255
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+ X_test = X_test.astype('float32') / 255
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+
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+ # Convert class vectors to binary class matrices
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+ y_train = to_categorical(y_train, num_classes=10)
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+ y_test = to_categorical(y_test, num_classes=10)
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+
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+ # Create the model
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+ model = Sequential()
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+ model.add(Conv2D(32, (3, 3), input_shape=(32, 32, 3)))
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+ model.add(Activation('relu'))
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+ model.add(MaxPooling2D(pool_size=(2, 2)))
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+
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+ model.add(Conv2D(64, (3, 3)))
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+ model.add(Activation('relu'))
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+ model.add(MaxPooling2D(pool_size=(2, 2)))
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+
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+ model.add(Flatten())
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+ model.add(Dense(64))
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+ model.add(Activation('relu'))
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+ model.add(Dropout(0.5))
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+ model.add(Dense(10))
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+ model.add(Activation('softmax'))
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+
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+ # Compile the model
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+ model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])
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+
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+ # Train the model
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+ model.fit(X_train, y_train, batch_size=32, epochs=10, validation_split=0.2)
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+
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  pipeline_tag: text-generation
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  base_model: codellama/CodeLlama-13b-Instruct-hf
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  ---