Model: aiplanet/panda-coder-13B
Author: aiplanet
Pipeline: text-generation
Library: transformers
Tags: transformers, safetensors, llama, text-generation, conversational, en, dataset:TokenBender/code_instructions_122k_alpaca_style, dataset:nickrosh/Evol-Instruct-Code-80k-v1, base_model:codellama/CodeLlama-13b-Instruct-hf, base_model:finetune:codellama/CodeLlama-13b-Instruct-hf, license:apache-2.0, text-generation-inference, endpoints_compatible, region:us
Base models (finetune): id: codellama/CodeLlama-13b-Instruct-hf
Card metadata: library_name: transformers
license: apache-2.0
datasets: TokenBender/code_instructions_122k_alpaca_style, nickrosh/Evol-Instruct-Code-80k-v1
metrics: accuracy
language: en
widget: text: ### Instruction: write a neural network complete code in Keras 
### Input: Use cifar dataset 
### Response:

output: text: from keras.datasets import cifar10 import numpy as np from keras.models import Sequential from keras.layers import Dense, Dropout, Activation, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.utils import to_categorical from keras.optimizers import Adam
# Load the CIFAR-10 dataset (X_train, y_train), (X_test, y_test) = cifar10.load_data()
# Normalize pixel values to be between 0 and 1 X_train = X_train.astype('float32') / 255 X_test = X_test.astype('float32') / 255
# Convert class vectors to binary class matrices y_train = to_categorical(y_train, num_classes=10) y_test = to_categorical(y_test, num_classes=10)
# Create the model model = Sequential() model.add(Conv2D(32, (3, 3), input_shape=(32, 32, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3))) model.add(Activation('relu')) model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten()) model.add(Dense(64)) model.add(Activation('relu')) model.add(Dropout(0.5)) model.add(Dense(10)) model.add(Activation('softmax'))
# Compile the model model.compile(loss='categorical_crossentropy', optimizer=Adam(), metrics=['accuracy'])
# Train the model model.fit(X_train, y_train, batch_size=32, epochs=10, validation_split=0.2)
pipeline_tag: text-generation
base_model: codellama/CodeLlama-13b-Instruct-hf
Likes: 13 | All-time downloads: 26563
Created: 2023-10-03 14:44:33