Sci-Fi Image Generator Prompt: Quantum Flux, Cyborg Oasis, Alien Metropolis, Galactic Nexus, Celestial Portal, Techno-Organic Fusion, Astral Frontier, Nano-Symbiosis, Cosmic Arcadia, Hyperdimensional Nexus, Synthetic Nebula, Bio-Mechanical Citadel, Virtual Cosmos, Transcendent Echoes, Futuristic Megalopolis, Exoplanetary Eden, Augmented Reality Dreamscape, Interstellar Beacon, Cybernetic Utopia, Robotic Serenity.

2、GEMMA-7B的使用

聊天

语言生成

前沿文本生成模型，能够理解文本、进行转换和生成代码。

T1、在线生成代码

Certainly! Here's a prompt in English that you can use to generate Python machine learning code:
"Please create a Python script that implements a machine learning algorithm for classifying iris flowers based on the iris dataset. The script should include steps for data loading, preprocessing, model training, and evaluation using accuracy as the metric. Use a popular machine learning library such as scikit-learn, and provide comments in the code for clarity."


#Import necessary libraries
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
 
#Load the iris dataset
iris_data = pd.read_csv('iris.csv')
 
#Preprocess the data
X = iris_data.drop('species', axis=1)
y = iris_data['species']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
#Train the logistic regression model
model = LogisticRegression()
model.fit(X_train, y_train)
 
#Evaluate the model
y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print('The accuracy of the model is:', accuracy)

T2、采用gemma-7b的API实现


from openai import OpenAI
 
client = OpenAI(
  base_url = "https://integrate.api.nvidia.com/v1",
  api_key = "$API_KEY_REQUIRED_IF_EXECUTING_OUTSIDE_NGC"
)
 
completion = client.chat.completions.create(
  model="google/gemma-7b",
  messages=[{"role":"user","content":"who are you?"}],
  temperature=0.5,
  top_p=1,
  max_tokens=1024,
  stream=True
)
 
for chunk in completion:
  if chunk.choices[0].delta.content is not None:
    print(chunk.choices[0].delta.content, end="")

3、LLAMA2-70B的使用

聊天

语言生成

前沿的大型语言人工智能模型，能够根据提示生成文本和代码。

T1、在线生成代码

Please create a Python script that implements a machine learning algorithm for classifying iris flowers based on the iris dataset. The script should include steps for data loading, preprocessing, model training, and evaluation using accuracy as the metric. Use a popular machine learning library such as scikit-learn, and provide comments in the code for clarity.


#Import necessary libraries
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.metrics import accuracy_score
 
#Load the iris dataset
iris = load_iris()
X = iris.data[:, :2]  #we only take the first two features.
y = iris.target
 
#Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
 
#Scale the data using StandardScaler
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)
 
#Train an SVM model on the scaled training data
svm = SVC(kernel='linear', C=1)
svm.fit(X_train_scaled, y_train)
 
#Evaluate the model on the test data
y_pred = svm.predict(X_test_scaled)
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy:.3f}")
 
#Use the trained model to make predictions on new data
new_data = [[5.1, 3.5]]
new_data = scaler.transform(new_data)
prediction = svm.predict(new_data)
print(f"Prediction: {prediction}")

T2、采用LLAMA2-70B的API实现


from openai import OpenAI
 
client = OpenAI(
  base_url = "https://integrate.api.nvidia.com/v1",
  api_key = "$API_KEY_REQUIRED_IF_EXECUTING_OUTSIDE_NGC"
)
 
completion = client.chat.completions.create(
  model="meta/llama2-70b",
  messages=[{"role":"user","content":"Please create a Python script that implements a machine learning algorithm for classifying iris flowers based on the iris dataset. The script should include steps for data loading, preprocessing, model training, and evaluation using accuracy as the metric. Use a popular machine learning library such as scikit-learn, and provide comments in the code for clarity."}],
  temperature=0.5,
  top_p=1,
  max_tokens=1024,
  stream=True
)
 
for chunk in completion:
  if chunk.choices[0].delta.content is not None:
    print(chunk.choices[0].delta.content, end="")

4、StabilityAI的Stable-Video-Diffusion使用

Stable-Video-Diffusion

图像到视频

NVIDIA NIM

稳定视频扩散（SVD）是一种生成扩散模型，利用单个图像作为条件帧来合成视频序列。

5、MistralAI的Mistral-7B-Instruct-v0.2使用

Mistral-7B-Instruct-v0.2

语言生成

NVIDIA NIM

这个LLM能够遵循指令，完成请求，并生成创造性的文本。

6、CodeLLAMA-70B的使用

聊天

代码生成

Nvidia官网人工智能大模型工具合集的案例应用

持续更新中……

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