nateraw/codellama-7b-instruct-hf 🔢📝 → 📝
Example Output
Output
Here is an example of how you can read in a csv file and calculate the 3 day rolling average of the "profit" column using the "date" column in Python:
import pandas as pd
# Read in the csv file
df = pd.read_csv('your_data.csv')
# Calculate the 3 day rolling average of the "profit" column using the "date" column
df['3_day_rolling_average'] = df['profit'].rolling(window=3).mean()
# Print the results
print(df)
This will give you the 3 day rolling average of the "profit" column for each row in the data frame.
Alternatively, you can also use the rolling_mean function from the pandas library:
df['3_day_rolling_average'] = df.rolling_mean(window=3, column='profit', date_column='date')
This will give you the same result as the previous code.
Performance Metrics
14.91s
Prediction Time
16.01s
Total Time
All Input Parameters
{
"top_k": 50,
"top_p": 0.95,
"message": "Write a function to load a csv file and find the 3 day rolling average of the \"profit\" column using the \"date\" column.",
"temperature": 1,
"max_new_tokens": 1024
}
Input Parameters
- top_k
- The number of highest probability tokens to consider for generating the output. If > 0, only keep the top k tokens with highest probability (top-k filtering).
- top_p
- A probability threshold for generating the output. If < 1.0, only keep the top tokens with cumulative probability >= top_p (nucleus filtering). Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751).
- message (required)
- temperature
- The value used to modulate the next token probabilities.
- max_new_tokens
- The maximum number of tokens the model should generate as output.
Output Schema
Output
Example Execution Logs
Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.
Version Details
- Version ID
efe614f6aa2d04c1e153dc33e4bc54a3ce97df8f3da7021d15a79233face5c30- Version Created
- September 19, 2023