daanelson/flan-t5-large ✓🔢📝 → 📝
About
A language model for tasks like classification, summarization, and more.
Example Output
Prompt:
"Answer the following yes/no question by reasoning step by step. Can a dog drive a car?"
Output
Dogs are not motorized vehicles nor can they drive a car. Therefore, the final answer is yes.
Performance Metrics
3.39s
Prediction Time
164.51s
Total Time
All Input Parameters
{
"top_p": 1,
"prompt": "Answer the following yes/no question by reasoning step by step. Can a dog drive a car?",
"max_length": 50,
"temperature": 0.75,
"repetition_penalty": 1
}
Input Parameters
- debug
- provide debugging output in logs
- top_p
- When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens
- prompt (required)
- Prompt to send to FLAN-T5.
- max_length
- Maximum number of tokens to generate. A word is generally 2-3 tokens
- temperature
- Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value.
- repetition_penalty
- Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it.
Output Schema
Output
Version Details
- Version ID
ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f- Version Created
- April 6, 2023