nateraw/aidc-ai-business-marcoroni-13b 🔢📝 → 📝

▶️ 90 runs 📅 Sep 2023 ⚙️ Cog 0.8.6
document-summarization question-answering text-generation

About

Example Output

Output

Why did the machine learning algorithm go to the gym? To increase its weights and bench press its performance.

Performance Metrics

2.99s Prediction Time
2.96s Total Time
All Input Parameters 
{
  "top_k": 50,
  "top_p": 0.4,
  "message": "Write a short joke about machine learning",
  "temperature": 0.9,
  "max_new_tokens": 256
}
Input Parameters
top_k Type: integerDefault: 50
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 Type: numberDefault: 1
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) Type: string
temperature Type: numberDefault: 1
The value used to modulate the next token probabilities.
max_new_tokens Type: integerDefault: 256
The maximum number of tokens the model should generate as output.
Output Schema

Output

Type: array • Items Type: string

Version Details
Version ID
6cf4ddac2546aa3d612ed47202489a417a86ae2e42ed5d5f80e04470e39904fd
Version Created
September 19, 2023
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