monsterapi/Falcon_40B_dolly15k

Finetuning Overview:

Model Used: tiiuae/falcon-40b
Dataset: Databricks-dolly-15k

Dataset Insights:

The Databricks-dolly-15k dataset, comprising over 15,000 records, stands as a testament to the dedication of numerous Databricks professionals. Aimed at refining the interactive capabilities of systems like ChatGPT, the dataset offers:

• Prompt/response pairs across eight distinct instruction categories.
• A blend of the seven categories from the InstructGPT paper and an open-ended category.
• Original content, devoid of generative AI influence and primarily offline-sourced, with exceptions for Wikipedia references.
• Interactive sessions where contributors could address and rephrase peer questions.

Note: Some data categories incorporate Wikipedia references, evident from bracketed citation numbers, e.g., [42]. Exclusion is recommended for downstream applications.

Finetuning Details:

Leveraging MonsterAPI's no-code LLM finetuner, our finetuning emphasized:

• Cost-Effectiveness: A complete run at just $11.8.
• Efficiency: Using an A6000 48GB GPU, the session concluded in 5 hours and 40 minutes.

Hyperparameters & Additional Details:

• Epochs: 1
• Learning Rate: 0.0002
• Data Split: Training 90% / Validation 10%
• Gradient Accumulation Steps: 4

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Prompt Structure:

### INSTRUCTION:
[instruction]

[context]

### RESPONSE:
[response]

Loss metrics

Training loss:

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license: apache-2.0