seanoliver / bob-dylan-fun-tuning

Llama fine-tune-athon project training llama2 on bob dylan lyrics.

  • Public
  • 36 runs
  • A100 (80GB)
Iterate in playground

Input

*string
Shift + Return to add a new line

Prompt to send to the model.

integer
(minimum: 1)

Maximum number of tokens to generate. A word is generally 2-3 tokens

Default: 128

integer
(minimum: -1)

Minimum number of tokens to generate. To disable, set to -1. A word is generally 2-3 tokens.

Default: -1

number
(minimum: 0.01, maximum: 5)

Adjusts randomness of outputs, greater than 1 is random and 0 is deterministic, 0.75 is a good starting value.

Default: 0.75

number
(minimum: 0, maximum: 1)

When decoding text, samples from the top p percentage of most likely tokens; lower to ignore less likely tokens

Default: 0.9

integer
(minimum: 0)

When decoding text, samples from the top k most likely tokens; lower to ignore less likely tokens

Default: 50

string
Shift + Return to add a new line

A comma-separated list of sequences to stop generation at. For example, '<end>,<stop>' will stop generation at the first instance of 'end' or '<stop>'.

boolean

provide debugging output in logs

Default: false

Output

! The RAG vs. Fine Tuning competition is now in its second year, and this year we’re doing things a little differently. We’ve got a great new prize for the winner, and we’re inviting people to write a song about the competition, in the style of Bob Dylan! The prize for this year’s RAG vs. Fine Tuning competition is a free ticket to the UK RAG 2016, plus a free one-year subscription to the RAG. And we’re asking people to submit a song in the style of Bob Dylan about
Generated in

Run time and cost

This model costs approximately $0.036 to run on Replicate, or 27 runs per $1, but this varies depending on your inputs. It is also open source and you can run it on your own computer with Docker.

This model runs on Nvidia A100 (80GB) GPU hardware. Predictions typically complete within 26 seconds. The predict time for this model varies significantly based on the inputs.

Readme

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