adirik/mamba-2.8b-slimpj

Base version of Mamba 2.8B Slim Pyjama, a 2.8 billion parameter state space language model

  • Public
  • 26 runs

Run time and cost

This model runs on Nvidia A40 GPU hardware. Predictions typically complete within 144 seconds. The predict time for this model varies significantly based on the inputs.

Readme

Mamba

Mamba is a large language model with state space model architecture showing promising performance on information-dense data such as language modeling. See the original repo and paper for details.

Basic Usage

The API input arguments are as follows:

  • prompt: The text prompt for Mamba.
  • 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.
  • top_p: Samples from the top p percentage of most likely tokens during text decoding, lower to ignore less likely tokens.
  • top_k: Samples from the top k most likely tokens during text decoding, lower to ignore less likely tokens.
  • repetition_penalty: Penalty for repeated words in generated text; 1 is no penalty, values greater than 1 discourage repetition, less than 1 encourage it.
  • seed: The seed parameter for deterministic text generation. A specific seed can be used to reproduce results or left blank for random generation.

References

@article{mamba,
  title={Mamba: Linear-Time Sequence Modeling with Selective State Spaces},
  author={Gu, Albert and Dao, Tri},
  journal={arXiv preprint arXiv:2312.00752},
  year={2023}
}