Returns the configuration of a causal model
Usage
causal_config(
model = getOption("pangoling.causal.default"),
checkpoint = NULL,
config_model = NULL
)Arguments
- model
Name of a pre-trained model or folder.
- checkpoint
Folder of a checkpoint.
- config_model
List with other arguments that control how the model from Hugging Face is accessed.
Details
A causal language model (also called GPT-like, auto-regressive, or decoder model) is a type of large language model usually used for text-generation that can predict the next word (or more accurately in fact token) based on a preceding context.
If not specified, the causal model that will be used is the one set in
specified in the global option pangoling.causal.default, this can be
accessed via getOption("pangoling.causal.default") (by default
"gpt2"). To change the default option
use options(pangoling.causal.default = "newcausalmodel").
A list of possible causal models can be found in Hugging Face website.
Using the config_model and config_tokenizer arguments, it's possible to
control how the model and tokenizer from Hugging Face is accessed, see the
Python method
from_pretrained
for details.
In case of errors when a new model is run, check the status of https://status.huggingface.co/
See also
Other causal model functions:
causal_lp_mats(),
causal_lp(),
causal_next_tokens_tbl(),
causal_preload(),
causal_tokens_lp_tbl()