Simple python interface to access models from Genta API.
Project description
genta-api
Genta Python API Package.
Installation
This API wrapper can be installed by cloning the repository and running pip install .
in the root directory. You can also use pip install genta
Examples
You can use this module like so:
from genta_api import GentaAPI
api = GentaAPI(token='your-token')
response, statusCode = api.ChatCompletion(
chat_history=[
{ "role": "user", "text": "Hello, how are you?" },
{ "role": "assistant", "text": "I'm fine, thank you." },
{ "role": "user", "text": "That's good to hear." },
],
model_name='Starstreak',
best_of=1,
decoder_input_details=True,
details=True,
do_sample=True,
max_new_tokens=128,
repetition_penalty=1.03,
return_full_text=False,
seed=None,
stop=[],
temperature=0.7,
top_k=50,
top_n_tokens=None,
top_p=0.95,
truncate=None,
typical_p=0.95,
watermark=False,
)
print(response, statusCode)
A variety of options can be configured when calling the ChatCompletion
method:
chat_history
: a list of dictionaries, each containing a "role" (user, assistant, or system) and "text".model_name
: the name of the machine learning model to use. By default, it uses 'Starstreak'.best_of
: an integer specifying the number of responses the model should generate. The best one will be chosen. Default is 1.decoder_input_details
,details
,do_sample
: boolean flags to control various model behaviors.max_new_tokens
: the maximum output length that the model will produce.repetition_penalty
: a penalty for repetitive output. Default is 1.03.return_full_text
: whether the output includes original input text as well. Default is False.seed
: an integer to seed random responses.stop
: a list of stop tokens.temperature
: a measure of randomness in the output.top_k
,top_n_tokens
,top_p
,truncate
,typical_p
: control parameters for the output.watermark
: whether to include a watermark in the output. Default is False.
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