Text Generation
Transformers
Safetensors
Finnish
llama
finnish
conversational
text-generation-inference
Ahma-3B / EasyLM /scripts /lm_eval_json.py
aapot
Add easylm training code
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import json
import mlxu
from EasyLM.serving import LMClient
FLAGS, FLAGS_DEF = mlxu.define_flags_with_default(
input_file='',
output_file='',
prefix_field='prefix',
text_field='text',
until_field='until',
eval_type='loglikelihood',
lm_client=LMClient.get_default_config(),
)
def main(argv):
lm_client = LMClient(FLAGS.lm_client)
with mlxu.open_file(FLAGS.input_file, 'r') as fin:
input_data = json.load(fin)
if FLAGS.eval_type == 'loglikelihood':
prefix = input_data[FLAGS.prefix_field]
text = input_data[FLAGS.text_field]
loglikelihoods, is_greedys = lm_client.loglikelihood(prefix, text)
output_data = {
'loglikelihood': loglikelihoods,
'is_greedy': is_greedys,
}
elif FLAGS.eval_type == 'loglikelihood_rolling':
text = input_data[FLAGS.text_field]
loglikelihoods, is_greedys = lm_client.loglikelihood_rolling(text)
output_data = {
'loglikelihood': loglikelihoods,
'is_greedy': is_greedys,
}
elif FLAGS.eval_type == 'greedy_until':
prefix = input_data[FLAGS.prefix_field]
until = input_data[FLAGS.until_field]
output_data = {'output_text': lm_client.greedy_until(prefix, until)}
elif FLAGS.eval_type == 'generate':
prefix = input_data[FLAGS.prefix_field]
output_data = {'output_text': lm_client.generate(prefix)}
else:
raise ValueError(f'Unknown eval_type: {FLAGS.eval_type}')
with mlxu.open_file(FLAGS.output_file, 'w') as fout:
json.dump(output_data, fout)
if __name__ == "__main__":
mlxu.run(main)