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from transformers import AutoTokenizer, AutoModelForCausalLM | |
class Petals: | |
"""Petals Bloom models.""" | |
def __init__( | |
self, | |
model_name="bigscience/bloom-petals", | |
temperature=0.7, | |
max_new_tokens=256, | |
top_p=0.9, | |
top_k=None, | |
do_sample=True, | |
max_length=None | |
): | |
self.model_name = model_name | |
self.temperature = temperature | |
self.max_new_tokens = max_new_tokens | |
self.top_p = top_p | |
self.top_k = top_k | |
self.do_sample = do_sample | |
self.max_length = max_length | |
self.tokenizer = AutoTokenizer.from_pretrained(model_name) | |
self.model = AutoModelForCausalLM.from_pretrained(model_name) | |
def _default_params(self): | |
"""Get the default parameters for calling Petals API.""" | |
return { | |
"temperature": self.temperature, | |
"max_new_tokens": self.max_new_tokens, | |
"top_p": self.top_p, | |
"top_k": self.top_k, | |
"do_sample": self.do_sample, | |
"max_length": self.max_length, | |
} | |
def generate(self, prompt): | |
"""Generate text using the Petals API.""" | |
params = self._default_params() | |
inputs = self.tokenizer(prompt, return_tensors="pt")["input_ids"] | |
outputs = self.model.generate(inputs, **params) | |
return self.tokenizer.decode(outputs[0]) |