Potential Maintainers

Maintainers:

https://gist.github.com/GrahamcOfBorg/fe6fd64579db2302b7c736c6787f8e2d.js

from transformers import BloomForCausalLM, BloomTokenizerFast

Załaduj model i tokenizer

model = BloomForCausalLM.from_pretrained("bigscience/bloom")
tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom")

Przykład generowania tekstu

input_text = "Hello, how are you?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(inputs["input_ids"], max_length=50)

Dekoduj wynik

result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)

https://gist.github.com/GrahamcOfBorg/fe6fd64579db2302b7c736c6787f8e2d.js

from transformers import BloomForCausalLM, BloomTokenizerFast

Załaduj model i tokenizer

model = BloomForCausalLM.from_pretrained("bigscience/bloom")
tokenizer = BloomTokenizerFast.from_pretrained("bigscience/bloom")

Przykład generowania tekstu

input_text = "Hello, how are you?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(inputs["input_ids"], max_length=50)

Dekoduj wynik

result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)

100000 usd

It is like a handy guide for loading and using Bloom models having clear examples for tokenization and text generation really helps streamline experimentation. Step-by-step tutorials like this make complex setups feel approachable, much like how platforms like win pkr 888 keep everything simple and engaging for users.

This example makes loading a model and tokenizer with Hugging Face Transformers really easy to follow. The step-by-step text generation and decoding is super clear definitely helpful for anyone experimenting with NLP and even testing ideas for ar999game.