rebekah0302/Glo-Bus

from transformers import pipeline, set_seed generator = pipeline('text-generation', model='gpt2') set_seed(42) generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)

input_text = "The future of AI is" inputs = tokenizer(input_text, return_tensors="pt")

output = model.generate(**inputs, max_length=100) print(tokenizer.decode(output[0], skip_special_tokens=True))

Use a pipeline as a high-level helper

from transformers import pipeline

messages = [ {"role": "user", "content": "Who are you?"}, ] pipe = pipeline("text-generation", model="deepseek-ai/DeepSeek-R1", trust_remote_code=True) pipe(messages) from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("rebekah0302/Glo-Bus", set_active=True) from datasets import load_dataset

Login using e.g. huggingface-cli login to access this dataset

ds = load_dataset("open-thoughts/OpenThoughts-114k", "default") from adapters import AutoAdapterModel

model = AutoAdapterModel.from_pretrained("undefined") model.load_adapter("rebekah0302/Glo-Bus", set_active=True) from transformers import TrainingArguments, Trainer

training_args = TrainingArguments( output_dir="./results", evaluation_strategy="epoch", per_device_train_batch_size=4, per_device_eval_batch_size=4, num_train_epochs=3, save_total_limit=2, logging_dir="./logs", )

trainer = Trainer( model=model, args=training_args, train_dataset=tokenized_datasets["train"], )

trainer.train() from huggingface_hub import notebook_login

notebook_login() model.push_to_hub("your-huggingface-username/custom-gpt") tokenizer.push_to_hub("your-huggingface-username/custom-gpt") pip install gradio import gradio as gr from transformers import pipeline

generator = pipeline("text-generation", model="your-huggingface-username/custom-gpt")

def chatbot(prompt): return generator(prompt, max_length=100)[0]["generated_text"]

iface = gr.Interface(fn=chatbot, inputs="text", outputs="text") iface.launch()