README.md

---
license: mit
datasets:
- open-thoughts/OpenThoughts-114k
language:
- en
metrics:
- accuracy
base_model:
- deepseek-ai/DeepSeek-R1
new_version: deepseek-ai/DeepSeek-R1
pipeline_tag: question-answering
library_name: adapter-transformers
---
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()