from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("shivam909067/Sifera-V1")
tokenizer = AutoTokenizer.from_pretrained("shivam909067/Sifera-V1")

messages = [
    {"role": "system", "content": "You are Sifera, an AI assistant for note-taking."},
    {"role": "user", "content": "Summarize this text: ..."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=500)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Training

Fine-tuned using LoRA on custom note-taking and summarization datasets.