from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

tokenizer = AutoTokenizer.from_pretrained("Saif658/Saif-1.1")
model = AutoModelForCausalLM.from_pretrained(
    "Saif658/Saif-1.1",
    torch_dtype=torch.float16,
    device_map="auto"
)

messages = [{"role": "user", "content": "your message here"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to("cuda")
outputs = model.generate(inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Limitations

Base model: Saif658/Saif-1.0
Fine-tuned by: Saif658
Training: QLoRA 4-bit, 500 steps
Dataset: angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k
License: Apache 2.0

Small 3B model — may struggle with very complex reasoning or long context tasks.