Update README.md

Added text-generation pipeline documentation

README.md

@@ -52,11 +52,17 @@ model_name = "Deci/DeciLM-7B"
 device = "cuda" # for GPU usage or "cpu" for CPU usage
 
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, trust_remote_code=True).to(device)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", trust_remote_code=True).to(device)
 
 inputs = tokenizer.encode("In a shocking finding, scientists discovered a herd of unicorns living in", return_tensors="pt").to(device)
 outputs = model.generate(inputs, max_new_tokens=100, do_sample=True, top_p=0.95)
 print(tokenizer.decode(outputs[0]))
+
+# The model can also be used via the text-generation pipeline interface
+from transformers import pipeline
+generator = pipeline("text-generation", "Deci/DeciLM-7B", torch_dtype="auto", trust_remote_code=True, device=device)
+outputs = generator("In a shocking finding, scientists discovered a herd of unicorns living in", max_new_tokens=100, do_sample=True, top_p=0.95)
+print(outputs[0]["generated_text"])
 ```
 
 ## Evaluation