Update code examples with diffusion-specific parameters

README.md

@@ -164,17 +164,17 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 tokenizer = AutoTokenizer.from_pretrained("codelion/dhara-70m")
 model = AutoModelForCausalLM.from_pretrained("codelion/dhara-70m", trust_remote_code=True)
 
-# Generate text
+# Generate text using diffusion sampling
 inputs = tokenizer("The future of AI is", return_tensors="pt")
 outputs = model.generate(
     **inputs,
-    max_length=50,
+    max_new_tokens=40,          # Generate 40 new tokens
+    num_diffusion_steps=10,      # Diffusion denoising steps (higher = better quality)
     do_sample=True,
     temperature=0.8,
-    top_p=0.9,
-    pad_token_id=tokenizer.eos_token_id
+    top_p=0.9
 )
-print(tokenizer.decode(outputs[0]))
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
 ### Batch Generation (High Throughput)