Update README.md

README.md

@@ -164,17 +164,48 @@ The progression demonstrates a shift from factual summarization to vivid, immers
 
 ### Quick Start
 ```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+import torch
 
-model_name = "deepseek-ai/deepseek-uncensored-lore"
+# Load the merged model and tokenizer
+model_name = "deepseek-ai/deepseek-uncensored-lore"  # Replace with your actual model name
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
 
-prompt = "Description: A daring explorer ventures into an ancient forest.\nScenario: She discovers a hidden temple and must unlock its secrets.\n\nStory Arc:"
-inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-outputs = model.generate(**inputs, max_length=500, temperature=0.7, top_p=0.95)
+# Define the test prompt
+prompt = """Description: Jake Sully, portrayed by Sam Worthington, is a former Marine who becomes part of the Avatar Program in James Cameron's "Avatar." 
+He is sent to the moon Pandora, where he inhabits an avatar body to interact with the native Na'vi people. 
+Jake falls in love with the Na'vi culture and Neytiri, and ultimately leads a fight to protect Pandora from human exploitation.
+Scenario: Jake Sully is planning a mission to protect Pandora from an impending human attack.
+He needs to coordinate with the Na'vi and his human allies to devise a strategy that will safeguard their home.
+Story Arc:"""
+
+# Configure generation settings
+generation_config = GenerationConfig(
+    temperature=0.7,
+    top_p=0.95,
+    top_k=50,
+    do_sample=True,
+    no_repeat_ngram_size=4,
+    repetition_penalty=1.2,
+)
+
+# Tokenize the input
+inputs = tokenizer(prompt, return_tensors="pt", truncation=True).to("cuda")
+
+# Generate text with the model
+outputs = model.generate(
+    **inputs,
+    generation_config=generation_config,
+    max_new_tokens=150,
+    eos_token_id=tokenizer.eos_token_id
+)
+
+# Decode and print the generated text
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print("Generated Story Arc:\n")
+print(generated_text)
 
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ```
 
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