Update README.md

README.md

@@ -51,28 +51,66 @@ To utilize the LlamaLens model for inference, follow these steps:
 Use the transformers library to load the LlamaLens model and its tokenizer:
 
 ```python
-from transformers import pipeline
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Define model path
+MODEL_PATH = "QCRI/LlamaLens"
+
+# Load model and tokenizer
+device_map = "auto"
+model = AutoModelForCausalLM.from_pretrained(MODEL_PATH, device_map=device_map)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH, trust_remote_code=True)
+tokenizer.pad_token = tokenizer.eos_token
 
-model_name = "QCRI/LlamaLens"
-pipe = pipeline("text-generation", model=model_name)
 ```
 3. **Prepare the Input:**:
 Tokenize your input text:
 ```python
-input_text = "Your input text here"
-system_message = "Your system message text here"
+# Define task and input text
+task = "classification"  # Change to "summarization" for summarization tasks
+instruction = (
+    "Analyze the text and indicate if it shows an emotion, then label it as joy, love, fear,"
+    " anger, sadness, or surprise. Return only the label without any explanation, justification, or additional text."
+)
+input_text = "I am not creating anything I feel satisfied with."
+output_prefix = "Summary: " if task == "summarization" else "Label: "
+
+# Define messages for chat-based prompt format
 messages = [
-    {"role": "system", "content": system_message},
-    {"role": "user", "content": input_text},
+    {"role": "system", "content": "You are a social media expert providing accurate analysis and insights."},
+    {"role": "user", "content": f"{instruction}\nInput: {input_text}"},
+    {"role": "assistant", "content": output_prefix}
 ]
 
+# Tokenize input
+input_ids = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=False,
+    continue_final_message=True,
+    tokenize=True,
+    padding=True,
+    return_tensors="pt"
+).to(model.device)
+
+
 
 ```
 4. **Generate the Output:**:
 Generate a response using the model:
 ```python
-generated_text = pipe(messages, num_return_sequences=1)
-print(generated_text)
+# Generate response
+outputs = model.generate(
+    input_ids,
+    max_new_tokens=128,
+    do_sample=False,
+    eos_token_id=tokenizer.eos_token_id,
+    pad_token_id=tokenizer.eos_token_id,
+    temperature=0.001
+)
+
+# Decode and print response
+response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
+print(response)
 ```
 
 ## Results