Update README.md

README.md

@@ -100,151 +100,6 @@ base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T
 
 # Usage Example
 
-```python
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel
-
-# Define the base model and the LoRA model repositories
-base_model_name = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"
-repo_id_sentiment = "BEncoderRT/IMDB-Sentiment-LoRA-TinyLlama-1.1B"
-repo_id_translation = "BEncoderRT/EN-FR-Translation-LoRA-TinyLlama-1.1B"
-
-# Load the tokenizer (assuming it's consistent across tasks and already defined earlier)
-tokenizer = AutoTokenizer.from_pretrained(base_model_name)
-tokenizer.pad_token = tokenizer.eos_token
-
-# Load the base model once
-base_model_for_inference = AutoModelForCausalLM.from_pretrained(
-    base_model_name,
-    device_map="auto"
-)
-
-# Load the first adapter (sentiment) on top of the base model
-# This creates the initial PeftModel instance with the 'sentiment' adapter active
-multi_task_model = PeftModel.from_pretrained(
-    base_model_for_inference,
-    repo_id_sentiment,
-    adapter_name="sentiment" # Name this adapter
-)
-
-# Load the second adapter (translation) into the existing PeftModel instance
-multi_task_model.load_adapter(
-    repo_id_translation,
-    adapter_name="translation" # Name this adapter
-)
-
-multi_task_model.eval() # Set the model to evaluation mode
-
-print("Base model loaded and both sentiment and translation adapters attached.")
-print(f"Loaded adapters: {list(multi_task_model.peft_config.keys())}")
-```
-
-```python
-import torch
-
-def multi_task_inference(model, tokenizer, prompt_text, task_type, max_new_tokens=100):
-    if task_type == "sentiment":
-        model.set_adapter("sentiment")
-        formatted_prompt = (
-            "### Task: Sentiment Analysis\n"
-            "### Review:\n"
-            f"{prompt_text}\n"
-            "### Answer:\n"
-        )
-    elif task_type == "translation":
-        model.set_adapter("translation")
-        formatted_prompt = (
-            "### Task: Translation (English to French)\n"
-            "### English:\n"
-            f"{prompt_text}\n"
-            "### French:\n"
-        )
-    else:
-        raise ValueError("Invalid task_type. Must be 'sentiment' or 'translation'.")
-
-    inputs = tokenizer(formatted_prompt, return_tensors="pt", truncation=True, max_length=512).to(model.device)
-
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=max_new_tokens,
-            do_sample=True,
-            temperature=0.7,
-            top_k=50,
-            top_p=0.95,
-            eos_token_id=tokenizer.eos_token_id,
-            pad_token_id=tokenizer.pad_token_id
-        )
-
-    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    # Extracting the answer based on task type
-    if task_type == "sentiment":
-        answer_start_index = generated_text.find("### Answer:\n")
-        if answer_start_index != -1:
-            extracted_answer = generated_text[answer_start_index + len("### Answer:\n"):].strip()
-            if 'positive' in extracted_answer.lower():
-                return 'positive'
-            elif 'negative' in extracted_answer.lower():
-                return 'negative'
-            return extracted_answer.split('\n')[0].strip() # Fallback for sentiment
-        return generated_text
-    elif task_type == "translation":
-        answer_start_index = generated_text.find("### French:\n")
-        if answer_start_index != -1:
-            extracted_answer = generated_text[answer_start_index + len("### French:\n"):].strip()
-            end_of_french = extracted_answer.find("###")
-            if end_of_french != -1:
-                extracted_answer = extracted_answer[:end_of_french].strip()
-            return extracted_answer
-        return generated_text
-
-# --- Test Cases ---
-print("Testing Sentiment Analysis:")
-positive_review = "This movie was absolutely fantastic! The acting was superb and the story was captivating."
-print(f"Review: {positive_review}")
-print(f"Sentiment: {multi_task_inference(multi_task_model, tokenizer, positive_review, 'sentiment')}\n")
-
-negative_review = "I found this film to be incredibly boring and predictable. A complete waste of time."
-print(f"Review: {negative_review}")
-print(f"Sentiment: {multi_task_inference(multi_task_model, tokenizer, negative_review, 'sentiment')}\n")
-
-print("Testing English-French Translation:")
-english_sentence_1 = "The quick brown fox jumps over the lazy dog."
-print(f"English: {english_sentence_1}")
-print(f"French: {multi_task_inference(multi_task_model, tokenizer, english_sentence_1, 'translation')}\n")
-
-english_sentence_2 = "Life is beautiful."
-print(f"English: {english_sentence_2}")
-print(f"French: {multi_task_inference(multi_task_model, tokenizer, english_sentence_2, 'translation')}")
-```
-
-```
-Testing Sentiment Analysis:
-Review: This movie was absolutely fantastic! The acting was superb and the story was captivating.
-Sentiment: positive
-
-Review: I found this film to be incredibly boring and predictable. A complete waste of time.
-Sentiment: negative
-
-Testing English-French Translation:
-English: The quick brown fox jumps over the lazy dog.
-French: Le chien laissé derrière lui, il n'y a pas de fauve.
-
-English: Life is beautiful.
-French: La vie est beau.
-```
-
-This project demonstrates:
-
-- ✅ True **multi-task inference** with LoRA
-- ✅ No catastrophic forgetting
-- ✅ No base model retraining
-- ✅ Clean task separation via adapters
-- ✅ Efficient GPU memory usage
-
-This pattern scales naturally to **more tasks** (e.g. summarization, classification, safety).
-
 ```python
 from transformers import AutoTokenizer, AutoModelForCausalLM