Update README.md

README.md

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----
-license: mit
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+---
+license: mit
+---
+Model Summary
+
+OpenCelestial_1 is a compact and efficient language model fine-tuned on a greeting dataset. It demonstrates that small LLMs can achieve remarkable conversational capabilities, even when trained on consumer-grade hardware.
+
+Based on the GPT-2 architecture, OpenCelestial_1 is optimized for clear, polite, and structured responses, making it ideal for use cases such as:
+
+    Chatbots
+    Instruction-following assistants
+    Lightweight deployments on limited hardware
+
+Model Training
+
+    Base Model: openai-community/gpt2
+    Dataset: Custom greeting dataset with structured "User" and "AI" dialogue pairs.
+    Hardware: Fine-tuned on a single NVIDIA RTX 3060.
+    Optimization: Fine-tuning utilized LoRA (Low-Rank Adaptation) to improve memory efficiency.
+
+Usage Example
+
+To interact with OpenCelestial_1, use the following Python script:
+
+pip install transformers torch
+
+Copy and paste the following script:
+
+
+
+```python3
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import torch
+
+# Load the model and tokenizer
+model_path = "./gpt2_lora_alpaca_gpt4"
+model = GPT2LMHeadModel.from_pretrained(model_path)
+tokenizer = GPT2Tokenizer.from_pretrained(model_path)
+
+# Set the pad token to the EOS token if not already set
+tokenizer.pad_token = tokenizer.eos_token
+
+print("Chatbot is ready! Type 'exit' to quit.")
+
+while True:
+    user_input = input("You: ")
+    if user_input.lower() == "exit":
+        print("Chatbot: Goodbye!")
+        break
+
+    # Define the system prompt and the full prompt
+    system_prompt = "You are an intelligent AI assistant that will answer every question to the best of your ability. Be clear and polite with your answers."
+    prompt = f"{system_prompt}\n### Instruction:\n{user_input}\n### Response:"
+
+    # Tokenize the input
+    inputs = tokenizer(
+        prompt,
+        return_tensors="pt",
+        padding=True,
+        truncation=True,
+        max_length=1024,
+    )
+    input_ids = inputs.input_ids.to(model.device)
+    attention_mask = inputs.attention_mask.to(model.device)
+
+    # Generate the response
+    with torch.no_grad():
+        outputs = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            max_new_tokens=150,
+            pad_token_id=tokenizer.eos_token_id,
+            do_sample=True,
+            temperature=0.7,
+            top_k=50,
+            top_p=0.95,
+        )
+
+    # Decode the response and clean it up
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    clean_response = response.split("### Response:")[-1].strip()
+    print(f"Chatbot: {clean_response}")
+```
+
+Example Outputs
+
+Prompt: Hello there!
+Response: Hello there! I am just an AI assistant, but I’m here to help you with anything you need.
+
+Prompt: Can you tell me a joke?
+Response: Sure! Why don’t skeletons fight each other? Because they don’t have the guts!
+
+Prompt: What is the capital of France?
+Response: The capital of France is Paris.
+Training Details
+
+    LoRA Configuration:
+        Rank (r): 4
+        Alpha: 16
+        Dropout: 0.1
+        Target Modules: GPT-2’s attention layers (attn.c_attn)
+    Training Arguments:
+        Mixed precision: Enabled (fp16)
+        Epochs: 3
+        Batch size: 2 (to fit GPU memory)
+        Learning rate: 5e-5
+
+Performance
+
+OpenCelestial_1 demonstrates:
+
+    Clear conversational ability with polite, structured responses.
+    Low resource requirements, suitable for GPUs like the RTX 3060.
+    Consistency in instruction-following tasks.
+
+Intended Use
+
+This model is designed for:
+
+    Conversational AI applications.
+    Instruction-based assistants that respond politely and clearly.
+    Lightweight deployments for hobbyists, small-scale developers, or educational purposes.
+
+Limitations
+
+    Responses may still contain hallucinations or factual inaccuracies.
+    Performance is limited to the dataset scope and GPT-2’s inherent capabilities.
+
+Citation
+
+If you use OpenCelestial_1 in your work, please consider citing:
+
+@misc{OpenCelestial_1,
+  author = {Your Name or Organization},
+  title = {OpenCelestial_1: A Compact GPT-2 Fine-Tuned Model},
+  year = {2024},
+  howpublished = {\url{https://huggingface.co/your_username/OpenCelestial_1}},
+}
+
+Acknowledgments
+
+    Base Model: openai-community/gpt2
+    Fine-tuned using the LoRA technique for efficient memory usage.
+    Developed on a single NVIDIA RTX 3060 GPU.