Update README.md with improved model card

README.md

@@ -6,16 +6,115 @@ tags:
   - lora
   - adapters
   - tinyllama
+  - youtube
+  - conversational
+  - text-generation
+license: apache-2.0
 ---
 
 # TinyLlama YouTube Replies (LoRA)
 
-LoRA adapters to fine-tune `TinyLlama-1.1B-Chat-v1.0` for generating concise, friendly replies to AI/ML YouTube comments.
+This model is a **LoRA fine-tuned** version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0), designed to generate **concise, friendly, and domain-specific replies** to YouTube comments on AI/ML-related content. Using Low-Rank Adaptation (LoRA), this project demonstrates the ability to fine-tune a lightweight language model for conversational tasks. While the model may occasionally produce out-of-context replies and could benefit from further optimization, it effectively showcases a functional fine-tuning pipeline.
+
+## Model Details
+
+- **Base Model**: [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0)
+- **Fine-Tuning Method**: LoRA (Low-Rank Adaptation)
+- **Task**: Generating short, engaging replies to AI/ML YouTube comments
+- **Language**: English
+- **License**: Apache 2.0
+
+## Intended Use
+
+This model is intended for:
+- Generating polite and engaging replies to AI/ML-related YouTube comments.
+- Demonstrating a fine-tuning project using LoRA for lightweight adaptation.
+- Research or educational purposes in conversational AI.
+
+**Not Intended For**:
+- Production environments without further optimization.
+- Non-English text generation.
+- Applications requiring high contextual accuracy without human review.
 
 ## Usage
+
+To use this model, you need the `transformers` and `peft` libraries. Below is an example of how to load and generate replies:
+
 ```python
+import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 from peft import PeftModel
-base = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
-tok = AutoTokenizer.from_pretrained("your-username/tinyllama-youtube-replies-lora")
-model = PeftModel.from_pretrained(base, "your-username/tinyllama-youtube-replies-lora")
+
+# Load the base model, tokenizer, and LoRA adapters
+base_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
+adapter_id = "AdamDE/tinyllama-custom-youtube-replies"
+tokenizer = AutoTokenizer.from_pretrained(adapter_id)
+base_model = AutoModelForCausalLM.from_pretrained(base_id, torch_dtype=torch.float16, device_map="auto")
+model = PeftModel.from_pretrained(base_model, adapter_id)
+
+# Prepare input
+messages = [
+    {"role": "system", "content": "You are an AI/ML tutorial creator replying to YouTube comments. "
+                                  "Provide concise, friendly, and domain-specific help, encourage engagement, "
+                                  "and keep a positive tone with occasional emojis when appropriate."},
+    {"role": "user", "content": "Your enthusiasm is contagious!"}
+]
+inputs = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
+
+# Generate reply
+with torch.no_grad():
+    out = model.generate(inputs, max_new_tokens=128, temperature=0.7, top_p=0.9, pad_token_id=tokenizer.eos_token_id)
+reply = tokenizer.decode(out[0], skip_special_tokens=True)
+print(reply)
+# Example output: "Haha, thanks! 😂 What's your favorite part?"
+```
+
+### Requirements
+```bash
+pip install transformers peft torch
+```
+
+### Notes
+- Use a clear, comment-like prompt for best results.
+- Adjust `max_new_tokens`, `temperature`, and `top_p` to control reply length and creativity.
+- The model may occasionally generate out-of-context replies, indicating room for further optimization.
+
+## Training Details
+
+- **Dataset**: Custom JSON dataset of AI/ML YouTube comments and replies, split into train, validation, and test sets.
+- **Training Procedure**: LoRA fine-tuning with 4-bit quantization (NF4) and mixed precision (bf16/fp16).
+- **Hyperparameters**:
+  - LoRA Rank (r): 16
+  - LoRA Alpha: 32
+  - LoRA Dropout: 0.05
+  - Epochs: 5
+  - Learning Rate: 2e-4
+  - Optimizer: Paged AdamW 8-bit
+  - Scheduler: Cosine with 10% warmup
+- **Evaluation Metrics**:
+  - BLEU and ROUGE scores computed on the test set (see training script for details).
+- **Training Features**:
+  - Gradient checkpointing for memory efficiency.
+  - Early stopping with patience of 2 epochs based on validation loss.
+  - Custom data collator for padding and label masking.
+
+## Performance
+
+The model achieves reasonable performance for a fine-tuning project, with BLEU and ROUGE scores indicating decent reply quality. However, occasional out-of-context responses suggest potential improvements in dataset quality or hyperparameter tuning.
+
+## Limitations
+
+- May generate out-of-context or generic replies, requiring human review.
+- Optimized for AI/ML YouTube comments; performance may vary for other domains.
+- Limited to English-language inputs and outputs.
+
+## Ethical Considerations
+
+- Generated replies should be reviewed to ensure they are appropriate and constructive.
+- Use responsibly to foster positive community interactions.
+
+## Contact
+
+For questions or feedback, please contact [Your Contact Info, e.g., GitHub, email, or Hugging Face profile].
+
+---