README.md

---
library_name: peft
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
pipeline_tag: text-generation
tags:
  - lora
  - adapters
  - tinyllama
  - youtube
  - conversational
  - text-generation
license: apache-2.0
---

# TinyLlama YouTube Replies (LoRA)

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

# 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.