README.md

---
library_name: transformers
license: mit
datasets:
- roneneldan/TinyStories
- Salesforce/wikitext
- abhinand/alpaca-gpt4-sharegpt
- shibing624/sharegpt_gpt4
- ChristophSchuhmann/basic-math-problems-with-step-by-step-solutions
- ajibawa-2023/SlimOrca-ShareGPT
- junelee/wizard_vicuna_70k
- meta-math/MetaMathQA
- HuggingFaceH4/MATH-500
- hkust-nlp/dart-math-pool-math
- TIGER-Lab/MathInstruct
- LimYeri/Python_Code_Instructions
language:
- en
pipeline_tag: text-generation
---

# Arsh-llm: A Compact 500M Parameter Powerhouse 🚀

**Arsh-llm** is a 500-million-parameter language model built on the Llama architecture, designed to shine in generating creative stories, coherent text, and functional code. Pretrained for 35 hours on a T4 GPU using a curated mix of small yet powerful datasets, and fine-tuned for 20 hours on conversational data, this model is a lean, mean, text-generating machine with massive potential. With a training loss between **1.2–1.9**, it’s already showing promise and is ready to level up with more training. Buckle up—this is just the beginning! 😎

## Model Overview

- **Architecture**: Llama-based causal language model
- **Parameters**: 500M
- **Context Length**: 128 tokens
- **Pretraining Duration**: \~35 hours on NVIDIA T4 GPU
- **Fine-tuning Duration**: \~20 hours on conversational datasets
- **Training Loss**: 1.2–1.9 (with room to improve!)
- **Library**: Transformers (Hugging Face)
- **License**: MIT

## Datasets

Arsh-llm was trained on a diverse set of datasets to ensure versatility in storytelling, text generation, and code-related tasks:

- **roneneldan/TinyStories**: Short, creative stories for narrative generation.
- **Salesforce/wikitext**: Wikipedia-based text for general knowledge and coherence.
- **abhinand/alpaca-gpt4-sharegpt**: Instruction-based conversational data for task-oriented responses.
- **shibing624/sharegpt_gpt4**: High-quality conversational data for chat-like interactions.
- **ChristophSchuhmann/basic-math-problems-with-step-by-step-solutions**: Math problems with solutions to boost logical reasoning.

Fine-tuning was performed on a structured ShareGPT chat template to enhance conversational abilities, making Arsh-llm a great starting point for dialogue-based applications.

## Use Cases

Arsh-llm is a versatile model with applications in:

- **Creative Writing**: Generate engaging short stories or narrative prompts.
- **Code Generation**: Produce functional code snippets for various programming tasks.
- **Conversational AI**: Power chatbots or assistants with natural dialogue.
- **Educational Tools**: Assist with math problem-solving or explain concepts step-by-step.

> **Note**: This model is a work in progress. For production-grade performance, further pretraining on larger datasets and post-training on conversational data is recommended.

## Getting Started

To use Arsh-llm, you can load it directly from Hugging Face:

```python
import torch
from transformers import pipeline, set_seed

# Set up the text-generation pipeline
model_name = "arshiaafshani/Arsh-llm"
chatbot = pipeline(
    "text-generation",
    model=model_name,
    device=0 if torch.cuda.is_available() else -1
)

# Ensure that bos_token and eos_token are explicitly set as strings
chatbot.tokenizer.bos_token = "<sos>"
chatbot.tokenizer.eos_token = "<|endoftext|>"

# Set seed for reproducibility (optional)
set_seed(42)

print("Arsh llm is ready! Type 'exit' to end the conversation.")

# Initialize the conversation history
conversation_history = []

conversation_history.append({"role": "system", "content": "You are a helpful assistant."})

while True:
    user_input = input("You: ").strip()
    if user_input.lower() == "exit":
        print("Exited from the chat. Bye!")
        break

    # Append user message to the conversation history
    conversation_history.append({"role": "user", "content": user_input})

    # Prepare the messages with the conversation history and an empty assistant turn
    messages = conversation_history + [{"role": "assistant", "content": ""}]

    # Use the tokenizer's apply_chat_template() method to format the prompt.
    prompt = chatbot.tokenizer.apply_chat_template(messages, tokenize=False)

    # Generate text using the formatted prompt.
    response = chatbot(
        prompt,
        do_sample=True,
        max_new_tokens=512,
        top_k=50,
        temperature=0.6,
        num_return_sequences=1,
        repetition_penalty=1.1,
        pad_token_id=chatbot.tokenizer.eos_token_id,
        min_new_tokens=20
    )

    # The returned 'generated_text' includes the prompt plus the generation.
    full_text = response[0]["generated_text"]
    # Extract the assistant's response by removing the prompt portion.
    bot_response = full_text[len(prompt):].strip()
    print(f"Bot: {bot_response}")

```

## Training Details

- **Pretraining**: Conducted on a T4 GPU for \~35 hours using a mix of TinyStories, WikiText, and other datasets to build a strong foundation in text and story generation.
- **Fine-tuning**: 20 hours on ShareGPT-based conversational data with a structured chat template to enhance dialogue capabilities.
- **Hardware**: NVIDIA T4 GPU (15GB VRAM).
- **Training Loss**: Achieved 1.2–1.9, indicating solid performance with significant potential for improvement through extended training.

## Limitations

- **Current Stage**: Arsh-llm is not yet fully optimized. It performs well for its size but requires additional training to compete with larger models.
- **Dataset Size**: Pretrained on relatively small datasets, which limits its generalization. Scaling up to larger datasets will unlock its full potential.
- **Context Length**: Limited to 128 tokens, which may constrain performance on longer sequences.
- **Not Production-Ready**: This model is best used as a base for further fine-tuning rather than as a standalone solution.

## Future Plans

The journey doesn’t end here! Arsh-llm is set to evolve with:

- **Extended Pretraining**: Leveraging larger datasets for broader knowledge and better generalization.
- **Conversational Fine-tuning**: Enhancing dialogue capabilities with advanced post-training techniques.
- **Benchmarking**: Evaluating performance against similar models (e.g., TinyLlama, Phi-1.5) on tasks like MMLU, HumanEval, and GSM8K.
- **Community Feedback**: Incorporating user insights to refine and improve the model.

Stay tuned—Arsh-llm is on its way to becoming a legend! 🔥

## License

This model is licensed under the MIT License, allowing for flexible use in both research and commercial applications. Feel free to build upon, modify, or share it!

## Acknowledgments

- Built with ❤️ by Arshia Afshani.
- Powered by the Hugging Face Transformers library.
- Thanks to the open-source community for providing the amazing datasets that made this model possible.

---

**Ready to take Arsh-llm for a spin?** Clone it, train it, and let’s make it a superstar together! 🌟 For questions, feedback, or collabs, reach out via Hugging Face or open an issue in the repo.