Update README.md

README.md

@@ -1,53 +1,114 @@
 ---
 library_name: transformers
 license: mit
-base_model: arshiaafshani/Arsh-llm
-tags:
-- generated_from_trainer
-model-index:
-- name: Arsh-llm
-  results: []
+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
+language:
+- en
+pipeline_tag: text-generation
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
+# Arsh-llm: A Compact 500M Parameter Powerhouse 🚀
 
-# Arsh-llm
+**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 5 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! 😎
 
-This model is a fine-tuned version of [arshiaafshani/Arsh-llm](https://huggingface.co/arshiaafshani/Arsh-llm) on an unknown dataset.
+## Model Overview
 
-## Model description
+- **Architecture**: Llama-based causal language model
+- **Parameters**: 500M
+- **Context Length**: 128 tokens
+- **Pretraining Duration**: \~35 hours on NVIDIA T4 GPU
+- **Fine-tuning Duration**: \~15 hours on conversational datasets
+- **Training Loss**: 1.2–1.9 (with room to improve!)
+- **Library**: Transformers (Hugging Face)
+- **License**: MIT
 
-More information needed
+## Datasets
 
-## Intended uses & limitations
+Arsh-llm was trained on a diverse set of datasets to ensure versatility in storytelling, text generation, and code-related tasks:
 
-More information needed
+- **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.
 
-## Training and evaluation data
+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.
 
-More information needed
+## Use Cases
 
-## Training procedure
+Arsh-llm is a versatile model with applications in:
 
-### Training hyperparameters
+- **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.
 
-The following hyperparameters were used during training:
-- learning_rate: 3e-05
-- train_batch_size: 4
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 12
-- total_train_batch_size: 48
-- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
-- lr_scheduler_type: linear
-- lr_scheduler_warmup_steps: 2000
-- training_steps: 800
-- mixed_precision_training: Native AMP
+> **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.
 
-### Framework versions
+## Getting Started
 
-- Transformers 4.52.2
-- Pytorch 2.6.0+cu124
-- Datasets 3.6.0
-- Tokenizers 0.21.1
+To use Arsh-llm, you can load it directly from Hugging Face:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("arshiaafshani/Arsh-llm")
+tokenizer = AutoTokenizer.from_pretrained("arshiaafshani/Arsh-llm")
+
+# Example: Generate a response
+messages = [{"role": "user", "content": "Write a short story about a brave robot."}]
+input_text = tokenizer.apply_chat_template(messages, tokenize=False)
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=200)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## 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**: 5 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.