cmagganas/instruct-tuned-llama-7b-hf-alpaca_gpt4_5_000_samples
The cmagganas/instruct-tuned-llama-7b-hf-alpaca_gpt4_5_000_samples model is a 7 billion parameter LLaMA-2 based language model, fine-tuned by cmagganas for instruction following. It utilizes 4-bit quantization and Flash Attention for efficient processing. Optimized on a subset of the Alpaca-GPT-4 dataset, this model excels at generating coherent and contextually relevant responses to complex prompts, making it suitable for tasks like text completion and question answering.
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Model Overview
This model, cmagganas/instruct-tuned-llama-7b-hf-alpaca_gpt4_5_000_samples, is an instruction-tuned language model built upon the LLaMA-2 architecture with 7 billion parameters. It has been optimized to effectively understand and generate text based on given instructions and contexts.
Key Capabilities & Features
- Instruction Following: Designed to generate coherent and contextually relevant responses to a wide range of prompts and instructions.
- Efficient Architecture: Incorporates 4-bit quantization and Flash Attention for improved performance and efficiency.
- Fine-tuned on Alpaca-GPT-4: Trained on a subset of the Alpaca-GPT-4 dataset, enhancing its ability to handle complex prompts.
- Versatile Applications: Suitable for various natural language processing tasks including text completion, question answering, and summarization.
Training Details
The model was fine-tuned using the HF's Transformers, Peft, and TRL libraries. It employed Paged AdamW 32-bit optimization with a learning rate of 2e-4 and a maximum sequence length of 2048 tokens.
Intended Use Cases
This model is particularly well-suited for applications requiring accurate and contextually appropriate responses to detailed instructions. It demonstrates significant improvements over the base LLaMA-2 model in generating aligned responses.
Limitations
Users should be aware of potential biases inherited from training data, context sensitivity limitations, and performance variations for tasks significantly different from its training data.