--- license: mit datasets: - mlabonne/guanaco-llama2-1k language: - en base_model: - NousResearch/Llama-2-7b-chat-hf pipeline_tag: text-generation tags: - llama - causal-lm - text-generation - fine-tuned library_name: adapter-transformers --- mlabonne/guanaco-llama2-1k language: en base_model: NousResearch/Llama-2-7b-chat-hf pipeline_tag: text-generation tags: llama causal-lm text-generation fine-tuned Fine-tuned LLaMA Model This repository contains a fine-tuned version of the LLaMA model, optimized for enhanced text generation capabilities. Model Description Model Architecture: LLaMA (Large Language Model Meta AI) Base Model: NousResearch/Llama-2-7b-chat-hf Training Type: Fine-tuning Language(s): English License: MIT Intended Uses This model is designed for: Text generation Conversation completion Natural language understanding tasks Training and Evaluation The model was fine-tuned on mlabonne/guanaco-llama2-1k dataset. from transformers import AutoTokenizer, AutoModelForCausalLM # Load model and tokenizer tokenizer = AutoTokenizer.from_pretrained("Smruti612/Llama-2-7b-chat-finetune_revise_smart") model = AutoModelForCausalLM.from_pretrained("Smruti612/Llama-2-7b-chat-finetune_revise_smart") # Example usage text = "Your input text here" inputs = tokenizer(text, return_tensors="pt") outputs = model.generate(**inputs, max_length=100) result = tokenizer.decode(outputs[0], skip_special_tokens=True) print(result) Model Details Base Model: Llama-2-7b-chat-hf Training Dataset: guanaco-llama2-1k @misc{Smruti612/Llama-2-7b-chat-finetune_revise_smart, author = {Smruti Sonekar}, title = {Fine-tuned LLaMA Model}, year = {2024}, publisher = {Hugging Face}, journal = {Hugging Face Model Hub}, } Limitations Model outputs may occasionally be inaccurate or contain biases Performance may vary depending on the specific use case Limited by context window size Acknowledgments This model builds upon the LLaMA architecture developed by Meta AI. We acknowledge their contribution to the field of large language models.