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.