Llama-2-7b-chat-finetune/README.md

license, datasets, language, base_model, pipeline_tag, library_name, finetuned_model, model_type, finetuned_task, tags, metrics

license	datasets	language	base_model	pipeline_tag	library_name	finetuned_model	model_type	finetuned_task	tags	metrics
mit	mlabonne/guanaco-llama2-1k	en	NousResearch/Llama-2-7b-chat-hf	text-generation	transformers	true	causal-lm	instruction-following	instruction-following text-generation fine-tuned llama2 causal-language-model QLoRa 4-bit-quantization low-memory training-optimized	accuracy loss

Llama-2-7B-Chat Fine-Tuned Model

This model is a fine-tuned version of Llama-2-7B-Chat model, optimized for instruction-following tasks. It has been trained on the mlabonne/guanaco-llama2-1k dataset and is optimized for efficient text generation across various NLP tasks, including question answering, summarization, and text completion.

Model Details

• Base Model: NousResearch/Llama-2-7b-chat-hf
• Fine-Tuning Task: Instruction-following
• Training Dataset: mlabonne/guanaco-llama2-1k
• Optimized For: Text generation, question answering, summarization, and more.
• Fine-Tuned Parameters:
  • LoRA (Low-Rank Adaption) applied for efficient training with smaller parameter updates.
  • Quantized to 4-bit for memory efficiency and better GPU utilization.
  • Training includes gradient accumulation, gradient checkpointing, and weight decay to prevent overfitting and enhance memory efficiency.

Usage

You can use this fine-tuned model with the Hugging Face transformers library. Below is an example of how to load and use the model for text generation.

from transformers import AutoTokenizer, AutoModelForCausalLM

# Load pre-trained model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("https://huggingface.co/devshaheen/llama-2-7b-chat-finetune")
model = AutoModelForCausalLM.from_pretrained("https://huggingface.co/devshaheen/llama-2-7b-chat-finetune")

# Example text generation
input_text = "What is the capital of France?"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generated_text)