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

```python
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)