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

# 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)






