---
language: en
datasets:
- truthful_qa
license: apache-2.0
tags:
- qlora
- falcon
- fine-tuning
- nlp
- causal-lm
- h100
library_name: peft
base_model: tiiuae/falcon-7b-instruct
---

# Falcon-7B QLoRA Fine-Tuned on TruthfulQA

## Model Description

This model is a fine-tuned version of the `tiiuae/falcon-7b-instruct` using the QLoRA technique on the [TruthfulQA](https://huggingface.co/datasets/truthful_qa) dataset.

## Training

- **Base Model**: [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct)
- **Dataset**: [TruthfulQA](https://huggingface.co/datasets/truthful_qa)
- **Training Technique**: QLoRA
- **Hardware**: H100 GPUs
- **Epochs**: 10
- **Batch Size**: 16
- **Learning Rate**: 2e-4


### How to Use

```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

# Load the base model
base_model_name = "tiiuae/falcon-7b-instruct"
base_model = AutoModelForCausalLM.from_pretrained(base_model_name)
tokenizer = AutoTokenizer.from_pretrained(base_model_name)

# Load the adapter and apply it to the base model
adapter_repo_name = "MohammadOthman/falcon-7b-qlora-truthfulqa"
model = PeftModel.from_pretrained(base_model, adapter_repo_name)

# Move model to GPU if available
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)

# Function to generate text
def generate_text(prompt, max_length=100, num_return_sequences=1):
    # Tokenize the input prompt
    inputs = tokenizer(prompt, return_tensors="pt").to(device)
    
    # Generate text
    outputs = model.generate(
        input_ids=inputs["input_ids"],
        attention_mask=inputs["attention_mask"],
        max_length=max_length,
        num_return_sequences=num_return_sequences,
        do_sample=True,
        temperature=0.7
    )
    
    # Decode and print the output
    for i, output in enumerate(outputs):
        print(f"Generated Text {i+1}: {tokenizer.decode(output, skip_special_tokens=True)}")

# Example usage
prompt = "Once upon a time in a land far, far away"
generate_text(prompt)
```