This model is a fine-tuned version of the Falcon-RW-1B model, specifically adapted for generating advertising content. The fine-tuning process utilized a dataset containing ad-related text, formatted as structured prompt-response pairs.

Developed by: Adnane Touiyate
Funded by [optional]: Adnane10
Shared by [optional]: Adnane10
Model type: Falcon-RW-1B (Causal Language Model)
Language(s) (NLP): English
License: MIT
Finetuned from model [optional]: tiiuae/falcon-rw-1b

Uses

Direct Use

This model can be used for generating advertising content based on structured prompts. It is useful for marketers and advertisers who need AI-generated ad copies.

Downstream Use [optional]

The model can be further fine-tuned for specific ad categories or integrated into larger marketing automation workflows.

Out-of-Scope Use

This model is not intended for generating non-advertising-related content, and its performance may be suboptimal in general text generation tasks beyond its training scope.

Bias, Risks, and Limitations

Since the model has been fine-tuned on advertising content, it may inherit biases present in the dataset. Users should be cautious when generating ads to ensure they meet ethical and regulatory standards.

Recommendations

Users should validate the generated content for appropriateness, compliance, and factual accuracy before using it in real-world applications.

How to Get Started with the Model

Use the code below to load and use the model:

python

from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-rw-1b")
model = AutoModelForCausalLM.from_pretrained("path_to_finetuned_model")

def generate_ad(prompt):
    inputs = tokenizer(prompt, return_tensors="pt").to('cuda')
    outputs = model.generate(**inputs, max_length=100)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

print(generate_ad("Introducing our latest product: "))

Training Details

Training Data

The model was trained on fixed_ads_list.json, a dataset containing structured ad-related prompts and responses.

Training Procedure

Preprocessing: Tokenized text in the format ### Prompt: [User Input] ### Response: [Ad Text]
Quantization: Used 4-bit quantization (NF4) with bitsandbytes for efficiency.
Fine-tuning method: LoRA (Low-Rank Adaptation) for efficient adaptation.
Hardware: GPU-accelerated training.

Training Hyperparameters

Learning Rate: 1e-4
Batch Size: 2 (per device)
Gradient Accumulation: 8 steps
Epochs: 6
Precision: BF16
Evaluation Strategy: Epoch-based
Early Stopping: Enabled after 2 epochs without improvement

Evaluation

Testing Data, Factors & Metrics

Metrics: BLEU and ROUGE scores
Results: Sample evaluation showed:

Environmental Impact

Hardware Type: NVIDIA P100 GPU
Hours used: ~54 minutes
Cloud Provider: Kaggle

Model Architecture and Objective

The Falcon-RW-1B model is a causal language model optimized for text generation.

Compute Infrastructure

Hardware

GPUs (NVIDIA P100)
Used bitsandbytes for memory-efficient training

Software

transformers
datasets
peft
torch
accelerate
bitsandbytes

Model Card Authors

Adnane Touiyate (@Adnane10)

Contact

For questions or collaborations, reach out via LinkedIn or email: adnanetouiayte11@gmail.com

Adnane10/NovaAdsAI

# Model Card for Falcon-RW-1B Fine-Tuned Model

Model Details

Model Description