Update README.md

README.md

@@ -1,199 +1,144 @@
 ---
 library_name: transformers
-tags: []
+tags:
+  - text-generation
+  - ad-generation
+  - marketing
+  - transformers
+  - pytorch
+  - beam-search
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+# 🧃 Model Card: Ad Generator for Marketing Copy
 
+This is a fine-tuned version of Microsoft's [`phi-2`](https://huggingface.co/microsoft/phi-2) language model, adapted for generating high-quality marketing content such as ad copy, slogans, and promotional text. It uses prompt-response training to structure outputs fluently and persuasively.
 
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+A fine-tuned Causal Language Model (CLM) based on `microsoft/phi-2`, optimized to produce structured marketing text with consistent formatting and clarity.
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Developed by:** Adnane Touiyate
+- **Shared by :** [Adnane10](https://huggingface.co/Adnane10)
+- **Model type:** Causal Language Model (phi-2)
+- **Language(s):** English
+- **License:** MIT
+- **Finetuned from model:** microsoft/phi-2
 
 ## Uses
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
+### ✅ Direct Use
 
-### Downstream Use [optional]
+Marketing teams can input a product name and short description to generate ad copy
+Copywriters seeking inspiration or quick content drafts
+Startup founders, product teams, or solopreneurs generating headlines and taglines
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+### 🚫 Out-of-Scope Use
 
-[More Information Needed]
+Not intended for factual, academic, or scientific content generation
+Not suitable for generating personal, sensitive, or confidential information
+May not generalize well to domains outside of marketing or product promotion
 
-### Out-of-Scope Use
+## ⚠️ Bias, Risks, and Limitations
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+While the model generates fluent and persuasive marketing text, it may:
 
-[More Information Needed]
+Include overly generic, exaggerated, or unverifiable claims
+Mimic clichés or stereotypes from marketing-focused training data
+Lack fact-checking for health-related, numerical, or product safety statements
 
-## Bias, Risks, and Limitations
+### 🔍 Recommendations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+Use human review and editing before publishing outputs
+Consider further fine-tuning the model on your brand voice, domain, or regulatory constraints if needed
 
-[More Information Needed]
+## 🚀 How to Get Started with the Model
 
-### Recommendations
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+tokenizer = AutoTokenizer.from_pretrained("Adnane10/AdsGeniusAI")
+model = AutoModelForCausalLM.from_pretrained("Adnane10/AdsGeniusAI")
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+prompt = "Create an ad for a vegan skincare brand that emphasizes natural ingredients and sustainability."
+inputs = tokenizer(prompt, return_tensors='pt').to('cuda')
+output = model.generate(**inputs, max_length=256, num_beams=5, temperature=0.7)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
 
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
+## 📊 Training Details
 
 ### Training Data
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+Fine-tuned on a dataset of curated product advertisements and promotional templates, covering sectors such as:
 
-[More Information Needed]
+Food & Beverage
+Tech & Gadgets
+Beauty & Skincare
+Fitness & Wellness
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
+Precision: fp16 mixed precision
+Quantization: 4-bit (nf4) using BitsAndBytes
+Optimizer: AdamW
+Scheduler: Linear warmup + cosine decay
+Epochs: 3–6 (early stopping used)
+Framework: Hugging Face transformers, peft, accelerate, and bitsandbytes
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+## 📈 Evaluation
 
-[More Information Needed]
+Metrics
 
-#### Metrics
+BLEU / ROUGE: For structural and surface evaluation
+Human Evaluation: Based on fluency, creativity, and relevance
+Manual Checks: On repetition and prompt adherence
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
+## 🌱 Environmental Impact
 
 <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
 
 Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
+- **Hardware Type:** NVIDIA Tesla T4
+- **Hours used:** ~2 hours
+- **Cloud Provider:** Kaggle
+- **Estimated Carbon Emission:** < 0.5 kg CO₂
 
-#### Software
+## 🔧 Technical Specifications 
 
-[More Information Needed]
 
-## Citation [optional]
+- **Base Model:** `microsoft/phi-2` (~2.7B parameters)  
+- **Tokenizer:** `AutoTokenizer` from `phi-2`  
+- **Quantization:** 4-bit (NF4 with FP16 compute)  
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+**Libraries Used:**
+- `transformers`
+- `peft`
+- `accelerate`
+- `bitsandbytes`
 
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+### Model Architecture and Objective
+The model is based on Microsoft’s `phi-2`, a small-scale language model focused on reasoning and general-purpose NLP tasks. It was fine-tuned as a Causal Language Model (CLM) to generate high-quality, structured advertising copy using prompt-response style formatting. Quantized to 4-bit using `bitsandbytes` for efficiency.
 
-[More Information Needed]
+## 📚 Citation [optional]
 
-## More Information [optional]
+@misc{freshpress-adgen,
+  title={FreshPress Ad Generator},
+  author={Adnane Touiyate},
+  year={2025},
+  url={https://huggingface.co/Adnane10/phi2-marketing-generator},
+  note={Fine-tuned Phi-2 model for marketing and ad copy generation}
+}
 
-[More Information Needed]
 
-## Model Card Authors [optional]
+## ✍️ Model Card Authors
 
-[More Information Needed]
+**Adnane Touiyate** ([@Adnane10](https://huggingface.co/Adnane10))
 
-## Model Card Contact
+## 📬 Contact
 
-[More Information Needed]
+For questions or collaborations, reach out via [LinkedIn](https://www.linkedin.com/in/adnanetouiyate/) or email: [adnanetouiayte11@gmail.com](mailto:adnanetouiayte11@gmail.com)