Update README.md

README.md

@@ -1,11 +1,17 @@
 ---
 library_name: transformers
-tags: []
+tags:
+  - text-generation
+  - ad-generation
+  - marketing
+  - transformers
+  - pytorch
+  - beam-search
 ---
 
-# Model Card for Model ID
+# # Model Card for Falcon-RW-1B Fine-Tuned Model
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model is a fine-tuned version of `tiiuae/falcon-rw-1b` trained on an advertising-related dataset to generate ad text based on prompts.
 
 
 
@@ -13,25 +19,16 @@ tags: []
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+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.
 
-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:** Adnane Touiyate
+- **Funded by [optional]:** [Adnane10](https://huggingface.co/Adnane10)
+- **Shared by [optional]:** [Adnane10](https://huggingface.co/Adnane10)
+- **Model type:** Falcon-RW-1B (Causal Language Model)
+- **Language(s) (NLP):** English
+- **License:** MIT
+- **Finetuned from model [optional]:** `tiiuae/falcon-rw-1b`
 
-- **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]
 
 ## Uses
 
@@ -39,161 +36,106 @@ This is the model card of a 🤗 transformers model that has been pushed on the
 
 ### Direct Use
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+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.
 
-[More Information Needed]
 
 ### Downstream Use [optional]
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+The model can be further fine-tuned for specific ad categories or integrated into larger marketing automation workflows.
 
-[More Information Needed]
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+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.
 
-[More Information Needed]
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+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
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further 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 get started with the model.
-
-[More Information Needed]
+Use the code below to load and use the model:
 
-## Training Details
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-### Training Data
+tokenizer = AutoTokenizer.from_pretrained("tiiuae/falcon-rw-1b")
+model = AutoModelForCausalLM.from_pretrained("path_to_finetuned_model")
 
-<!-- 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. -->
+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)
 
-[More Information Needed]
+print(generate_ad("Introducing our latest product: "))
+```
 
-### Training Procedure
+## Training Details
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+### Training Data
 
-#### Preprocessing [optional]
+The model was trained on `fixed_ads_list.json`, a dataset containing structured ad-related prompts and responses.
 
-[More Information Needed]
+### 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
 
-- **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]
+- **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
 
-<!-- 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
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+- **Metrics:** BLEU and ROUGE scores
+- **Results:** Sample evaluation showed:
 
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
 
 ## 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]
+- **Hardware Type:** NVIDIA P100 GPU
+- **Hours used:** ~54 minutes
+- **Cloud Provider:** Kaggle
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The Falcon-RW-1B model is a causal language model optimized for text generation.
 
 ### Compute Infrastructure
 
-[More Information Needed]
-
 #### Hardware
 
-[More Information Needed]
+- GPUs (NVIDIA P100)
+- Used `bitsandbytes` for memory-efficient training
 
 #### Software
 
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**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. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
+- `transformers`
+- `datasets`
+- `peft`
+- `torch`
+- `accelerate`
+- `bitsandbytes`
 
-## 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)