Update README.md

README.md

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 ---
 base_model: meta-llama/Llama-3.2-1B
 library_name: peft
+license: mit
+datasets:
+- dair-ai/emotion
+language:
+- en
+metrics:
+- accuracy
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+# **Model Card for LLaMA-3-2-LoRA-EmotionTune**
 
+## **Model Details**
 
+- **Model Description:**  
+  LLaMA-3-2-LoRA-EmotionTune is a causal language model fine-tuned using Low-Rank Adaptation (LoRA) on a curated emotion dataset. The dataset consists of user-generated text annotated with emotion labels (sadness, joy, love, anger, fear, or surprise). This fine-tuning enables the model to perform efficient emotion classification while preserving the core strengths of the base LLaMA-3.2-1B-Instruct model.
 
-## Model Details
+- **Developed by:**  
+  Taha Majlesi
 
-### Model Description
+- **Funded by (optional):**  
+  tahamajs
 
-<!-- Provide a longer summary of what this model is. -->
+- **Shared by (optional):**  
+  tahamajs
 
+- **Model type:**  
+  Causal Language Model with LoRA-based fine-tuning for emotion classification
 
+- **Language(s) (NLP):**  
+  English
 
-- **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]
+- **License:**  
+  [Choose a license, e.g., Apache-2.0, MIT]
 
-### Model Sources [optional]
+- **Finetuned from model (optional):**  
+  LLaMA-3.2-1B-Instruct
 
-<!-- Provide the basic links for the model. -->
+- **Model Sources (optional):**  
+  Original LLaMA model and publicly available emotion datasets
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+- **Repository:**  
+  [https://huggingface.co/your-username/LLaMA-3-2-LoRA-EmotionTune](https://huggingface.co/your-username/LLaMA-3-2-LoRA-EmotionTune)
 
-## Uses
+- **Paper (optional):**  
+  For LoRA: *LoRA: Low-Rank Adaptation of Large Language Models* (Hu et al., 2021)  
+  For LLaMA: [Reference paper details if available]
 
-<!-- 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]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### 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.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## 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. -->
-
-[More Information Needed]
-
-### 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]
+- **Demo (optional):**  
+  [Link to interactive demo if available]
 
+---
 
-#### Training Hyperparameters
+## **Uses**
 
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+- **Direct Use:**  
+  Emotion classification and sentiment analysis on short text inputs.
 
-#### Speeds, Sizes, Times [optional]
+- **Downstream Use (optional):**  
+  Can be integrated into affective computing systems, chatbots, content moderation pipelines, or any application requiring real-time sentiment detection.
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+- **Out-of-Scope Use:**  
+  Not recommended for critical decision-making systems (e.g., mental health diagnostics) or for applications in languages other than English without further adaptation.
 
-[More Information Needed]
+---
 
-## Evaluation
+## **Bias, Risks, and Limitations**
 
-<!-- This section describes the evaluation protocols and provides the results. -->
+- **Bias and Risks:**  
+  The model may inherit biases present in the training data, potentially misclassifying nuanced emotions or reflecting cultural biases in emotional expression.
+  
+- **Limitations:**  
+  - Limited to six predefined emotion categories.  
+  - Performance may degrade for longer texts or in ambiguous contexts.  
+  - The model is fine-tuned on a specific emotion dataset and may not generalize well across all domains.
 
-### Testing Data, Factors & Metrics
+---
 
-#### Testing Data
+## **Recommendations**
 
-<!-- This should link to a Dataset Card if possible. -->
+Users (both direct and downstream) should be aware of the model’s inherent biases and limitations. We recommend additional validation and fine-tuning before deploying this model in sensitive or high-stakes environments.
 
-[More Information Needed]
+---
 
-#### Factors
+## **How to Get Started with the Model**
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+To load and use the model with Hugging Face Transformers and the PEFT library, try the following code snippet:
 
-[More Information Needed]
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
 
-#### Metrics
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained("meta-llama/LLaMA-3.2-1B-Instruct")
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/LLaMA-3.2-1B-Instruct")
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+# Load fine-tuned LoRA adapter
+model = PeftModel.from_pretrained(base_model, "your-username/LLaMA-3-2-LoRA-EmotionTune")
 
-[More Information Needed]
+# Example usage:
+input_text = "I feel so happy today!"
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=50)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 
-### Results
+> **Demo:** An interactive demo for LLaMA-3-2-LoRA-EmotionTune is available on Hugging Face Spaces at [https://huggingface.co/spaces/your-username/demo-name](https://huggingface.co/spaces/your-username/demo-name).
 
-[More Information Needed]
+---
 
-#### Summary
+## **Training Details**
 
+- **Training Data:**  
+  A curated emotion dataset consisting of user-generated text annotated with emotion labels (sadness, joy, love, anger, fear, surprise).
 
+- **Training Procedure:**  
+  Fine-tuning was performed on the LLaMA-3.2-1B-Instruct model using the LoRA method, which adapts selected attention layers using a low-rank approach. This method leverages a small subset of trainable parameters while keeping the majority of the model frozen.
 
-## Model Examination [optional]
+- **Preprocessing (optional):**  
+  Text normalization, tokenization using the Hugging Face tokenizer, and train/validation splitting.
 
-<!-- Relevant interpretability work for the model goes here -->
+- **Training Hyperparameters:**  
+  - **LoRA rank (r):** 16  
+  - **lora_alpha:** 32  
+  - **lora_dropout:** 0.1  
+  - **Learning rate:** 2e-5  
+  - **Batch size:** 32  
+  - **Epochs:** Early stopping applied around epoch 10 to prevent overfitting.
 
-[More Information Needed]
+- **Speeds, Sizes, Times (optional):**  
+  Training conducted on an NVIDIA Tesla T4 GPU for approximately 10–12 hours.
 
-## Environmental Impact
+---
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+## **Evaluation**
 
-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).
+- **Testing Data:**  
+  A held-out subset of the emotion-annotated dataset (e.g., 100 samples).
 
-- **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]
+- **Factors:**  
+  The evaluation focused on the model’s ability to classify emotions within short text outputs.
 
-## Technical Specifications [optional]
+- **Metrics:**  
+  Accuracy and Micro F1 score.
 
-### Model Architecture and Objective
+- **Results:**  
+  The fine-tuned model achieved an accuracy and Micro F1 score of approximately 31% on short-text generation tasks (5–100 token outputs), outperforming the base and instruction-tuned models.
 
-[More Information Needed]
+---
 
-### Compute Infrastructure
 
-[More Information Needed]
+## **Technical Specifications **
 
-#### Hardware
+- **Model Architecture and Objective:**  
+  Based on LLaMA-3.2-1B-Instruct, the model is fine-tuned using LoRA to specifically classify text into emotion categories.
 
-[More Information Needed]
+- **Compute Infrastructure:**  
+  Hugging Face Transformers, PEFT library, and PyTorch.
 
-#### Software
+- **Hardware:**  
+  NVIDIA Tesla T4 or equivalent GPU.
 
-[More Information Needed]
+- **Software:**  
+  Python, PyTorch, Hugging Face Transformers, PEFT 0.14.0.
 
-## 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. -->
+## **Citation**
 
 **BibTeX:**
 
-[More Information Needed]
+```bibtex
+@inproceedings{hu2021lora,
+  title={LoRA: Low-Rank Adaptation of Large Language Models},
+  author={Hu, Edward J and others},
+  booktitle={Proceedings of ICLR},
+  year={2021}
+}
+```
 
 **APA:**
 
-[More Information Needed]
-
-## Glossary [optional]
+Hu, E. J., et al. (2021). LoRA: Low-Rank Adaptation of Large Language Models. In *Proceedings of ICLR*.
 
-<!-- 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]
+---
 
-## Model Card Authors [optional]
+## **Additional Information **
 
-[More Information Needed]
+- **Model Card Authors:**  
+  Taha Majlesi
 
-## Model Card Contact
+- **Model Card Contact:**  
+  [email protected]
 
-[More Information Needed]
-### Framework versions
+- **Framework Versions:**  
+  - PEFT: 0.14.0  
+  - Transformers: [version]  
+  - PyTorch: [version]
 
-- PEFT 0.14.0
+---