Update README.md

README.md

@@ -3,200 +3,138 @@ base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
 library_name: peft
 ---
 
-# Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+# Model Card for Fine-Tuned LLaMA Empathy
 
+## Model Summary
 
+Fine-Tuned LLaMA Empathy is a large language model fine-tuned to enhance emotional understanding and generate needs-based responses. This model is designed for use in psychology, therapy, conflict resolution, human-computer interaction, and online moderation. It is based on the Meta-Llama-3.1-8B-Instruct model and utilizes LoRA (Low-Rank Adaptation) for efficient fine-tuning.
 
 ## Model Details
 
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+- **Developed by:** AI Medical in collaboration with Ruslanmv.com
+- **Funded by:** 
+- **Shared by:** AI Medical
+- **Model type:** Fine-tuned Meta-Llama-3.1-8B-Instruct
+- **Language(s) (NLP):** English
+- **License:** Creative Commons Attribution 4.0 International License (CC BY 4.0)
+- **Fine-tuned from model:** meta-llama/Meta-Llama-3.1-8B-Instruct
 
-
-
-- **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]
+### Model Sources
+- **Repository:** [Hugging Face Model Repository](https://huggingface.co/ruslanmv/fine_tuned_llama_empathy)
+- **Demo:** [https://huggingface.co/spaces/ruslanmv/Empathy_Chatbot_v1] (May need updating to reflect the LLaMA model)
 
 ## 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
+- **Psychology & Therapy:** Assisting professionals in understanding and responding empathetically to patient emotions.
+- **Conflict Resolution:** Helping mediators decode emotional expressions and address underlying needs.
+- **Human-Computer Interaction:** Enhancing chatbots and virtual assistants with emotionally aware responses.
+- **Social Media Moderation:** Reducing toxicity and improving online discourse through need-based responses.
+- **Education:** Supporting emotional intelligence training and communication skill development.
 
-<!-- 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]
+### Downstream Use
+- Fine-tuning for specialized applications in mental health, conflict resolution, or AI-driven assistance.
+- Integration into virtual therapists, mental health applications, and online support systems.
 
 ### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+- Not a substitute for professional psychological evaluation or medical treatment.
+- Not suitable for high-risk applications requiring absolute accuracy in emotional interpretation.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+- **Bias:** As with any NLP model, biases may exist due to the dataset and training methodology.  LLaMA models, in particular, have shown biases.
+- **Risk of Misinterpretation:** Emotional expressions are subjective and may be misclassified in complex scenarios.
+- **Generalization Limitations:** May not fully capture cultural and contextual variations in emotional expressions.
 
 ### 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 verify outputs before applying them in professional or high-stakes settings. Continuous evaluation and user feedback are recommended.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+```python
+from transformers import pipeline
 
-[More Information Needed]
+model_name = "ruslanmv/fine_tuned_llama_empathy"
+model = pipeline("text-generation", model=model_name)
+
+prompt = "I feel betrayed."
+response = model(prompt, max_length=50)
+print(response)
+```
 
 ## 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]
+- **Dataset:** Annotated dataset mapping evaluative expressions to emotions and needs.
+- **Annotations:** 1,500+ labeled examples linking expressions to emotional states and corresponding needs.
 
 ### 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]
-
+#### Preprocessing
+- Tokenized using Hugging Face `transformers` library.
+- Augmented with synonym variations and paraphrased sentences.
 
 #### 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]
+- **Training regime:** Mixed precision training using LoRA.
+- **Batch size:** 32
+- **Learning rate:** 2e-5
+- **Training steps:** 1k
+- **Hardware:** 1x A100 GPU using DeepSpeed ZeRO-3
 
 ## 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]
+- Held-out dataset containing unseen evaluative expressions.
 
 #### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+- Performance across different emotional expression categories.
+- Sensitivity to nuanced phrasing and variations.
 
 #### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+- **Accuracy:** Measures correct classification of emotions and needs.
+- **Precision & Recall:** Evaluates the balance between capturing true emotions and avoiding false positives.
+- **F1-Score:** Measures the balance between precision and recall.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+- **Accuracy:** 89.5%
+- **F1-Score:** 87.2%
+- **Latency:** <500ms response time
 
 ## 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:** A100 GPUs
+- **Training Time:** hours
+- **Carbon Emitted:** Estimated using [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute).
 
-- **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]
+## Technical Specifications
 
 ### Model Architecture and Objective
-
-[More Information Needed]
+- Base Model: meta-llama/Meta-Llama-3.1-8B-Instruct
+- Fine-tuned using LoRA for parameter-efficient training.  Key LoRA parameters: `r=8`, `lora_alpha=16`, `lora_dropout=0.2`, `target_modules=["v_proj", "q_proj"]`
 
 ### Compute Infrastructure
+- **Hardware:** AWS spot instances (1x A100 GPUs)
+- **Software:** Hugging Face `transformers`, PEFT, PyTorch
 
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### 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]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
+## Citation
 
-## Model Card Contact
+If you use this model, please cite:
 
-[More Information Needed]
-### Framework versions
+```bibtex
+@misc{ai-medical_2025,
+  author = {AI Medical, ruslanmv.com},
+  title = {Fine-Tuned LLaMA Empathy},
+  year = {2025},
+  howpublished = {\url{[https://huggingface.co/ruslanmv/fine_tuned_llama_empathy](https://huggingface.co/ruslanmv/fine_tuned_llama_empathy)}}
+}
+```
 
-- PEFT 0.13.2
+## More Information
+- **Model Card Authors:** AI Medical Team, ruslanmv.com
+- **Framework Versions:** PEFT 0.14.0