Create README.md

README.md

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+---
+datasets:
+- nbertagnolli/counsel-chat
+language:
+- en
+base_model:
+- meta-llama/Llama-3.2-3B-Instruct
+---
+# Suicide and Mental Health Support LLaMA
+
+This model is a **fine-tuned LLaMA-based** (or derivative) model designed to (1) **detect suicidal or self-harm risk** in text, and (2) **provide a short therapeutic-style reply** if suicidality is detected. We combined multiple datasets to train this model, including:
+
+- **Reddit-based** suicide detection data (r/SuicideWatch, r/depression, r/teenagers),
+- **Twitter** suicidal-intent classification data,
+- **CounselChat**: a dataset of mental-health counseling Q&A,
+- **PAIR**: short counseling interactions with high- and medium-quality reflections.
+
+> **DISCLAIMER**: This model is **not** a substitute for professional mental-health services or emergency intervention. If you or someone you know is in crisis, **seek professional help** (e.g., call emergency services or hotlines like `988` in the US). This model may be **incorrect** or incomplete. Use responsibly, and see **Limitations** below.
+
+---
+
+## Model Details
+
+- **Base Model**: LLaMA-based architecture from [Unslo … or “my changes”].  
+- **Parameter-Efficient Fine-tuning**: We used **LoRA** adapters or 4-bit quantization to reduce GPU memory usage.  
+- **Data**: 
+  1. **Suicide detection** (Reddit & Twitter) – labeled as “suicidal” vs. “non-suicidal.”  
+  2. **Therapeutic Q&A** (CounselChat & PAIR) – used to produce empathetic, reflective responses.  
+- **Intended Use**: 
+  - For research on suicidal ideation detection and mental-health conversation modeling.  
+  - For demonstration or proof-of-concept.  
+
+---
+
+## Training Approach
+
+1. **Data Preprocessing**: We unified suicidal posts as `"suicidal"` and non-suicidal posts as `"non-suicidal"`.  
+2. **Multi-Task Instruction**: We used short prompts for classification tasks, and Q&A style prompts for therapy.  
+3. **Oversampling**: To ensure the model doesn’t just classify everything as “suicidal,” we oversampled the therapy data.  
+4. **Hyperparameters**: 
+   - Batch Size: 2  
+   - Max Steps: 60 (example short run)  
+   - Learning Rate: 2e-4  
+   - Mixed Precision (fp16) or bf16 depending on the GPU  
+
+---
+
+## Usage
+
+**Classification Example**:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+# or from unsloth import FastLanguageModel if you used Unsloth
+
+text = "Life is too painful. I'm done. I want to end it."
+
+# 1) Classify
+classification = model("Determine if the following text is suicidal:\n" + text)
+print("Classification:", classification)
+# e.g., "suicidal"
+
+# 2) Therapeutic Response Example:
+
+response = model("Respond like a therapist:\n" + text, max_new_tokens=256)
+print("Therapy-Style Reply:", response)
+```
+
+## Limitations & Caveats
+1. **Not a Medical Professional**: This model does not replace mental-health professionals.
+2. **Potential for Harmful or Inaccurate Content**: Large language models may produce misleading or harmful text.
+3. **Biased Data**: Reddit, Twitter, or crowd-annotated counseling data can carry biases and incomplete perspectives.
+4. **Over-Classification or Under-Classification**: The model might incorrectly label or fail to detect self-harm.
+
+## Ethical and Responsible Use
+- **Self-Harm & Crisis**: If you suspect someone is in crisis, direct them to professional hotlines or emergency resources.
+
+- **Data Privacy**: The training data might include personal text from Reddit/Twitter. We have made efforts to remove personally identifying information, but use responsibly.
+
+## Thank You
+
+Thank you for checking out our model. We hope this can encourage research into safe, responsible, and helpful mental-health assistant approaches. Please reach out or open an issue if you have suggestions or concerns.