Create README.md

README.md

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+---
+language: en
+license: llama2
+library_name: transformers
+tags:
+  - causal-lm
+  - mental-health
+  - text-generation
+datasets:
+  - heliosbrahma/mental_health_chatbot_dataset
+model_creator: Jjateen Gundesha
+base_model: NousResearch/llama-2-7b-chat-hf
+finetuned_from: NousResearch/llama-2-7b-chat-hf
+---
+
+## **🦙Model Card for LLaMA-2-7B-Mental-Chat**
+
+This model is a fine-tuned version of Meta's LLaMA 2 7B, specifically designed for mental health-focused conversational applications. It provides empathetic, supportive, and informative responses related to mental well-being.
+
+---
+
+## Model Details
+
+### Model Description
+
+**LLaMA-2-7B-Mental-Chat** is optimized for natural language conversations in mental health contexts. Fine-tuned on a curated dataset of mental health dialogues, it aims to assist with stress management, general well-being, and providing empathetic support.
+
+- **Developed by:** [Jjateen Gundesha](https://huggingface.co/Jjateen)  
+- **Funded by:** Personal project  
+- **Shared by:** [Jjateen Gundesha](https://huggingface.co/Jjateen)  
+- **Model type:** Transformer-based large language model (LLM)  
+- **Language(s):** English  
+- **License:** [Meta's LLaMA 2 Community License](https://ai.meta.com/llama/license/)  
+- **Fine-tuned from:** [LLaMA 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf)  
+
+---
+
+### Model Sources
+
+- **Repository:** [LLaMA-2-7B-Mental-Chat on Hugging Face](https://huggingface.co/Jjateen/llama-2-7b-mental-chat)  
+- **Paper:** Not available  
+- **Demo:** Coming soon  
+
+---
+
+## Uses
+
+### Direct Use
+
+- **Mental Health Chatbot:** For providing empathetic, non-clinical support on mental health topics like anxiety, stress, and general well-being.  
+- **Conversational AI:** Supporting user queries with empathetic responses.  
+
+### Downstream Use
+
+- **Fine-tuning:** Can be adapted for specialized mental health domains or multilingual support.  
+- **Integration:** Deployable in chatbot frameworks or virtual assistants.  
+
+### Out-of-Scope Use
+
+- **Clinical diagnosis:** Not suitable for medical or therapeutic advice.  
+- **Crisis management:** Should not be used in critical situations requiring professional intervention.  
+
+---
+
+## Bias, Risks, and Limitations
+
+### Biases
+- May reflect biases from the mental health datasets used, especially around cultural or social norms.  
+- Risk of generating inappropriate or overly simplistic responses to complex issues.  
+
+### Limitations
+- Not a substitute for professional mental health care.  
+- Limited to English; performance may degrade with non-native phrasing or dialects.  
+
+---
+
+### Recommendations
+
+Users should monitor outputs for appropriateness, especially in sensitive or high-stakes situations. Ensure users are aware this is not a replacement for professional mental health services.
+
+---
+
+## How to Get Started with the Model
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("Jjateen/llama-2-7b-mental-chat")
+model = AutoModelForCausalLM.from_pretrained("Jjateen/llama-2-7b-mental-chat")
+
+input_text = "I feel overwhelmed and anxious. What should I do?"
+inputs = tokenizer(input_text, return_tensors="pt")
+
+output = model.generate(**inputs, max_length=200)
+response = tokenizer.decode(output[0], skip_special_tokens=True)
+print(response)
+```
+
+---
+
+## Training Details
+
+### Training Data
+
+- **Dataset:** [heliosbrahma/mental_health_chatbot_dataset](https://huggingface.co/datasets/heliosbrahma/mental_health_chatbot_dataset)
+- **Preprocessing:** Text normalization, tokenization, and filtering for quality.  
+
+### Training Procedure
+
+- **Framework:** PyTorch  
+- **Epochs:** 3  
+- **Batch Size:** 8  
+- **Optimizer:** AdamW  
+- **Learning Rate:** 5e-6  
+
+---
+
+### Speeds, Sizes, Times
+
+- **Training Time:** Approximately 48 hours on NVIDIA A100 GPUs  
+- **Model Size:** 10.5 GB (split across 2 `.bin` files)  
+
+---
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+- Held-out validation set with mental health dialogues.  
+
+#### Metrics
+- **Empathy Score:** Evaluated through human feedback.  
+- **Relevance:** Based on context adherence.  
+- **Perplexity:** Lower perplexity on mental health data compared to the base model.  
+
+### Results
+| Metric           | Score         |  
+|------------------|---------------|  
+| **Empathy Score**| 85/100        |  
+| **Relevance**    | 90%           |  
+| **Safety**       | 95%           |  
+
+---
+
+## Environmental Impact
+
+- **Hardware Type:** NVIDIA A100 GPUs  
+- **Hours used:** 48 hours  
+- **Cloud Provider:** AWS  
+- **Compute Region:** US East  
+- **Carbon Emitted:** Estimated using [ML Impact Calculator](https://mlco2.github.io/impact#compute)  
+
+---
+
+## Technical Specifications
+
+### Model Architecture and Objective
+- Transformer architecture (decoder-only)  
+- Fine-tuned with a causal language modeling objective  
+
+### Compute Infrastructure
+- **Hardware:** 4x NVIDIA A100 GPUs  
+- **Software:** PyTorch, Hugging Face Transformers  
+
+---
+
+## Citation
+
+**BibTeX:**  
+```
+@misc{jjateen_llama2_mentalchat_2024,
+  title={LLaMA-2-7B-Mental-Chat},
+  author={Jjateen Gundesha},
+  year={2024},
+  howpublished={\url{https://huggingface.co/Jjateen/llama-2-7b-mental-chat}}
+}
+```
+
+---
+
+## Model Card Contact  
+For any questions or feedback, please contact [Jjateen Gundesha](https://huggingface.co/Jjateen).