aryan27
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llama-therapy-lora

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-HEAD
-# Lora-Therapist
-Meet Lora - Your Own Therapist, a fine-tuned LLM on the data-set of therapy sessions. It uses Parameter Efficient fine tuning (PEFT). Using Low Rank Adaptation where we only train a particular number of parameters, we trained Meta Llama 7b on Tesla T4 GPU. The dataset can be found on HuggingFace.
-=======
 ---
-base_model: meta-llama/Llama-2-7b-hf
 library_name: peft
 ---
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-### Framework versions
-- PEFT 0.15.2
->>>>>>> 7eef288 (added adapters)

 ---
+license: apache-2.0
+tags:
+  - peft
+  - lora
+  - mental-health
+  - therapy
+  - transformers
+  - llama
+  - 4-bit
+  - bitsandbytes
 library_name: peft
+language:
+  - en
+datasets:
+  - your-username/therapy-dataset
+model_name: Meet Lora - Your Own Therapist
+model_type: llama
+inference: false
+pipeline_tag: text-generation
 ---
+# Meet Lora – Your Own Therapist 🧠💬
+**Meet Lora** is a fine-tuned LLM built to simulate a therapist, trained on a dataset of therapy sessions.
+It leverages **Parameter-Efficient Fine-Tuning (PEFT)** using **LoRA (Low-Rank Adaptation)** on **Meta LLaMA 2 7B**.
+The model was fine-tuned on a **Tesla T4 GPU**, selectively training a small number of parameters to efficiently adapt the base model to the domain of mental health and therapy.
+> 💡 Dataset used is available on [Hugging Face 🤗](https://huggingface.co/datasets/your-username/therapy-dataset)
+---
+## 🔧 How to Use
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+# Load the base model in 4-bit precision
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_quant_type="nf4"
+)
+base_model = AutoModelForCausalLM.from_pretrained(
+    "meta-llama/Llama-2-7b-hf",
+    quantization_config=bnb_config,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-hf")
+# Load the adapter
+model = PeftModel.from_pretrained(base_model, "your-username/meet-lora-therapist")
+# System prompt to guide behavior
+system_prompt = (
+    "You are a compassionate and thoughtful therapist. "
+    "Your responses are empathetic, non-judgmental, and helpful."
+)
+# 🧩 Function to generate response
+def generate_therapy_response(user_input: str):
+    prompt = f"{system_prompt}\nUser: {user_input}\nTherapist:"
+    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
+    with torch.no_grad():
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=200,
+            do_sample=True,
+            temperature=0.7,
+            top_p=0.9,
+            repetition_penalty=1.1,
+            pad_token_id=tokenizer.eos_token_id
+        )
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Strip off the prompt from the generated text
+    response_only = response.split("Therapist:")[-1].strip()
+    return response_only
+# 💬 Example usage
+response = generate_therapy_response("I feel demotivated because of my breakup")
+print(response)