Update README.md

README.md

@@ -45,7 +45,6 @@ The goal of this fine-tuning is to enhance:
 - Training precision: 4-bit base + 16-bit adapters
 
 
-
 # 🎯 Intended Use
 
 This model is intended for:
@@ -72,5 +71,49 @@ The model aims to produce:
 
 This model should not be used for crisis intervention or high-risk mental health scenarios.
 
-
-
+# How to get started with Model
+
+``` Python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from peft import PeftModel
+
+
+tokenizer = AutoTokenizer.from_pretrained("unsloth/qwen3-14b-unsloth-bnb-4bit")
+base_model = AutoModelForCausalLM.from_pretrained(
+    "unsloth/qwen3-14b-unsloth-bnb-4bit",
+    device_map={"": 0}
+)
+
+model = PeftModel.from_pretrained(base_model,"khazarai/Med-R1-14B")
+
+question = """
+How can someone work through and move past deeply painful memories associated with trauma, understanding that "moving past" doesn't mean forgetting but rather integrating the experience in a healthy way?
+"""
+
+messages = [
+    {"role" : "user", "content" : question}
+]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize = False,
+    add_generation_prompt = True,
+    enable_thinking = True,
+)
+
+from transformers import TextStreamer
+_ = model.generate(
+    **tokenizer(text, return_tensors = "pt").to("cuda"),
+    max_new_tokens = 2048,
+    temperature = 0.6,
+    top_p = 0.95,
+    top_k = 20,
+    streamer = TextStreamer(tokenizer, skip_prompt = True),
+)
+```
+
+# 🧪 Future Work
+
+- Domain expansion to broader emotional intelligence tasks
+- Controlled reasoning output (hidden CoT vs visible CoT)
+- Evaluation via human annotation
+- Cross-cultural emotional adaptation