Update README.md

README.md

@@ -8,28 +8,30 @@ pipeline_tag: text-generation
 datasets:
 - ShenLab/MentalChat16K
 tags:
-- usloth
-metrics:
-- accuracy
+- unsloth
+- lora
+- peft
+- mental-health
 ---
+
 # TinyLlama MentalChat LoRA
 
-This repository contains a **LoRA adapter** fine-tuned on the
-[ShenLab/MentalChat16K](https://huggingface.co/datasets/ShenLab/MentalChat16K)
-dataset for **mental health–related supportive dialogue**.
+This repository contains a **LoRA adapter** fine-tuned on the  
+[ShenLab/MentalChat16K](https://huggingface.co/datasets/ShenLab/MentalChat16K) dataset  
+for **mental health–related supportive dialogue**.
 
-⚠️ This is **not a full model**. It is a lightweight LoRA adapter that must be
-used together with the base model.
+⚠️ **This is not a full model.**  
+It is a lightweight **LoRA adapter** that must be used together with the base model.
 
 ---
 
 ## 🔍 Model Overview
 
-- **Base Model**: TinyLlama/TinyLlama-1.1B-Chat-v1.0
-- **Fine-tuning Method**: LoRA (PEFT)
-- **Domain**: Mental health supportive conversations
-- **Language**: English
-- **Parameter Size (Adapter)**: ~50MB
+- **Base Model**: TinyLlama/TinyLlama-1.1B-Chat-v1.0  
+- **Fine-tuning Method**: LoRA (PEFT)  
+- **Domain**: Mental health supportive conversations  
+- **Language**: English  
+- **Adapter Size**: ~50 MB  
 
 ---
 
@@ -38,9 +40,9 @@ used together with the base model.
 The model was fine-tuned using the **MentalChat16K** dataset, which consists of
 mental health–related conversations between users and assistants.
 
-- Dataset: `ShenLab/MentalChat16K`
-- Language: English
-- Task: Supportive, empathetic responses in mental health contexts
+- **Dataset**: `ShenLab/MentalChat16K`  
+- **Language**: English  
+- **Task**: Supportive, empathetic responses in mental health contexts  
 
 ---
 
@@ -48,18 +50,19 @@ mental health–related conversations between users and assistants.
 
 ### Load Base Model + LoRA Adapter
 
+```python
 from unsloth import FastLanguageModel
 from peft import PeftModel
 import torch
 
-# Base model
+# Load base model
 base_model, tokenizer = FastLanguageModel.from_pretrained(
     "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
     max_seq_length=2048,
     load_in_4bit=True,
 )
 
-# LoRA model
+# Load LoRA adapter
 lora_model = PeftModel.from_pretrained(
     base_model,
     "BEncoderRT/tinyllama-mentalchat-lora",
@@ -82,6 +85,7 @@ def generate(model, prompt, max_new_tokens=200):
 
     return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
+
 prompt = """### Instruction:
 I feel empty and hopeless lately. Nothing seems meaningful.
 
@@ -93,4 +97,3 @@ print(generate(base_model, prompt))
 
 print("\n=== LoRA Model ===")
 print(generate(lora_model, prompt))
-