UEC-InabaLab
/

Llama-3.1-KokoroChat-High

@@ -14,99 +14,51 @@ datasets:
 - UEC-InabaLab/KokoroChat
 ---
-# Model Card for Model ID
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 - UEC-InabaLab/KokoroChat
 ---
+# 🧠 KokoroChat-High (LoRA Adapter for Japanese Counseling Dialogue)
+This repository contains the **LoRA adapter weights** for KokoroChat-High, a version of the KokoroChat model fine-tuned on **high-feedback counseling dialogues** (client score ≥ 70 and ≤ 98) from the [KokoroChat dataset](https://huggingface.co/datasets/your-username/kokorochat).
+The base model is [tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3), and this adapter enhances it for generating **high-quality, empathetic Japanese counseling responses**.
+---
+## 💡 What is "Kokoro-High"?
+- ✅ Trained on **2,601 dialogues**
+- ✅ All sessions have **client feedback scores between 70 and 98**
+- ✅ Represents high-quality, successful counseling interactions
+---
+## 🧾 Model Details
+- **Base Model**: [`tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3`](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3)
+- **Fine-tuning Method**: PEFT (LoRA)
+- **Adapter Size**: ~1.1GB (`adapter_model.safetensors`)
+- **Language**: Japanese
+- **Training Data**: KokoroChat-High subset
+---
+## ⚙️ Usage Instructions
+This repository contains **only the LoRA adapter**. You must load the original base model and then apply this adapter:
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+base_model_id = "tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3"
+adapter_id = "your-username/kokorochat-high-lora"
+tokenizer = AutoTokenizer.from_pretrained(base_model_id)
+base_model = AutoModelForCausalLM.from_pretrained(
+    base_model_id,
+    device_map="auto",
+    torch_dtype="auto",
+    quantization_config=BitsAndBytesConfig(load_in_4bit=True)
+)
+model = PeftModel.from_pretrained(base_model, adapter_id)
+model = model.merge_and_unload()