Update README.md

README.md

@@ -13,53 +13,40 @@ tags:
 - structured-output
 ---
 
-＜【課題】ここは自分で記入して下さい＞
+LoRA adapter Repo ID: Mani124124/structeval-lora
+Base model ID used for training: unsloth/Qwen3-4B-Instruct-2507
 
-This repository provides a **LoRA adapter** fine-tuned from
-**unsloth/Qwen3-4B-Instruct-2507** using **QLoRA (4-bit, Unsloth)**.
+This repository provides a LoRA adapter fine-tuned from unsloth/Qwen3-4B-Instruct-2507.
 
-This repository contains **LoRA adapter weights only**.
-The base model must be loaded separately.
+This repository contains LoRA adapter weights only. The base model must be loaded separately.
 
 ## Training Objective
-
-This adapter is trained to improve **structured output accuracy**
-(JSON / YAML / XML / TOML / CSV).
-
-Loss is applied only to the final assistant output,
-while intermediate reasoning (Chain-of-Thought) is masked.
+This adapter is trained to improve structured output accuracy (JSON / YAML / XML / TOML / CSV).
 
 ## Training Configuration
-
-- Base model: unsloth/Qwen3-4B-Instruct-2507
-- Method: QLoRA (4-bit)
-- Max sequence length: 256
-- Epochs: 1
-- Learning rate: 5e-05
-- LoRA: r=16, alpha=32
+Base model: unsloth/Qwen3-4B-Instruct-2507
+Method: LoRA (PEFT)
+Max sequence length: 256
+Epochs: 1
+Learning rate: 5e-05
+LoRA: r=16, alpha=32
 
 ## Usage
-
-```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 from peft import PeftModel
 import torch
 
 base = "unsloth/Qwen3-4B-Instruct-2507"
-adapter = "your_id/your-repo"
+adapter = "Mani124124/structeval-lora"
 
-tokenizer = AutoTokenizer.from_pretrained(base)
+tokenizer = AutoTokenizer.from_pretrained(base, trust_remote_code=True)
 model = AutoModelForCausalLM.from_pretrained(
     base,
     torch_dtype=torch.float16,
     device_map="auto",
+    trust_remote_code=True,
 )
 model = PeftModel.from_pretrained(model, adapter)
-```
 
 ## Sources & Terms (IMPORTANT)
-
 Training data: u-10bei/structured_data_with_cot_dataset_512_v5
-
-Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
-Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.