Update README.md

README.md

@@ -2,201 +2,89 @@
 base_model: HuggingFaceTB/SmolLM2-360M-Instruct
 library_name: peft
 ---
+# ACT-R Adapter for PEFT
 
-# Model Card for Model ID
+## 📌 概要
+`summerstars/ACT-R-adapter` は、Hugging Faceの`SmolLM2-360M-Instruct`モデルに基づいて、ACT-Rモデルを効率的に微調整（PEFT）するためのアダプターです。このアダプターは、少ないパラメータで大規模言語モデルを適応させることができ、ACT-Rのような認知アーキテクチャに基づく推論を効率的に行えるようにします。
 
-<!-- Provide a quick summary of what the model is/does. -->
-
-
-
-## Model Details
-
-### Model Description
-
-<!-- Provide a longer summary of what this model is. -->
-
-
-
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-
-### Model Sources [optional]
-
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
+---
 
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+## 🚀 必要なライブラリ
 
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+以下のライブラリが必要です：
 
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
+```bash
+pip install transformers peft
+```
 
-## Technical Specifications [optional]
+---
 
-### Model Architecture and Objective
+## 🔧 使用方法
 
-[More Information Needed]
+### 1. モデルとアダプターのロード
 
-### Compute Infrastructure
+まず、以下のコードで、`summerstars/ACT-R-adapter`をロードして、`SmolLM2-360M-Instruct`のモデルを基にしたACT-Rアダプターを適用します。
 
-[More Information Needed]
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM
 
-#### Hardware
+# ベースモデルのロード
+base_model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM2-360M-Instruct")
 
-[More Information Needed]
+# ACT-R用アダプターを適用
+model = PeftModel.from_pretrained(base_model, "summerstars/ACT-R-adapter")
+```
 
-#### Software
+このコードは、Hugging Faceの`SmolLM2-360M-Instruct`という事前学習済みのモデルをベースとして、`summerstars/ACT-R-adapter`を使ってPEFTを適用しています。
 
-[More Information Needed]
+### 2. 推論の実行
 
-## Citation [optional]
+ACT-Rアダプターを使った推論を行うためには、以下のコードを使用します。
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+```python
+from transformers import pipeline
 
-**BibTeX:**
+# パイプラインの設定
+actr_pipeline = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=base_model.tokenizer  # ベースモデルのトークナイザーを使用
+)
 
-[More Information Needed]
+# 推論関数の定義
+def generate_actr_text(prompt, max_length=200, temperature=0.7, top_p=0.95, top_k=50):
+    response = actr_pipeline(prompt, max_length=max_length, temperature=temperature, top_p=top_p, top_k=top_k, do_sample=True)
+    return response[0]["generated_text"]
 
-**APA:**
+# 例: 推論の実行
+actr_prompt = "Analyze the impact of AI on human cognition."
+print("【ACT-R Model Output】")
+print(generate_actr_text(actr_prompt))
+```
 
-[More Information Needed]
+このコードでは、ACT-Rアダプターを使って、指定したプロンプトに基づいてAIの影響を分析するための推論を行います。
 
-## Glossary [optional]
+---
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+## ⚙️ 設定
 
-[More Information Needed]
+アダプターを使用して微調整されたACT-Rモデルの設定は、以下のようにカスタマイズできます：
 
-## More Information [optional]
+- **max_length**: 生成するテキストの最大長
+- **temperature**: 生成時のランダム性（高い値はランダムで多様な出力を生成）
+- **top_p**: トークンの確率分布の上位p%から生成する（Nucleus Sampling）
+- **top_k**: 上位k個のトークンから生成する（Top-k Sampling）
 
-[More Information Needed]
+---
 
-## Model Card Authors [optional]
+## 🧠 参考文献
 
-[More Information Needed]
+- Anderson, J. R., & Lebiere, C. (1998). *The Atomic Components of Thought: A Propositional Theory of Cognitive Representations*. Erlbaum.
+- PEFT論文: *Parameter-Efficient Fine-Tuning* by Houlsby et al. (2019)
 
-## Model Card Contact
+---
 
-[More Information Needed]
-### Framework versions
+## 📜 ライセンス
 
-- PEFT 0.14.0
+このプロジェクトは `Apache 2.0` ライセンスのもとで公開されています。