📖 [docs] 英語READMEの更新

docs/README.en.md

@@ -44,34 +44,33 @@ license: mit
 
 ## 🚀 Project Overview
 
-**Llama-finetune-sandbox** provides an experimental environment for learning and verifying Llama model fine-tuning.  You can try various fine-tuning methods, customize models, and evaluate their performance.  It caters to a wide range of users, from beginners to researchers.  Version 0.1.0 includes a repository name change, a major README update, and the addition of a Llama model fine-tuning tutorial.
+**Llama-finetune-sandbox** provides an experimental environment for learning and verifying Llama model fine-tuning.  You can try various fine-tuning methods, customize models, and evaluate performance.  It caters to a wide range of users, from beginners to researchers. Version 0.2.0 includes updated and improved documentation.  In particular,  guides for efficient model operation using Ollama and LiteLLM have been added, and the README.md and implementation example guides have been made easier to understand.
+
 
 ## ✨ Main Features
 
-1. **Diverse Fine-tuning Methods**:
+1. **Diverse Fine-tuning Methods:**
    - LoRA (Low-Rank Adaptation)
    - QLoRA (Quantized LoRA)
-   - ⚠️~Full Fine-tuning~
-   - ⚠️~Parameter-Efficient Fine-tuning (PEFT)~
 
-2. **Flexible Model Configuration**:
+2. **Flexible Model Configuration:**
    - Customizable maximum sequence length
    - Various quantization options
    - Multiple attention mechanisms
 
-3. **Well-Equipped Experiment Environment**:
+3. **Well-equipped Experiment Environment:**
    - Performance evaluation tools
    - Memory usage optimization
-   - Visualization of experimental results
+   - Experiment result visualization
 
-## 📚 Examples
+## 📚 Implementation Examples
 
-This repository includes the following examples:
+This repository includes the following implementation examples:
 
 ### High-Speed Fine-tuning using Unsloth
- - High-speed fine-tuning implementation for Llama-3.2-1B/3B models
+ - High-speed fine-tuning implementation of Llama-3.2-1B/3B models
    - → See [`Llama_3_2_1B+3B_Conversational_+_2x_faster_finetuning_JP.md`](sandbox/Llama_3_2_1B+3B_Conversational_+_2x_faster_finetuning_JP.md) for details.
-   - → [Use this to convert from Markdown to Notebook format](https://huggingface.co/spaces/MakiAi/JupytextWebUI)
+   - → [Use this to convert from markdown format to notebook format](https://huggingface.co/spaces/MakiAi/JupytextWebUI)
  - [📒Notebook here](https://colab.research.google.com/drive/1AjtWF2vOEwzIoCMmlQfSTYCVgy4Y78Wi?usp=sharing)
 
 ### Efficient Model Operation using Ollama and LiteLLM
@@ -79,6 +78,7 @@ This repository includes the following examples:
  - → See [`efficient-ollama-colab-setup-with-litellm-guide.md`](sandbox/efficient-ollama-colab-setup-with-litellm-guide.md) for details.
  - [📒Notebook here](https://colab.research.google.com/drive/1buTPds1Go1NbZOLlpG94VG22GyK-F4GW?usp=sharing)
 
+
 ## 🛠️ Environment Setup
 
 1. Clone the repository:
@@ -87,9 +87,9 @@ git clone https://github.com/Sunwood-ai-labs/Llama-finetune-sandbox.git
 cd Llama-finetune-sandbox
 ```
 
-## 📝 Adding Examples
+## 📝 Adding Implementation Examples
 
-1. Add a new implementation to the `examples/` directory.
+1. Add new implementations to the `examples/` directory.
 2. Add necessary settings and utilities to `utils/`.
 3. Update documentation and tests.
 4. Create a pull request.
@@ -104,9 +104,10 @@ cd Llama-finetune-sandbox
 ## 📚 References
 
 - [HuggingFace PEFT Documentation](https://huggingface.co/docs/peft)
-- [About Llama Models](https://github.com/facebookresearch/llama)
-- [Fine-tuning Best Practices](https://github.com/Sunwood-ai-labs/Llama-finetune-sandbox/wiki)
+- [About Llama models](https://github.com/facebookresearch/llama)
+- [Fine-tuning best practices](https://github.com/Sunwood-ai-labs/Llama-finetune-sandbox/wiki)
 
-## ⚖️ License
+## 📄 License
 
-This project is licensed under the MIT License.
+This project is licensed under the MIT License.
+```