Merge pull request #14 from Sunwood-ai-labs/translate-readme-11573465431

docs/README.en.md

@@ -44,7 +44,7 @@ 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 0.2.0 includes updated and improved documentation.  Specifically, it adds guides for efficient model operation using Ollama and LiteLLM, and improves the readability of the README.md and implementation example guides.
+**Llama-finetune-sandbox** provides an experimental environment for learning and validating 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 0.3.0 included improved documentation and an updated English README.
 
 
 ## ✨ Main Features
@@ -59,35 +59,36 @@ license: mit
    - Multiple attention mechanisms
 
 3. **Experiment Environment Setup**:
-   - Performance evaluation tools
+   - Performance evaluation tools (added in v0.3.0, later removed)
    - Memory usage optimization
-   - Visualization of experimental results
+   - Experiment result visualization
 
-## 📚 Implementation Examples
 
-This repository includes the following implementation examples:
+## 📚 Examples
+
+This repository includes the following examples:
 
 ### High-Speed Fine-tuning using Unsloth
- - Implementation of high-speed fine-tuning for 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)
+ - Implementation of high-speed fine-tuning for 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. (Japanese)
+   - → Use [this tool](https://huggingface.co/spaces/MakiAi/JupytextWebUI) to convert from markdown to notebook format.
  - [📒Notebook here](https://colab.research.google.com/drive/1AjtWF2vOEwzIoCMmlQfSTYCVgy4Y78Wi?usp=sharing)
 
-### Efficient Model Operation using Ollama and LiteLLM
- - Setup and operation guide on Google Colab
+### Efficient Model Deployment using Ollama and LiteLLM
+ - Setup and deployment guide for Google Colab.
  - → 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)
 
 ### LLM Evaluation System (LLMs as a Judge)
- - Implementation of a system for automatically evaluating the quality of LLM responses.
+ - Implementation of a system for automatically evaluating the quality of LLM responses (added in v0.3.0, later removed).
  - Uses LLMs as evaluators to assess the responses of other LLMs (LLMs as a Judge method).
- - Quantitative quality assessment and feedback generation using a 4-level rating scale.
+ - Quantitative quality assessment and feedback generation using a 4-point rating scale.
  - → See [`llm-evaluator-notebook.md`](sandbox/llm-evaluator-notebook.md) for details.
  - Efficient evaluation system using Gemini and LiteLLM.
  - [📒Notebook here](https://colab.research.google.com/drive/1haO44IeseQ3OL92HlsINAgBI_yA1fxcJ?usp=sharing)
 
 
-## 🛠️ Environment Setup
+## 🔧 Setup
 
 1. Clone the repository:
 ```bash
@@ -95,19 +96,19 @@ git clone https://github.com/Sunwood-ai-labs/Llama-finetune-sandbox.git
 cd Llama-finetune-sandbox
 ```
 
-## 📝 Adding Implementation Examples
+## 📝 Adding Examples
 
 1. Add new implementations to the `examples/` directory.
-2. Add necessary settings and utilities to `utils/`.
+2. Add necessary configurations and utilities to `utils/`.
 3. Update documentation and tests.
 4. Create a pull request.
 
 ## 🤝 Contributions
 
-- Implementation of new fine-tuning methods
-- Bug fixes and feature improvements
-- Documentation improvements
-- Addition of usage examples
+- Implementation of new fine-tuning methods.
+- Bug fixes and feature improvements.
+- Documentation improvements.
+- Adding usage examples.
 
 ## 📚 References