Merge pull request #12 from Sunwood-ai-labs/translate-readme-11573137120

docs/README.en.md

@@ -44,33 +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 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.
+**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.
 
 
 ## ✨ Main Features
 
-1. **Diverse Fine-tuning Methods:**
+1. **Diverse Fine-tuning Methods**:
    - LoRA (Low-Rank Adaptation)
    - QLoRA (Quantized LoRA)
 
-2. **Flexible Model Configuration:**
+2. **Flexible Model Configuration**:
    - Customizable maximum sequence length
    - Various quantization options
    - Multiple attention mechanisms
 
-3. **Well-equipped Experiment Environment:**
+3. **Experiment Environment Setup**:
    - Performance evaluation tools
    - Memory usage optimization
-   - Experiment result visualization
+   - Visualization of experimental results
 
 ## 📚 Implementation Examples
 
 This repository includes the following implementation examples:
 
 ### High-Speed Fine-tuning using Unsloth
- - High-speed fine-tuning implementation of Llama-3.2-1B/3B models
+ - 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 format to notebook format](https://huggingface.co/spaces/MakiAi/JupytextWebUI)
+   - → [Use this to convert from markdown 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
@@ -78,6 +78,14 @@ This repository includes the following implementation 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)
 
+### LLM Evaluation System (LLMs as a Judge)
+ - Implementation of a system for automatically evaluating the quality of LLM responses.
+ - 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.
+ - → 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