Update README.md

README.md

@@ -6,7 +6,7 @@ base_model:
 - Qwen/Qwen3-0.6B
 library_name: transformers
 ---
-# Vex-Amber-Mini 1.0
+# Vex-Amber-Mini 1.1
 
 > ⚡ **World Record Holder: Most Parameter-Efficient Sub-1B Language Model**
 
@@ -24,7 +24,7 @@ library_name: transformers
 
 ## Overview
 
-**Vex-Amber-Mini 1.0** is a groundbreaking small language model (SLM) that holds the **world record for the most parameter-efficient model with fewer than 1 billion parameters**. Meticulously optimized for code generation and general-purpose text tasks, it delivers exceptional performance within a compact 0.6B parameter framework.
+**Vex-Amber-Mini 1.1** is a groundbreaking small language model (SLM) that holds the **world record for the most parameter-efficient model with fewer than 1 billion parameters**. Meticulously optimized for code generation and general-purpose text tasks, it delivers exceptional performance within a compact 0.6B parameter framework.
 
 - **Base Model:** [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
 - **Fine-tuning:** LoRA with optional full-weight fine-tuning for enhanced adaptability
@@ -36,7 +36,7 @@ library_name: transformers
 
 ## Installation
 
-To harness the power of **Vex-Amber-Mini 1.0**, install the required dependencies:
+To harness the power of **Vex-Amber-Mini 1.1**, install the required dependencies:
 
 ```bash
 pip install transformers torch
@@ -74,7 +74,7 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 - **Architecture:** Transformer-based, derived from [Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
 - **Training Data:** Fine-tuned on a curated dataset optimized for code generation and versatile text tasks
-- **Performance:** Achieves a HumanEval Pass@1 score of **19.51%**, setting a benchmark for sub-1B models and earning the title of the **most parameter-efficient sub-1B model**
+- **Performance:** Achieves a HumanEval Pass@1 score of **20.12%**, setting a benchmark for sub-1B models and earning the title of the **most parameter-efficient sub-1B model**
 - **Use Cases:** Ideal for code generation, text completion, and lightweight NLP applications
 - **Context Length:** Supports up to 2048 tokens for efficient processing
 
@@ -82,11 +82,11 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 ## Performance Metrics
 
-The following table compares the HumanEval performance of **Vex-Amber-Mini 1.0** against other code generation models. Note that scores for rival models are approximate, as indicated by "~", based on available benchmarks:
+The following table compares the HumanEval performance of **Vex-Amber-Mini 1.1** against other code generation models. Note that scores for rival models are approximate, as indicated by "~", based on available benchmarks:
 
 | Model                  | Parameters | HumanEval Pass@1 | Notes                                                          |
 |------------------------|------------|------------------|----------------------------------------------------------------|
-| **Vex-Amber-Mini 1.0** | 0.6B       | 19.51%           | Compact model optimized for code generation.                   |
+| **Vex-Amber-Mini 1.0** | 0.6B       | 20.21%           | Compact model optimized for code generation.                   |
 | **Code Llama**         | 7B         | ~24%             | Developed by Meta, optimized for code tasks.                   |
 | **StarCoder**          | 7B         | ~25%             | Developed by Hugging Face and ServiceNow, fine-tuned for code. |
 | **CodeGen**            | 6B         | ~22%             | Developed by Salesforce, optimized for code generation.        |
@@ -130,4 +130,4 @@ For inquiries, collaboration, or to report issues, please visit:
 
 ## Contribute
 
-We warmly welcome contributions! Please submit pull requests or issues via the [GitHub repository](https://github.com/Arioron-International/Vex-Amber-Mini-1.0) to help refine and elevate **Vex-Amber-Mini 1.0**.
+We warmly welcome contributions! Please submit pull requests or issues via the [GitHub repository](https://github.com/Arioron-International/Vex-Amber-Mini-1.0) to help refine and elevate **Vex-Amber-Mini 1.1**.