Update README.md

README.md

@@ -1,3 +1,6 @@
+
+
+````markdown
 ---
 license: apache-2.0
 language:
@@ -15,20 +18,14 @@ pipeline_tag: text-classification
 
 # BenchHub-Cat-7b
 
-**BenchHub-Cat-7b** is a 7B parameter instruction-tuned language model that performs structured classification of natural language queries into three dimensions:
-
-- `subject`: Topic domain of the query (e.g., law, health, travel)
-- `skill`: Type of skill or task (e.g., reasoning, explanation, comparison)
-- `target`: General or cultural-specific target audience
-
-It is based on the Qwen2.5-7B-Instruct architecture and trained on a mixture of synthetic and GPT-generated instruction data.
+**BenchHub-Cat-7b** is a category classification model based on **Qwen2.5-7B**, fine-tuned to assign natural language queries to structured category triplets: `(subject, skill, target)`.
 
 ## 🔧 Model Details
 
-- **Base Model**: Qwen2.5-7B-Instruct
-- **Task**: Structured triple-label classification
-- **Prompt Format**: Instruction-style with output structure
-- **Training Framework**: Axolotl + DeepSpeed ZeRO-3
+- **Base Model**: [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
+- **Task**: Structured multi-label classification (triple: subject, skill, target)
+- **Prompting Style**: Instruction-style with expected format output
+- **Training Framework**: [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) + DeepSpeed ZeRO-3
 
 ## 🧪 Training Configuration
 
@@ -41,19 +38,40 @@ It is based on the Qwen2.5-7B-Instruct architecture and trained on a mixture of
 | Scheduler                | Cosine Decay         |
 | Warmup Ratio             | 0.05                 |
 | Optimizer                | Method from [19]     |
+| Trainer                  | DeepSpeed ZeRO-3     |
 | Hardware                 | 4× A6000 48GB GPUs   |
 | Training Time            | ~5 hours per run     |
 
 ## 🧠 Intended Use
 
-**Input**: Open-ended natural language queries  
-**Output**: Structured classification result with 3 fields
+**Input**: Natural language question or instruction  
+**Output**: Triplet `(subject, skill, target)`, such as:
+
+```yaml
+{ "subject_type": "history",  
+"task_type": "reasoning",  
+"target_type": "korea"}
+````
+
+
+## ✨ Prompt Example
+
+```
+### Instruction:
+Classify the following query into subject, skill, and target.
+
+### Query:
+How did Confucianism shape education in East Asia?
+
+### Output:
+{ "subject_type": "history",  
+"task_type": "reasoning",  
+"target_type": "korea"}
+```
+
+## 📜 License
 
-### Example Categories:
-- `subject`: education, health, history, law, etc.
-- `skill`: reasoning, recall, summarization, etc.
-- `target`: general, cultural-specific
+Apache 2.0
 
-### ✨ Example Prompt & Output
 
-#### 📝 Prompt
+````