Update README.md

README.md

@@ -1,34 +1,52 @@
 ---
 library_name: transformers
-license: other
+license: apache-2.0
 base_model: Qwen/Qwen3-8B-Base
 tags:
 - llama-factory
 - full
 - generated_from_trainer
+- text2diagram
+- plantuml
+- code-generation
 model-index:
 - name: pumlGenV1-1
   results: []
 ---
 
-<!-- This model card has been generated automatically according to the information the Trainer had access to. You
-should probably proofread and complete it, then remove this comment. -->
-
 # pumlGenV1-1
 
-This model is a fine-tuned version of [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) on the pumlGen dataset.
+This model is a fine-tuned version of [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base) on the pumlGen dataset. It specializes in generating PlantUML diagrams from natural language questions.
 
 ## Model description
 
-More information needed
+pumlGenV1-1 is a specialized language model that converts complex questions into structured PlantUML diagrams. The model takes philosophical, historical, legal, or analytical questions as input and generates comprehensive PlantUML code that visualizes the relationships, hierarchies, and connections between concepts mentioned in the question.
+
+Key features:
+- Generates syntactically correct PlantUML diagrams
+- Creates structured visualizations with packages, entities, and relationships
+- Adds contextual notes and annotations
+- Handles complex domain-specific topics across various fields
 
 ## Intended uses & limitations
 
-More information needed
+### Intended uses
+- **Educational purposes**: Creating visual diagrams to explain complex concepts
+- **Research visualization**: Mapping relationships between ideas, theories, or historical events
+- **Documentation**: Generating diagrams for technical or conceptual documentation
+- **Analysis tools**: Visualizing interconnections in philosophical, legal, or social topics
+
+### Limitations
+- The model is specifically trained for PlantUML output format
+- Best performance on analytical, philosophical, historical, and conceptual questions
+- May require post-processing for specific PlantUML styling preferences
+- Generated diagrams should be reviewed for accuracy and completeness
 
 ## Training and evaluation data
 
-More information needed
+The model was trained on the pumlGen dataset, which consists of question-answer pairs where:
+- **Input**: Complex analytical questions about various topics (philosophy, history, law, social sciences)
+- **Output**: Corresponding PlantUML diagram code that visualizes the concepts and relationships
 
 ## Training procedure
 
@@ -50,11 +68,53 @@ The following hyperparameters were used during training:
 
 ### Training results
 
-
+The model demonstrates strong capabilities in:
+- Generating valid PlantUML syntax
+- Creating meaningful entity relationships
+- Adding appropriate annotations and notes
+- Structuring complex information hierarchically
 
 ### Framework versions
 
 - Transformers 4.52.3
 - Pytorch 2.6.0+cu124
 - Datasets 3.6.0
-- Tokenizers 0.21.1
+- Tokenizers 0.21.1
+
+## Usage Example
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("your-username/pumlGenV1-1")
+tokenizer = AutoTokenizer.from_pretrained("your-username/pumlGenV1-1")
+
+# Prepare the input in conversation format
+question = "What role does the annual flooding of the Nile play in the overall agricultural success and survival of the kingdoms along its banks?"
+
+messages = [
+    {"from": "human", "value": question},
+]
+
+# Format the input (adjust based on your specific tokenizer's chat template)
+input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(input_text, return_tensors="pt")
+
+# Generate PlantUML diagram
+outputs = model.generate(
+    **inputs, 
+    max_length=2048,
+    temperature=0.7,
+    do_sample=True,
+    pad_token_id=tokenizer.eos_token_id
+)
+
+# Decode and extract the PlantUML code
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+# Extract the PlantUML code from the response (between @startuml and @enduml)
+plantuml_code = response.split("@startuml")[-1].split("@enduml")[0]
+plantuml_code = "@startuml" + plantuml_code + "@enduml"
+
+print(plantuml_code)
+```