readme

README.md

@@ -3,4 +3,43 @@ license: mit
 language:
 - en
 pipeline_tag: text-classification
----
+---
+# BogoAI Model
+
+BogoAI is a conceptual model inspired by Bogo Sort and the infinite monkey theorem. It generates random outputs and is not intended for practical use. It has a time complexity og O(n!) were as n is the length of the output text;
+
+## Model Details
+
+- **Vocabulary Size**: 152064
+- **Tokenizer**: Qwen/Qwen2.5-72B-Instruct
+
+## Installation
+
+To use this model, install the required libraries:
+
+```bash
+pip install transformers huggingface_hub
+```
+
+## Usage
+
+Here's how to load and use the BogoAI model:
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModel
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Hugo0123/BogoAI")
+model = AutoModel.from_pretrained("Hugo0123/BogoAI")
+# Example input
+input_text = "Example input text"
+input_ids = tokenizer.encode(input_text, return_tensors='pt')
+# Generate random output
+output_ids = model(input_ids=input_ids)
+output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+print("Output:", output_text)
+```
+
+## License
+
+MIT

readme.md

@@ -1,42 +0,0 @@
-# BogoAI Model
-
-BogoAI is a conceptual model inspired by Bogo Sort and the infinite monkey theorem. It generates random outputs and is not intended for practical use. It has a time complexity og O(n!) were as n is the length of the output text;
-
-## Model Details
-
-- **Vocabulary Size**: 152064
-- **Tokenizer**: Qwen/Qwen2.5-72B-Instruct
-
-## Installation
-
-To use this model, install the required libraries:
-
-```bash
-pip install transformers huggingface_hub
-```
-
-## Usage
-
-Here's how to load and use the BogoAI model:
-
-```python
-import torch
-from transformers import AutoTokenizer, AutoModel
-
-# Load tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained("Hugo0123/BogoAI")
-model = AutoModel.from_pretrained("Hugo0123/BogoAI")
-
-# Example input
-input_text = "Example input text"
-input_ids = tokenizer.encode(input_text, return_tensors='pt')
-
-# Generate random output
-output_ids = model(input_ids=input_ids)
-output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
-print("Output:", output_text)
-```
-
-## License
-
-MIT