Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,72 @@
+---
+license: apache-2.0
+tags:
+  - text-generation
+  - instruction-tuned
+  - maincoder
+  - gguf
+  - chatbot
+library_name: llama.cpp
+language: en
+datasets:
+  - custom
+model-index:
+  - name: Corelyn Neosepcyn Leon
+    results: []
+---
+
+
+# Corelyn NeoMini GGUF Model
+
+## Specifications :
+- Model Name: Corelyn Neosepcyn Leon
+- Base Name: Leon_1B
+- Type: Instruct / Fine-tuned
+- Architecture: Maincoder
+- Size: 1B parameters
+- Organization: Corelyn
+
+## Model Overview
+
+Corelyn Neosepcyn Leon is a 1-billion parameter LLaMA-based instruction-tuned model, designed for general-purpose assistant tasks and knowledge extraction. It is a fine-tuned variant optimized for instruction-following use cases.
+
+- Fine-tuning type: Instruct
+
+- Base architecture: Maincoder
+
+- Parameter count: 3B
+
+
+### This model is suitable for applications such as:
+
+- Algorithms
+
+- Websites
+
+- Python, JavaScript, Java...
+
+- Code and text generation
+
+## Usage
+
+Download from : [LeonCode_1B](https://huggingface.co/CorelynAI/LeonCode/blob/main/LeonCode_1B.gguf)
+
+```python
+
+# pip install pip install llama-cpp-python
+
+from llama_cpp import Llama
+
+# Load the model (update the path to where your .gguf file is)
+llm = Llama(model_path="path/to/the/file/LeonCode_1B.gguf")
+
+# Create chat completion
+response = llm.create_chat_completion(
+    messages=[{"role": "user", "content": "Create a python sorting algorithm"}]
+)
+
+# Print the generated text
+print(response.choices[0].message["content"])
+
+
+```