Update README.md

changed to fine_tune as its not mor

README.md

@@ -2,7 +2,6 @@
 license: mit
 library_name: transformers
 tags:
-- mixture-of-recursions
 - adaptive-computation
 - early-exiting
 - llama
@@ -17,26 +16,13 @@ pipeline_tag: text-generation
 model_type: llama
 ---
 
-# Mixture-of-Recursions (MoR): Learning Dynamic Recursive Depths for Adaptive Token-Level Computation
-
-<div align="center">
-
-[![Paper](https://img.shields.io/badge/Paper-arXiv:2507.10524-Green)](https://arxiv.org/abs/2507.10524)
-[![GitHub](https://img.shields.io/badge/GitHub-mixture_of_recursions-blue)](https://github.com/raymin0223/mixture_of_recursions)
-[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
+# Model Fine tunning on ineweb-edu-dedup, Hugging face open datasets
 
 </div>
 
 ## Model Description
 
-This is a **Mixture-of-Recursions (MoR)** model that implements adaptive token-level computation through dynamic recursive depths. MoR addresses key bottlenecks in early-exiting techniques by introducing a unified framework that tackles both missing Key-Value (KV) cache problems and inefficient batched inference.
 
-**Key Features:**
-- 🚀 **Up to 2× greater inference throughput** compared to standard transformers at similar accuracy
-- 🧠 **Dynamic routing mechanism** that assigns optimal recursion depth to each token
-- 💾 **Recursion-wise KV caching strategy** that optimizes memory usage
-- ⚡ **Efficient batched inference** through parameter sharing
-- 🎯 **End-to-end trainable** architecture
 
 ### Model Details
 
@@ -64,7 +50,7 @@ import torch
 from transformers import AutoTokenizer, AutoModelForCausalLM
 
 # Load model and tokenizer
-model_name = "your-username/mixture-of-recursions-360m"
+model_name = "your-username/fine_tune"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForCausalLM.from_pretrained(
     model_name,