Update README.md

Updated model name in README

README.md

@@ -8,12 +8,13 @@ tags:
 - lightweight
 - edge-deployment
 - neo-agent
+- neo
 base_model: HuggingFaceTB/SmolLM2-135M
 datasets:
 - glaiveai/glaive-function-calling-v2
 - NousResearch/hermes-function-calling-v1
 model-index:
-- name: SmolLM2-135M-Function-Calling-NEO
+- name: SmolLM2-135M-Function-Calling
   results:
   - task:
       type: function-calling
@@ -30,17 +31,17 @@ model-index:
       name: Function Name Accuracy (Internal Validation)
 ---
 
-# SmolLM2-135M-Function-Calling-NEO
+# SmolLM2-135M-Function-Calling
 
 ## Model Description
 
-SmolLM2-135M-Function-Calling-NEO is a fine-tuned version of HuggingFaceTB/SmolLM2-135M specifically optimized for function calling tasks. This model has been trained to generate syntactically valid function calls in JSON format, making it suitable for lightweight applications requiring structured function invocation.
+SmolLM2-135M-Function-Calling is a fine-tuned version of HuggingFaceTB/SmolLM2-135M specifically optimized for function calling tasks. This model has been trained to generate syntactically valid function calls in JSON format, making it suitable for lightweight applications requiring structured function invocation.
 
 **Key Achievement**: This model achieves **92.18% Structural Validity on BFCL** and **97.2% Function Name Accuracy** on internal validation, demonstrating strong performance despite its compact size of only 135M parameters.
 
 ## Attribution
 
-The dataset combination, training strategy, and execution were autonomously achieved by NEO.
+The dataset combination, training strategy, and execution were autonomously achieved by [NEO](https://heyneo.so/).
 
 ## Performance Metrics
 
@@ -66,7 +67,7 @@ This model is specifically designed for:
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model_name = "SmolLM2-135M-Function-Calling-NEO"
+model_name = "SmolLM2-135M-Function-Calling"
 device = "cuda" if torch.cuda.is_available() else "cpu"
 
 tokenizer = AutoTokenizer.from_pretrained(model_name)
@@ -143,8 +144,8 @@ Expected output:
 If you use this model, please cite:
 
 ```bibtex
-@misc{smollm2-function-calling-neo,
-  title={SmolLM2-135M-Function-Calling-NEO: Lightweight Function Calling Model},
+@misc{smollm2-function-calling,
+  title={SmolLM2-135M-Function-Calling: Lightweight Function Calling Model},
   author={NEO Agent},
   year={2024},
   publisher={HuggingFace},