Update README.md

README.md

@@ -16,7 +16,7 @@ We're excited to release lightweight Hammer 2.0 models ([0.5B](https://huggingfa
 **Hammer** is a series of cutting-edge Large Language Models (LLMs) crafted to boost the critical capability of AI agents: function calling. Differing from existing models focusing on training data refinement, Hammer optimizes performance primarily through advanced training techniques. Focusing on on-device applications, we release a number of models from [1.5B](https://huggingface.co/MadeAgents/Hammer-1.5b), [4B](https://huggingface.co/MadeAgents/Hammer-4b) to [7B](https://huggingface.co/MadeAgents/Hammer-7b) parameters.
 
 ## Model Details
-Hammer2.0 finetuned based on [Qwen 2.0 series](https://huggingface.co/collections/Qwen/qwen2-6659360b33528ced941e557f) using function masking techniques. It's trained using the [APIGen Function Calling Datasets](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) containing 60,000 samples, supplemented by [xlam-irrelevance-7.5k](https://huggingface.co/datasets/MadeAgents/xlam-irrelevance-7.5k) we generated. Hammer has achieved exceptional performances across numerous function calling benchmarks. For more details, please refer to [Hammer: Robust Function-Calling for On-Device Language Models via Function Masking](https://arxiv.org/abs/2410.04587) and [Hammer GitHub repository](https://github.com/MadeAgents/Hammer).
+Hammer finetuned based on [Qwen 2.0 series](https://huggingface.co/collections/Qwen/qwen2-6659360b33528ced941e557f) using function masking techniques. It's trained using the [APIGen Function Calling Datasets](https://huggingface.co/datasets/Salesforce/xlam-function-calling-60k) containing 60,000 samples, supplemented by [xlam-irrelevance-7.5k](https://huggingface.co/datasets/MadeAgents/xlam-irrelevance-7.5k) we generated. Hammer has achieved exceptional performances across numerous function calling benchmarks. For more details, please refer to [Hammer: Robust Function-Calling for On-Device Language Models via Function Masking](https://arxiv.org/abs/2410.04587) and [Hammer GitHub repository](https://github.com/MadeAgents/Hammer).
 
 ## Evaluation
 First, we evaluate Hammer series on the Berkeley Function-Calling Leaderboard (BFCL-v2):