Update README.md

README.md

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 # Hammer2.0-3b Function Calling Model
 
 ## Introduction
-We're excited to release lightweight Hammer 2.0 models(  [0.5B](https://huggingface.co/MadeAgents/Hammer2.0-0.5b ) ,  [1.5B](https://huggingface.co/MadeAgents/Hammer2.0-1.5b ) , [3B](https://huggingface.co/MadeAgents/Hammer2.0-3b ), and  [7B](https://huggingface.co/MadeAgents/Hammer2.0-7b )) with strong function calling capability, which empower developers to build personalized, on-device agentic applications.
+We're excited to release lightweight Hammer 2.0 models ([0.5B](https://huggingface.co/MadeAgents/Hammer2.0-0.5b) ,   [1.5B](https://huggingface.co/MadeAgents/Hammer2.0-1.5b) ,   [3B](https://huggingface.co/MadeAgents/Hammer2.0-3b) ,  and  [7B](https://huggingface.co/MadeAgents/Hammer2.0-7b)) with strong function calling capability, which empower developers to build personalized, on-device agentic applications.
 
 ## Model Details
-Hammer2.0 finetuned based on [Qwen 2.5 series](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e ) and [Qwen 2.5 coder series](https://huggingface.co/collections/Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f ) 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 [7,500 irrelevance detection data](https://huggingface.co/datasets/MadeAgents/XLAM-7.5k-Irrelevance ) we generated. Hammer2.0 has achieved exceptional performances across numerous benchmarks including [Berkley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html ), [API-Bank](https://arxiv.org/abs/2304.08244 ), [Tool-Alpaca](https://arxiv.org/abs/2306.05301 ), [Nexus Raven](https://github.com/nexusflowai/NexusRaven-V2 ) and [Seal-Tools](https://arxiv.org/abs/2405.08355 ). For detailed training procedures, please refer to our paper [```Hammer: Robust Function-Calling for On-Device Language Models via Function Masking```](https://arxiv.org/pdf/2410.04587 ) and the [Hammer GitHub repository](https://github.com/MadeAgents/Hammer ) .
+Hammer2.0 finetuned based on [Qwen 2.5 series](https://huggingface.co/collections/Qwen/qwen25-66e81a666513e518adb90d9e) and [Qwen 2.5 coder series](https://huggingface.co/collections/Qwen/qwen25-coder-66eaa22e6f99801bf65b0c2f) 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 [7,500 irrelevance detection data](https://huggingface.co/datasets/MadeAgents/XLAM-7.5k-Irrelevance) we generated. Hammer2.0 has achieved exceptional performances across numerous benchmarks including [Berkley Function Calling Leaderboard](https://gorilla.cs.berkeley.edu/leaderboard.html), [API-Bank](https://arxiv.org/abs/2304.08244), [Tool-Alpaca](https://arxiv.org/abs/2306.05301), [Nexus Raven](https://github.com/nexusflowai/NexusRaven-V2) and [Seal-Tools](https://arxiv.org/abs/2405.08355). For detailed training procedures, please refer to our paper [Hammer: Robust Function-Calling for On-Device Language Models via Function Masking](https://arxiv.org/abs/2410.04587) and the [Hammer GitHub repository](https://github.com/MadeAgents/Hammer) .
 
 
 ## Evaluation