Update README.md

README.md

@@ -2,6 +2,7 @@
 license: cc-by-4.0
 datasets:
 - Salesforce/xlam-function-calling-60k
+- MadeAgents/XLAM-7.5k-Irrelevance
 base_model: Qwen/Qwen2-7B-Instruct
 ---
 # Hammer-7b Function Calling Model
@@ -10,7 +11,7 @@ base_model: Qwen/Qwen2-7B-Instruct
 Hammer-7b is a cutting-edge Large Language Model (LLM) crafted to boost the critical capability of AI agents: function calling. Differing from existing models focusing on traning data refinement, Hammer-7b optimizes performance primarily through advanced training techniques. 
 
 ## Model Details
-Hammer-7b is a finetuned model built upon [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct). 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 we generated. Employing innovative training techniques like function masking, function shuffling, and prompt optimization, Hammer-7b 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).
+Hammer-7b is a finetuned model built upon [Qwen2-7B-Instruct](https://huggingface.co/Qwen/Qwen2-7B-Instruct). 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. Employing innovative training techniques like function masking, function shuffling, and prompt optimization, Hammer-7b 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).
 
 ## Tuning Details
 Thanks so much for your attention, a report with all the technical details leading to our models will be published soon.