Update README.md

README.md

@@ -21,7 +21,8 @@ We're excited to introduce Hammer 2.0, the latest in our Hammer Large Language M
 Hammer-4b is a finetuned model built upon [Qwen1.5-4B-Chat](https://huggingface.co/Qwen/Qwen1.5-4B-Chat). 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-4b 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.
+A report with all the technical details leading to our models has been published at "[Hammer: Robust Function-Calling for On-Device Language Models via Function Masking](https://arxiv.org/abs/2410.04587)". All the code for data process, model tuning, and evaluation will also be open-sourced very soon.
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 ## Evaluation
 First, we evaluate Hammer series on the Berkeley Function-Calling Leaderboard (BFCL):