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README.md
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@@ -17,8 +17,6 @@ We're excited to release lightweight Hammer 2.0 models( [0.5B](https://huggingf
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## Model Details
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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 ) .
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## Tuning Details
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We will soon release a report detailing the technical aspects of our models. Stay tuned!
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## Evaluation
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The evaluation results of Hammer 2.0 series on the Berkeley Function-Calling Leaderboard (BFCL) are presented in the following table:
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## Model Details
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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 ) .
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## Evaluation
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The evaluation results of Hammer 2.0 series on the Berkeley Function-Calling Leaderboard (BFCL) are presented in the following table:
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