Improve model card: Add pipeline tag, library name, and explicit links

#1
by nielsr HF Staff - opened
Files changed (1) hide show
  1. README.md +13 -12
README.md CHANGED
@@ -1,24 +1,24 @@
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  ---
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- license: apache-2.0
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  base_model:
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  - Qwen/Qwen3-4B-Instruct-2507
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- task_categories:
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- - question-answering
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- - text-generation
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  language:
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  - en
 
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  tags:
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  - agent
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  - Agentic Learning
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  - tool use
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  - BFCL
 
 
 
 
 
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  ---
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-
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  # FunReason-MT Technical Report: Overcoming the Complexity Barrier in Multi-Turn Function Calling
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- [![arXiv](https://img.shields.io/badge/arXiv-2510.24645-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2510.24645) [![Model](https://img.shields.io/badge/Hugging%20Face-Model-yellow?logo=huggingface)](https://huggingface.co/Bingguang/FunReason-MT) [![Dataset](https://img.shields.io/badge/Hugging%20Face-Dataset-yellow?logo=huggingface)](https://huggingface.co/datasets/Bingguang/FunReason-MT)
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-
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  ## Model Overview
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@@ -30,9 +30,7 @@ FunReason-MT-4B achieves state-of-the-art results on the **Berkeley Function-Cal
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  - **Size:** 4 Billion parameters
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  - **Key Capability:** Advanced Multi-Turn Function Calling and Agentic Tool-Use
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- The full usage of the model is in this [pull request](https://github.com/ShishirPatil/gorilla/pull/1229)
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-
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-
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  ## 📊 Evaluation Results
@@ -195,8 +193,11 @@ class FunReasonMTHandler(OSSHandler):
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  cleaned_response = model_response
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  if "</think>" in model_response:
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  parts = model_response.split("</think>")
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- reasoning_content = parts[0].rstrip("\n").split("<think>")[-1].lstrip("\n")
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- cleaned_response = parts[-1].lstrip("\n")
 
 
 
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  else:
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  cleaned_response = "response outputs too long or no slash think in response."
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  print("cleaned_response: ", cleaned_response)
 
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  ---
 
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  base_model:
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  - Qwen/Qwen3-4B-Instruct-2507
 
 
 
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  language:
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  - en
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+ license: apache-2.0
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  tags:
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  - agent
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  - Agentic Learning
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  - tool use
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  - BFCL
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+ task_categories:
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+ - question-answering
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+ - text-generation
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+ pipeline_tag: text-generation
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+ library_name: transformers
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  ---
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  # FunReason-MT Technical Report: Overcoming the Complexity Barrier in Multi-Turn Function Calling
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+ [![arXiv](https://img.shields.io/badge/arXiv-2510.24645-b31b1b.svg?logo=arXiv)](https://arxiv.org/abs/2510.24645) [![Paper](https://img.shields.io/badge/Hugging%20Face-Paper-yellow?logo=huggingface)](https://huggingface.co/papers/2510.24645) [![Model](https://img.shields.io/badge/Hugging%20Face-Model-yellow?logo=huggingface)](https://huggingface.co/Bingguang/FunReason-MT) [![Dataset](https://img.shields.io/badge/Hugging%20Face-Dataset-yellow?logo=huggingface)](https://huggingface.co/datasets/Bingguang/FunReason-MT) [![GitHub](https://img.shields.io/badge/GitHub-Code-181717?logo=github)](https://github.com/inclusionAI/AWorld-RL) [![Project Page](https://img.shields.io/badge/Project-AWorld-green)](https://github.com/inclusionAI/AWorld)
 
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  ## Model Overview
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  - **Size:** 4 Billion parameters
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  - **Key Capability:** Advanced Multi-Turn Function Calling and Agentic Tool-Use
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+ The full usage of the model is in the [AWorld-RL GitHub repository](https://github.com/inclusionAI/AWorld-RL).
 
 
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  ## 📊 Evaluation Results
 
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  cleaned_response = model_response
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  if "</think>" in model_response:
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  parts = model_response.split("</think>")
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+ reasoning_content = parts[0].rstrip("
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+ ").split("<think>")[-1].lstrip("
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+ ")
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+ cleaned_response = parts[-1].lstrip("
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+ ")
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  else:
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  cleaned_response = "response outputs too long or no slash think in response."
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  print("cleaned_response: ", cleaned_response)