jianguozhang nielsr HF Staff commited on
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Update model card with paper link and correct pipeline tag (#6)

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- Update model card with paper link and correct pipeline tag (ebc6d02e440a360feb1497ad89c281f7d5e7c919)


Co-authored-by: Niels Rogge <[email protected]>

Files changed (1) hide show
  1. README.md +36 -14
README.md CHANGED
@@ -1,16 +1,10 @@
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  ---
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- extra_gated_heading: Acknowledge to follow corresponding license to access the repository
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- extra_gated_button_content: Agree and access repository
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- extra_gated_fields:
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- First Name: text
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- Last Name: text
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- Country: country
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- Affiliation: text
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- license: cc-by-nc-4.0
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  datasets:
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  - Salesforce/xlam-function-calling-60k
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  language:
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  - en
 
 
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  pipeline_tag: text-generation
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  tags:
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  - function-calling
@@ -18,9 +12,16 @@ tags:
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  - tool-use
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  - mistral
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  - pytorch
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- library_name: transformers
 
 
 
 
 
 
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  ---
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  <p align="center">
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  <img width="500px" alt="xLAM" src="https://huggingface.co/datasets/jianguozhang/logos/resolve/main/xlam-no-background.png">
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  </p>
@@ -191,15 +192,35 @@ def build_conversation_history_prompt(conversation_history: str):
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  })
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  history_string = json.dumps(parsed_history)
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- return f"\n[BEGIN OF HISTORY STEPS]\n{history_string}\n[END OF HISTORY STEPS]\n"
 
 
 
 
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  # Helper function to build the input prompt for our model
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  def build_prompt(task_instruction: str, format_instruction: str, tools: list, query: str, conversation_history: list):
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- prompt = f"[BEGIN OF TASK INSTRUCTION]\n{task_instruction}\n[END OF TASK INSTRUCTION]\n\n"
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- prompt += f"[BEGIN OF AVAILABLE TOOLS]\n{json.dumps(xlam_format_tools)}\n[END OF AVAILABLE TOOLS]\n\n"
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- prompt += f"[BEGIN OF FORMAT INSTRUCTION]\n{format_instruction}\n[END OF FORMAT INSTRUCTION]\n\n"
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- prompt += f"[BEGIN OF QUERY]\n{query}\n[END OF QUERY]\n\n"
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  if len(conversation_history) > 0: prompt += build_conversation_history_prompt(conversation_history)
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  return prompt
@@ -479,4 +500,5 @@ If you find this repo helpful, please consider to cite our papers:
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  journal={arXiv preprint arXiv:2402.15506},
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  year={2024}
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  }
 
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  ```
 
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  ---
 
 
 
 
 
 
 
 
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  datasets:
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  - Salesforce/xlam-function-calling-60k
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  language:
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  - en
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+ library_name: transformers
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+ license: cc-by-nc-4.0
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  pipeline_tag: text-generation
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  tags:
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  - function-calling
 
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  - tool-use
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  - mistral
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  - pytorch
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+ extra_gated_heading: Acknowledge to follow corresponding license to access the repository
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+ extra_gated_button_content: Agree and access repository
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+ extra_gated_fields:
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+ First Name: text
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+ Last Name: text
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+ Country: country
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+ Affiliation: text
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  ---
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+ ```markdown
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  <p align="center">
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  <img width="500px" alt="xLAM" src="https://huggingface.co/datasets/jianguozhang/logos/resolve/main/xlam-no-background.png">
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  </p>
 
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  })
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  history_string = json.dumps(parsed_history)
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+ return f"
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+ [BEGIN OF HISTORY STEPS]
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+ {history_string}
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+ [END OF HISTORY STEPS]
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+ "
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  # Helper function to build the input prompt for our model
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  def build_prompt(task_instruction: str, format_instruction: str, tools: list, query: str, conversation_history: list):
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+ prompt = f"[BEGIN OF TASK INSTRUCTION]
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+ {task_instruction}
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+ [END OF TASK INSTRUCTION]
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+
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+ "
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+ prompt += f"[BEGIN OF AVAILABLE TOOLS]
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+ {json.dumps(xlam_format_tools)}
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+ [END OF AVAILABLE TOOLS]
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+
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+ "
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+ prompt += f"[BEGIN OF FORMAT INSTRUCTION]
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+ {format_instruction}
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+ [END OF FORMAT INSTRUCTION]
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+
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+ "
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+ prompt += f"[BEGIN OF QUERY]
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+ {query}
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+ [END OF QUERY]
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+
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+ "
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  if len(conversation_history) > 0: prompt += build_conversation_history_prompt(conversation_history)
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  return prompt
 
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  journal={arXiv preprint arXiv:2402.15506},
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  year={2024}
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  }
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+ ```
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  ```