Update README.md
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README.md
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@@ -50,7 +50,6 @@ import json
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from typing import Any, Dict, List
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# Specify the desired model name here
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model_name = "katanemo/Arch-Agent-3B"
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model = AutoModelForCausalLM.from_pretrained(
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Please use the recommended prompt for each model.
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TASK_PROMPT = (
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"You are a helpful assistant designed to assist with the user query by making one or more function calls if needed."
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"\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\n"
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}
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]
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# Helper function to create the system prompt for our model
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def format_prompt(tools: List[Dict[str, Any]]):
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tool_text = "\n".join(
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)
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return TASK_PROMPT.format(tool_text=tool_text)
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system_prompt = format_prompt(tools)
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messages = [
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{"role": "user", "content": "What is the weather in Seattle?"},
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]
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#### 2.2.3 Run inference
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model_inputs = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
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).to(model.device)
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from typing import Any, Dict, List
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "katanemo/Arch-Agent-3B"
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model = AutoModelForCausalLM.from_pretrained(
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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TASK_PROMPT = (
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"You are a helpful assistant designed to assist with the user query by making one or more function calls if needed."
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"\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\n"
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}
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]
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# Helper function to create the system prompt for our model
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def format_prompt(tools: List[Dict[str, Any]]):
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tool_text = "\n".join(
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)
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return TASK_PROMPT.format(tool_text=tool_text)
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system_prompt = format_prompt(tools)
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messages = [
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{"role": "user", "content": "What is the weather in Seattle?"},
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]
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model_inputs = tokenizer.apply_chat_template(
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messages, add_generation_prompt=True, return_tensors="pt", return_dict=True
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).to(model.device)
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