jeffreymeetkai
commited on
Commit
•
b1f1e88
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Parent(s):
d301c4d
update jinja chat template + readme usage
Browse files- README.md +4 -5
- tokenization_functionary.py +0 -524
- tokenizer_config.json +2 -5
README.md
CHANGED
@@ -23,13 +23,13 @@ The model determines when to execute functions, whether in parallel or serially,
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## How to Get Started
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We provide custom code for
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("meetkai/functionary-small-
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model = AutoModelForCausalLM.from_pretrained("meetkai/functionary-small-
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tools = [
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{
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@@ -53,7 +53,6 @@ tools = [
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messages = [{"role": "user", "content": "What is the weather in Istanbul and Singapore respectively?"}]
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final_prompt = tokenizer.apply_chat_template(messages, tools, add_generation_prompt=True, tokenize=False)
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tokenizer.padding_side = "left"
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inputs = tokenizer(final_prompt, return_tensors="pt").to("cuda")
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pred = model.generate_tool_use(**inputs, max_new_tokens=128, tokenizer=tokenizer)
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print(tokenizer.decode(pred.cpu()[0]))
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@@ -63,7 +62,7 @@ print(tokenizer.decode(pred.cpu()[0]))
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We convert function definitions to a similar text to TypeScript definitions. Then we inject these definitions as system prompts. After that, we inject the default system prompt. Then we start the conversation messages.
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This formatting is also available via our vLLM server which we process the functions into Typescript definitions encapsulated in a system message
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```python
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from openai import OpenAI
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## How to Get Started
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+
We provide custom code for parsing raw model responses into a JSON object containing `role`, `content` and `tool_calls` fields. This enables the users to read the function-calling output of the model easily.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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tokenizer = AutoTokenizer.from_pretrained("meetkai/functionary-small-v3.2")
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model = AutoModelForCausalLM.from_pretrained("meetkai/functionary-small-v3.2", device_map="auto", trust_remote_code=True)
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tools = [
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{
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messages = [{"role": "user", "content": "What is the weather in Istanbul and Singapore respectively?"}]
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final_prompt = tokenizer.apply_chat_template(messages, tools, add_generation_prompt=True, tokenize=False)
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inputs = tokenizer(final_prompt, return_tensors="pt").to("cuda")
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pred = model.generate_tool_use(**inputs, max_new_tokens=128, tokenizer=tokenizer)
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print(tokenizer.decode(pred.cpu()[0]))
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We convert function definitions to a similar text to TypeScript definitions. Then we inject these definitions as system prompts. After that, we inject the default system prompt. Then we start the conversation messages.
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+
This formatting is also available via our vLLM server which we process the functions into Typescript definitions encapsulated in a system message using a pre-defined Transformers Jinja chat template. This means that the lists of messages can be formatted for you with the apply_chat_template() method within our server:
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```python
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from openai import OpenAI
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tokenization_functionary.py
DELETED
@@ -1,524 +0,0 @@
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# Copyright (c) 2024, MeetKai Inc. All rights reserved.
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from copy import deepcopy
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import json
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from typing import Any, Dict, List, Literal, Optional, Union
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import jsonref
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from pydantic import BaseModel, Field, model_validator
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from typing_extensions import Self
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from transformers.tokenization_utils_base import BatchEncoding
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from transformers.tokenization_utils_fast import PreTrainedTokenizerFast
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from transformers.utils import TensorType, logging
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logger = logging.get_logger(__name__)
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SYSTEM_PROMPT = """A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. The assistant calls functions with appropriate input when necessary"""
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CODE_INTERPRETER_SYSTEM_PROMPT = """When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0 seconds. The drive at '/mnt/data' can be used to save and persist user files."""
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class Function(BaseModel):
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name: str
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description: Optional[str] = Field(default="")
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parameters: Optional[dict] = None
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class Tool(BaseModel):
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type: Literal["function", "code_interpreter"]
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function: Optional[Function] = None
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@model_validator(mode="after")
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def check_type_function_matches(self) -> Self:
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if self.type == "function":
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assert self.function is not None, '"function" must contain function description when `"type": "function"`'
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else:
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assert self.function is None, '"function" must not be provided when `"type": "code_interpreter"`'
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return self
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def convert_data_type(param_type: str) -> str:
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"""convert data_type to typescript data type
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Args:
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param_type (str): param_type
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Returns:
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str: param type in typescript
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"""
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if param_type == "integer" or param_type == "float":
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return "number"
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return param_type
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def get_param_type(param: Dict) -> str:
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"""get param_type of parameter
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Args:
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param (Dict): param dict in properties
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Returns:
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str: _description_
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"""
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param_type = "any"
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if "type" in param:
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raw_param_type = param["type"]
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if type(raw_param_type) is list:
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param_type = " | ".join(raw_param_type)
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else:
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param_type = raw_param_type
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else: # in many cases, the json schema contains: oneOf instead of "type"
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if "oneOf" in param:
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one_of_types = []
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for item in param["oneOf"]:
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if "type" in item:
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one_of_types.append(convert_data_type(item["type"]))
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one_of_types = list(set(one_of_types))
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param_type = " | ".join(one_of_types)
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return convert_data_type(param_type)
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def get_format_param(param: Dict) -> Optional[str]:
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"""Get "format" from param. There are cases where format is not directly in param but in oneOf
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Args:
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param (Dict): _description_
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Returns:
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Optional[str]: _description_
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"""
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if "format" in param:
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return param["format"]
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if "oneOf" in param:
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formats = []
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for item in param["oneOf"]:
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if "format" in item:
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formats.append(item["format"])
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if len(formats) > 0:
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return " or ".join(formats)
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return None
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def get_param_info(param: Dict) -> Optional[str]:
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"""get additional information about parameter such as: format, default value, min, max, ...
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Args:
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param (Dict): _description_
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Returns:
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Optional[str]: _description_
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"""
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param_type = param.get("type", "any")
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info_list = []
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if "description" in param:
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desc = param["description"]
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if not desc.endswith("."):
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desc += "."
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info_list.append(desc)
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if "default" in param:
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default_value = param["default"]
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if param_type == "string":
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default_value = f'"{default_value}"' # if string --> add ""
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info_list.append(f"Default={default_value}.")
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format_param = get_format_param(param)
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if format_param is not None:
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info_list.append("Format=" + format_param)
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for field, field_name in [
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("maximum", "Maximum"),
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("minimum", "Minimum"),
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("maxLength", "Maximum length"),
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("minLength", "Minimum length"),
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]:
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if field in param:
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info_list.append(f"{field_name}=" + str(param[field]))
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if len(info_list) > 0:
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result = "// " + " ".join(info_list)
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result = result.replace("\n", " ")
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return result
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return None
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def append_new_param_info(
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info_list: List[str],
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param_declaration: str,
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comment_info: Optional[str],
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examples_info: List,
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depth: int,
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):
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"""Append a new parameter with comment to the info_list
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Args:
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info_lines (List[str]): current info_list
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param_declaration (str): param: type
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comment_info (Optional[str]): information of comment
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examples_info (List): information of examples given
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depth (int): level of nested param
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"""
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offset = ""
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if depth >= 1:
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offset = "".join([" " for _ in range(depth)])
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if comment_info is not None:
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# if depth == 0: # format: //comment\nparam: type
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info_list.append(f"{offset}{comment_info}")
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if len(examples_info) > 0:
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for example in examples_info:
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info_list.append(f"{offset}{example}")
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info_list.append(f"{offset}{param_declaration}")
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# else: # format: param: type // comment
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# info_list.append(f"{offset}{param_declaration} {comment_info}")
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else:
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info_list.append(f"{offset}{param_declaration}")
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def get_examples_info(param_name: str, examples: List) -> List:
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"""get information about examples provided
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Args:
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param_name (str): _description_
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examples (List): _description_
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Returns:
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List: _description_
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"""
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examples_list = [f"// Example {param_name}:"]
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for example in examples:
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if isinstance(example, dict) or isinstance(example, list):
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example_str = json.dumps(example, ensure_ascii=False).replace('\n', '\\n')
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else:
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example_str = str(example).replace('\n', '\\n')
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examples_list.append(f"// {example_str}")
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return examples_list
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def get_enum_option_str(enum_options: List) -> str:
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"""get enum option separated by: "|"
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Args:
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enum_options (List): list of options
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Returns:
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_type_: concatenation of options separated by "|"
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"""
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# if each option is string --> add quote
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return " | ".join([f'"{v}"' if type(v) is str else str(v) for v in enum_options])
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def get_array_typescript(
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param_name: Optional[str], param_dic: dict, depth: int = 0
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) -> str:
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"""recursive implementation for generating type script of array
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Args:
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param_name (Optional[str]): name of param, optional
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param_dic (dict): param_dic
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depth (int, optional): nested level. Defaults to 0.
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Returns:
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_type_: typescript of array
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"""
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offset = ""
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if depth >= 1:
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offset = "".join([" " for _ in range(depth)])
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items_info = param_dic.get("items", {})
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if len(items_info) == 0:
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if param_name is not None:
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return f"{offset}{param_name}: []"
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else:
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return "[]"
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array_type = get_param_type(items_info)
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if array_type == "object":
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info_lines = []
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child_lines = get_parameter_typescript(
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items_info.get("properties", {}), items_info.get("required", []), depth + 1
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)
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# if comment_info is not None:
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# info_lines.append(f"{offset}{comment_info}")
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if param_name is not None:
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info_lines.append(f"{offset}{param_name}" + ": {")
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else:
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info_lines.append(f"{offset}" + "{")
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info_lines.extend(child_lines)
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info_lines.append(f"{offset}" + "}[]")
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return "\n".join(info_lines)
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elif array_type == "array":
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item_info = get_array_typescript(None, items_info, depth + 1)
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if param_name is None:
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return f"{item_info}[]"
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return f"{offset}{param_name}: {item_info.strip()}[]"
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else:
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if "enum" in items_info:
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item_type = get_enum_option_str(items_info["enum"])
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if param_name is None:
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return f"({item_type})[]"
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else:
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return f"{offset}{param_name}: ({item_type})[]"
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else:
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if param_name is None:
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return f"{array_type}[]"
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else:
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return f"{offset}{param_name}: {array_type}[],"
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-
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-
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def get_parameter_typescript(properties, required_params, depth=0) -> List[str]:
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"""Recursion, returning the information about parameters including data type, description and other information
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These kinds of information will be put into the prompt
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Args:
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properties (_type_): properties in parameters
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required_params (_type_): List of required parameters
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depth (int, optional): the depth of params (nested level). Defaults to 0.
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Returns:
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_type_: list of lines containing information about all parameters
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"""
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tp_lines = []
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for param_name, param in properties.items():
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# Sometimes properties have "required" field as a list of string.
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# Even though its supposed to be not under properties. So we skip it
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if not isinstance(param, dict):
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continue
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# Param Description
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comment_info = get_param_info(param)
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# Param Examples
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examples_info = []
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if "examples" in param:
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examples_info = get_examples_info(param_name, param["examples"])
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# Param Name declaration
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param_declaration = f"{param_name}"
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if isinstance(required_params, list):
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if param_name not in required_params:
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param_declaration += "?"
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param_type = get_param_type(param)
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offset = ""
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if depth >= 1:
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offset = "".join([" " for _ in range(depth)])
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if param_type == "object": # param_type is object
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child_lines = get_parameter_typescript(
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param.get("properties", {}), param.get("required", []), depth + 1
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)
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if comment_info is not None:
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tp_lines.append(f"{offset}{comment_info}")
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if len(examples_info) > 0:
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for example in examples_info:
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tp_lines.append(f"{offset}{example}")
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param_declaration += ": {"
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tp_lines.append(f"{offset}{param_declaration}")
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tp_lines.extend(child_lines)
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tp_lines.append(f"{offset}" + "},")
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-
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elif param_type == "array": # param_type is an array
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item_info = param.get("items", {})
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if "type" not in item_info: # don't know type of array
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param_declaration += ": [],"
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append_new_param_info(
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tp_lines, param_declaration, comment_info, examples_info, depth
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)
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else:
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array_declaration = get_array_typescript(
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param_declaration, param, depth
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)
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if not array_declaration.endswith(","):
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array_declaration += ","
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if comment_info is not None:
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tp_lines.append(f"{offset}{comment_info}")
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if len(examples_info) > 0:
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for example in examples_info:
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tp_lines.append(f"{offset}{example}")
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tp_lines.append(array_declaration)
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else:
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if "enum" in param:
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324 |
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param_type = get_enum_option_str(param["enum"])
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325 |
-
# param_type = " | ".join([f'"{v}"' for v in param["enum"]])
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326 |
-
if "nullable" in param and param["nullable"] is True:
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327 |
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param_type += " | null"
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328 |
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param_declaration += f": {param_type},"
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329 |
-
append_new_param_info(
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330 |
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tp_lines, param_declaration, comment_info, examples_info, depth
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331 |
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)
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332 |
-
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return tp_lines
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334 |
-
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335 |
-
def generate_schema_from_functions(
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336 |
-
functions: List[Function], namespace="functions"
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337 |
-
) -> str:
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338 |
-
"""
|
339 |
-
Convert functions schema to a schema that language models can understand.
|
340 |
-
"""
|
341 |
-
|
342 |
-
schema = "// Supported function definitions that should be called when necessary.\n"
|
343 |
-
schema += f"namespace {namespace} {{\n\n"
|
344 |
-
|
345 |
-
for function in functions:
|
346 |
-
# Convert a Function object to dict, if necessary
|
347 |
-
if not isinstance(function, dict):
|
348 |
-
function = function.model_dump()
|
349 |
-
function_name = function.get("name", None)
|
350 |
-
if function_name is None:
|
351 |
-
continue
|
352 |
-
|
353 |
-
description = function.get("description", "")
|
354 |
-
schema += f"// {description}\n"
|
355 |
-
schema += f"type {function_name}"
|
356 |
-
|
357 |
-
parameters = function.get("parameters", None)
|
358 |
-
if parameters is not None and parameters.get("properties") is not None:
|
359 |
-
parameters = deepcopy(jsonref.JsonRef.replace_refs(parameters))
|
360 |
-
schema += " = (_: {\n"
|
361 |
-
required_params = parameters.get("required", [])
|
362 |
-
tp_lines = get_parameter_typescript(
|
363 |
-
parameters.get("properties"),
|
364 |
-
required_params,
|
365 |
-
0,
|
366 |
-
)
|
367 |
-
schema += "\n".join(tp_lines)
|
368 |
-
schema += "\n}) => any;\n\n"
|
369 |
-
else:
|
370 |
-
# Doesn't have any parameters
|
371 |
-
schema += " = () => any;\n\n"
|
372 |
-
|
373 |
-
schema += f"}} // namespace {namespace}"
|
374 |
-
|
375 |
-
return schema
|
376 |
-
|
377 |
-
class FunctionaryTokenizer(PreTrainedTokenizerFast):
|
378 |
-
def apply_chat_template(
|
379 |
-
self,
|
380 |
-
conversation: Union[List[Dict[str, str]], List[List[Dict[str, str]]], str],
|
381 |
-
tools: Optional[List[Dict[str, Any]]],
|
382 |
-
chat_template: Optional[str] = None,
|
383 |
-
add_generation_prompt: bool = False,
|
384 |
-
tokenize: bool = True,
|
385 |
-
padding: bool = False,
|
386 |
-
truncation: bool = False,
|
387 |
-
max_length: Optional[int] = None,
|
388 |
-
return_tensors: Optional[Union[str, TensorType]] = None,
|
389 |
-
return_dict: bool = False,
|
390 |
-
tokenizer_kwargs: Optional[Dict[str, Any]] = None,
|
391 |
-
**kwargs,
|
392 |
-
) -> Union[str, List[int], List[str], List[List[int]], BatchEncoding]:
|
393 |
-
|
394 |
-
if return_dict and not tokenize:
|
395 |
-
raise ValueError(
|
396 |
-
"`return_dict=True` is incompatible with `tokenize=False`, because there is no dict "
|
397 |
-
"of tokenizer outputs to return."
|
398 |
-
)
|
399 |
-
|
400 |
-
if tokenizer_kwargs is None:
|
401 |
-
tokenizer_kwargs = {}
|
402 |
-
|
403 |
-
using_default_template = False
|
404 |
-
|
405 |
-
# First, handle the cases when the model has a dict of multiple templates
|
406 |
-
if isinstance(self.chat_template, dict) or (
|
407 |
-
self.chat_template is None and isinstance(self.default_chat_template, dict)
|
408 |
-
):
|
409 |
-
if self.chat_template is not None:
|
410 |
-
template_dict = self.chat_template
|
411 |
-
using_default_dict = False
|
412 |
-
else:
|
413 |
-
template_dict = self.default_chat_template
|
414 |
-
using_default_dict = True
|
415 |
-
if chat_template is not None and chat_template in template_dict:
|
416 |
-
# The user can pass the name of a template to the chat template argument instead of an entire template
|
417 |
-
chat_template = template_dict[chat_template]
|
418 |
-
if using_default_dict:
|
419 |
-
using_default_template = True
|
420 |
-
elif chat_template is None and "default" in template_dict:
|
421 |
-
chat_template = template_dict["default"]
|
422 |
-
if using_default_dict:
|
423 |
-
using_default_template = True
|
424 |
-
elif chat_template is None:
|
425 |
-
raise ValueError(
|
426 |
-
"This model has multiple chat templates with no default specified! Please either pass a chat "
|
427 |
-
"template or the name of the template you wish to use to the `chat_template` argument. Available "
|
428 |
-
f"template names are {sorted(template_dict.keys())}."
|
429 |
-
)
|
430 |
-
elif chat_template is None:
|
431 |
-
# These are the cases when the model has a single template
|
432 |
-
# priority: `chat_template` argument > `tokenizer.chat_template` > `tokenizer.default_chat_template
|
433 |
-
if self.chat_template is not None:
|
434 |
-
chat_template = self.chat_template
|
435 |
-
else:
|
436 |
-
chat_template = self.default_chat_template
|
437 |
-
using_default_template = True
|
438 |
-
|
439 |
-
if using_default_template:
|
440 |
-
logger.warning_once(
|
441 |
-
"No chat template is set for this tokenizer, falling back to a default class-level template. This is "
|
442 |
-
"very error-prone, because models are often trained with templates different from the class default! "
|
443 |
-
"Default chat templates are a legacy feature and will be removed in Transformers v4.43, at which "
|
444 |
-
"point any code depending on them will stop working. We recommend setting a valid chat template before "
|
445 |
-
"then to ensure that this model continues working without issues."
|
446 |
-
)
|
447 |
-
|
448 |
-
PYTHON_RUN_SYS_MSG = "When you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0 seconds. The drive at '/mnt/data' can be used to save and persist user files."
|
449 |
-
SYSTEM_CONTENT = """You are capable of executing available function(s) if required.
|
450 |
-
Only execute function(s) when absolutely necessary.
|
451 |
-
Ask for the required input to:recipient==all
|
452 |
-
Use JSON for function arguments.
|
453 |
-
Respond in this format:
|
454 |
-
>>>${recipient}
|
455 |
-
${content}
|
456 |
-
Available functions:
|
457 |
-
"""
|
458 |
-
|
459 |
-
# Prepare tools/functions into schema
|
460 |
-
functions_pydantic_to_render = []
|
461 |
-
has_code_interpreter = False
|
462 |
-
if tools is not None:
|
463 |
-
for item in tools:
|
464 |
-
if (
|
465 |
-
"function" in item and item["function"] is not None
|
466 |
-
): # new data format: tools: [{"type": xx, "function": xxx}]
|
467 |
-
functions_pydantic_to_render.append(item["function"])
|
468 |
-
elif "type" in item and item["type"] == "code_interpreter":
|
469 |
-
has_code_interpreter = True
|
470 |
-
else:
|
471 |
-
functions_pydantic_to_render.append(item) # old format
|
472 |
-
|
473 |
-
conversation.insert(
|
474 |
-
0,
|
475 |
-
{
|
476 |
-
"role": "system",
|
477 |
-
"content": SYSTEM_CONTENT + generate_schema_from_functions(functions_pydantic_to_render),
|
478 |
-
},
|
479 |
-
)
|
480 |
-
if has_code_interpreter:
|
481 |
-
conversation.insert(1, {"role": "system", "content": PYTHON_RUN_SYS_MSG})
|
482 |
-
|
483 |
-
# Compilation function uses a cache to avoid recompiling the same template
|
484 |
-
compiled_template = self._compile_jinja_template(chat_template)
|
485 |
-
|
486 |
-
if isinstance(conversation, (list, tuple)) and (
|
487 |
-
isinstance(conversation[0], (list, tuple)) or hasattr(conversation[0], "messages")
|
488 |
-
):
|
489 |
-
conversations = conversation
|
490 |
-
is_batched = True
|
491 |
-
else:
|
492 |
-
conversations = [conversation]
|
493 |
-
is_batched = False
|
494 |
-
|
495 |
-
rendered = []
|
496 |
-
template_kwargs = {**self.special_tokens_map, **kwargs} # kwargs overwrite special tokens if both are present
|
497 |
-
for chat in conversations:
|
498 |
-
if hasattr(chat, "messages"):
|
499 |
-
# Indicates it's a Conversation object
|
500 |
-
chat = chat.messages
|
501 |
-
rendered_chat = compiled_template.render(
|
502 |
-
messages=chat, add_generation_prompt=add_generation_prompt, **template_kwargs
|
503 |
-
)
|
504 |
-
rendered.append(rendered_chat)
|
505 |
-
|
506 |
-
if not is_batched:
|
507 |
-
rendered = rendered[0]
|
508 |
-
|
509 |
-
if tokenize:
|
510 |
-
out = self(
|
511 |
-
rendered,
|
512 |
-
padding=padding,
|
513 |
-
truncation=truncation,
|
514 |
-
max_length=max_length,
|
515 |
-
add_special_tokens=False,
|
516 |
-
return_tensors=return_tensors,
|
517 |
-
**tokenizer_kwargs,
|
518 |
-
)
|
519 |
-
if return_dict:
|
520 |
-
return out
|
521 |
-
else:
|
522 |
-
return out["input_ids"]
|
523 |
-
else:
|
524 |
-
return rendered
|
|
|
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|
tokenizer_config.json
CHANGED
@@ -2050,7 +2050,7 @@
|
|
2050 |
}
|
2051 |
},
|
2052 |
"bos_token": "<|begin_of_text|>",
|
2053 |
-
"chat_template": "{# version=v3.llama3 #}{% for message in messages %}\n{% if message['role'] == 'user' or message['role'] == 'system' %}\n{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }}{% elif message['role'] == 'tool' %}\n{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n' + message['content'] + '<|eot_id|>' }}{% else %}\n{{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'}}{% if message['content'] is not none %}\n{{ '>>>all\n' + message['content'] }}{% endif %}\n{% if 'tool_calls' in message and message['tool_calls'] is not none %}\n{% for tool_call in message['tool_calls'] %}\n{{ '>>>' + tool_call['function']['name'] + '\n' + tool_call['function']['arguments'] }}{% endfor %}\n{% endif %}\n{{ '<|eot_id|>' }}{% endif %}\n{% endfor %}\n{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\n\n>>>' }}{% endif %}",
|
2054 |
"clean_up_tokenization_spaces": true,
|
2055 |
"eos_token": "<|eot_id|>",
|
2056 |
"legacy": true,
|
@@ -2061,8 +2061,5 @@
|
|
2061 |
"model_max_length": 16384,
|
2062 |
"pad_token": "<|eot_id|>",
|
2063 |
"padding_side": "right",
|
2064 |
-
"tokenizer_class": "PreTrainedTokenizerFast"
|
2065 |
-
"auto_map": {
|
2066 |
-
"AutoTokenizer": ["tokenization_functionary.FunctionaryTokenizer", null]
|
2067 |
-
}
|
2068 |
}
|
|
|
2050 |
}
|
2051 |
},
|
2052 |
"bos_token": "<|begin_of_text|>",
|
2053 |
+
"chat_template": "{# version=v3.llama3 #}{%- macro append_new_param_info(param_declaration, comment_info, examples_info, depth) -%}\n {%- set offset = \"\" -%}\n {%- if depth >= 1 -%}\n {%- set offset = \" \" * depth -%}\n {%- endif -%}\n {%- if comment_info != \"<|NONE|>\" -%}\n {{ \"\\n\" + offset + comment_info }}\n {%- if examples_info | length > 0 -%}\n {# Append each example info #}\n {%- for example in examples_info -%}\n {{ \"\\n\" + offset + \"// \" + example|string|replace(\"'\", '\"') }}\n {%- endfor -%}\n {%- endif -%}\n {%- endif -%}\n {{ \"\\n\" + offset + param_declaration }}\n{%- endmacro -%}\n\n{%- macro convert_data_type(param_type) -%}\n {%- if param_type == \"integer\" or param_type == \"float\" -%}\n {{ \"number\" }}\n {%- else -%}\n {{ param_type }}\n {%- endif -%}\n{%- endmacro -%}\n\n{%- macro get_param_type(param) -%}\n {%- set param_type = \"any\" -%}\n\n {%- if \"type\" in param -%}\n {%- set raw_param_type = param[\"type\"] -%}\n {%- if raw_param_type is iterable and raw_param_type is not string -%}\n {%- set param_type = raw_param_type | join(\" | \") -%}\n {%- else -%}\n {%- set param_type = raw_param_type -%}\n {%- endif -%}\n {{ convert_data_type(param_type) }}\n {%- elif \"oneOf\" in param -%}\n {%- set one_of_types = param[\"oneOf\"]|selectattr(\"type\", \"defined\")|list -%}\n {%- set one_of_types = one_of_types|map(attribute=\"type\")|unique|list -%}\n {{ convert_data_type(one_of_types | join(\" | \")) }}\n {%- endif -%}\n{%- endmacro -%}\n\n{%- macro get_format_param(param) -%}\n {%- if \"format\" in param -%}\n {{ param[\"format\"] }}\n {%- elif \"oneOf\" in param -%}\n {%- set formats = [] -%}\n {%- for item in param[\"oneOf\"] -%}\n {%- if \"format\" in item -%}\n {%- if item[\"format\"] == param[\"oneOf\"][-1][\"format\"] -%}\n {{ item[\"format\"] }}\n {%- else -%}\n {{ item[\"format\"] + \" or \"}}\n {%- endif -%}\n {%- endif -%}\n {%- endfor -%}\n {%- else -%}\n {{ \"<|NONE|>\" }}\n {%- endif -%}\n{%- endmacro -%}\n\n{%- macro get_param_info(param) -%}\n {%- set param_type = param.get(\"type\", \"any\") -%}\n {%- set format_param = get_format_param(param) -%}\n\n {%- if \"description\" in param or \"default\" in param or format_param != \"<|NONE|>\" or param[\"maximum\"] or param[\"minimum\"] or param[\"maxLength\"] or param[\"minLength\"] -%}\n {{ \"//\" }}\n {%- if \"description\" in param -%}\n {%- set desc = param[\"description\"] -%}\n {%- if not desc.endswith(\".\") -%}\n {%- set desc = desc + \".\" -%}\n {%- endif -%}\n {{ \" \" + desc }}\n {%- endif -%}\n\n {%- if \"default\" in param -%}\n {%- set default_value = param[\"default\"] -%}\n {%- if param_type == \"string\" -%}\n {%- set default_value = '\"' ~ default_value ~ '\"' -%}\n {%- endif -%}\n {{ \" Default=\" ~ default_value ~ \".\" }}\n {%- endif -%}\n\n {%- set format_param = get_format_param(param) -%}\n {%- if format_param != \"<|NONE|>\" -%}\n {{ \" Format=\" ~ format_param }}\n {%- endif -%}\n\n {%- for field, field_name in [(\"maximum\", \"Maximum\"), (\"minimum\", \"Minimum\"), (\"maxLength\", \"Maximum length\"), (\"minLength\", \"Minimum length\")] -%}\n {%- if field in param -%}\n {{ \" \" + field_name ~ \"=\" ~ param[field] }}\n {%- endif -%}\n {%- endfor -%}\n {%- else -%}\n {{ \"<|NONE|>\"}}\n {%- endif -%}\n{%- endmacro -%}\n\n{%- macro get_enum_option_str(enum_options) -%}\n {%- for v in enum_options -%}\n {%- if v is string -%}\n {{ '\"' + v + '\"' }}\n {%- else -%}\n {{ v }}\n {%- endif -%}\n {%- if enum_options|length > 0 and v != enum_options[-1] -%}\n {{ \" | \" }}\n {%- endif -%}\n {%- endfor -%}\n{%- endmacro -%}\n\n{%- macro get_array_typescript(param_name, param_dic, depth) -%}\n {%- set offset = '' -%}\n {%- if depth >= 1 -%}\n {%- set offset = \" \" * depth -%}\n {%- endif -%}\n {%- set items_info = param_dic.get('items', {}) -%}\n\n {%- if items_info|length == 0 -%}\n {%- if param_name is not none -%}\n {{ \"\\n\" + offset + param_name + \": []\" }}\n {%- else -%}\n {{ \"\\n\" + offset + \"[]\" }}\n {%- endif -%}\n {%- else -%}\n {%- set array_type = get_param_type(items_info) -%}\n {%- if array_type == 'object' -%}\n {%- if param_name -%}\n {{ \"\\n\" + offset + param_name + \": {\" }}\n {%- else -%}\n {{ \"\\n\" + offset + \"{\" }}\n {%- endif -%}\n {{ get_parameter_typescript(items_info.get('properties', {}), items_info.get('required', []), depth + 1) -}}\n {{- \"\\n\" + offset + \"}[]\" }}\n {%- elif array_type == 'array' -%}\n {%- set item_info = get_array_typescript(None, items_info, depth + 1) -%}\n {%- if not param_name -%}\n {{ \"\\n\" + item_info + \"[]\" }}\n {%- else -%}\n {{ \"\\n\" + offset + param_name + \": \" + item_info|trim + \"[]\" }}\n {%- endif -%}\n {%- else -%}\n {%- if 'enum' in items_info -%}\n {%- set item_type = get_enum_option_str(items_info['enum']) -%}\n {%- if param_name is none -%}\n {{ \"(\" + item_type + \")[]\"}}\n {%- else -%}\n {{ \"\\n\" + offset + param_name + \": (\" + item_type + \")[]\" }}\n {%- endif -%}\n {%- else -%}\n {%- if param_name is none -%}\n {{ \"\\n\" + array_type + \"[]\" }}\n {%- else -%}\n {{ \"\\n\" + offset + param_name + \": \" + array_type + \"[],\" }}\n {%- endif -%}\n {%- endif -%}\n {%- endif -%}\n {%- endif -%}\n{%- endmacro -%}\n\n{%- macro get_parameter_typescript(properties, required_params, depth=0) -%}\n {%- set res = \"\" -%}\n {%- for param_name, param in properties.items() -%}\n {%- if param is mapping -%}\n {%- set comment_info = get_param_info(param) -%}\n {# Param Examples #}\n {%- set examples_info = [] -%}\n {%- if \"examples\" in param -%}\n {%- set examples_info = [\"Example \" + param_name + \":\"] -%}\n {%- set examples_info = examples_info + param[\"examples\"] -%}\n {%- endif -%}\n\n {# Param Name declaration #}\n {%- set param_declaration = param_name -%}\n {%- if required_params is iterable and param_name not in required_params -%}\n {%- set param_declaration = param_declaration + \"?\" -%}\n {%- endif -%}\n\n {%- set param_type = get_param_type(param) -%}\n\n {# Handle indentation based on depth #}\n {%- set offset = \"\" -%}\n {%- if depth >= 1 -%}\n {%- set offset = \" \" * depth -%}\n {%- endif -%}\n\n {%- if param_type == \"object\" -%}\n {%- if comment_info != \"<|NONE|>\" -%}\n {{ \"\\n\" + offset + comment_info }}\n {%- endif -%}\n {%- if examples_info|length > 0 -%}\n {%- for example in examples_info -%}\n {{ \"\\n\" + offset + \"// \" + example|string|replace(\"'\", '\"') }}\n {%- endfor -%}\n {%- endif -%}\n {%- set param_declaration = param_declaration + \": {\" -%}\n {{ \"\\n\" + offset + param_declaration -}}\n {{- get_parameter_typescript(param.get(\"properties\", {}), param.get(\"required\", []), depth + 1) -}}\n {{- \"\\n\" + offset + \"},\" }}\n {%- elif param_type == \"array\" -%}\n {%- set item_info = param.get(\"items\", {}) -%}\n {%- if \"type\" not in item_info -%}\n {%- set param_declaration = param_declaration + \": [],\" -%}\n {{ append_new_param_info(param_declaration, comment_info, examples_info, depth) }}\n {%- else -%}\n {%- if comment_info != \"<|NONE|>\" -%}\n {{ \"\\n\" + offset + comment_info }}\n {%- endif -%}\n {%- if examples_info|length > 0 -%}\n {%- for example in examples_info -%}\n {{ \"\\n\" + offset + \"// \" + example|string|replace(\"'\", '\"') }}\n {%- endfor -%}\n {%- endif -%}\n {%- set array_declaration = get_array_typescript(param_declaration, param, depth) -%}\n {%- if not array_declaration.endswith(\",\") -%}\n {%- set array_declaration = array_declaration + \",\" -%}\n {%- endif -%}\n {{ array_declaration}}\n {%- endif -%}\n {%- else -%}\n {%- if \"enum\" in param -%}\n {%- set param_type = get_enum_option_str(param[\"enum\"]) -%}\n {%- endif -%}\n {%- if \"nullable\" in param and param[\"nullable\"] -%}\n {%- set param_type = param_type + \" | null\" -%}\n {%- endif -%}\n {%- set param_declaration = param_declaration + \": \" + param_type + \",\" -%}\n {{ append_new_param_info(param_declaration, comment_info, examples_info, depth) }}\n {%- endif -%}\n {%- endif -%}\n {%- endfor -%}\n{%- endmacro -%}\n\n{%- macro generate_schema_from_functions(functions, namespace='functions') -%}\n {{ \"// Supported function definitions that should be called when necessary.\\n\" -}}\n {{- \"namespace \" + namespace + \" {\\n\\n\" -}}\n\n {%- for function in functions -%}\n {%- if function.get(\"function\") is not none -%}\n {%- set function = function.get(\"function\") -%}\n {%- endif -%}\n\n {%- set function_name = function.get(\"name\") -%}\n {%- if function_name is not none -%}\n {%- set description = function.get('description', '') -%}\n {%- set parameters = function.get('parameters', {}) -%}\n {{- \"// \" + description + \"\\n\" -}}\n {{- \"type \" + function_name -}}\n {%- if parameters is not none and parameters.get(\"properties\") is not none -%}\n {{- \" = (_: {\" -}}\n {%- set required_params = parameters.get(\"required\", []) -%}\n {{ get_parameter_typescript(parameters.get(\"properties\"), required_params, 0) -}}\n {{- \"\\n}) => any;\\n\\n\" }}\n {%- else -%}\n {{ \" = () => any;\\n\\n\" }}\n {%- endif -%}\n {%- endif -%}\n {%- endfor -%}\n {{ \"} // namespace \" + namespace }}\n{%- endmacro -%}\n{%- if not tools -%}\n {%- set tools = [] -%}\n{%- endif -%}\n{{ '<|start_header_id|>system<|end_header_id|>\\n\\nYou are capable of executing available function(s) if required.\\nOnly execute function(s) when absolutely necessary.\\nAsk for the required input to:recipient==all\\nUse JSON for function arguments.\\nRespond in this format:\\n>>>${recipient}\\n${content}\\nAvailable functions:\\n' + generate_schema_from_functions(tools) + '<|eot_id|>' -}}\n{%- if tools|length > 0 and tools|selectattr(\"type\", \"equalto\", \"code_interpreter\")|list|length > 0 -%}\n {{ '<|start_header_id|>system<|end_header_id|>\\n\\nWhen you send a message containing Python code to python, it will be executed in a stateful Jupyter notebook environment. python will respond with the output of the execution or time out after 60.0 seconds. The drive at \\'/mnt/data\\' can be used to save and persist user files.<|eot_id|>' }}\n{%- endif -%}\n{%- for message in messages -%}\n {%- if message['role'] == 'user' or message['role'] == 'system' -%}\n {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n' + message['content'] + '<|eot_id|>' }}\n {%- elif message['role'] == 'tool' -%}\n {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n' + message['content'] + '<|eot_id|>' }}\n {%- else -%}\n {{ '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'}}\n {%- if message['content'] is not none -%}\n {{ '>>>all\\n' + message['content'] }}\n {% endif %}\n {%- if 'tool_calls' in message and message['tool_calls'] is not none -%}\n {{ '>>>' + tool_call['function']['name'] + '\\n' + tool_call['function']['arguments'] }}\n {%- endif -%}\n {{ '<|eot_id|>' }}\n {%- endif -%}\n{%- endfor -%}\n{% if add_generation_prompt %}{{ '<|start_header_id|>assistant<|end_header_id|>\\n\\n>>>' }}{% endif %}",
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2054 |
"clean_up_tokenization_spaces": true,
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2055 |
"eos_token": "<|eot_id|>",
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2056 |
"legacy": true,
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2061 |
"model_max_length": 16384,
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2062 |
"pad_token": "<|eot_id|>",
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2063 |
"padding_side": "right",
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2064 |
+
"tokenizer_class": "PreTrainedTokenizerFast"
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|
2065 |
}
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