Text Generation
Transformers
TensorBoard
Safetensors
biology
genomics
rna
sequence-generation
regression
reinforcement-learning
git-lfs
Instructions to use JoyXiangLab/rnaseek-full with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use JoyXiangLab/rnaseek-full with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="JoyXiangLab/rnaseek-full")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("JoyXiangLab/rnaseek-full", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use JoyXiangLab/rnaseek-full with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "JoyXiangLab/rnaseek-full" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JoyXiangLab/rnaseek-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/JoyXiangLab/rnaseek-full
- SGLang
How to use JoyXiangLab/rnaseek-full with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "JoyXiangLab/rnaseek-full" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JoyXiangLab/rnaseek-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "JoyXiangLab/rnaseek-full" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "JoyXiangLab/rnaseek-full", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use JoyXiangLab/rnaseek-full with Docker Model Runner:
docker model run hf.co/JoyXiangLab/rnaseek-full
| # Copyright 2025 the LlamaFactory team. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import ast | |
| import json | |
| import re | |
| from abc import ABC, abstractmethod | |
| from dataclasses import dataclass | |
| from datetime import datetime | |
| from typing import Any, NamedTuple, Union | |
| from typing_extensions import override | |
| class FunctionCall(NamedTuple): | |
| name: str | |
| arguments: str | |
| DEFAULT_TOOL_PROMPT = ( | |
| "You have access to the following tools:\n{tool_text}" | |
| "Use the following format if using a tool:\n" | |
| "```\n" | |
| "Action: tool name (one of [{tool_names}])\n" | |
| "Action Input: the input to the tool, in a JSON format representing the kwargs " | |
| """(e.g. ```{{"input": "hello world", "num_beams": 5}}```)\n""" | |
| "```\n" | |
| ) | |
| GLM4_TOOL_PROMPT = ( | |
| "你是一个名为 ChatGLM 的人工智能助手。你是基于智谱 AI 公司训练的语言模型 GLM-4 模型开发的," | |
| "你的任务是针对用户的问题和要求提供适当的答复和支持。\n\n# 可用工具{tool_text}" | |
| ) | |
| GLM4_MOE_TOOL_PROMPT = ( | |
| "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\n" | |
| "You are provided with function signatures within <tools></tools> XML tags:\n<tools>{tool_text}" | |
| "\n</tools>\n\nFor each function call, output the function name and arguments within the following XML format:" | |
| "\n<tool_call>{{function-name}}" | |
| "\n<arg_key>{{arg-key-1}}</arg_key>" | |
| "\n<arg_value>{{arg-value-1}}</arg_value>" | |
| "\n<arg_key>{{arg-key-2}}</arg_key>" | |
| "\n<arg_value>{{arg-value-2}}</arg_value>" | |
| "\n...\n</tool_call>\n" | |
| ) | |
| LLAMA3_TOOL_PROMPT = ( | |
| "Cutting Knowledge Date: December 2023\nToday Date: {date}\n\n" | |
| "You have access to the following functions. To call a function, please respond with JSON for a function call. " | |
| """Respond in the format {{"name": function name, "parameters": dictionary of argument name and its value}}. """ | |
| "Do not use variables.\n\n{tool_text}" | |
| ) | |
| MINIMAX_M1_TOOL_PROMPT = ( | |
| "You are provided with these tools:\n<tools>\n{tool_text}</tools>\n\n" | |
| "If you need to call tools, please respond with <tool_calls></tool_calls> XML tags, and provide tool-name and " | |
| "json-object of arguments, following the format below:\n<tool_calls>\n" | |
| """{{"name": <tool-name-1>, "arguments": <args-json-object-1>}}\n...\n</tool_calls>""" | |
| ) | |
| MINIMAX_M2_TOOL_PROMPT = ( | |
| "\n\n# Tools\n\nYou may call one or more tools to assist with the user query.\n" | |
| "Here are the tools available in JSONSchema format:\n\n<tools>\n{tool_text}</tools>\n\n" | |
| "When making tool calls, use XML format to invoke tools and pass parameters:\n" | |
| """\n<minimax:tool_call>\n<invoke name="tool-name-1">\n<parameter name="param-key-1">param-value-1</parameter>\n""" | |
| """<parameter name="param-key-2">param-value-2</parameter>\n...\n</invoke>\n</minimax:tool_call>""" | |
| ) | |
| QWEN_TOOL_PROMPT = ( | |
| "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\n" | |
| "You are provided with function signatures within <tools></tools> XML tags:\n<tools>{tool_text}" | |
| "\n</tools>\n\nFor each function call, return a json object with function name and arguments within " | |
| """<tool_call></tool_call> XML tags:\n<tool_call>\n{{"name": <function-name>, """ | |
| """"arguments": <args-json-object>}}\n</tool_call>""" | |
| ) | |
| QWEN35_TOOL_PROMPT = ( | |
| "\n\n# Tools\n\nYou have access to the following functions:\n\n<tools>{tool_text}" | |
| "\n</tools>\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n" | |
| "<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n" | |
| "<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n" | |
| "</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n" | |
| "- Function calls MUST follow the specified format: " | |
| "an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n" | |
| "- Required parameters MUST be specified\n" | |
| "- You may provide optional reasoning for your function call in natural language " | |
| "BEFORE the function call, but NOT after\n" | |
| "- If there is no function call available, answer the question like normal with your current knowledge " | |
| "and do not tell the user about function calls\n</IMPORTANT>" | |
| ) | |
| SEED_TOOL_PROMPT = ( | |
| "system\nYou are Doubao, a helpful AI assistant. You may call one or more functions to assist with the user query." | |
| "Tool List:\nYou are authorized to use the following tools (described in JSON Schema format). Before performing " | |
| "any task, you must decide how to call them based on the descriptions and parameters of these tools.{tool_text}\n" | |
| "工具调用请遵循如下格式:\n<seed:tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>value_1" | |
| "</parameter>\n<parameter=example_parameter_2>This is the value for the second parameter\nthat can span\nmultiple " | |
| "lines</parameter>\n</function>\n</seed:tool_call>\n" | |
| ) | |
| LING_TOOL_PROMPT = ( | |
| "# Tools\n\nYou may call one or more functions to assist with the user query.\n\n" | |
| "You are provided with function signatures within <tools></tools> XML tags:\n<tools>{tool_text}" | |
| "\n</tools>\n\nFor each function call, return a json object with function name and arguments within " | |
| """<tool_call></tool_call> XML tags:\n<tool_call>\n{{"name": <function-name>, """ | |
| """"arguments": <args-json-object>}}\n</tool_call>""" | |
| ) | |
| LFM2_TOOL_PROMPT = "List of tools: <|tool_list_start|>{tool_text}<|tool_list_end|>" | |
| class ToolUtils(ABC): | |
| """Base class for tool utilities.""" | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| r"""Generate the system message describing all the available tools.""" | |
| ... | |
| def function_formatter(functions: list["FunctionCall"]) -> str: | |
| r"""Generate the assistant message including all the tool calls.""" | |
| ... | |
| def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
| r"""Extract all the function calls from the assistant message. | |
| It should be an inverse function of `function_formatter`. | |
| """ | |
| ... | |
| class DefaultToolUtils(ToolUtils): | |
| r"""Default tool using template.""" | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| tool_text = "" | |
| tool_names = [] | |
| for tool in tools: | |
| tool = tool.get("function", "") if tool.get("type") == "function" else tool | |
| param_text = "" | |
| for name, param in tool["parameters"]["properties"].items(): | |
| required, enum, items = "", "", "" | |
| if name in tool["parameters"].get("required", []): | |
| required = ", required" | |
| if param.get("enum", None): | |
| enum = ", should be one of [{}]".format(", ".join(param["enum"])) | |
| if param.get("items", None): | |
| items = ", where each item should be {}".format(param["items"].get("type", "")) | |
| param_text += " - {name} ({type}{required}): {desc}{enum}{items}\n".format( | |
| name=name, | |
| type=param.get("type", ""), | |
| required=required, | |
| desc=param.get("description", ""), | |
| enum=enum, | |
| items=items, | |
| ) | |
| tool_text += "> Tool Name: {name}\nTool Description: {desc}\nTool Args:\n{args}\n".format( | |
| name=tool["name"], desc=tool.get("description", ""), args=param_text | |
| ) | |
| tool_names.append(tool["name"]) | |
| return DEFAULT_TOOL_PROMPT.format(tool_text=tool_text, tool_names=", ".join(tool_names)) | |
| def function_formatter(functions: list["FunctionCall"]) -> str: | |
| return "\n".join([f"Action: {name}\nAction Input: {arguments}" for name, arguments in functions]) | |
| def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
| regex = re.compile(r"Action:\s*([a-zA-Z0-9_]+)\s*Action Input:\s*(.+?)(?=\s*Action:|\s*$)", re.DOTALL) | |
| action_match: list[tuple[str, str]] = re.findall(regex, content) | |
| if not action_match: | |
| return content | |
| results = [] | |
| for match in action_match: | |
| tool_name = match[0].strip() | |
| tool_input = match[1].strip().strip('"').strip("```") | |
| try: | |
| arguments = json.loads(tool_input) | |
| results.append(FunctionCall(tool_name, json.dumps(arguments, ensure_ascii=False))) | |
| except json.JSONDecodeError: | |
| return content | |
| return results | |
| class Gemma4ToolUtils(ToolUtils): | |
| r"""Gemma-4 tool using template.""" | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| def _format_parameters(properties: dict[str, Any]) -> str: | |
| parts: list[str] = [] | |
| for name, schema in properties.items(): | |
| item_parts: list[str] = [] | |
| if schema.get("description"): | |
| item_parts.append(f'description:<|"|>{schema["description"]}<|"|>') | |
| if schema.get("type"): | |
| item_parts.append(f'type:<|"|>{str(schema["type"]).upper()}<|"|>') | |
| parts.append(f"{name}:{{{','.join(item_parts)}}}") | |
| return ",".join(parts) | |
| declarations: list[str] = [] | |
| for tool in tools: | |
| function_data = tool.get("function", tool) if tool.get("type") == "function" else tool | |
| declaration = ( | |
| f"declaration:{function_data['name']}" | |
| + "{" | |
| + f'description:<|"|>{function_data.get("description", "")}<|"|>' | |
| ) | |
| params = function_data.get("parameters") | |
| if params: | |
| param_parts: list[str] = [] | |
| if params.get("properties"): | |
| param_parts.append(f"properties:{{{_format_parameters(params['properties'])}}}") | |
| if params.get("required"): | |
| required_text = ",".join(f'<|"|>{item}<|"|>' for item in params["required"]) | |
| param_parts.append(f"required:[{required_text}]") | |
| if params.get("type"): | |
| param_parts.append(f'type:<|"|>{str(params["type"]).upper()}<|"|>') | |
| declaration += f",parameters:{{{','.join(param_parts)}}}" | |
| response_declaration = function_data.get("response") | |
| if response_declaration: | |
| response_parts: list[str] = [] | |
| if response_declaration.get("description"): | |
| response_parts.append(f'description:<|"|>{response_declaration["description"]}<|"|>') | |
| response_type = str(response_declaration.get("type", "")).upper() | |
| if response_type == "OBJECT": | |
| response_parts.append(f'type:<|"|>{response_type}<|"|>') | |
| declaration += f",response:{{{','.join(response_parts)}}}" | |
| declarations.append(declaration + "}") | |
| return "\n".join(declarations) | |
| def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
| regex = re.compile(r"<\|tool_call\>call:([^{\s]+)\{(.*?)\}<tool_call\|>", re.DOTALL) | |
| matches = re.findall(regex, content) | |
| if not matches: | |
| return content | |
| def _parse_arguments(arg_text: str) -> Any: | |
| text = arg_text.strip() | |
| if not text: | |
| return {} | |
| # `function_formatter` writes dict arguments as `k:v,...` inside `{...}`. | |
| # The extractor captures only the inner text, so re-wrap it to parse as JSON object. | |
| object_like_text = "{" + text + "}" | |
| # Convert Gemma string markers (<|"|>value<|"|>) to valid JSON strings. | |
| normalized = re.sub( | |
| r"<\|\"\|\>(.*?)<\|\"\|\>", | |
| lambda m: json.dumps(m.group(1), ensure_ascii=False), | |
| object_like_text, | |
| flags=re.DOTALL, | |
| ) | |
| # Quote unquoted object keys so the payload can be parsed by json.loads. | |
| normalized = re.sub(r"(^|[{\s,])([A-Za-z_][A-Za-z0-9_]*)(\s*:)", r'\1"\2"\3', normalized) | |
| try: | |
| return json.loads(normalized) | |
| except json.JSONDecodeError: | |
| pass | |
| try: | |
| return json.loads(text) | |
| except json.JSONDecodeError: | |
| return text | |
| results: list[FunctionCall] = [] | |
| for name, arg_block in matches: | |
| parsed_arguments = _parse_arguments(arg_block) | |
| if isinstance(parsed_arguments, str): | |
| arguments = parsed_arguments | |
| else: | |
| arguments = json.dumps(parsed_arguments, ensure_ascii=False) | |
| results.append(FunctionCall(name.strip(), arguments)) | |
| return results | |
| def function_formatter(functions: list["FunctionCall"]) -> str: | |
| def _format_argument(argument: Any, escape_keys: bool = True) -> str: | |
| if isinstance(argument, str): | |
| return f'<|"|>{argument}<|"|>' | |
| if isinstance(argument, bool): | |
| return "true" if argument else "false" | |
| if isinstance(argument, dict): | |
| items: list[str] = [] | |
| for key in sorted(argument.keys()): | |
| formatted_key = f'<|"|>{key}<|"|>' if escape_keys else str(key) | |
| formatted_value = _format_argument(argument[key], escape_keys=escape_keys) | |
| items.append(f"{formatted_key}:{formatted_value}") | |
| return "{" + ",".join(items) + "}" | |
| if isinstance(argument, (list, tuple)): | |
| return "[" + ",".join(_format_argument(item, escape_keys=escape_keys) for item in argument) + "]" | |
| if argument is None: | |
| return "null" | |
| return str(argument) | |
| function_texts: list[str] = [] | |
| for function in functions: | |
| name = function.name | |
| raw_arguments = function.arguments | |
| try: | |
| parsed_arguments = json.loads(raw_arguments) | |
| except (TypeError, json.JSONDecodeError): | |
| parsed_arguments = raw_arguments | |
| call_text = f"<|tool_call>call:{name}" + "{" | |
| if isinstance(parsed_arguments, dict): | |
| args_text = [] | |
| for key in sorted(parsed_arguments.keys()): | |
| value_text = _format_argument(parsed_arguments[key], escape_keys=False) | |
| args_text.append(f"{key}:{value_text}") | |
| call_text += ",".join(args_text) | |
| elif isinstance(parsed_arguments, str): | |
| call_text += parsed_arguments | |
| else: | |
| call_text += _format_argument(parsed_arguments, escape_keys=False) | |
| call_text += "}<tool_call|>" | |
| function_texts.append(call_text) | |
| return "".join(function_texts) | |
| class GLM4ToolUtils(ToolUtils): | |
| r"""GLM-4 tool using template.""" | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| tool_text = "" | |
| for tool in tools: | |
| tool = tool.get("function", "") if tool.get("type") == "function" else tool | |
| tool_text += "\n\n## {name}\n\n{body}\n在调用上述函数时,请使用 Json 格式表示调用的参数。".format( | |
| name=tool["name"], body=json.dumps(tool, indent=4, ensure_ascii=False) | |
| ) | |
| return GLM4_TOOL_PROMPT.format(tool_text=tool_text) | |
| def function_formatter(functions: list["FunctionCall"]) -> str: | |
| if len(functions) > 1: | |
| raise ValueError("GLM-4 does not support parallel functions.") | |
| return f"{functions[0].name}\n{functions[0].arguments}" | |
| def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
| if "\n" not in content: | |
| return content | |
| tool_name, tool_input = content.split("\n", maxsplit=1) | |
| try: | |
| arguments = json.loads(tool_input.strip()) | |
| except json.JSONDecodeError: | |
| return content | |
| return [FunctionCall(tool_name, json.dumps(arguments, ensure_ascii=False))] | |
| class Llama3ToolUtils(ToolUtils): | |
| r"""Llama 3.x tool using template with `tools_in_user_message=False`. | |
| Reference: https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/#json-based-tool-calling | |
| """ | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| date = datetime.now().strftime("%d %b %Y") | |
| tool_text = "" | |
| for tool in tools: | |
| wrapped_tool = tool if tool.get("type") == "function" else {"type": "function", "function": tool} | |
| tool_text += json.dumps(wrapped_tool, indent=4, ensure_ascii=False) + "\n\n" | |
| return LLAMA3_TOOL_PROMPT.format(date=date, tool_text=tool_text) | |
| def function_formatter(functions: list["FunctionCall"]) -> str: | |
| function_objects = [{"name": name, "parameters": json.loads(arguments)} for name, arguments in functions] | |
| return json.dumps(function_objects[0] if len(function_objects) == 1 else function_objects, ensure_ascii=False) | |
| def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
| try: | |
| tools = json.loads(content.strip()) | |
| except json.JSONDecodeError: | |
| return content | |
| tools = [tools] if not isinstance(tools, list) else tools | |
| try: | |
| return [FunctionCall(tool["name"], json.dumps(tool["parameters"], ensure_ascii=False)) for tool in tools] | |
| except KeyError: | |
| return content | |
| class MiniMaxM1ToolUtils(ToolUtils): | |
| r"""MiniMax-M1 tool using template.""" | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| tool_text = "" | |
| for tool in tools: | |
| tool = tool.get("function", "") if tool.get("type") == "function" else tool | |
| tool_text += json.dumps(tool, ensure_ascii=False) + "\n" | |
| return MINIMAX_M1_TOOL_PROMPT.format(tool_text=tool_text) | |
| def function_formatter(functions: list["FunctionCall"]) -> str: | |
| function_texts = [] | |
| for func in functions: | |
| name, arguments = func.name, json.loads(func.arguments) | |
| function_texts.append(json.dumps({"name": name, "arguments": arguments}, ensure_ascii=False)) | |
| return "<tool_calls>\n" + "\n".join(function_texts) + "\n</tool_calls>" | |
| def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
| regex = re.compile(r"<tool_calls>\s*(.+?)\s*</tool_calls>", re.DOTALL) | |
| tool_match = re.search(regex, content) | |
| if not tool_match: | |
| return content | |
| tool_calls_content = tool_match.group(1) | |
| results = [] | |
| for line in tool_calls_content.split("\n"): | |
| line = line.strip() | |
| if not line: | |
| continue | |
| try: | |
| tool_call = json.loads(line) | |
| results.append(FunctionCall(tool_call["name"], json.dumps(tool_call["arguments"], ensure_ascii=False))) | |
| except json.JSONDecodeError: | |
| continue | |
| return results | |
| class MiniMaxM2ToolUtils(ToolUtils): | |
| r"""MiniMax-M2 tool using template.""" | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| tool_text = "" | |
| for tool in tools: | |
| tool = tool.get("function", "") if tool.get("type") == "function" else tool | |
| tool_text += "<tool>" + json.dumps(tool, ensure_ascii=False) + "</tool>\n" | |
| return MINIMAX_M2_TOOL_PROMPT.format(tool_text=tool_text) | |
| def function_formatter(functions: list["FunctionCall"]) -> str: | |
| function_texts = [] | |
| for func in functions: | |
| name, arguments = func.name, json.loads(func.arguments) | |
| prompt = f'<invoke name="{name}">' | |
| for key, value in arguments.items(): | |
| prompt += f'\n<parameter name="{key}">' | |
| if not isinstance(value, str): | |
| value = json.dumps(value, ensure_ascii=False) | |
| prompt += value + "</parameter>" | |
| prompt += "\n</invoke>" | |
| function_texts.append(prompt) | |
| return "\n".join(function_texts) | |
| def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
| regex = re.compile(r"<minimax:tool_call>\s*(.+?)\s*</minimax:tool_call>", re.DOTALL) | |
| tool_match = re.search(regex, content) | |
| if not tool_match: | |
| return content | |
| tool_calls_content = tool_match.group(1) | |
| invoke_regex = re.compile(r"<invoke name=\"(.*?)\">(.*?)</invoke>", re.DOTALL) | |
| results = [] | |
| for func_name, params_block in re.findall(invoke_regex, tool_calls_content): | |
| args_dict = {} | |
| param_pattern = re.compile(r"<parameter name=\"(.*?)\">(.*?)</parameter>", re.DOTALL) | |
| for key, raw_value in re.findall(param_pattern, params_block): | |
| value = raw_value.strip() | |
| try: | |
| parsed_value = json.loads(value) | |
| except json.JSONDecodeError: | |
| parsed_value = raw_value | |
| args_dict[key] = parsed_value | |
| results.append(FunctionCall(func_name.strip(), json.dumps(args_dict, ensure_ascii=False))) | |
| return results | |
| class MistralToolUtils(ToolUtils): | |
| r"""Mistral v0.3 tool using template.""" | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| wrapped_tools = [] | |
| for tool in tools: | |
| wrapped_tools.append(tool if tool.get("type") == "function" else {"type": "function", "function": tool}) | |
| return "[AVAILABLE_TOOLS] " + json.dumps(wrapped_tools, ensure_ascii=False) + "[/AVAILABLE_TOOLS]" | |
| def function_formatter(functions: list["FunctionCall"]) -> str: | |
| return json.dumps( | |
| [{"name": name, "arguments": json.loads(arguments)} for name, arguments in functions], ensure_ascii=False | |
| ) | |
| def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
| try: | |
| tools = json.loads(content.strip()) | |
| except json.JSONDecodeError: | |
| return content | |
| tools = [tools] if not isinstance(tools, list) else tools | |
| try: | |
| return [FunctionCall(tool["name"], json.dumps(tool["arguments"], ensure_ascii=False)) for tool in tools] | |
| except KeyError: | |
| return content | |
| class QwenToolUtils(ToolUtils): | |
| r"""Qwen 2.5 tool using template.""" | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| tool_text = "" | |
| for tool in tools: | |
| wrapped_tool = tool if tool.get("type") == "function" else {"type": "function", "function": tool} | |
| tool_text += "\n" + json.dumps(wrapped_tool, ensure_ascii=False) | |
| return QWEN_TOOL_PROMPT.format(tool_text=tool_text) | |
| def function_formatter(functions: list["FunctionCall"]) -> str: | |
| function_texts = [ | |
| json.dumps({"name": name, "arguments": json.loads(arguments)}, ensure_ascii=False) | |
| for name, arguments in functions | |
| ] | |
| return "\n".join([f"<tool_call>\n{text}\n</tool_call>" for text in function_texts]) | |
| def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
| regex = re.compile(r"<tool_call>(.+?)</tool_call>(?=\s*<tool_call>|\s*$)", re.DOTALL) | |
| tool_match: list[str] = re.findall(regex, content) | |
| if not tool_match: | |
| return content | |
| results = [] | |
| for tool in tool_match: | |
| try: | |
| tool = json.loads(tool.strip()) | |
| except json.JSONDecodeError: | |
| return content | |
| if "name" not in tool or "arguments" not in tool: | |
| return content | |
| results.append(FunctionCall(tool["name"], json.dumps(tool["arguments"], ensure_ascii=False))) | |
| return results | |
| class Qwen35ToolUtils(ToolUtils): | |
| r"""Qwen 3.5 tool using template.""" | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| tool_text = "" | |
| for tool in tools: | |
| tool = tool.get("function", tool) if tool.get("type") == "function" else tool | |
| tool_text += "\n" + json.dumps(tool, ensure_ascii=False) | |
| return QWEN35_TOOL_PROMPT.format(tool_text=tool_text) | |
| def function_formatter(functions: list["FunctionCall"]) -> str: | |
| function_texts = [] | |
| for func in functions: | |
| name, arguments = func.name, json.loads(func.arguments) | |
| prompt = f"<tool_call>\n<function={name}>" | |
| for key, value in arguments.items(): | |
| prompt += f"\n<parameter={key}>" | |
| if not isinstance(value, str): | |
| value = json.dumps(value, ensure_ascii=False) | |
| prompt += f"\n{value}\n</parameter>" | |
| prompt += "\n</function>\n</tool_call>" | |
| function_texts.append(prompt) | |
| return "\n".join(function_texts) | |
| def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
| results = [] | |
| regex = re.compile(r"<tool_call>\s*<function=\s*([^\s<>]+)\s*(.*?)\s*</function>\s*</tool_call>", re.DOTALL) | |
| for func_name, params_block in re.findall(regex, content): | |
| args_dict = {} | |
| param_pattern = re.compile(r"<parameter=(.*?)>(.*?)</parameter>", re.DOTALL) | |
| for key, raw_value in re.findall(param_pattern, params_block.strip()): | |
| value = raw_value.strip() | |
| try: | |
| parsed_value = json.loads(value) | |
| except json.JSONDecodeError: | |
| parsed_value = raw_value.strip() | |
| args_dict[key] = parsed_value | |
| results.append(FunctionCall(func_name.strip(), json.dumps(args_dict, ensure_ascii=False))) | |
| return results if results else content | |
| class GLM4MOEToolUtils(QwenToolUtils): | |
| r"""GLM-4-MOE tool using template.""" | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| tool_text = "" | |
| for tool in tools: | |
| wrapped_tool = tool if tool.get("type") == "function" else {"type": "function", "function": tool} | |
| tool_text += "\n" + json.dumps(wrapped_tool, ensure_ascii=False) | |
| return GLM4_MOE_TOOL_PROMPT.format(tool_text=tool_text) | |
| def function_formatter(functions: list["FunctionCall"]) -> str: | |
| function_json = [ | |
| {"func_name": name, "func_key_values": json.loads(arguments)} for name, arguments in functions | |
| ] | |
| function_texts = [] | |
| for func in function_json: | |
| prompt = "\n<tool_call>" + func["func_name"] | |
| for key, value in func["func_key_values"].items(): | |
| prompt += "\n<arg_key>" + key + "</arg_key>" | |
| if not isinstance(value, str): | |
| value = json.dumps(value, ensure_ascii=False) | |
| prompt += "\n<arg_value>" + value + "</arg_value>" | |
| function_texts.append(prompt) | |
| return "\n".join(function_texts) | |
| class SeedToolUtils(ToolUtils): | |
| r"""Seed tool using template.""" | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| return SEED_TOOL_PROMPT.format(tool_text="\n" + json.dumps(tools, ensure_ascii=False)) | |
| def function_formatter(functions: list["FunctionCall"]) -> str: | |
| function_json = [ | |
| {"func_name": name, "func_key_values": json.loads(arguments)} for name, arguments in functions | |
| ] | |
| function_texts = [] | |
| for func in function_json: | |
| prompt = "\n<seed:tool_call>\n<function=" + func["func_name"] | |
| for key, value in func["func_key_values"].items(): | |
| prompt += "\n<parameter=" + key + ">" | |
| if not isinstance(value, str): | |
| value = json.dumps(value, ensure_ascii=False) | |
| prompt += value + "</parameter>" | |
| prompt += "\n</function>\n</seed:tool_call>" | |
| function_texts.append(prompt) | |
| return "\n".join(function_texts) | |
| def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
| results = [] | |
| regex = re.compile( | |
| r"<seed:tool_call>\s*<function=\s*([^\s<]+)\s*(.*?)\s*</function>\s*</seed:tool_call>", re.DOTALL | |
| ) | |
| for func_name, params_block in re.findall(regex, content): | |
| args_dict = {} | |
| param_pattern = re.compile(r"<parameter=(.*?)>(.*?)</parameter>", re.DOTALL) | |
| for key, raw_value in re.findall(param_pattern, params_block.strip()): | |
| value = raw_value.strip() | |
| try: | |
| parsed_value = json.loads(value) | |
| except json.JSONDecodeError: | |
| parsed_value = raw_value | |
| args_dict[key] = parsed_value | |
| results.append(FunctionCall(func_name.strip(), json.dumps(args_dict, ensure_ascii=False))) | |
| return results if results else content | |
| class LingToolUtils(QwenToolUtils): | |
| r"""Ling v2 tool using template.""" | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| tool_text = "" | |
| for tool in tools: | |
| wrapped_tool = tool if tool.get("type") == "function" else {"type": "function", "function": tool} | |
| tool_text += "\n" + json.dumps(wrapped_tool, ensure_ascii=False) | |
| return LING_TOOL_PROMPT.format(tool_text=tool_text) + "\n" + "detailed thinking off" | |
| class LFM2ToolUtils(ToolUtils): | |
| r"""LFM2.5 tool using template with Pythonic function call syntax.""" | |
| def tool_formatter(tools: list[dict[str, Any]]) -> str: | |
| tool_list = [] | |
| for tool in tools: | |
| tool = tool.get("function", tool) if tool.get("type") == "function" else tool | |
| tool_list.append(tool) | |
| return LFM2_TOOL_PROMPT.format(tool_text=json.dumps(tool_list, ensure_ascii=False)) | |
| def function_formatter(functions: list["FunctionCall"]) -> str: | |
| calls = [] | |
| for name, args_json in functions: | |
| args = json.loads(args_json) | |
| kwargs_parts = [] | |
| for key, value in args.items(): | |
| if isinstance(value, str): | |
| kwargs_parts.append(f'{key}="{value}"') | |
| else: | |
| kwargs_parts.append(f"{key}={json.dumps(value, ensure_ascii=False)}") | |
| calls.append(f"{name}({', '.join(kwargs_parts)})") | |
| return f"<|tool_call_start|>[{', '.join(calls)}]<|tool_call_end|>" | |
| def _ast_to_value(node: ast.AST) -> Any: | |
| """Convert an AST node to a Python value, handling JSON-style booleans/null.""" | |
| # Handle JSON-style true/false/null as Name nodes | |
| if isinstance(node, ast.Name): | |
| if node.id == "true": | |
| return True | |
| elif node.id == "false": | |
| return False | |
| elif node.id == "null": | |
| return None | |
| else: | |
| raise ValueError(f"Unknown identifier: {node.id}") | |
| # Use literal_eval for other cases (strings, numbers, lists, dicts) | |
| return ast.literal_eval(node) | |
| def tool_extractor(content: str) -> Union[str, list["FunctionCall"]]: | |
| # Extract content between tool call markers | |
| start_marker = "<|tool_call_start|>" | |
| end_marker = "<|tool_call_end|>" | |
| start_idx = content.find(start_marker) | |
| if start_idx == -1: | |
| return content | |
| end_idx = content.find(end_marker, start_idx) | |
| if end_idx == -1: | |
| return content | |
| tool_call_str = content[start_idx + len(start_marker) : end_idx].strip() | |
| # Parse Pythonic function call syntax using AST | |
| try: | |
| tree = ast.parse(tool_call_str, mode="eval") | |
| except SyntaxError: | |
| return content | |
| # Handle both single call and list of calls | |
| if isinstance(tree.body, ast.List): | |
| call_nodes = tree.body.elts | |
| elif isinstance(tree.body, ast.Call): | |
| call_nodes = [tree.body] | |
| else: | |
| return content | |
| results = [] | |
| for node in call_nodes: | |
| if not isinstance(node, ast.Call): | |
| return content | |
| # Extract function name | |
| if isinstance(node.func, ast.Name): | |
| func_name = node.func.id | |
| else: | |
| return content | |
| # Extract keyword arguments | |
| args_dict = {} | |
| for keyword in node.keywords: | |
| key = keyword.arg | |
| try: | |
| value = LFM2ToolUtils._ast_to_value(keyword.value) | |
| except (ValueError, SyntaxError): | |
| return content | |
| args_dict[key] = value | |
| results.append(FunctionCall(func_name, json.dumps(args_dict, ensure_ascii=False))) | |
| return results if results else content | |
| TOOLS = { | |
| "default": DefaultToolUtils(), | |
| "gemma4": Gemma4ToolUtils(), | |
| "glm4": GLM4ToolUtils(), | |
| "llama3": Llama3ToolUtils(), | |
| "lfm2": LFM2ToolUtils(), | |
| "minimax1": MiniMaxM1ToolUtils(), | |
| "minimax2": MiniMaxM2ToolUtils(), | |
| "mistral": MistralToolUtils(), | |
| "qwen": QwenToolUtils(), | |
| "qwen3_5": Qwen35ToolUtils(), | |
| "glm4_moe": GLM4MOEToolUtils(), | |
| "seed_oss": SeedToolUtils(), | |
| "ling": LingToolUtils(), | |
| } | |
| def get_tool_utils(name: str) -> "ToolUtils": | |
| tool_utils = TOOLS.get(name, None) | |
| if tool_utils is None: | |
| raise ValueError(f"Tool utils `{name}` not found.") | |
| return tool_utils | |