diff --git a/README.md b/README.md index 04c3ec4..6eb39a1 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,9 @@ --- pipeline_tag: text-generation -license: MIT License +license: mit +library_name: transformers --- +
@@ -155,7 +157,7 @@ We look forward to your feedback and to collaborating with developers and resear ## How to Use -- Our product **MiniMax Agent**, built on MiniMax-M2, is now **publicly available and free** for a limited time: https://agent.minimaxi.io/ +- Our product **MiniMax Agent**, built on MiniMax-M2, is now **publicly available and free** for a limited time: https://agent.minimax.io/ - The MiniMax-M2 API is now live on the **MiniMax Open Platform** and is **free** for a limited time: https://platform.minimax.io/docs/guides/text-generation diff --git a/config.json b/config.json index 622a352..5979a6d 100644 --- a/config.json +++ b/config.json @@ -79,7 +79,7 @@ "layernorm_mlp_beta": 1.0, "max_position_embeddings": 196608, "mlp_intermediate_size": 8192, - "model_type": "mixtral", + "model_type": "minimax", "mtp_transformer_layers": 1, "num_attention_heads": 48, "num_experts_per_tok": 8, @@ -114,4 +114,4 @@ "use_qk_norm": true, "use_routing_bias": true, "vocab_size": 200064 -} +} \ No newline at end of file diff --git a/docs/sglang_deploy_guide.md b/docs/sglang_deploy_guide.md new file mode 100644 index 0000000..3138745 --- /dev/null +++ b/docs/sglang_deploy_guide.md @@ -0,0 +1,118 @@ +# MiniMax M2 Model SGLang Deployment Guide + +[English Version](./sglang_deploy_guide.md) | [Chinese Version](./sglang_deploy_guide_cn.md) + +We recommend using [SGLang](https://github.com/sgl-project/sglang) to deploy the [MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) model. SGLang is a high-performance inference engine with excellent serving throughput, efficient and intelligent memory management, powerful batch request processing capabilities, and deeply optimized underlying performance. We recommend reviewing SGLang's official documentation to check hardware compatibility before deployment. + +## Applicable Models + +This document applies to the following models. You only need to change the model name during deployment. + +- [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) + +The deployment process is illustrated below using MiniMax-M2 as an example. + +## System Requirements + +- OS: Linux + +- Python: 3.9 - 3.12 + +- GPU: + + - compute capability 7.0 or higher + + - Memory requirements: 220 GB for weights, 240 GB per 1M context tokens + +The following are recommended configurations; actual requirements should be adjusted based on your use case: + +- 4x 96GB GPUs: Supported context length of up to 400K tokens. + +- 8x 144GB GPUs: Supported context length of up to 3M tokens. + +## Deployment with Python + +It is recommended to use a virtual environment (such as **venv**, **conda**, or **uv**) to avoid dependency conflicts. + +We recommend installing SGLang in a fresh Python environment: + +```bash +git clone -b v0.5.4.post1 https://github.com/sgl-project/sglang.git +cd sglang + +# Install the python packages +pip install --upgrade pip +pip install -e "python" +``` + +Run the following command to start the SGLang server. SGLang will automatically download and cache the MiniMax-M2 model from Hugging Face. + +4-GPU deployment command: + +```bash +python -m sglang.launch_server \ + --model-path MiniMaxAI/MiniMax-M2 \ + --tp-size 4 \ + --tool-call-parser minimax-m2 \ + --reasoning-parser minimax-append-think \ + --host 0.0.0.0 \ + --trust-remote-code \ + --port 8000 \ + --mem-fraction-static 0.85 +``` + +8-GPU deployment command: + +```bash +python -m sglang.launch_server \ + --model-path MiniMaxAI/MiniMax-M2 \ + --tp-size 8 \ + --ep-size 8 \ + --tool-call-parser minimax-m2 \ + --trust-remote-code \ + --host 0.0.0.0 \ + --reasoning-parser minimax-append-think \ + --port 8000 \ + --mem-fraction-static 0.85 +``` + +## Testing Deployment + +After startup, you can test the SGLang OpenAI-compatible API with the following command: + +```bash +curl http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "MiniMaxAI/MiniMax-M2", + "messages": [ + {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]}, + {"role": "user", "content": [{"type": "text", "text": "Who won the world series in 2020?"}]} + ] + }' +``` + +## Common Issues + +### Hugging Face Network Issues + +If you encounter network issues, you can set up a proxy before pulling the model. + +```bash +export HF_ENDPOINT=https://hf-mirror.com +``` + +### MiniMax-M2 model is not currently supported + +Please upgrade to the latest stable version, >= v0.5.4.post3. + +## Getting Support + +If you encounter any issues while deploying the MiniMax model: + +- Contact our technical support team through official channels such as email at [model@minimax.io](mailto:model@minimax.io) + +- Submit an issue on our [GitHub](https://github.com/MiniMax-AI) repository + +We continuously optimize the deployment experience for our models. Feedback is welcome! + diff --git a/docs/sglang_deploy_guide_cn.md b/docs/sglang_deploy_guide_cn.md new file mode 100644 index 0000000..013d9b1 --- /dev/null +++ b/docs/sglang_deploy_guide_cn.md @@ -0,0 +1,115 @@ +# MiniMax M2 模型 SGLang 部署指南 + +[英文版](./sglang_deploy_guide.md) | [中文版](./sglang_deploy_guide_cn.md) + +我们推荐使用 [SGLang](https://github.com/sgl-project/sglang) 来部署 [MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) 模型。SGLang 是一个高性能的推理引擎，其具有卓越的服务吞吐、高效智能的内存管理机制、强大的批量请求处理能力、深度优化的底层性能等特性。我们建议在部署之前查看 SGLang 的官方文档以检查硬件兼容性。 + +## 本文档适用模型 + +本文档适用以下模型，只需在部署时修改模型名称即可。 + +- [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) + +以下以 MiniMax-M2 为例说明部署流程。 + +## 环境要求 + +- OS：Linux + +- Python：3.9 - 3.12 + +- GPU： + + - compute capability 7.0 or higher + + - 显存需求：权重需要 220 GB，每 1M 上下文 token 需要 240 GB + +以下为推荐配置，实际需求请根据业务场景调整： + +- 96G x4 GPU：支持 40 万 token 的总上下文。 + +- 144G x8 GPU：支持长达 300 万 token 的总上下文。 + +## 使用 Python 部署 + +建议使用虚拟环境（如 **venv**、**conda**、**uv**）以避免依赖冲突。 + +建议在全新的 Python 环境中安装 SGLang: +```bash +git clone -b v0.5.4.post1 https://github.com/sgl-project/sglang.git +cd sglang + +# Install the python packages +pip install --upgrade pip +pip install -e "python" +``` + +运行如下命令启动 SGLang 服务器，SGLang 会自动从 Huggingface 下载并缓存 MiniMax-M2 模型。 + +4 卡部署命令： + +```bash +python -m sglang.launch_server \ + --model-path MiniMaxAI/MiniMax-M2 \ + --tp-size 4 \ + --tool-call-parser minimax-m2 \ + --reasoning-parser minimax-append-think \ + --host 0.0.0.0 \ + --trust-remote-code \ + --port 8000 \ + --mem-fraction-static 0.85 +``` + +8 卡部署命令： + +```bash +python -m sglang.launch_server \ + --model-path MiniMaxAI/MiniMax-M2 \ + --tp-size 8 \ + --ep-size 8 \ + --tool-call-parser minimax-m2 \ + --trust-remote-code \ + --host 0.0.0.0 \ + --reasoning-parser minimax-append-think \ + --port 8000 \ + --mem-fraction-static 0.85 +``` + +## 测试部署 + +启动后，可以通过如下命令测试 SGLang OpenAI 兼容接口： + +```bash +curl http://localhost:8000/v1/chat/completions \ + -H "Content-Type: application/json" \ + -d '{ + "model": "MiniMaxAI/MiniMax-M2", + "messages": [ + {"role": "system", "content": [{"type": "text", "text": "You are a helpful assistant."}]}, + {"role": "user", "content": [{"type": "text", "text": "Who won the world series in 2020?"}]} + ] + }' +``` + +## 常见问题 + +### Huggingface 网络问题 + +如果遇到网络问题，可以设置代理后再进行拉取。 + +```bash +export HF_ENDPOINT=https://hf-mirror.com +``` + +### MiniMax-M2 model is not currently supported + +请升级到最新的稳定版本, >= v0.5.4.post1. + +## 获取支持 + +如果在部署 MiniMax 模型过程中遇到任何问题： + +- 通过邮箱 [model@minimax.io](mailto:model@minimax.io) 等官方渠道联系我们的技术支持团队 + +- 在我们的 [GitHub](https://github.com/MiniMax-AI) 仓库提交 Issue +我们会持续优化模型的部署体验，欢迎反馈！ diff --git a/docs/tool_calling_guide.md b/docs/tool_calling_guide.md new file mode 100644 index 0000000..0fbf40e --- /dev/null +++ b/docs/tool_calling_guide.md @@ -0,0 +1,485 @@ +# MiniMax-M2 Tool Calling Guide + +[English Version](./tool_calling_guide.md) | [Chinese Version](./tool_calling_guide_cn.md) + +## Introduction + +The MiniMax-M2 model supports tool calling capabilities, enabling the model to identify when external tools need to be called and output tool call parameters in a structured format. This document provides detailed instructions on how to use the tool calling features of MiniMax-M2. + +## Basic Example + +The following Python script implements a weather query tool call example based on the OpenAI SDK: + +```python +from openai import OpenAI +import json + +client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy") + +def get_weather(location: str, unit: str): + return f"Getting the weather for {location} in {unit}..." + +tool_functions = {"get_weather": get_weather} + +tools = [{ + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string", "description": "City and state, e.g., 'San Francisco, CA'"}, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]} + }, + "required": ["location", "unit"] + } + } +}] + +response = client.chat.completions.create( + model=client.models.list().data[0].id, + messages=[{"role": "user", "content": "What's the weather like in San Francisco? use celsius."}], + tools=tools, + tool_choice="auto" +) + +print(response) + +tool_call = response.choices[0].message.tool_calls[0].function +print(f"Function called: {tool_call.name}") +print(f"Arguments: {tool_call.arguments}") +print(f"Result: {get_weather(**json.loads(tool_call.arguments))}") +``` + +**Output Example:** +``` +Function called: get_weather +Arguments: {"location": "San Francisco, CA", "unit": "celsius"} +Result: Getting the weather for San Francisco, CA in celsius... +``` + +## Manually Parsing Model Output + +**We strongly recommend using vLLM or SGLang for parsing tool calls.** If you cannot use the built-in parser of inference engines (e.g., vLLM and SGLang) that support MiniMax-M2, or need to use other inference frameworks (such as transformers, TGI, etc.), you can manually parse the model's raw output using the following method. This approach requires you to parse the XML tag format of the model output yourself. + +### Example Using Transformers + +Here is a complete example using the transformers library: + +```python +from transformers import AutoTokenizer + +def get_default_tools(): + return [ + { + "name": "get_current_weather", + "description": "Get the latest weather for a location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "A certain city, such as Beijing, Shanghai" + } + }, + } + "required": ["location"], + "type": "object" + } + ] + +# Load model and tokenizer +tokenizer = AutoTokenizer.from_pretrained(model_id) +prompt = "What's the weather like in Shanghai today?" +messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": prompt}, +] + +# Enable function calling tools +tools = get_default_tools() + +# Apply chat template and include tool definitions +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + tools=tools +) + +# Send request (using any inference service) +import requests +payload = { + "model": "MiniMaxAI/MiniMax-M2", + "prompt": text, + "max_tokens": 4096 +} +response = requests.post( + "http://localhost:8000/v1/completions", + headers={"Content-Type": "application/json"}, + json=payload, + stream=False, +) + +# Model output needs manual parsing +raw_output = response.json()["choices"][0]["text"] +print("Raw output:", raw_output) + +# Use the parsing function below to process the output +tool_calls = parse_tool_calls(raw_output, tools) +``` + +## 🛠️ Tool Call Definition + +### Tool Structure + +Tool calls need to define the `tools` field in the request body. Each tool consists of the following parts: + +```json +{ + "tools": [ + { + "name": "search_web", + "description": "Search function.", + "parameters": { + "properties": { + "query_list": { + "description": "Keywords for search, list should contain 1 element.", + "items": { "type": "string" }, + "type": "array" + }, + "query_tag": { + "description": "Category of query", + "items": { "type": "string" }, + "type": "array" + } + }, + "required": [ "query_list", "query_tag" ], + "type": "object" + } + } + ] +} +``` + +**Field Descriptions:** +- `name`: Function name +- `description`: Function description +- `parameters`: Function parameter definition + - `properties`: Parameter property definition, where key is the parameter name and value contains detailed parameter description + - `required`: List of required parameters + - `type`: Parameter type (usually "object") + +### Internal Processing Format + +When processing within the MiniMax-M2 model, tool definitions are converted to a special format and concatenated to the input text. Here is a complete example: + +``` +]~!b[]~b]system +You are a helpful assistant. + +# Tools +You may call one or more tools to assist with the user query. +Here are the tools available in JSONSchema format: + + +{"name": "search_web", "description": "Search function.", "parameters": {"type": "object", "properties": {"query_list": {"type": "array", "items": {"type": "string"}, "description": "Keywords for search, list should contain 1 element."}, "query_tag": {"type": "array", "items": {"type": "string"}, "description": "Category of query"}}, "required": ["query_list", "query_tag"]}} + + +When making tool calls, use XML format to invoke tools and pass parameters: + + + +param-value-1 +param-value-2 +... + +[e~[ +]~b]user +When were the latest announcements from OpenAI and Gemini?[e~[ +]~b]ai + +``` + +**Format Description:** + +- `]~!b[]~b]system`: System message start marker +- `[e~[`: Message end marker +- `]~b]user`: User message start marker +- `]~b]ai`: Assistant message start marker +- `]~b]tool`: Tool result message start marker +- `...`: Tool definition area, each tool is wrapped with `` tag, content is JSON Schema +- `...`: Tool call area +- `...`: Thinking process marker during generation + +### Model Output Format + +MiniMax-M2 uses structured XML tag format: + +```xml + + +["technology", "events"] +["\"OpenAI\" \"latest\" \"release\""] + + +["technology", "events"] +["\"Gemini\" \"latest\" \"release\""] + + +``` + +Each tool call uses the `` tag, and parameters use the `` tag wrapper. + +## Manually Parsing Tool Call Results + +### Parsing Tool Calls + +MiniMax-M2 uses structured XML tags, which require a different parsing approach. The core function is as follows: + +```python +import re +import json +from typing import Any, Optional, List, Dict + + +def extract_name(name_str: str) -> str: + """Extract name from quoted string""" + name_str = name_str.strip() + if name_str.startswith('"') and name_str.endswith('"'): + return name_str[1:-1] + elif name_str.startswith("'") and name_str.endswith("'"): + return name_str[1:-1] + return name_str + + +def convert_param_value(value: str, param_type: str) -> Any: + """Convert parameter value based on parameter type""" + if value.lower() == "null": + return None + + param_type = param_type.lower() + + if param_type in ["string", "str", "text"]: + return value + elif param_type in ["integer", "int"]: + try: + return int(value) + except (ValueError, TypeError): + return value + elif param_type in ["number", "float"]: + try: + val = float(value) + return val if val != int(val) else int(val) + except (ValueError, TypeError): + return value + elif param_type in ["boolean", "bool"]: + return value.lower() in ["true", "1"] + elif param_type in ["object", "array"]: + try: + return json.loads(value) + except json.JSONDecodeError: + return value + else: + # Try JSON parsing, return string if failed + try: + return json.loads(value) + except json.JSONDecodeError: + return value + + +def parse_tool_calls(model_output: str, tools: Optional[List[Dict]] = None) -> List[Dict]: + """ + Extract all tool calls from model output + + Args: + model_output: Complete output text from the model + tools: Tool definition list for getting parameter type information, format can be: + - [{"name": "...", "parameters": {...}}] + - [{"type": "function", "function": {"name": "...", "parameters": {...}}}] + + Returns: + Parsed tool call list, each element contains name and arguments fields + + Example: + >>> tools = [{ + ... "name": "get_weather", + ... "parameters": { + ... "type": "object", + ... "properties": { + ... "location": {"type": "string"}, + ... "unit": {"type": "string"} + ... } + ... } + ... }] + >>> output = ''' + ... + ... San Francisco + ... celsius + ... + ... ''' + >>> result = parse_tool_calls(output, tools) + >>> print(result) + [{'name': 'get_weather', 'arguments': {'location': 'San Francisco', 'unit': 'celsius'}}] + """ + # Quick check if tool call marker is present + if "" not in model_output: + return [] + + tool_calls = [] + + try: + # Match all blocks + tool_call_regex = re.compile(r"(.*?)", re.DOTALL) + invoke_regex = re.compile(r"", re.DOTALL) + parameter_regex = re.compile(r"", re.DOTALL) + + # Iterate through all tool_call blocks + for tool_call_match in tool_call_regex.findall(model_output): + # Iterate through all invokes in this block + for invoke_match in invoke_regex.findall(tool_call_match): + # Extract function name + name_match = re.search(r'^([^>]+)', invoke_match) + if not name_match: + continue + + function_name = extract_name(name_match.group(1)) + + # Get parameter configuration + param_config = {} + if tools: + for tool in tools: + tool_name = tool.get("name") or tool.get("function", {}).get("name") + if tool_name == function_name: + params = tool.get("parameters") or tool.get("function", {}).get("parameters") + if isinstance(params, dict) and "properties" in params: + param_config = params["properties"] + break + + # Extract parameters + param_dict = {} + for match in parameter_regex.findall(invoke_match): + param_match = re.search(r'^([^>]+)>(.*)', match, re.DOTALL) + if param_match: + param_name = extract_name(param_match.group(1)) + param_value = param_match.group(2).strip() + + # Remove leading and trailing newlines + if param_value.startswith('\n'): + param_value = param_value[1:] + if param_value.endswith('\n'): + param_value = param_value[:-1] + + # Get parameter type and convert + param_type = "string" + if param_name in param_config: + if isinstance(param_config[param_name], dict) and "type" in param_config[param_name]: + param_type = param_config[param_name]["type"] + + param_dict[param_name] = convert_param_value(param_value, param_type) + + tool_calls.append({ + "name": function_name, + "arguments": param_dict + }) + + except Exception as e: + print(f"Failed to parse tool calls: {e}") + return [] + + return tool_calls +``` + +**Usage Example:** + +```python +# Define tools +tools = [ + { + "name": "get_weather", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string"}, + "unit": {"type": "string"} + }, + "required": ["location", "unit"] + } + } +] + +# Model output +model_output = """Let me help you query the weather. + + +San Francisco +celsius + +""" + +# Parse tool calls +tool_calls = parse_tool_calls(model_output, tools) + +# Output results +for call in tool_calls: + print(f"Function called: {call['name']}") + print(f"Arguments: {call['arguments']}") + # Output: Function called: get_weather + # Arguments: {'location': 'San Francisco', 'unit': 'celsius'} +``` + +### Executing Tool Calls + +After parsing is complete, you can execute the corresponding tool and construct the return result: + +```python +def execute_function_call(function_name: str, arguments: dict): + """Execute function call and return result""" + if function_name == "get_weather": + location = arguments.get("location", "Unknown location") + unit = arguments.get("unit", "celsius") + # Build function execution result + return { + "role": "tool", + "content": [ + { + "name": function_name, + "type": "text", + "text": json.dumps({ + "location": location, + "temperature": "25", + "unit": unit, + "weather": "Sunny" + }, ensure_ascii=False) + } + ] + } + elif function_name == "search_web": + query_list = arguments.get("query_list", []) + query_tag = arguments.get("query_tag", []) + # Simulate search results + return { + "role": "tool", + "content": [ + { + "name": function_name, + "type": "text", + "text": f"Search keywords: {query_list}, Category: {query_tag}\nSearch results: Relevant information found" + } + ] + } + + return None +``` + +### Returning Tool Execution Results to the Model + +After successfully parsing tool calls, you should add the tool execution results to the conversation history so that the model can access and utilize this information in subsequent interactions. Refer to [chat_template.jinja](https://huggingface.co/MiniMaxAI/MiniMax-M2/blob/main/chat_template.jinja) for concatenation format. + +## References + +- [MiniMax-M2 Model Repository](https://github.com/MiniMax-AI/MiniMax-M2) +- [vLLM Project Homepage](https://github.com/vllm-project/vllm) +- [SGLang Project Homepage](https://github.com/sgl-project/sglang) +- [OpenAI Python SDK](https://github.com/openai/openai-python) \ No newline at end of file diff --git a/docs/tool_calling_guide_cn.md b/docs/tool_calling_guide_cn.md new file mode 100644 index 0000000..c70660f --- /dev/null +++ b/docs/tool_calling_guide_cn.md @@ -0,0 +1,485 @@ +# MiniMax-M2 工具调用指南 + +[英文版](./tool_calling_guide.md) | [中文版](./tool_calling_guide_cn.md) + +## 简介 + +MiniMax-M2 模型支持工具调用功能，使模型能够识别何时需要调用外部工具，并以结构化格式输出工具调用参数。本文档提供了有关如何使用 MiniMax-M2 工具调用功能的详细说明。 + +## 基础示例 + +以下 Python 脚本基于 OpenAI SDK 实现了一个天气查询工具调用示例： + +```python +from openai import OpenAI +import json + +client = OpenAI(base_url="http://localhost:8000/v1", api_key="dummy") + +def get_weather(location: str, unit: str): + return f"Getting the weather for {location} in {unit}..." + +tool_functions = {"get_weather": get_weather} + +tools = [{ + "type": "function", + "function": { + "name": "get_weather", + "description": "Get the current weather in a given location", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string", "description": "City and state, e.g., 'San Francisco, CA'"}, + "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]} + }, + "required": ["location", "unit"] + } + } +}] + +response = client.chat.completions.create( + model=client.models.list().data[0].id, + messages=[{"role": "user", "content": "What's the weather like in San Francisco? use celsius."}], + tools=tools, + tool_choice="auto" +) + +print(response) + +tool_call = response.choices[0].message.tool_calls[0].function +print(f"Function called: {tool_call.name}") +print(f"Arguments: {tool_call.arguments}") +print(f"Result: {get_weather(**json.loads(tool_call.arguments))}") +``` + +**输出示例：** +``` +Function called: get_weather +Arguments: {"location": "San Francisco, CA", "unit": "celsius"} +Result: Getting the weather for San Francisco, CA in celsius... +``` + +## 手动解析模型输出 + +**我们强烈建议使用 vLLM 或 SGLnag 来解析工具调用。** 如果您无法使用支持 MiniMax-M2 的推理引擎（如 vLLM 和 SGLang）的内置解析器，或需要使用其他推理框架（如 transformers、TGI 等），您可以使用以下方法手动解析模型的原始输出。这种方法需要您自己解析模型输出的 XML 标签格式。 + +### 使用 Transformers 的示例 + +这是一个使用 transformers 库的完整示例： + +```python +from transformers import AutoTokenizer + +def get_default_tools(): + return [ + { + "name": "get_current_weather", + "description": "Get the latest weather for a location", + "parameters": { + "type": "object", + "properties": { + "location": { + "type": "string", + "description": "A certain city, such as Beijing, Shanghai" + } + }, + } + "required": ["location"], + "type": "object" + } + ] + +# Load model and tokenizer +tokenizer = AutoTokenizer.from_pretrained(model_id) +prompt = "What's the weather like in Shanghai today?" +messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": prompt}, +] + +# Enable function calling tools +tools = get_default_tools() + +# Apply chat template and include tool definitions +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True, + tools=tools +) + +# Send request (using any inference service) +import requests +payload = { + "model": "MiniMaxAI/MiniMax-M2", + "prompt": text, + "max_tokens": 4096 +} +response = requests.post( + "http://localhost:8000/v1/completions", + headers={"Content-Type": "application/json"}, + json=payload, + stream=False, +) + +# Model output needs manual parsing +raw_output = response.json()["choices"][0]["text"] +print("Raw output:", raw_output) + +# Use the parsing function below to process the output +tool_calls = parse_tool_calls(raw_output, tools) +``` + +## 🛠️ 工具调用定义 + +### 工具结构 + +工具调用需要在请求体中定义 `tools` 字段。每个工具由以下部分组成： + +```json +{ + "tools": [ + { + "name": "search_web", + "description": "Search function.", + "parameters": { + "properties": { + "query_list": { + "description": "Keywords for search, list should contain 1 element.", + "items": { "type": "string" }, + "type": "array" + }, + "query_tag": { + "description": "Category of query", + "items": { "type": "string" }, + "type": "array" + } + }, + "required": [ "query_list", "query_tag" ], + "type": "object" + } + } + ] +} +``` + +**字段说明：** +- `name`：函数名称 +- `description`：函数描述 +- `parameters`：函数参数定义 + - `properties`：参数属性定义，其中键是参数名称，值包含详细的参数描述 + - `required`：必需参数列表 + - `type`：参数类型（通常为 "object"） + +### 内部处理格式 + +在 MiniMax-M2 模型内部处理时，工具定义会被转换为特殊格式并连接到输入文本中。以下是一个完整示例： + +``` +]~!b[]~b]system +You are a helpful assistant. + +# Tools +You may call one or more tools to assist with the user query. +Here are the tools available in JSONSchema format: + + +{"name": "search_web", "description": "Search function.", "parameters": {"type": "object", "properties": {"query_list": {"type": "array", "items": {"type": "string"}, "description": "Keywords for search, list should contain 1 element."}, "query_tag": {"type": "array", "items": {"type": "string"}, "description": "Category of query"}}, "required": ["query_list", "query_tag"]}} + + +When making tool calls, use XML format to invoke tools and pass parameters: + + + +param-value-1 +param-value-2 +... + +[e~[ +]~b]user +When were the latest announcements from OpenAI and Gemini?[e~[ +]~b]ai + +``` + +**格式说明：** + +- `]~!b[]~b]system`：系统消息开始标记 +- `[e~[`：消息结束标记 +- `]~b]user`：用户消息开始标记 +- `]~b]ai`：助手消息开始标记 +- `]~b]tool`：工具结果消息开始标记 +- `...`：工具定义区域，每个工具都用 `` 标签包装，内容为 JSON Schema +- `...`：工具调用区域 +- `...`：生成过程中的思考过程标记 + +### 模型输出格式 + +MiniMax-M2 使用结构化的 XML 标签格式： + +```xml + + +["technology", "events"] +["\"OpenAI\" \"latest\" \"release\""] + + +["technology", "events"] +["\"Gemini\" \"latest\" \"release\""] + + +``` + +每个工具调用使用 `` 标签，参数使用 `` 标签包装。 + +## 手动解析工具调用结果 + +### 解析工具调用 + +MiniMax-M2 使用结构化的 XML 标签，这需要一种不同的解析方法。核心函数如下： + +```python +import re +import json +from typing import Any, Optional, List, Dict + + +def extract_name(name_str: str) -> str: + """Extract name from quoted string""" + name_str = name_str.strip() + if name_str.startswith('"') and name_str.endswith('"'): + return name_str[1:-1] + elif name_str.startswith("'") and name_str.endswith("'"): + return name_str[1:-1] + return name_str + + +def convert_param_value(value: str, param_type: str) -> Any: + """Convert parameter value based on parameter type""" + if value.lower() == "null": + return None + + param_type = param_type.lower() + + if param_type in ["string", "str", "text"]: + return value + elif param_type in ["integer", "int"]: + try: + return int(value) + except (ValueError, TypeError): + return value + elif param_type in ["number", "float"]: + try: + val = float(value) + return val if val != int(val) else int(val) + except (ValueError, TypeError): + return value + elif param_type in ["boolean", "bool"]: + return value.lower() in ["true", "1"] + elif param_type in ["object", "array"]: + try: + return json.loads(value) + except json.JSONDecodeError: + return value + else: + # Try JSON parsing, return string if failed + try: + return json.loads(value) + except json.JSONDecodeError: + return value + + +def parse_tool_calls(model_output: str, tools: Optional[List[Dict]] = None) -> List[Dict]: + """ + Extract all tool calls from model output + + Args: + model_output: Complete output text from the model + tools: Tool definition list for getting parameter type information, format can be: + - [{"name": "...", "parameters": {...}}] + - [{"type": "function", "function": {"name": "...", "parameters": {...}}}] + + Returns: + Parsed tool call list, each element contains name and arguments fields + + Example: + >>> tools = [{ + ... "name": "get_weather", + ... "parameters": { + ... "type": "object", + ... "properties": { + ... "location": {"type": "string"}, + ... "unit": {"type": "string"} + ... } + ... } + ... }] + >>> output = ''' + ... + ... San Francisco + ... celsius + ... + ... ''' + >>> result = parse_tool_calls(output, tools) + >>> print(result) + [{'name': 'get_weather', 'arguments': {'location': 'San Francisco', 'unit': 'celsius'}}] + """ + # Quick check if tool call marker is present + if "" not in model_output: + return [] + + tool_calls = [] + + try: + # Match all blocks + tool_call_regex = re.compile(r"(.*?)", re.DOTALL) + invoke_regex = re.compile(r"", re.DOTALL) + parameter_regex = re.compile(r"", re.DOTALL) + + # Iterate through all tool_call blocks + for tool_call_match in tool_call_regex.findall(model_output): + # Iterate through all invokes in this block + for invoke_match in invoke_regex.findall(tool_call_match): + # Extract function name + name_match = re.search(r'^([^>]+)', invoke_match) + if not name_match: + continue + + function_name = extract_name(name_match.group(1)) + + # Get parameter configuration + param_config = {} + if tools: + for tool in tools: + tool_name = tool.get("name") or tool.get("function", {}).get("name") + if tool_name == function_name: + params = tool.get("parameters") or tool.get("function", {}).get("parameters") + if isinstance(params, dict) and "properties" in params: + param_config = params["properties"] + break + + # Extract parameters + param_dict = {} + for match in parameter_regex.findall(invoke_match): + param_match = re.search(r'^([^>]+)>(.*)', match, re.DOTALL) + if param_match: + param_name = extract_name(param_match.group(1)) + param_value = param_match.group(2).strip() + + # Remove leading and trailing newlines + if param_value.startswith('\n'): + param_value = param_value[1:] + if param_value.endswith('\n'): + param_value = param_value[:-1] + + # Get parameter type and convert + param_type = "string" + if param_name in param_config: + if isinstance(param_config[param_name], dict) and "type" in param_config[param_name]: + param_type = param_config[param_name]["type"] + + param_dict[param_name] = convert_param_value(param_value, param_type) + + tool_calls.append({ + "name": function_name, + "arguments": param_dict + }) + + except Exception as e: + print(f"Failed to parse tool calls: {e}") + return [] + + return tool_calls +``` + +**使用示例：** + +```python +# Define tools +tools = [ + { + "name": "get_weather", + "parameters": { + "type": "object", + "properties": { + "location": {"type": "string"}, + "unit": {"type": "string"} + }, + "required": ["location", "unit"] + } + } +] + +# Model output +model_output = """Let me help you query the weather. + + +San Francisco +celsius + +""" + +# Parse tool calls +tool_calls = parse_tool_calls(model_output, tools) + +# Output results +for call in tool_calls: + print(f"Function called: {call['name']}") + print(f"Arguments: {call['arguments']}") + # Output: Function called: get_weather + # Arguments: {'location': 'San Francisco', 'unit': 'celsius'} +``` + +### 执行工具调用 + +完成解析后，您可以执行相应的工具并构造返回结果： + +```python +def execute_function_call(function_name: str, arguments: dict): + """Execute function call and return result""" + if function_name == "get_weather": + location = arguments.get("location", "Unknown location") + unit = arguments.get("unit", "celsius") + # Build function execution result + return { + "role": "tool", + "content": [ + { + "name": function_name, + "type": "text", + "text": json.dumps({ + "location": location, + "temperature": "25", + "unit": unit, + "weather": "Sunny" + }, ensure_ascii=False) + } + ] + } + elif function_name == "search_web": + query_list = arguments.get("query_list", []) + query_tag = arguments.get("query_tag", []) + # Simulate search results + return { + "role": "tool", + "content": [ + { + "name": function_name, + "type": "text", + "text": f"Search keywords: {query_list}, Category: {query_tag}\nSearch results: Relevant information found" + } + ] + } + + return None +``` + +### 将工具执行结果返回给模型 + +在成功解析工具调用后，您应该将工具执行结果添加到对话历史中，以便模型在后续交互中可以访问和利用这些信息。请参考 [chat_template.jinja](https://huggingface.co/MiniMaxAI/MiniMax-M2/blob/main/chat_template.jinja) 了解连接格式。 + +## 参考文献 + +- [MiniMax-M2 模型仓库](https://github.com/MiniMax-AI/MiniMax-M2) +- [vLLM 项目主页](https://github.com/vllm-project/vllm) +- [SGLang 项目主页](https://github.com/sgl-project/sglang) +- [OpenAI Python SDK](https://github.com/openai/openai-python) \ No newline at end of file diff --git a/docs/vllm_deploy_guide.md b/docs/vllm_deploy_guide.md index a6d3cf6..e738274 100644 --- a/docs/vllm_deploy_guide.md +++ b/docs/vllm_deploy_guide.md @@ -1,7 +1,17 @@ # MiniMax M2 Model vLLM Deployment Guide +[English Version](./vllm_deploy_guide.md) | [Chinese Version](./vllm_deploy_guide_cn.md) + We recommend using [vLLM](https://docs.vllm.ai/en/stable/) to deploy the [MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) model. vLLM is a high-performance inference engine with excellent serving throughput, efficient and intelligent memory management, powerful batch request processing capabilities, and deeply optimized underlying performance. We recommend reviewing vLLM's official documentation to check hardware compatibility before deployment. +## Applicable Models + +This document applies to the following models. You only need to change the model name during deployment. + +- [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) + +The deployment process is illustrated below using MiniMax-M2 as an example. + ## System Requirements - OS: Linux @@ -12,25 +22,22 @@ We recommend using [vLLM](https://docs.vllm.ai/en/stable/) to deploy the [MiniMa - compute capability 7.0 or higher - - Memory requirements: 220 GB for weights, 60 GB per 1M context tokens + - Memory requirements: 220 GB for weights, 240 GB per 1M context tokens The following are recommended configurations; actual requirements should be adjusted based on your use case: -- 4x 96GB GPUs: Supports context input of up to 400K tokens. +- 4x 96GB GPUs: Supported context length of up to 400K tokens. -- 8x 144GB GPUs: Supports context input of up to 3M tokens. +- 8x 144GB GPUs: Supported context length of up to 3M tokens. ## Deployment with Python -It is recommended to use a virtual environment (such as venv, conda, or uv) to avoid dependency conflicts. We recommend installing vLLM in a fresh Python environment: +It is recommended to use a virtual environment (such as **venv**, **conda**, or **uv**) to avoid dependency conflicts. + +We recommend installing vLLM in a fresh Python environment: ```bash -# Not yet released, please install nightly build -uv pip install -U vllm \ - --torch-backend=auto \ - --extra-index-url https://wheels.vllm.ai/nightly -# If released, install using uv -uv pip install "vllm" --torch-backend=auto +uv pip install 'triton-kernels @ git+https://github.com/triton-lang/triton.git@v3.5.0#subdirectory=python/triton_kernels' vllm --extra-index-url https://wheels.vllm.ai/nightly --prerelease=allow ``` Run the following command to start the vLLM server. vLLM will automatically download and cache the MiniMax-M2 model from Hugging Face. @@ -38,12 +45,21 @@ Run the following command to start the vLLM server. vLLM will automatically down 4-GPU deployment command: ```bash -SAFETENSORS_FAST_GPU=1 VLLM_USE_V1=0 vllm serve \ - --model MiniMaxAI/MiniMax-M2 \ - --trust-remote-code \ - --enable-expert-parallel --tensor-parallel-size 4 \ +SAFETENSORS_FAST_GPU=1 vllm serve \ + MiniMaxAI/MiniMax-M2 --trust-remote-code \ + --tensor-parallel-size 4 \ --enable-auto-tool-choice --tool-call-parser minimax_m2 \ - --reasoning-parser minimax_m2 + --reasoning-parser minimax_m2_append_think +``` + +8-GPU deployment command: + +```bash +SAFETENSORS_FAST_GPU=1 vllm serve \ + MiniMaxAI/MiniMax-M2 --trust-remote-code \ + --enable_expert_parallel --tensor-parallel-size 8 \ + --enable-auto-tool-choice --tool-call-parser minimax_m2 \ + --reasoning-parser minimax_m2_append_think ``` ## Testing Deployment @@ -76,11 +92,23 @@ export HF_ENDPOINT=https://hf-mirror.com This vLLM version is outdated. Please upgrade to the latest version. +### torch.AcceleratorError: CUDA error: an illegal memory access was encountered +Add `--compilation-config "{\"cudagraph_mode\": \"PIECEWISE\"}"` to the startup parameters to resolve this issue. For example: + +```bash +SAFETENSORS_FAST_GPU=1 vllm serve \ + MiniMaxAI/MiniMax-M2 --trust-remote-code \ + --enable_expert_parallel --tensor-parallel-size 8 \ + --enable-auto-tool-choice --tool-call-parser minimax_m2 \ + --reasoning-parser minimax_m2_append_think \ + --compilation-config "{\"cudagraph_mode\": \"PIECEWISE\"}" +``` + ## Getting Support If you encounter any issues while deploying the MiniMax model: -- Contact our technical support team through official channels such as email at api@minimaxi.com +- Contact our technical support team through official channels such as email at [model@minimax.io](mailto:model@minimax.io) - Submit an issue on our [GitHub](https://github.com/MiniMax-AI) repository diff --git a/docs/vllm_deploy_guide_cn.md b/docs/vllm_deploy_guide_cn.md index f41a06d..db76ed7 100644 --- a/docs/vllm_deploy_guide_cn.md +++ b/docs/vllm_deploy_guide_cn.md @@ -1,7 +1,17 @@ # MiniMax M2 模型 vLLM 部署指南 +[英文版](./vllm_deploy_guide.md) | [中文版](./vllm_deploy_guide_cn.md) + 我们推荐使用 [vLLM](https://docs.vllm.ai/en/stable/) 来部署 [MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) 模型。vLLM 是一个高性能的推理引擎，其具有卓越的服务吞吐、高效智能的内存管理机制、强大的批量请求处理能力、深度优化的底层性能等特性。我们建议在部署之前查看 vLLM 的官方文档以检查硬件兼容性。 +## 本文档适用模型 + +本文档适用以下模型，只需在部署时修改模型名称即可。 + +- [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2) + +以下以 MiniMax-M2 为例说明部署流程。 + ## 环境要求 - OS：Linux @@ -12,24 +22,21 @@ - compute capability 7.0 or higher - - 显存需求：权重需要 220 GB，每 1M 上下文 token 需要 60 GB + - 显存需求：权重需要 220 GB，每 1M 上下文 token 需要 240 GB 以下为推荐配置，实际需求请根据业务场景调整： -- 96G x4 GPU：支持 40 万 token 的上下文输入。 +- 96G x4 GPU：支持 40 万 token 的总上下文。 -- 144G x8 GPU：支持长达 300 万 token 的上下文输入。 +- 144G x8 GPU：支持长达 300 万 token 的总上下文。 ## 使用 Python 部署 -建议使用虚拟环境（如 venv、conda、uv）以避免依赖冲突。建议在全新的 Python 环境中安装 vLLM： +建议使用虚拟环境（如 **venv**、**conda**、**uv**）以避免依赖冲突。 + +建议在全新的 Python 环境中安装 vLLM： ```bash -# 尚未 release，请安装 nightly 构建 -uv pip install -U vllm \ - --torch-backend=auto \ - --extra-index-url https://wheels.vllm.ai/nightly -# 如果 release，使用 uv 安装 -uv pip install "vllm" --torch-backend=auto +uv pip install 'triton-kernels @ git+https://github.com/triton-lang/triton.git@v3.5.0#subdirectory=python/triton_kernels' vllm --extra-index-url https://wheels.vllm.ai/nightly --prerelease=allow ``` 运行如下命令启动 vLLM 服务器，vLLM 会自动从 Huggingface 下载并缓存 MiniMax-M2 模型。 @@ -37,12 +44,21 @@ uv pip install "vllm" --torch-backend=auto 4 卡部署命令： ```bash -SAFETENSORS_FAST_GPU=1 VLLM_USE_V1=0 vllm serve \ - --model MiniMaxAI/MiniMax-M2 \ - --trust-remote-code \ - --enable-expert-parallel --tensor-parallel-size 4 \ +SAFETENSORS_FAST_GPU=1 vllm serve \ + MiniMaxAI/MiniMax-M2 --trust-remote-code \ + --tensor-parallel-size 4 \ --enable-auto-tool-choice --tool-call-parser minimax_m2 \ - --reasoning-parser minimax_m2 + --reasoning-parser minimax_m2_append_think +``` + +8 卡部署命令： + +```bash +SAFETENSORS_FAST_GPU=1 vllm serve \ + MiniMaxAI/MiniMax-M2 --trust-remote-code \ + --enable_expert_parallel --tensor-parallel-size 8 \ + --enable-auto-tool-choice --tool-call-parser minimax_m2 \ + --reasoning-parser minimax_m2_append_think ``` ## 测试部署 @@ -75,11 +91,23 @@ export HF_ENDPOINT=https://hf-mirror.com 该 vLLM 版本过旧，请升级到最新版本。 +### torch.AcceleratorError: CUDA error: an illegal memory access was encountered +在启动参数添加 `--compilation-config "{\"cudagraph_mode\": \"PIECEWISE\"}"` 可以解决。例如： + +```bash +SAFETENSORS_FAST_GPU=1 vllm serve \ + MiniMaxAI/MiniMax-M2 --trust-remote-code \ + --enable_expert_parallel --tensor-parallel-size 8 \ + --enable-auto-tool-choice --tool-call-parser minimax_m2 \ + --reasoning-parser minimax_m2_append_think \ + --compilation-config "{\"cudagraph_mode\": \"PIECEWISE\"}" +``` + ## 获取支持 如果在部署 MiniMax 模型过程中遇到任何问题： -- 通过邮箱 api@minimaxi.com 等官方渠道联系我们的技术支持团队 +- 通过邮箱 [model@minimax.io](mailto:model@minimax.io) 等官方渠道联系我们的技术支持团队 - 在我们的 [GitHub](https://github.com/MiniMax-AI) 仓库提交 Issue 我们会持续优化模型的部署体验，欢迎反馈！ diff --git a/figures/Bench.png b/figures/Bench.png index 7e3bfc3..bdeafaf 100644 Binary files a/figures/Bench.png and b/figures/Bench.png differ