Add files using upload-large-folder tool

.gitattributes

@@ -35,3 +35,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 model.safetensors.index.json filter=lfs diff=lfs merge=lfs -text
 figures/agent_teams.gif filter=lfs diff=lfs merge=lfs -text
+figures/banner.png filter=lfs diff=lfs merge=lfs -text
+figures/mle_bench.png filter=lfs diff=lfs merge=lfs -text
+figures/agent_harness.png filter=lfs diff=lfs merge=lfs -text

chat_template.jinja

@@ -0,0 +1,159 @@
+{# ----------‑‑‑ special token variables ‑‑‑---------- #}
+{%- set toolcall_begin_token   = '<minimax:tool_call>'         -%}
+{%- set toolcall_end_token     = '</minimax:tool_call>'        -%}
+{#- Tool Rendering Functions ============================================== -#}
+{%- macro render_tool_namespace(namespace_name, tool_list) -%}
+{%- for tool in tool_list -%}
+<tool>{{ tool.function | tojson(ensure_ascii=False) }}</tool>
+{% endfor -%}
+{%- endmacro -%}
+{%- macro visible_text(content) -%}
+    {%- if content is string -%}
+        {{ content }}
+    {%- elif content is iterable and content is not mapping -%}
+        {%- for item in content -%}
+            {%- if item is mapping and item.type == 'text' -%}
+                {{- item.text }}
+            {%- elif item is string -%}
+                {{- item }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{- content }}
+    {%- endif -%}
+{%- endmacro -%}
+{#- System Message Construction ============================================ -#}
+{%- macro build_system_message(system_message) -%}
+    {%- if system_message and system_message.content -%}
+        {{- visible_text(system_message.content) }}
+    {%- else -%}
+        {%- if model_identity is not defined -%}
+            {%- set model_identity = "You are a helpful assistant. Your name is MiniMax-M2.7 and is built by MiniMax." -%}
+        {%- endif -%}
+        {{- model_identity }}
+    {%- endif -%}
+    
+    {#- Handle current_date -#}
+    {%- if system_message and system_message.current_date -%}
+        {{- '\n' ~ 'Current date: ' + system_message.current_date }}
+    {%- endif -%}
+    {#- Handle current_location -#}
+    {%- if system_message and system_message.current_location -%}
+        {{- '\n' ~ 'Current location: ' + system_message.current_location }}
+    {%- endif -%}
+{%- endmacro -%}
+{#- Main Template Logic ================================================= -#}
+{#- Extract system message (only first message if it's system) -#}
+{%- set system_message = none -%}
+{%- set conversation_messages = messages -%}
+{%- if messages and messages[0].role == "system" -%}
+    {%- set system_message = messages[0] -%}
+    {%- set conversation_messages = messages[1:] -%}
+{%- endif -%}
+{#- Get the last user message turn, for interleved thinking -#}
+{%- set ns = namespace(last_user_index=-1) %}
+{% for m in conversation_messages %}
+    {%- if m.role == 'user' %}
+        {% set ns.last_user_index = loop.index0 -%}
+    {%- endif %}
+{%- endfor %}
+{#- Render system message -#}
+{{- ']~!b[' ~ ']~b]system' ~ '\n' }}
+{{- build_system_message(system_message) }}
+{#- Render tools if available -#}
+{%- if tools -%}
+    {{- '\n\n' ~ '# Tools' ~ '\n' ~ 'You may call one or more tools to assist with the user query.\nHere are the tools available in JSONSchema format:' ~ '\n' }}
+    {{- '\n' ~ '<tools>' ~ '\n' }}
+    {{- render_tool_namespace("functions", tools) }}
+    {{- '</tools>' ~ '\n\n' }}
+{{- 'When making tool calls, use XML format to invoke tools and pass parameters:' ~ '\n' }}
+{{- '\n' ~ toolcall_begin_token }}
+<invoke name="tool-name-1">
+<parameter name="param-key-1">param-value-1</parameter>
+<parameter name="param-key-2">param-value-2</parameter>
+...
+</invoke>
+{{- '\n' ~ toolcall_end_token }}
+{%- endif -%}
+{{- '[e~[\n' }}
+
+{#- Render messages -#}
+{%- set last_tool_call = namespace(name=none) -%}
+{%- for message in conversation_messages -%}
+    {%- if message.role == 'assistant' -%}
+        {#- Only render reasoning_content if no user message follows -#}
+        {{- ']~b]ai' ~ '\n' }}
+
+        {%- set reasoning_content = '' %}
+        {%- set content = visible_text(message.content) %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].strip('\n').split('<think>')[-1].strip('\n') %}
+                {%- set content = content.split('</think>')[-1].strip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if reasoning_content and loop.index0 > ns.last_user_index -%}
+            {{- '<think>' ~ '\n' ~ reasoning_content ~ '\n' ~ '</think>' ~ '\n\n' }}
+        {%- endif -%}
+        {%- if content -%}
+            {{- content }}
+        {%- endif -%}
+        {%- if message.tool_calls -%}
+            {{- '\n' ~ toolcall_begin_token ~ '\n' }}
+
+            {%- for tool_call in message.tool_calls -%}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<invoke name="' + tool_call.name + '">' }}
+                {% set _args = tool_call.arguments %}
+                {%- for k, v in _args.items() %}
+                {{- '<parameter name="' + k + '">' }}
+                {{- v | tojson(ensure_ascii=False) if v is not string else v }}
+                {{- '</parameter>' }}
+                {% endfor %}
+                {{- '</invoke>' ~ '\n' }}
+            {%- endfor -%}
+            
+            {{- toolcall_end_token}}
+            {%- set last_tool_call.name = message.tool_calls[-1].name -%}
+        {%- else -%}
+            {%- set last_tool_call.name = none -%}
+        {%- endif -%}
+        {{- '[e~[' ~ '\n' }}
+        
+    {%- elif message.role == 'tool' -%}
+    {%- if last_tool_call.name is none -%}
+        {{- raise_exception("Message has tool role, but there was no previous assistant message with a tool call!") }}
+    {%- endif -%}
+    {%- if loop.first or (conversation_messages[loop.index0 - 1].role != 'tool') -%}
+        {{- ']~b]tool' }}
+    {%- endif -%}
+    {%- if message.content is string -%}
+        {{- '\n<response>' }}
+        {{- message.content }}
+        {{- '</response>' }}
+    {%- else -%}
+        {%- for tr in message.content -%}
+            {{- '\n<response>' }}
+            {{- tr.output if tr.output is defined else (tr.text if tr.type == 'text' and tr.text is defined else tr) }}
+            {{- '\n</response>' }}
+        {%- endfor -%}
+    {%- endif -%}
+    {%- if loop.last or (conversation_messages[loop.index0 + 1].role != 'tool') -%}
+        {{- '[e~[\n' -}}
+    {%- endif -%}
+        
+    {%- elif message.role == 'user' -%}
+        {{- ']~b]user' ~ '\n' }}
+        {{- visible_text(message.content) }}
+        {{- '[e~[' ~ '\n' }}
+    {%- endif -%}
+{%- endfor -%}
+
+{#- Generation prompt -#}
+{%- if add_generation_prompt -%}
+{{- ']~b]ai' ~ '\n' ~ '<think>' ~ '\n' }}
+{%- endif -%}

config.json

@@ -0,0 +1,113 @@
+{
+  "name_or_path": "tclf90/MiniMax-M2.7-AWQ",
+  "architectures": [
+    "MiniMaxM2ForCausalLM"
+  ],
+  "attn_type_list": [
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1,
+    1
+  ],
+  "auto_map": {
+    "AutoConfig": "configuration_minimax_m2.MiniMaxM2Config",
+    "AutoModelForCausalLM": "modeling_minimax_m2.MiniMaxM2ForCausalLM"
+  },
+  "dtype": "float16",
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 3072,
+  "intermediate_size": 1536,
+  "max_position_embeddings": 196608,
+  "model_type": "minimax_m2",
+  "mtp_transformer_layers": 1,
+  "num_attention_heads": 48,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 62,
+  "num_key_value_heads": 8,
+  "num_local_experts": 256,
+  "num_mtp_modules": 3,
+  "qk_norm_type": "per_layer",
+  "rms_norm_eps": 1e-06,
+  "rope_theta": 5000000,
+  "rotary_dim": 64,
+  "scoring_func": "sigmoid",
+  "shared_intermediate_size": 0,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.46.1",
+  "use_cache": true,
+  "use_mtp": true,
+  "use_qk_norm": true,
+  "use_routing_bias": true,
+  "vocab_size": 200064,
+  "quantization_config": {
+    "quant_method": "awq",
+    "bits": 4,
+    "group_size": 128,
+    "version": "gemm",
+    "zero_point": true,
+    "modules_to_not_convert": [
+      "self_attn",
+      "block_sparse_moe.gate",
+      "model.layers.0."
+    ]
+  }
+}

docs/sglang_deploy_guide.md

@@ -0,0 +1,112 @@
+# MiniMax M2.7 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-M2.7](https://huggingface.co/MiniMaxAI/MiniMax-M2.7) 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-M2.7](https://huggingface.co/MiniMaxAI/MiniMax-M2.7)
+- [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5)
+- [MiniMaxAI/MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1)
+- [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2)
+
+The deployment process is illustrated below using MiniMax-M2.7 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:
+
+- **96G x4** GPU: Supports a total KV Cache capacity of 400K tokens.
+
+- **144G x8** GPU: Supports a total KV Cache capacity of up to 3M tokens.
+
+> **Note**: The values above represent the total aggregate hardware KV Cache capacity. The maximum context length per individual sequence remains **196K** 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
+uv venv
+source .venv/bin/activate
+uv pip install sglang
+```
+
+Run the following command to start the SGLang server. SGLang will automatically download and cache the MiniMax-M2.7 model from Hugging Face.
+
+4-GPU deployment command:
+
+```bash
+python -m sglang.launch_server \
+    --model-path MiniMaxAI/MiniMax-M2.7 \
+    --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-M2.7 \
+    --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-M2.7",
+        "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
+
+### MiniMax-M2 model is not currently supported
+
+Please upgrade to the latest stable version, >= v0.5.4.post1.
+
+## 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!
+

docs/tool_calling_guide.md

@@ -0,0 +1,487 @@
+# MiniMax-M2.7 Tool Calling Guide
+
+[English Version](./tool_calling_guide.md) | [Chinese Version](./tool_calling_guide_cn.md)
+
+MiniMax-M2.7 supports the same toolcall syntax as MiniMax-M2.
+
+## Introduction
+
+The MiniMax-M2.7 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-M2.7.
+
+## 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-M2.7, 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-M2.7",
+    "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-M2.7 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:
+
+<tools>
+<tool>{"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"]}}</tool>
+</tools>
+
+When making tool calls, use XML format to invoke tools and pass parameters:
+
+<minimax:tool_call>
+<invoke name="tool-name-1">
+<parameter name="param-key-1">param-value-1</parameter>
+<parameter name="param-key-2">param-value-2</parameter>
+...
+</invoke>
+[e~[
+]~b]user
+When were the latest announcements from OpenAI and Gemini?[e~[
+]~b]ai
+<think>
+```
+
+**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
+- `<tools>...</tools>`: Tool definition area, each tool is wrapped with `<tool>` tag, content is JSON Schema
+- `<minimax:tool_call>...</minimax:tool_call>`: Tool call area
+- `<think>...</think>`: Thinking process marker during generation
+
+### Model Output Format
+
+MiniMax-M2.7 uses structured XML tag format:
+
+```xml
+<minimax:tool_call>
+<invoke name="search_web">
+<parameter name="query_tag">["technology", "events"]</parameter>
+<parameter name="query_list">["\"OpenAI\" \"latest\" \"release\""]</parameter>
+</invoke>
+<invoke name="search_web">
+<parameter name="query_tag">["technology", "events"]</parameter>
+<parameter name="query_list">["\"Gemini\" \"latest\" \"release\""]</parameter>
+</invoke>
+</minimax:tool_call>
+```
+
+Each tool call uses the `<invoke name="function_name">` tag, and parameters use the `<parameter name="parameter_name">` tag wrapper.
+
+## Manually Parsing Tool Call Results
+
+### Parsing Tool Calls
+
+MiniMax-M2.7 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 = '''<minimax:tool_call>
+        ... <invoke name="get_weather">
+        ... <parameter name="location">San Francisco</parameter>
+        ... <parameter name="unit">celsius</parameter>
+        ... </invoke>
+        ... </minimax:tool_call>'''
+        >>> 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 "<minimax:tool_call>" not in model_output:
+        return []
+    
+    tool_calls = []
+    
+    try:
+        # Match all <minimax:tool_call> blocks
+        tool_call_regex = re.compile(r"<minimax:tool_call>(.*?)</minimax:tool_call>", re.DOTALL)
+        invoke_regex = re.compile(r"<invoke name=(.*?)</invoke>", re.DOTALL)
+        parameter_regex = re.compile(r"<parameter name=(.*?)</parameter>", 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.
+<minimax:tool_call>
+<invoke name="get_weather">
+<parameter name="location">San Francisco</parameter>
+<parameter name="unit">celsius</parameter>
+</invoke>
+</minimax:tool_call>"""
+
+# 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-M2.7/blob/main/chat_template.jinja) for concatenation format.
+
+## References
+
+- [MiniMax-M2.7 Model Repository](https://github.com/MiniMax-AI/MiniMax-M2.7)
+- [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)

docs/vllm_deploy_guide_cn.md

@@ -0,0 +1,128 @@
+# MiniMax M2.7 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-M2.7](https://huggingface.co/MiniMaxAI/MiniMax-M2.7) 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-M2.7](https://huggingface.co/MiniMaxAI/MiniMax-M2.7)
+- [MiniMaxAI/MiniMax-M2.5](https://huggingface.co/MiniMaxAI/MiniMax-M2.5)
+- [MiniMaxAI/MiniMax-M2.1](https://huggingface.co/MiniMaxAI/MiniMax-M2.1)
+- [MiniMaxAI/MiniMax-M2](https://huggingface.co/MiniMaxAI/MiniMax-M2)
+
+以下以 MiniMax-M2.7 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**：总 KV Cache 容量支持 40 万 token。
+
+- **144G x8 GPU**：总 KV Cache 容量支持高达 300 万 token。
+
+> **注**：以上数值为硬件支持的最大并发缓存总量，模型单序列（Single Sequence）长度上限仍为 196k。
+
+## 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：
+
+```bash
+uv venv
+source .venv/bin/activate
+uv pip install vllm --torch-backend=auto
+```
+
+运行如下命令启动 vLLM 服务器，vLLM 会自动从 Huggingface 下载并缓存 MiniMax-M2.7 模型。
+
+4 卡部署命令：
+
+```bash
+SAFETENSORS_FAST_GPU=1 vllm serve \
+    MiniMaxAI/MiniMax-M2.7 --trust-remote-code \
+    --tensor-parallel-size 4 \
+    --enable-auto-tool-choice --tool-call-parser minimax_m2 \
+    --reasoning-parser minimax_m2_append_think
+```
+
+8 卡部署命令：
+
+```bash
+SAFETENSORS_FAST_GPU=1 vllm serve \
+    MiniMaxAI/MiniMax-M2.7 --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
+
+启动后，可以通过如下命令测试 vLLM OpenAI 兼容接口：
+
+```bash
+curl http://localhost:8000/v1/chat/completions \
+    -H "Content-Type: application/json" \
+    -d '{
+        "model": "MiniMaxAI/MiniMax-M2.7",
+        "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
+
+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-M2.7 --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\"}"
+```
+
+### 模型输出乱码
+
+如果您在使用 vLLM 运行这些模型时遇到输出乱码，可以升级到最新版本（请至少确保版本在提交 [cf3eacfe58fa9e745c2854782ada884a9f992cf7](https://github.com/vllm-project/vllm/commit/cf3eacfe58fa9e745c2854782ada884a9f992cf7) 之后）。
+
+## 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
+
+- 通过我们的 [官方企业微信交流群](https://github.com/MiniMax-AI/MiniMax-AI.github.io/blob/main/images/wechat-qrcode.jpeg) 反馈
+
+我们会持续优化模型的部署体验，欢迎反馈！

model-00039-of-00044.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:70f22a75ed40f37ef5d3ab3b3c003e735c72b088f3834022795856cb9d5d9481
+size 2999141944

model-00042-of-00044.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d4e7feb101da477ae04dc237b5d47b52c748886206c8867ed7ba68bd4b9d16c2
+size 2999679088

tokenizer_config.json

@@ -0,0 +1,495 @@
+{
+  "added_tokens_decoder": {
+  "200000": {
+      "content": "]!p~[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200001": {
+      "content": "<fim_prefix>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200002": {
+      "content": "<fim_middle>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200003": {
+      "content": "<fim_suffix>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200004": {
+      "content": "<fim_pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200005": {
+      "content": "<reponame>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200006": {
+      "content": "<filename>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200007": {
+      "content": "<gh_stars>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200008": {
+      "content": "<issue_start>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200009": {
+      "content": "<issue_comment>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200010": {
+      "content": "<issue_closed>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200011": {
+    "content": "<jupyter_start>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false,
+    "special": true
+  },
+  "200012": {
+      "content": "<jupyter_text>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200013": {
+      "content": "<jupyter_code>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200014": {
+      "content": "<jupyter_output>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200015": {
+    "content": "<empty_output>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false,
+    "special": true
+  },
+  "200016": {
+      "content": "<commit_before>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200017": {
+      "content": "<commit_msg>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200018": {
+      "content": "<commit_after>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200019": {
+      "content": "]~b]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200020": {
+      "content": "[e~[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200021": {
+      "content": "]!d~[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200022": {
+      "content": "<function_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200023": {
+      "content": "<code_interpreter>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200024": {
+      "content": "]<]speech[>[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200025": {
+      "content": "]<]image[>[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200026": {
+      "content": "]<]video[>[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200027": {
+      "content": "]<]start of speech[>[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200028": {
+      "content": "]<]end of speech[>[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200029": {
+      "content": "]<]start of image[>[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200030": {
+      "content": "]<]end of image[>[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200031": {
+      "content": "]<]start of video[>[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200032": {
+      "content": "]<]end of video[>[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200033": {
+      "content": "]<]vision pad[>[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200034": {
+      "content": "]~!b[",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200035": {
+      "content": "<jupyter_error>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200036": {
+      "content": "<add_file>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+  },
+  "200037": {
+      "content": "<delete_file>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200038": {
+      "content": "<rename_file>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200039": {
+      "content": "<edit_file>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200040": {
+      "content": "<commit_message>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200041": {
+      "content": "<empty_source_file>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200042": {
+      "content": "<repo_struct>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+  },
+  "200043": {
+    "content": "<code_context>",
+    "single_word": false,
+    "lstrip": false,
+    "rstrip": false,
+    "normalized": false,
+    "special": true
+  },
+  "200044": {
+    "content": "<file_content>",
+    "single_word": false,
+    "lstrip": false,
+    "rstrip": false,
+    "normalized": false,
+    "special": true
+  },
+  "200045": {
+    "content": "<source_files>",
+    "single_word": false,
+    "lstrip": false,
+    "rstrip": false,
+    "normalized": false,
+    "special": true
+  },
+  "200046": {
+    "content": "<pr_start>",
+    "single_word": false,
+    "lstrip": false,
+    "rstrip": false,
+    "normalized": false,
+    "special": true
+  },
+  "200047": {
+    "content": "<review_comment>",
+    "single_word": false,
+    "lstrip": false,
+    "rstrip": false,
+    "normalized": false,
+    "special": true
+  },
+  "200048": {
+    "content": "<filepath>",
+    "single_word": false,
+    "lstrip": false,
+    "rstrip": false,
+    "normalized": false,
+    "special": true
+  },
+  "200049": {
+    "content": "<file_sep>",
+    "single_word": false,
+    "lstrip": false,
+    "rstrip": false,
+    "normalized": false,
+    "special": true
+  },
+  "200050": {
+    "content": "<think>",
+    "single_word": false,
+    "lstrip": false,
+    "rstrip": false,
+    "normalized": false,
+    "special": false
+  },
+  "200051": {
+    "content": "</think>",
+    "single_word": false,
+    "lstrip": false,
+    "rstrip": false,
+    "normalized": false,
+    "special": false
+  },
+  "200052": {
+    "content": "<minimax:tool_call>",
+    "single_word": false,
+    "lstrip": false,
+    "rstrip": false,
+    "normalized": false,
+    "special": false
+  },  
+  "200053": {
+    "content": "</minimax:tool_call>",
+    "single_word": false,
+    "lstrip": false,
+    "rstrip": false,
+    "normalized": false,
+    "special": false
+  }
+  },
+  "additional_special_tokens": [
+        "<code_interpreter>",
+        "<commit_after>",
+        "<commit_before>",
+        "<commit_msg>",
+        "<empty_output>",
+        "<filename>",
+        "<fim_middle>",
+        "<fim_pad>",
+        "<fim_prefix>",
+        "<fim_suffix>",
+        "<function_call>",
+        "<gh_stars>",
+        "]<]speech[>[",
+        "]<]image[>[",
+        "]<]video[>[",
+        "]<]start of speech[>[",
+        "]<]end of speech[>[",
+        "]<]start of image[>[",
+        "]<]end of image[>[",
+        "]<]start of video[>[",
+        "]<]end of video[>[",
+        "]<]vision pad[>[",
+        "]~!b[",
+        "<issue_closed>",
+        "<issue_comment>",
+        "<issue_start>",
+        "<jupyter_code>",
+        "<jupyter_output>",
+        "<jupyter_start>",
+        "<jupyter_text>",
+        "<reponame>",
+        "[e~[",
+        "]!d~[",
+        "]!p~[",
+        "]~b]",
+        "<jupyter_error>",
+        "<add_file>",
+        "<delete_file>",
+        "<rename_file>",
+        "<edit_file>",
+        "<commit_message>",
+        "<empty_source_file>",
+        "<repo_struct>",
+        "<code_context>",
+        "<file_content>",
+        "<source_files>",
+        "<pr_start>",
+        "<review_comment>",
+        "<filepath>",
+        "<file_sep>"
+    ],
+  "add_prefix_space": false,
+  "bos_token": "]~!b[",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "[e~[",
+  "model_max_length": 40960000,
+  "tokenizer_class": "GPT2Tokenizer",
+  "unk_token": "]!d~["
+}