Skip to content

GitLab

"model/models/mistral3/model_text.go" did not exist on "854a9195f351ffe7c8aaaad34d19022144963e51"
Commit 688d8492 authored by chenych's avatar chenych
Browse files

Update minimax-m2.1 tool call

parent 9d2097be
Hide whitespace changes
Inline Side-by-side
Showing with 1075 additions and 118 deletions
README.md
View file @ 688d8492
......@@ -38,6 +38,12 @@ docker run -it --shm-size 60g --network=host --name minimax_m2 --privileged --de
更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装。
vllm文件替换
```bash
# /path/of/vllm 可以通过 "pip show vllm" 中的 "Location" 字段来获取环境中vllm所在目录
cp codes/minimax_m2_tool_parser.py /path/of/vllm/entrypoints/openai/tool_parsers/minimax_m2_tool_parser.py
cp codes/minimax_m2_reasoning_parser.py /path/of/vllm/reasoning/minimax_m2_reasoning_parser.py
```
## 数据集
暂无
......@@ -71,11 +77,10 @@ cp /path/of/MiniMax/MiniMax-M2/vocab.json /path/of/MiniMax/MiniMax-M2-bf16
### vllm
#### 单机推理
- MiniMax-M2
```bash
## serve启动
export ALLREDUCE_STREAM_WITH_COMPUTE=1
export VLLM_MLA_DISABLE=0
export VLLM_USE_FLASH_MLA=1
vllm serve /path/of/MiniMax/MiniMax-M2-bf16/ \
--trust-remote-code \
......@@ -98,6 +103,38 @@ curl http://localhost:8000/v1/chat/completions \
}'
```
- MiniMax-M2.1
```bash
## serve启动
vllm serve /path/of/MiniMax/MiniMax-M2.1-bf16 \
--trust-remote-code \
--served-model-name minimax-m2.1 \
--max-model-len 32768 \
--dtype bfloat16 \
-tp 8 \
--port 8001 \
--enable-auto-tool-choice \
--tool-call-parser minimax-m2 \
--enable-expert-parallel \
--reasoning-parser minimax_m2
## client访问
curl http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "minimax-m2.1",
"messages": [
{
"role": "user",
"content": "牛顿提出了哪三大运动定律？请简要说明。"
}
]
}'
## 离线工具调用
python offline_tools.py
```
## 效果展示
- MiniMax-M2 模型效果
<div align=center>
......@@ -105,11 +142,15 @@ curl http://localhost:8000/v1/chat/completions \
</div>
- MiniMax-M2.1 模型效果
1. 对话
<div align=center>
<img src="./doc/result-minimax-m2_1.png"/>
</div>
2. 离线工具调用
<div align=center>
<img src="./doc/results-minimax-m2_1-tool.png"/>
</div>
### 精度
DCU与GPU精度一致，推理框架：vllm。
......@@ -117,8 +158,8 @@ DCU与GPU精度一致，推理框架：vllm。
## 预训练权重
| 模型名称 | 权重大小 | DCU型号 | 最低卡数需求 |下载地址|
|:-----:|:----------:|:----------:|:---------------------:|:----------:|
| MiniMax-M2 | 230 B | K100AI | 8 | [下载地址](https://huggingface.co/MiniMaxAI/MiniMax-M2) |
| MiniMax-M2.1 | 230 B | K100AI | 8 | [下载地址](https://www.modelscope.cn/models/MiniMax/MiniMax-M2.1) |
| MiniMax-M2 | 230 B | K100AI,BW1000 | 8 | [下载地址](https://huggingface.co/MiniMaxAI/MiniMax-M2) |
| MiniMax-M2.1 | 230 B | K100AI,BW1000 | 8 | [下载地址](https://www.modelscope.cn/models/MiniMax/MiniMax-M2.1) |
## 源码仓库及问题反馈
- https://developer.sourcefind.cn/codes/modelzoo/minimax-m2_vllm
......
codes/minimax_m2_reasoning_parser.py 0 → 100644
View file @ 688d8492
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
from collections.abc import Sequence
from vllm.entrypoints.openai.protocol import (
ChatCompletionRequest,
DeltaMessage,
ResponsesRequest,
)
from vllm.logger import init_logger
from vllm.reasoning import ReasoningParser, ReasoningParserManager
from vllm.reasoning.basic_parsers import BaseThinkingReasoningParser
from vllm.transformers_utils.tokenizer import AnyTokenizer
logger = init_logger(__name__)
@ReasoningParserManager.register_module("minimax_m2")
class MiniMaxM2ReasoningParser(BaseThinkingReasoningParser):
"""
Reasoning parser for MiniMax M2 model.
MiniMax M2 models don't generate <think> start token, only </think> end
token. All content before </think> is reasoning, content after is the
actual response.
"""
@property
def start_token(self) -> str:
"""The token that starts reasoning content."""
return "<think>"
@property
def end_token(self) -> str:
"""The token that ends reasoning content."""
return "</think>"
def extract_reasoning_streaming(
self,
previous_text: str,
current_text: str,
delta_text: str,
previous_token_ids: Sequence[int],
current_token_ids: Sequence[int],
delta_token_ids: Sequence[int],
) -> DeltaMessage | None:
"""
Extract reasoning content from a delta message for streaming.
MiniMax M2 models don't generate <think> start token, so we assume
all content is reasoning until we encounter the </think> end token.
"""
# Skip single end token
if len(delta_token_ids) == 1 and delta_token_ids[0] == self.end_token_id:
return None
# Check if end token has already appeared in previous tokens
# meaning we're past the reasoning phase
if self.end_token_id in previous_token_ids:
# We're past the reasoning phase, this is content
return DeltaMessage(content=delta_text)
# Check if end token is in delta tokens
if self.end_token_id in delta_token_ids:
# End token in delta, split reasoning and content
end_index = delta_text.find(self.end_token)
reasoning = delta_text[:end_index]
content = delta_text[end_index + len(self.end_token) :]
return DeltaMessage(
reasoning=reasoning if reasoning else None,
content=content if content else None,
)
# No end token yet, all content is reasoning
return DeltaMessage(reasoning=delta_text)
class MiniMaxM2AppendThinkReasoningParser(ReasoningParser):
"""
Reasoning parser for MiniMax M2 model.
"""
def __init__(self, tokenizer: AnyTokenizer, *args, **kwargs):
super().__init__(tokenizer, *args, **kwargs)
self.end_token_id = self.vocab.get("</think>")
def is_reasoning_end(self, input_ids: Sequence[int]) -> bool:
end_token_id = self.end_token_id
return any(input_id == end_token_id for input_id in reversed(input_ids))
def extract_content_ids(self, input_ids: list[int]) -> list[int]:
return input_ids
def extract_reasoning_streaming(
self,
previous_text: str,
current_text: str,
delta_text: str,
previous_token_ids: Sequence[int],
current_token_ids: Sequence[int],
delta_token_ids: Sequence[int],
) -> DeltaMessage | None:
if len(previous_token_ids) == 0:
delta_text = "<think>" + delta_text
return DeltaMessage(content=delta_text)
def extract_reasoning(
self, model_output: str, request: ChatCompletionRequest | ResponsesRequest
) -> tuple[str | None, str | None]:
return None, "<think>" + model_output
\ No newline at end of file
codes/minimax_m2_tool_parser.py 0 → 100644
View file @ 688d8492
# SPDX-License-Identifier: Apache-2.0
# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
import json
import uuid
from collections.abc import Sequence
from typing import Any
import regex as re
from vllm.entrypoints.openai.protocol import (
ChatCompletionRequest,
DeltaFunctionCall,
DeltaMessage,
DeltaToolCall,
ExtractedToolCallInformation,
FunctionCall,
ToolCall,
)
from vllm.entrypoints.openai.tool_parsers.abstract_tool_parser import (
ToolParser,
ToolParserManager
)
from vllm.logger import init_logger
from vllm.transformers_utils.tokenizer import AnyTokenizer
logger = init_logger(__name__)
@ToolParserManager.register_module("minimax-m2")
class MinimaxM2ToolParser(ToolParser):
def __init__(self, tokenizer: AnyTokenizer):
super().__init__(tokenizer)
self.prev_tool_call_arr: list[dict] = []
# Sentinel tokens
self.tool_call_start_token: str = "<minimax:tool_call>"
self.tool_call_end_token: str = "</minimax:tool_call>"
self.invoke_start_prefix: str = "<invoke name="
self.invoke_end_token: str = "</invoke>"
self.parameter_prefix: str = "<parameter name="
self.parameter_end_token: str = "</parameter>"
# Streaming state variables
self.current_tool_name_sent: bool = False
# Override base class type - we use string IDs for tool calls
self.current_tool_id: str | None = None # type: ignore
self.streamed_args_for_tool: list[str] = []
self.is_tool_call_started: bool = False
self.failed_count: int = 0
# Initialize streaming state variables
self.current_tool_index: int = 0
self.invoke_index: int = 0
self.header_sent: bool = False
self.current_function_name: str | None = None
self.current_param_name: str | None = None
self.current_param_value: str = ""
self.param_count: int = 0
self.in_param: bool = False
self.in_function: bool = False
self.accumulated_text: str = ""
self.json_started: bool = False
self.json_closed: bool = False
self.accumulated_params: dict = {}
self.streaming_request: ChatCompletionRequest | None = None
# Enhanced streaming state - reset for each new message
self._reset_streaming_state()
# Regex patterns for complete parsing
self.tool_call_complete_regex = re.compile(
r"<minimax:tool_call>(.*?)</minimax:tool_call>", re.DOTALL
)
# Improved regex: capture only the name attribute value (quoted or unquoted)
# and ignore any additional attributes that may follow
self.invoke_complete_regex = re.compile(
r"""
<invoke\s+name= # Match tag start and name attribute key
( # Start Group 1: Name value
"[^"]+" # Double-quoted string
| # OR
'[^']+' # Single-quoted string
| # OR
[^\s>]+ # Unquoted value (no whitespace or >)
) # End Group 1
(?:\s+[^>]*)? # Optional: Extra attributes (ignored)
\s*> # Closing bracket of opening tag
(.*?) # Group 2: Content (non-greedy)
</invoke> # Closing tag
""",
re.VERBOSE | re.DOTALL,
)
# Improved regex for parameters: capture name attribute and content separately
# Handles cases where model may include description text in attributes
self.parameter_complete_regex = re.compile(
r"""
<parameter\s+name= # Match tag start and name attribute key
( # Start Group 1: Name value
"[^"]+" # Double-quoted string
| # OR
'[^']+' # Single-quoted string
| # OR
[^\s>]+ # Unquoted value (no whitespace or >)
) # End Group 1
(?:\s+[^>]*)? # Optional: Extra attributes (ignored)
\s*> # Closing bracket of opening tag
(.*?) # Group 2: Content (non-greedy)
</parameter> # Closing tag
""",
re.VERBOSE | re.DOTALL,
)
if not self.model_tokenizer:
raise ValueError(
"The model tokenizer must be passed to the ToolParser "
"constructor during construction."
)
self.tool_call_start_token_id = self.vocab.get(self.tool_call_start_token)
self.tool_call_end_token_id = self.vocab.get(self.tool_call_end_token)
if self.tool_call_start_token_id is None or self.tool_call_end_token_id is None:
raise RuntimeError(
"MiniMax M2 Tool parser could not locate tool call start/end "
"tokens in the tokenizer!"
)
logger.debug(
"vLLM Successfully import tool parser %s !", self.__class__.__name__
)
def _generate_tool_call_id(self) -> str:
"""Generate a unique tool call ID."""
return f"call_{uuid.uuid4().hex[:24]}"
def _reset_streaming_state(self):
"""Reset all streaming state."""
self.current_tool_index = 0
self.invoke_index = 0
self.is_tool_call_started = False
self.header_sent = False
self.current_tool_id = None
self.current_function_name = None
self.current_param_name = None
self.current_param_value = ""
self.param_count = 0
self.in_param = False
self.in_function = False
self.accumulated_text = ""
self.json_started = False
self.json_closed = False
# Store accumulated parameters for type conversion
self.accumulated_params = {}
self.streaming_request = None
# Clear previous tool call history to avoid state pollution
self.prev_tool_call_arr.clear()
# Reset streamed args tracking
self.streamed_args_for_tool.clear()
def _extract_name(self, name_str: str) -> str:
"""Extract name from quoted string."""
name_str = name_str.strip()
if (
name_str.startswith('"')
and name_str.endswith('"')
or name_str.startswith("'")
and name_str.endswith("'")
):
return name_str[1:-1]
return name_str
def _parse_name_from_attributes(self, attr_section: str) -> str:
"""Helper to extract name from attribute section string.
Handles quoted and unquoted names, ignoring extra attributes."""
# Check for quoted name first
if attr_section.startswith('"'):
# Find closing quote
close_quote = attr_section.find('"', 1)
if close_quote != -1:
name_raw = attr_section[: close_quote + 1]
else:
name_raw = attr_section
elif attr_section.startswith("'"):
# Find closing single quote
close_quote = attr_section.find("'", 1)
if close_quote != -1:
name_raw = attr_section[: close_quote + 1]
else:
name_raw = attr_section
else:
# Unquoted name - take until first whitespace
space_idx = -1
for i, c in enumerate(attr_section):
if c.isspace():
space_idx = i
break
name_raw = attr_section[:space_idx] if space_idx != -1 else attr_section
return self._extract_name(name_raw)
def _convert_param_value(self, value: str, param_type: str) -> Any:
"""Convert parameter value to the correct type (legacy single-type version)."""
return self._convert_param_value_with_types(value, [param_type])
def _extract_types_from_schema(self, schema: Any) -> list[str]:
"""
Extract all possible types from a JSON schema definition.
Handles anyOf, oneOf, allOf, type arrays, and enum fields.
Args:
schema: The JSON schema definition for a parameter
Returns:
List of type strings (e.g., ["string", "integer", "null"])
"""
if schema is None:
return ["string"]
if not isinstance(schema, dict):
return ["string"]
types: set[str] = set()
# Handle direct "type" field
if "type" in schema:
type_value = schema["type"]
if isinstance(type_value, str):
types.add(type_value)
elif isinstance(type_value, list):
for t in type_value:
if isinstance(t, str):
types.add(t)
# Handle enum - infer types from enum values
if "enum" in schema and isinstance(schema["enum"], list) and schema["enum"]:
for value in schema["enum"]:
if value is None:
types.add("null")
elif isinstance(value, bool):
types.add("boolean")
elif isinstance(value, int):
types.add("integer")
elif isinstance(value, float):
types.add("number")
elif isinstance(value, str):
types.add("string")
elif isinstance(value, list):
types.add("array")
elif isinstance(value, dict):
types.add("object")
# Handle anyOf, oneOf, allOf - recursively extract types
for choice_field in ("anyOf", "oneOf", "allOf"):
if choice_field in schema and isinstance(schema[choice_field], list):
for choice in schema[choice_field]:
extracted = self._extract_types_from_schema(choice)
types.update(extracted)
# If no types found, default to string
if not types:
return ["string"]
return list(types)
def _convert_param_value_with_types(
self, value: str, param_types: list[str]
) -> Any:
"""
Convert parameter value to the correct type based on a list of possible types.
Tries each type in order until one succeeds.
Args:
value: The string value to convert
param_types: List of possible type strings
Returns:
The converted value
"""
# Check if the VALUE itself indicates null (not just if null is allowed)
if value.lower() in ("null", "none", "nil"):
return None
# Normalize types
normalized_types = [t.lower() for t in param_types]
# Try each type in order of preference (most specific first, string as fallback)
# Priority: integer > number > boolean > object > array > string
type_priority = [
"integer",
"int",
"number",
"float",
"boolean",
"bool",
"object",
"array",
"string",
"str",
"text",
]
for param_type in type_priority:
if param_type not in normalized_types:
continue
if param_type in ["string", "str", "text"]:
return value
elif param_type in ["integer", "int"]:
try:
return int(value)
except (ValueError, TypeError):
continue
elif param_type in ["number", "float"]:
try:
val = float(value)
return val if val != int(val) else int(val)
except (ValueError, TypeError):
continue
elif param_type in ["boolean", "bool"]:
lower_val = value.lower().strip()
if lower_val in ["true", "1", "yes", "on"]:
return True
elif lower_val in ["false", "0", "no", "off"]:
return False
continue
elif param_type in ["object", "array"]:
try:
return json.loads(value)
except json.JSONDecodeError:
continue
# Fallback: try JSON parse, then return as string
try:
return json.loads(value)
except json.JSONDecodeError:
return value
def _get_param_types_from_config(
self, param_name: str, param_config: dict
) -> list[str]:
"""
Get parameter types from parameter configuration.
Handles anyOf, oneOf, allOf, and direct type definitions.
Args:
param_name: The name of the parameter
param_config: The properties dict from the tool schema
Returns:
List of type strings
"""
if param_name not in param_config:
return ["string"]
param_schema = param_config[param_name]
if not isinstance(param_schema, dict):
return ["string"]
return self._extract_types_from_schema(param_schema)
def _parse_single_invoke(
self, invoke_str: str, tools: list | None
) -> ToolCall | None:
"""Parse a single <invoke> block.
Args:
invoke_str: For legacy regex, this is the full content after
'<invoke name='. For new regex with groups, this is
a tuple of (name, content).
tools: List of available tools for type information.
Returns:
Parsed ToolCall or None if parsing fails.
"""
# Handle both old format (string) and new format (tuple from regex groups)
if isinstance(invoke_str, tuple):
# New regex format: (name_raw, content)
function_name = self._extract_name(invoke_str[0])
invoke_content = invoke_str[1] if len(invoke_str) > 1 else ""
else:
# Fallback for unexpected string input
# (should generally be tuple from regex)
# Try to extract similarly to tuple case
match = self.invoke_complete_regex.search(invoke_str)
if match:
function_name = self._extract_name(match.group(1))
invoke_content = match.group(2)
else:
# Basic fallback if regex doesn't match
name_match = re.search(r"^([^>]+)", invoke_str)
if not name_match:
return None
function_name = self._extract_name(name_match.group(1))
# Extract content after the closing '>'
content_match = re.search(r"^[^>]+>(.*)", invoke_str, re.DOTALL)
invoke_content = content_match.group(1) if content_match else ""
# Get parameter configuration
param_config = {}
if tools:
for tool in tools:
if (
hasattr(tool, "function")
and tool.function.name == function_name
and hasattr(tool.function, "parameters")
):
params = tool.function.parameters
if isinstance(params, dict) and "properties" in params:
param_config = params["properties"]
break
# Extract parameters using the improved regex
param_dict = {}
for match in self.parameter_complete_regex.findall(invoke_content):
# match is now a tuple: (param_name_raw, param_value)
if isinstance(match, tuple) and len(match) >= 2:
param_name = self._extract_name(match[0])
param_value = match[1].strip()
else:
# Fallback for unexpected format
param_match = re.search(r"^([^>]+)>(.*)", str(match), re.DOTALL)
if not param_match:
continue
param_name = self._extract_name(param_match.group(1))
param_value = param_match.group(2).strip()
if param_value.startswith("\n"):
param_value = param_value[1:]
if param_value.endswith("\n"):
param_value = param_value[:-1]
# Get parameter types (supports anyOf/oneOf/allOf)
param_type = self._get_param_types_from_config(param_name, param_config)
# Convert value
param_dict[param_name] = self._convert_param_value_with_types(
param_value, param_type
)
return ToolCall(
type="function",
function=FunctionCall(
name=function_name,
arguments=json.dumps(param_dict, ensure_ascii=False),
),
)
def extract_tool_calls(
self,
model_output: str,
request: ChatCompletionRequest,
) -> ExtractedToolCallInformation:
"""Extract tool calls from complete model output (non-streaming)."""
# Quick check
if self.tool_call_start_token not in model_output:
return ExtractedToolCallInformation(
tools_called=False, tool_calls=[], content=model_output
)
try:
tool_calls = []
# Find all complete tool_call blocks
for tool_call_match in self.tool_call_complete_regex.findall(model_output):
# Find all invokes within this tool_call
for invoke_match in self.invoke_complete_regex.findall(tool_call_match):
tool_call = self._parse_single_invoke(
invoke_match, request.tools if request else None
)
if tool_call:
tool_calls.append(tool_call)
if not tool_calls:
return ExtractedToolCallInformation(
tools_called=False, tool_calls=[], content=model_output
)
# Update prev_tool_call_arr
self.prev_tool_call_arr.clear()
for tool_call in tool_calls:
self.prev_tool_call_arr.append(
{
"name": tool_call.function.name,
"arguments": tool_call.function.arguments,
}
)
# Extract content before first tool call
first_tool_idx = model_output.find(self.tool_call_start_token)
content = model_output[:first_tool_idx] if first_tool_idx > 0 else None
return ExtractedToolCallInformation(
tools_called=True, tool_calls=tool_calls, content=content
)
except Exception:
logger.exception("Error extracting tool calls")
return ExtractedToolCallInformation(
tools_called=False, tool_calls=[], content=model_output
)
def extract_tool_calls_streaming(
self,
previous_text: str,
current_text: str,
delta_text: str,
previous_token_ids: Sequence[int], # pylint: disable=unused-argument
current_token_ids: Sequence[int], # pylint: disable=unused-argument
delta_token_ids: Sequence[int],
request: ChatCompletionRequest,
) -> DeltaMessage | None:
"""Extract tool calls from streaming model output."""
# Store request for type conversion
if not previous_text or self.tool_call_start_token in delta_text:
self._reset_streaming_state()
self.streaming_request = request
# If no delta text, return None unless it's an EOS token after tools
if not delta_text:
# Check if this is an EOS token after all tool calls are complete
if delta_token_ids and self.tool_call_end_token_id not in delta_token_ids:
# Count complete tool calls
complete_calls = len(
self.tool_call_complete_regex.findall(current_text)
)
# If we have completed tool calls and populated prev_tool_call_arr
if complete_calls > 0 and len(self.prev_tool_call_arr) > 0:
# Check if all tool calls are closed
open_calls = current_text.count(
self.tool_call_start_token
) - current_text.count(self.tool_call_end_token)
if open_calls == 0:
# Return empty delta for finish_reason processing
return DeltaMessage(content="")
elif not self.is_tool_call_started and current_text:
# This is a regular content response that's now complete
return DeltaMessage(content="")
return None
# Update accumulated text
self.accumulated_text = current_text
# Check if we need to advance to next tool
if self.json_closed and not self.in_function:
# Check if this tool call has ended
invoke_ends = current_text.count(self.invoke_end_token)
if invoke_ends > self.current_tool_index:
# This tool has ended, advance to next
self.current_tool_index += 1
self.header_sent = False
self.param_count = 0
self.json_started = False
self.json_closed = False
self.in_function = False # Now we can safely set this to False
self.accumulated_params = {}
# Continue processing next tool
return None
# Handle normal content before tool calls
if not self.is_tool_call_started:
# Check if tool call is starting
if (
self.tool_call_start_token_id in delta_token_ids
or self.tool_call_start_token in delta_text
):
self.is_tool_call_started = True
# Return any content before the tool call
if self.tool_call_start_token in delta_text:
content_before = delta_text[
: delta_text.index(self.tool_call_start_token)
]
if content_before:
return DeltaMessage(content=content_before)
return None
else:
# Check if we're between tool calls - skip whitespace
if (
current_text.rstrip().endswith(self.tool_call_end_token)
and delta_text.strip() == ""
):
# We just ended a tool call, skip whitespace
return None
# Normal content, no tool call
return DeltaMessage(content=delta_text)
# Check if we're between tool calls (waiting for next one)
invoke_starts_count = current_text.count(self.invoke_start_prefix)
if self.current_tool_index >= invoke_starts_count:
# We're past all tool calls, shouldn't be here
return None
# Find the current tool call portion
invoke_start_positions: list[int] = []
idx = 0
while True:
idx = current_text.find(self.invoke_start_prefix, idx)
if idx == -1:
break
invoke_start_positions.append(idx)
idx += len(self.invoke_start_prefix)
if self.current_tool_index >= len(invoke_start_positions):
# No more tool calls to process yet
return None
invoke_start_idx = invoke_start_positions[self.current_tool_index]
# Find where this tool call ends (or current position if not ended yet)
invoke_end_idx = current_text.find(self.invoke_end_token, invoke_start_idx)
if invoke_end_idx == -1:
tool_text = current_text[invoke_start_idx:]
else:
tool_text = current_text[
invoke_start_idx : invoke_end_idx + len(self.invoke_end_token)
]
# Looking for function header
if not self.header_sent:
if self.invoke_start_prefix in tool_text:
func_start = tool_text.find(self.invoke_start_prefix) + len(
self.invoke_start_prefix
)
# Find the end of the opening tag
func_end = tool_text.find(">", func_start)
if func_end != -1:
# Found complete function name
# Handle cases where model may add extra attributes after name
attr_section = tool_text[func_start:func_end]
self.current_function_name = self._parse_name_from_attributes(
attr_section
)
self.current_tool_id = self._generate_tool_call_id()
self.header_sent = True
self.in_function = True
# Add to prev_tool_call_arr immediately when we detect a tool call
# Each tool call should be recorded regardless of function name
# Ensure we don't add the same tool call index multiple times
if len(self.prev_tool_call_arr) <= self.current_tool_index:
self.prev_tool_call_arr.append(
{
"name": self.current_function_name,
"arguments": {}, # Placeholder, will be updated later
}
)
# Initialize streamed_args_for_tool for this tool call
if len(self.streamed_args_for_tool) <= self.current_tool_index:
self.streamed_args_for_tool.append("")
# Send header with function info
return DeltaMessage(
tool_calls=[
DeltaToolCall(
index=self.current_tool_index,
id=self.current_tool_id,
function=DeltaFunctionCall(
name=self.current_function_name, arguments=""
),
type="function",
)
]
)
return None
# We've sent header, now handle function body
if self.in_function:
# Send opening brace if not sent yet
if self.in_function and not self.json_started:
self.json_started = True
# Update streamed_args_for_tool for opening brace
if self.current_tool_index < len(self.streamed_args_for_tool):
self.streamed_args_for_tool[self.current_tool_index] += "{"
return DeltaMessage(
tool_calls=[
DeltaToolCall(
index=self.current_tool_index,
function=DeltaFunctionCall(arguments="{"),
)
]
)
# Make sure json_started is set if we're processing parameters
if not self.json_started:
self.json_started = True
# Check for function end in accumulated text
if not self.json_closed and self.invoke_end_token in tool_text:
# Count total parameters in the tool text
total_param_count = tool_text.count(self.parameter_prefix)
# Only close JSON if all parameters have been processed
if self.param_count >= total_param_count:
# Close JSON
self.json_closed = True
# Extract complete tool call
# Find the invoke content
invoke_start = tool_text.find(self.invoke_start_prefix) + len(
self.invoke_start_prefix
)
invoke_content_end = tool_text.find(
self.invoke_end_token, invoke_start
)
if invoke_content_end != -1:
invoke_content = tool_text[invoke_start:invoke_content_end]
# Parse to get the complete arguments
try:
parsed_tool = self._parse_single_invoke(
invoke_content,
self.streaming_request.tools
if self.streaming_request
else None,
)
if parsed_tool and self.current_tool_index < len(
self.prev_tool_call_arr
):
# Update existing entry in prev_tool_call_arr
args = parsed_tool.function.arguments
self.prev_tool_call_arr[self.current_tool_index][
"arguments"
] = json.loads(args)
except Exception:
pass # Ignore parsing errors during streaming
result = DeltaMessage(
tool_calls=[
DeltaToolCall(
index=self.current_tool_index,
function=DeltaFunctionCall(arguments="}"),
)
]
)
# Update streamed_args_for_tool for closing brace
if self.current_tool_index < len(self.streamed_args_for_tool):
self.streamed_args_for_tool[self.current_tool_index] += "}"
# Reset state for next tool
self.json_closed = True
self.in_function = False
self.accumulated_params = {}
logger.debug("[M2_STREAMING] Tool call completed")
return result
else:
# Don't close JSON yet, continue processing parameters
return None
# Look for parameters
# Find all parameter starts
param_starts = []
idx = 0
while True:
idx = tool_text.find(self.parameter_prefix, idx)
if idx == -1:
break
param_starts.append(idx)
idx += len(self.parameter_prefix)
# Check if we should start a new parameter
if (
not self.in_param
and self.param_count < len(param_starts)
and len(param_starts) > self.param_count
):
# Process the next parameter
param_idx = param_starts[self.param_count]
param_start = param_idx + len(self.parameter_prefix)
remaining = tool_text[param_start:]
if ">" in remaining:
# We have the complete parameter name
# Handle cases where model may add extra attributes after name
# e.g., <parameter name="cmd" description="(e.g. ls)">
name_end = remaining.find(">")
attr_section = remaining[:name_end]
self.current_param_name = self._parse_name_from_attributes(
attr_section
)
# Find the parameter value
value_start = param_start + name_end + 1
value_text = tool_text[value_start:]
if value_text.startswith("\n"):
value_text = value_text[1:]
# Find where this parameter ends
param_end_idx = value_text.find(self.parameter_end_token)
if param_end_idx == -1:
# No closing tag, look for next parameter or function end
next_param_idx = value_text.find(self.parameter_prefix)
func_end_idx = value_text.find(self.invoke_end_token)
if next_param_idx != -1 and (
func_end_idx == -1 or next_param_idx < func_end_idx
):
param_end_idx = next_param_idx
elif func_end_idx != -1:
param_end_idx = func_end_idx
else:
# Neither found, check if tool call is complete
if self.invoke_end_token in tool_text:
# Tool call and parameter is complete
param_end_idx = len(value_text)
else:
# Still streaming, wait for more content
return None
if param_end_idx != -1:
# Complete parameter found
param_value = value_text[:param_end_idx]
if param_value.endswith("\n"):
param_value = param_value[:-1]
# Store raw value for later processing
self.accumulated_params[self.current_param_name] = param_value
# Get parameter configuration with anyOf support
param_config = {}
if self.streaming_request and self.streaming_request.tools:
for tool in self.streaming_request.tools:
if (
hasattr(tool, "function")
and tool.function.name == self.current_function_name
and hasattr(tool.function, "parameters")
):
params = tool.function.parameters
if (
isinstance(params, dict)
and "properties" in params
):
param_config = params["properties"]
break
# Get parameter types (supports anyOf/oneOf/allOf)
param_type = self._get_param_types_from_config(
self.current_param_name, param_config
)
converted_value = self._convert_param_value_with_types(
param_value, param_type
)
# Build JSON fragment based on the converted type
# Use json.dumps to properly serialize the value
serialized_value = json.dumps(
converted_value, ensure_ascii=False
)
if self.param_count == 0:
json_fragment = (
f'"{self.current_param_name}": {serialized_value}'
)
else:
json_fragment = (
f', "{self.current_param_name}": {serialized_value}'
)
self.param_count += 1
# Update streamed_args_for_tool for this tool call
if self.current_tool_index < len(self.streamed_args_for_tool):
self.streamed_args_for_tool[self.current_tool_index] += (
json_fragment
)
return DeltaMessage(
tool_calls=[
DeltaToolCall(
index=self.current_tool_index,
function=DeltaFunctionCall(arguments=json_fragment),
)
]
)
return None
\ No newline at end of file
config.json deleted 100644 → 0
View file @ 9d2097be
{
"architectures": [
"MiniMaxM2ForCausalLM"
],
"attention_dropout": 0.0,
"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"
},
"bos_token_id": null,
"eos_token_id": null,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 1536,
"layernorm_full_attention_beta": 1.0,
"layernorm_linear_attention_beta": 1.0,
"layernorm_mlp_beta": 1.0,
"max_position_embeddings": 196608,
"mlp_intermediate_size": 8192,
"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,
"output_router_logits": false,
"qk_norm_type": "per_layer",
"rms_norm_eps": 1e-06,
"rope_theta": 5000000,
"rotary_dim": 64,
"router_aux_loss_coef": 0.001,
"router_jitter_noise": 0.0,
"scoring_func": "sigmoid",
"shared_intermediate_size": 0,
"shared_moe_mode": "sigmoid",
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "4.57.1",
"use_cache": true,
"use_mtp": true,
"use_qk_norm": true,
"use_routing_bias": true,
"vocab_size": 200064
}
doc/result-minimax-m2_1.png
View replaced file @ 9d2097be View file @ 688d8492
doc/result-minimax-m2_1.png

232 KB | W: | H:

doc/result-minimax-m2_1.png

257 KB | W: | H:

doc/result-minimax-m2_1.png
doc/result-minimax-m2_1.png
doc/result-minimax-m2_1.png
doc/result-minimax-m2_1.png
  • 2-up
  • Swipe
  • Onion skin
offline_tools.py 0 → 100644
View file @ 688d8492
from openai import OpenAI
import json
client = OpenAI(base_url="http://localhost:8001/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))}")
\ No newline at end of file
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment