🚀 Mistral-Small-3.2-24B-Instruct-2506

Mistral-Small-3.2-24B-Instruct-2506是一款语言模型，它是Mistral-Small-3.1-24B-Instruct-2503的小版本更新。该模型在指令遵循、减少重复错误和函数调用等方面有显著提升，适用于图像文本到文本的转换任务。

支持语言

该模型支持多种语言，包括英语、法语、德语、西班牙语、葡萄牙语、意大利语、日语、韩语、俄语、中文、阿拉伯语、波斯语、印尼语、马来语、尼泊尔语、波兰语、罗马尼亚语、塞尔维亚语、瑞典语、土耳其语、乌克兰语、越南语、印地语和孟加拉语。

许可证

本项目采用Apache-2.0许可证。

基础模型

基于mistralai/Mistral-Small-3.2-24B-Instruct-2506基础模型构建。

额外说明

如果您想了解更多关于我们如何处理您的个人数据的信息，请阅读我们的隐私政策。

模型构建信息

• GGUF使用mistralai/Mistral-Small-3.1-24B-Instruct-2503的chat_template.json、preprocessor_config.json、processor_config.json、special_tokens_map.json、tokenizer.json和tokenizer_config.json创建。
• mmproj来自unsloth/Mistral-Small-3.1-24B-Instruct-2503-GGUF。

🚀 快速开始

主要特性

Mistral-Small-3.2-24B-Instruct-2506在以下几个方面进行了改进：

• 指令遵循：Small-3.2在遵循精确指令方面表现更出色。
• 重复错误：Small-3.2减少了无限生成或重复答案的问题。
• 函数调用：Small-3.2的函数调用模板更加健壮，具体可参考此处和示例。

基准测试结果

文本任务

在无限生成方面，Small 3.2在具有挑战性、长且重复的提示下，将无限生成情况减少了2倍。

在STEM相关任务中：

视觉任务

📦 安装指南

vLLM（推荐）

建议使用vLLM框架来使用该模型。

安装依赖

确保安装vLLM >= 0.9.1：

pip install vllm --upgrade

安装完成后，会自动安装mistral_common >= 1.6.2。您可以通过以下命令进行检查：

python -c "import mistral_common; print(mistral_common.__version__)"

您也可以使用Docker镜像或从Docker Hub获取。

启动服务

建议在服务器/客户端环境中使用Mistral-Small-3.2-24B-Instruct-2506。

1. 启动服务器：

vllm serve mistralai/Mistral-Small-3.2-24B-Instruct-2506 --tokenizer_mode mistral --config_format mistral --load_format mistral --tool-call-parser mistral --enable-auto-tool-choice --limit_mm_per_prompt 'image=10' --tensor-parallel-size 2

注意：在GPU上运行Mistral-Small-3.2-24B-Instruct-2506在bf16或fp16模式下大约需要55GB的GPU内存。

2. 您可以使用以下简单的Python代码片段来测试客户端：

💻 使用示例

基础用法

视觉推理

利用Mistral-Small-3.2-24B-Instruct-2506的视觉能力，根据给定场景做出最佳选择。

from datetime import datetime, timedelta
from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 131072
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    today = datetime.today().strftime("%Y-%m-%d")
    yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
    model_name = repo_id.split("/")[-1]
    return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
            },
            {"type": "image_url", "image_url": {"url": image_url}},
        ],
    },
]
response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
)
print(response.choices[0].message.content)

高级用法

函数调用

Mistral-Small-3.2-24B-Instruct-2506在通过vLLM进行函数/工具调用任务方面表现出色。

简单示例

from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 131072
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    return system_prompt
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
image_url = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/europe.png"
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_population",
            "description": "Get the up-to-date population of a given country.",
            "parameters": {
                "type": "object",
                "properties": {
                    "country": {
                        "type": "string",
                        "description": "The country to find the population of.",
                    },
                    "unit": {
                        "type": "string",
                        "description": "The unit for the population.",
                        "enum": ["millions", "thousands"],
                    },
                },
                "required": ["country", "unit"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "rewrite",
            "description": "Rewrite a given text for improved clarity",
            "parameters": {
                "type": "object",
                "properties": {
                    "text": {
                        "type": "string",
                        "description": "The input text to rewrite",
                    }
                },
            },
        },
    },
]
messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": "Could you please make the below article more concise?\n\nOpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership.",
    },
    {
        "role": "assistant",
        "content": "",
        "tool_calls": [
            {
                "id": "bbc5b7ede",
                "type": "function",
                "function": {
                    "name": "rewrite",
                    "arguments": '{"text": "OpenAI is an artificial intelligence research laboratory consisting of the non-profit OpenAI Incorporated and its for-profit subsidiary corporation OpenAI Limited Partnership."}',
                },
            }
        ],
    },
    {
        "role": "tool",
        "content": '{"action":"rewrite","outcome":"OpenAI is a FOR-profit company."}',
        "tool_call_id": "bbc5b7ede",
        "name": "rewrite",
    },
    {
        "role": "assistant",
        "content": "---\n\nOpenAI is a FOR-profit company.",
    },
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Can you tell me what is the biggest country depicted on the map?",
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": image_url,
                },
            },
        ],
    }
]
response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
    tools=tools,
    tool_choice="auto",
)
assistant_message = response.choices[0].message.content
print(assistant_message)

复杂示例

import json
from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 131072
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    return system_prompt
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
image_url = "https://math-coaching.com/img/fiche/46/expressions-mathematiques.jpg"
def my_calculator(expression: str) -> str:
    return str(eval(expression))
tools = [
    {
        "type": "function",
        "function": {
            "name": "my_calculator",
            "description": "A calculator that can evaluate a mathematical expression.",
            "parameters": {
                "type": "object",
                "properties": {
                    "expression": {
                        "type": "string",
                        "description": "The mathematical expression to evaluate.",
                    },
                },
                "required": ["expression"],
            },
        },
    },
    {
        "type": "function",
        "function": {
            "name": "rewrite",
            "description": "Rewrite a given text for improved clarity",
            "parameters": {
                "type": "object",
                "properties": {
                    "text": {
                        "type": "string",
                        "description": "The input text to rewrite",
                    }
                },
            },
        },
    },
]
messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": [
            {
                "type": "text",
                "text": "Can you calculate the results for all the equations displayed in the image? Only compute the ones that involve numbers.",
            },
            {
                "type": "image_url",
                "image_url": {
                    "url": image_url,
                },
            },
        ],
    },
]
response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
    tools=tools,
    tool_choice="auto",
)
tool_calls = response.choices[0].message.tool_calls
print(tool_calls)
results = []
for tool_call in tool_calls:
    function_name = tool_call.function.name
    function_args = tool_call.function.arguments
    if function_name == "my_calculator":
        result = my_calculator(**json.loads(function_args))
        results.append(result)
messages.append({"role": "assistant", "tool_calls": tool_calls})
for tool_call, result in zip(tool_calls, results):
    messages.append(
        {
            "role": "tool",
            "tool_call_id": tool_call.id,
            "name": tool_call.function.name,
            "content": result,
        }
    )
response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
)
print(response.choices[0].message.content)

指令遵循

Mistral-Small-3.2-24B-Instruct-2506能够精确遵循您的指令。

from openai import OpenAI
from huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 131072
client = OpenAI(
    api_key=openai_api_key,
    base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
    file_path = hf_hub_download(repo_id=repo_id, filename=filename)
    with open(file_path, "r") as file:
        system_prompt = file.read()
    return system_prompt
model_id = "mistralai/Mistral-Small-3.2-24B-Instruct-2506"
SYSTEM_PROMPT = load_system_prompt(model_id, "SYSTEM_PROMPT.txt")
messages = [
    {"role": "system", "content": SYSTEM_PROMPT},
    {
        "role": "user",
        "content": "Write me a sentence where every word starts with the next letter in the alphabet - start with 'a' and end wit"
    }
]
response = client.chat.completions.create(
    model=model,
    messages=messages,
    temperature=TEMP,
    max_tokens=MAX_TOK,
)
print(response.choices[0].message.content)

使用提示

⚠️ 重要提示

建议使用相对较低的温度，例如temperature=0.15。

确保为模型添加系统提示，以使其更好地满足您的需求。如果您想将模型用作通用助手，建议使用SYSTEM_PROMPT.txt文件中提供的提示。

📄 许可证

本项目采用Apache-2.0许可证。