模型简介
模型特点
模型能力
使用案例
🚀 Mistral-Small-3.2-24B-Instruct-2506
Mistral-Small-3.2-24B-Instruct-2506是一个图像文本到文本的模型,是Mistral-Small-3.1-24B-Instruct-2503的一个小更新版本。它在指令遵循、减少重复错误和函数调用等方面有所改进,能为用户提供更准确和高效的服务。
🚀 快速开始
本模型支持以下语言: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、孟加拉语。
许可证为Apache-2.0,库名称为vllm
。
✨ 主要特性
Mistral-Small-3.2-24B-Instruct-2506在以下几个方面进行了改进:
在其他所有方面,Small-3.2与Mistral-Small-3.1-24B-Instruct-2503相比,表现相当或略有提升。
其关键特性与Mistral-Small-3.1-24B-Instruct-2503相同。
📊 基准测试结果
我们将Mistral-Small-3.2-24B与Mistral-Small-3.1-24B-Instruct-2503进行了比较。如需查看与其他类似规模模型的更多比较,请参考Mistral-Small-3.1的基准测试。
文本性能
指令遵循/聊天/语气
模型 | Wildbench v2 | Arena Hard v2 | IF(内部;准确率) |
---|---|---|---|
Small 3.1 24B Instruct | 55.6% | 19.56% | 82.75% |
Small 3.2 24B Instruct | 65.33% | 43.1% | 84.78% |
无限生成情况
Small 3.2在处理具有挑战性、长且重复的提示时,将无限生成情况减少了一半。
模型 | 无限生成情况(内部;数值越低越好) |
---|---|
Small 3.1 24B Instruct | 2.11% |
Small 3.2 24B Instruct | 1.29% |
STEM领域
模型 | MMLU | MMLU Pro(5次少样本思维链) | MATH | GPQA Main(5次少样本思维链) | GPQA Diamond(5次少样本思维链) | MBPP Plus - Pass@5 | HumanEval Plus - Pass@5 | SimpleQA(总准确率) |
---|---|---|---|---|---|---|---|---|
Small 3.1 24B Instruct | 80.62% | 66.76% | 69.30% | 44.42% | 45.96% | 74.63% | 88.99% | 10.43% |
Small 3.2 24B Instruct | 80.50% | 69.06% | 69.42% | 44.22% | 46.13% | 78.33% | 92.90% | 12.10% |
视觉性能
模型 | MMMU | Mathvista | ChartQA | DocVQA | AI2D |
---|---|---|---|---|---|
Small 3.1 24B Instruct | 64.00% | 68.91% | 86.24% | 94.08% | 93.72% |
Small 3.2 24B Instruct | 62.50% | 67.09% | 87.4% | 94.86% | 92.91% |
📦 安装指南
vLLM(推荐)
我们建议使用vLLM来运行此模型。
安装步骤
确保安装vLLM >= 0.9.1
:
pip install vllm --upgrade
这样做应该会自动安装mistral_common >= 1.6.2
。
要检查是否安装成功,可以运行以下命令:
python -c "import mistral_common; print(mistral_common.__version__)"
你也可以使用现成的Docker镜像,或者从Docker Hub获取。
服务启动
我们建议在服务器/客户端环境中使用Mistral-Small-3.2-24B-Instruct-2506。
- 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格式下大约需要55GB的GPU内存。
- 你可以使用一个简单的Python代码片段来测试客户端。具体示例如下。
💻 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,在给定场景下做出最佳选择。
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)
# In this situation, you are playing a Pokémon game where your Pikachu (Level 42) is facing a wild Pidgey (Level 17). Here are the possible actions you can take and an analysis of each:
# 1. **FIGHT**:
# - **Pros**: Pikachu is significantly higher level than the wild Pidgey, which suggests that it should be able to defeat Pidgey easily. This could be a good opportunity to gain experience points and possibly items or money.
# - **Cons**: There is always a small risk of Pikachu fainting, especially if Pidgey has a powerful move or a status effect that could hinder Pikachu. However, given the large level difference, this risk is minimal.
# 2. **BAG**:
# - **Pros**: You might have items in your bag that could help in this battle, such as Potions, Poké Balls, or Berries. Using an item could help you capture the Pidgey or heal your Pikachu if needed.
# - **Cons**: Using items might not be necessary given the level difference. It could be more efficient to just fight and defeat the Pidgey quickly.
# 3. **POKÉMON**:
# - **Pros**: You might have another Pokémon in your party that is better suited for this battle or that you want to gain experience. Switching Pokémon could also be a strategic move if you want to train a lower-level Pokémon.
# - **Cons**: Switching Pokémon might not be necessary since Pikachu is at a significant advantage. It could also waste time and potentially give Pidgey a turn to attack.
# 4. **RUN**:
# - **Pros**: Running away could save time and conserve your Pokémon's health and resources. If you are in a hurry or do not need the experience or items, running away is a safe option.
# - **Cons**: Running away means you miss out on the experience points and potential items or money that you could gain from defeating the Pidgey. It also means you do not get the chance to capture the Pidgey if you wanted to.
# ### Recommendation:
# Given the significant level advantage, the best action is likely to **FIGHT**. This will allow you to quickly defeat the Pidgey, gain experience points, and potentially earn items or money. If you are concerned about Pikachu's health, you could use an item from your **BAG** to heal it before or during the battle. Running away or switching Pokémon does not seem necessary in this situation.
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。例如:
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)
# The biggest country depicted on the map is Russia.
messages.extend([
{"role": "assistant", "content": assistant_message},
{"role": "user", "content": "What is the population of that country in millions?"},
])
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
tools=tools,
tool_choice="auto",
)
print(response.choices[0].message.tool_calls)
# [ChatCompletionMessageToolCall(id='3e92V6Vfo', function=Function(arguments='{"country": "Russia", "unit": "millions"}', name='get_current_population'), type='function')]
指令遵循
Mistral-Small-3.2-24B-Instruct-2506能够严格遵循你的指令。
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 with 'z'.",
},
]
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
)
assistant_message = response.choices[0].message.content
print(assistant_message)
# Here's a sentence where each word starts with the next letter of the alphabet, starting from 'a' and ending with 'z':
# "Always brave ca
注意事项
⚠️ 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,以使其更好地满足你的需求。如果你想将该模型作为通用助手使用,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。









