🚀 Mixtral-8x22B-Instruct-v0.1模型卡片

Mixtral-8x22B-Instruct-v0.1大语言模型（LLM）是Mixtral-8x22B-v0.1的指令微调版本。

⚠️ 重要提示

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。

🚀 快速开始

💻 使用示例

基础用法

以下是使用mistral_common进行编码和解码的示例：

from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
 
mistral_models_path = "MISTRAL_MODELS_PATH"
 
tokenizer = MistralTokenizer.v3()
 
completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])
 
tokens = tokenizer.encode_chat_completion(completion_request).tokens

高级用法

以下是使用mistral_inference进行推理的示例：

from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
 
model = Transformer.from_folder(mistral_models_path)
out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)

result = tokenizer.decode(out_tokens[0])

print(result)

使用Hugging Face transformers准备输入

from transformers import AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mixtral-8x22B-Instruct-v0.1")

chat = [{"role": "user", "content": "Explain Machine Learning to me in a nutshell."}]

tokens = tokenizer.apply_chat_template(chat, return_dict=True, return_tensors="pt", add_generation_prompt=True)

使用Hugging Face transformers进行推理

from transformers import AutoModelForCausalLM
import torch

# You can also use 8-bit or 4-bit quantization here
model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x22B-Instruct-v0.1", torch_dtype=torch.bfloat16, device_map="auto")
model.to("cuda")
 
generated_ids = model.generate(**tokens, max_new_tokens=1000, do_sample=True)

# decode with HF tokenizer
result = tokenizer.decode(generated_ids[0])
print(result)

函数调用示例

from transformers import AutoModelForCausalLM
from mistral_common.protocol.instruct.messages import (
    AssistantMessage,
    UserMessage,
)
from mistral_common.protocol.instruct.tool_calls import (
    Tool,
    Function,
)
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.instruct.normalize import ChatCompletionRequest

device = "cuda" # the device to load the model onto

tokenizer_v3 = MistralTokenizer.v3()

mistral_query = ChatCompletionRequest(
    tools=[
        Tool(
            function=Function(
                name="get_current_weather",
                description="Get the current weather",
                parameters={
                    "type": "object",
                    "properties": {
                        "location": {
                            "type": "string",
                            "description": "The city and state, e.g. San Francisco, CA",
                        },
                        "format": {
                            "type": "string",
                            "enum": ["celsius", "fahrenheit"],
                            "description": "The temperature unit to use. Infer this from the users location.",
                        },
                    },
                    "required": ["location", "format"],
                },
            )
        )
    ],
    messages=[
        UserMessage(content="What's the weather like today in Paris"),
    ],
    model="test",
)

encodeds = tokenizer_v3.encode_chat_completion(mistral_query).tokens
model = AutoModelForCausalLM.from_pretrained("mistralai/Mixtral-8x22B-Instruct-v0.1")
model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
sp_tokenizer = tokenizer_v3.instruct_tokenizer.tokenizer
decoded = sp_tokenizer.decode(generated_ids[0])
print(decoded)

使用transformers进行函数调用

要使用此示例，您需要transformers版本4.42.0或更高版本。有关更多信息，请参阅transformers文档中的函数调用指南。

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "mistralai/Mixtral-8x22B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_id)

def get_current_weather(location: str, format: str):
    """
    Get the current weather

    Args:
        location: The city and state, e.g. San Francisco, CA
        format: The temperature unit to use. Infer this from the users location. (choices: ["celsius", "fahrenheit"])
    """
    pass

conversation = [{"role": "user", "content": "What's the weather like in Paris?"}]
tools = [get_current_weather]

# format and tokenize the tool use prompt 
inputs = tokenizer.apply_chat_template(
            conversation,
            tools=tools,
            add_generation_prompt=True,
            return_dict=True,
            return_tensors="pt",
)

model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")

inputs.to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1000)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

⚠️ 重要提示

由于篇幅原因，此示例未展示调用工具并将工具调用和工具结果添加到聊天历史记录的完整循环，以便模型在下次生成时使用它们。有关完整的工具调用示例，请参阅函数调用指南，并注意Mixtral 确实 使用工具调用ID，因此这些ID必须包含在您的工具调用和工具结果中。它们应该正好是9个字母数字字符。

指令分词器

此版本中包含的HuggingFace分词器应与我们自己的分词器匹配。您可以通过以下命令进行比较： pip install mistral-common

from mistral_common.protocol.instruct.messages import (
    AssistantMessage,
    UserMessage,
)
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.tokens.instruct.normalize import ChatCompletionRequest

from transformers import AutoTokenizer

tokenizer_v3 = MistralTokenizer.v3()

mistral_query = ChatCompletionRequest(
    messages=[
        UserMessage(content="How many experts ?"),
        AssistantMessage(content="8"),
        UserMessage(content="How big ?"),
        AssistantMessage(content="22B"),
        UserMessage(content="Noice 🎉 !"),
    ],
    model="test",
)
hf_messages = mistral_query.model_dump()['messages']

tokenized_mistral = tokenizer_v3.encode_chat_completion(mistral_query).tokens

tokenizer_hf = AutoTokenizer.from_pretrained('mistralai/Mixtral-8x22B-Instruct-v0.1')
tokenized_hf = tokenizer_hf.apply_chat_template(hf_messages, tokenize=True)

assert tokenized_hf == tokenized_mistral

函数调用和特殊标记

此分词器包含更多与函数调用相关的特殊标记：

• [TOOL_CALLS]
• [AVAILABLE_TOOLS]
• [/AVAILABLE_TOOLS]
• [TOOL_RESULTS]
• [/TOOL_RESULTS]

如果您想在函数调用中使用此模型，请确保以类似于我们在SentencePieceTokenizerV3中所做的方式应用它。

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