Mistral - Upsonic AI

Overview

Mistral AI provides high-performance open and commercial models including Mistral Large and Codestral. Known for strong performance and European values. Model Class: MistralModel

Authentication

export MISTRAL_API_KEY="..."

Examples

from upsonic import Agent, Task
from upsonic.models.mistral import MistralModel

model = MistralModel(model_name="mistral-large-latest")
agent = Agent(model=model)

task = Task("Hello, how are you?")
result = agent.do(task)
print(result)

Model Settings

You can set model parameters in two ways: on the model or on the Agent. On the model:

from upsonic import Agent, Task
from upsonic.models.mistral import MistralModel, MistralModelSettings

model = MistralModel(
    model_name="mistral-large-latest",
    settings=MistralModelSettings(max_tokens=1024, temperature=0.7)
)
agent = Agent(model=model)

On the Agent:

from upsonic import Agent, Task
from upsonic.models.mistral import MistralModelSettings

agent = Agent(
    model="mistral/mistral-large-latest",
    settings=MistralModelSettings(max_tokens=1024)
)

Parameters

Parameter	Type	Description	Default	Source
`max_tokens`	`int`	Maximum tokens to generate	Model default	Base
`temperature`	`float`	Sampling temperature (0.0-1.0)	0.7	Base
`top_p`	`float`	Nucleus sampling	1.0	Base
`seed`	`int`	Random seed	None	Base
`stop_sequences`	`list[str]`	Stop generation sequences	None	Base
`presence_penalty`	`float`	Token presence penalty	0.0	Base
`frequency_penalty`	`float`	Token frequency penalty	0.0	Base
`timeout`	`float`	Request timeout (seconds)	600	Base

​Overview

​Authentication

​Examples

​Model Settings

​Parameters

Overview

Authentication

Examples

Model Settings

Parameters