MistralLLM¶
MistralLLM
¶
Bases: AsyncLLM
Mistral LLM implementation running the async API client.
Attributes:
Name | Type | Description |
---|---|---|
model |
str
|
the model name to use for the LLM e.g. "mistral-tiny", "mistral-large", etc. |
endpoint |
str
|
the endpoint to use for the Mistral API. Defaults to "https://api.mistral.ai". |
api_key |
Optional[RuntimeParameter[SecretStr]]
|
the API key to authenticate the requests to the Mistral API. Defaults to |
max_retries |
RuntimeParameter[int]
|
the maximum number of retries to attempt when a request fails. Defaults to |
timeout |
RuntimeParameter[int]
|
the maximum time in seconds to wait for a response. Defaults to |
max_concurrent_requests |
RuntimeParameter[int]
|
the maximum number of concurrent requests to send. Defaults
to |
structured_output |
Optional[RuntimeParameter[InstructorStructuredOutputType]]
|
a dictionary containing the structured output configuration configuration
using |
_api_key_env_var |
str
|
the name of the environment variable to use for the API key. It is meant to be used internally. |
_aclient |
Optional[MistralAsyncClient]
|
the |
Runtime parameters
api_key
: the API key to authenticate the requests to the Mistral API.max_retries
: the maximum number of retries to attempt when a request fails. Defaults to5
.timeout
: the maximum time in seconds to wait for a response. Defaults to120
.max_concurrent_requests
: the maximum number of concurrent requests to send. Defaults to64
.
Examples:
Generate text:
```python
from distilabel.llms import MistralLLM
llm = MistralLLM(model="open-mixtral-8x22b")
llm.load()
# Call the model
output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
Generate structured data:
```python
from pydantic import BaseModel
from distilabel.llms import MistralLLM
class User(BaseModel):
name: str
last_name: str
id: int
llm = MistralLLM(
model="open-mixtral-8x22b",
api_key="api.key",
structured_output={"schema": User}
)
llm.load()
output = llm.generate(inputs=[[{"role": "user", "content": "Create a user profile for the following marathon"}]])
```
Source code in src/distilabel/llms/mistral.py
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|
model_name: str
property
¶
Returns the model name used for the LLM.
agenerate(input, max_new_tokens=None, temperature=None, top_p=None)
async
¶
Generates num_generations
responses for the given input using the MistralAI async
client.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input |
FormattedInput
|
a single input in chat format to generate responses for. |
required |
max_new_tokens |
Optional[int]
|
the maximum number of new tokens that the model will generate.
Defaults to |
None
|
temperature |
Optional[float]
|
the temperature to use for the generation. Defaults to |
None
|
top_p |
Optional[float]
|
the top-p value to use for the generation. Defaults to |
None
|
Returns:
Type | Description |
---|---|
GenerateOutput
|
A list of lists of strings containing the generated responses for each input. |
Source code in src/distilabel/llms/mistral.py
load()
¶
Loads the MistralAsyncClient
client to benefit from async requests.