LiteLLM¶
LiteLLM
¶
Bases: AsyncLLM
LiteLLM implementation running the async API client.
Attributes:
Name | Type | Description |
---|---|---|
model |
str
|
the model name to use for the LLM e.g. "gpt-3.5-turbo" or "mistral/mistral-large", etc. |
verbose |
RuntimeParameter[bool]
|
whether to log the LiteLLM client's logs. Defaults to |
structured_output |
Optional[RuntimeParameter[InstructorStructuredOutputType]]
|
a dictionary containing the structured output configuration configuration
using |
Runtime parameters
verbose
: whether to log the LiteLLM client's logs. Defaults toFalse
.
Examples:
Generate text:
```python
from distilabel.llms import LiteLLM
llm = LiteLLM(model="gpt-3.5-turbo")
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 LiteLLM
class User(BaseModel):
name: str
last_name: str
id: int
llm = LiteLLM(
model="gpt-3.5-turbo",
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/litellm.py
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|
model_name: str
property
¶
Returns the model name used for the LLM.
agenerate(input, num_generations=1, functions=None, function_call=None, temperature=1.0, top_p=1.0, stop=None, max_tokens=None, presence_penalty=None, frequency_penalty=None, logit_bias=None, user=None, metadata=None, api_base=None, api_version=None, api_key=None, model_list=None, mock_response=None, force_timeout=600, custom_llm_provider=None)
async
¶
Generates num_generations
responses for the given input using the LiteLLM async client.
Parameters:
Name | Type | Description | Default |
---|---|---|---|
input |
FormattedInput
|
a single input in chat format to generate responses for. |
required |
num_generations |
int
|
the number of generations to create per input. Defaults to
|
1
|
functions |
Optional[List]
|
a list of functions to apply to the conversation messages. Defaults to
|
None
|
function_call |
Optional[str]
|
the name of the function to call within the conversation. Defaults
to |
None
|
temperature |
Optional[float]
|
the temperature to use for the generation. Defaults to |
1.0
|
top_p |
Optional[float]
|
the top-p value to use for the generation. Defaults to |
1.0
|
stop |
Optional[Union[str, list]]
|
Up to 4 sequences where the LLM API will stop generating further tokens.
Defaults to |
None
|
max_tokens |
Optional[int]
|
The maximum number of tokens in the generated completion. Defaults to
|
None
|
presence_penalty |
Optional[float]
|
It is used to penalize new tokens based on their existence in the
text so far. Defaults to |
None
|
frequency_penalty |
Optional[float]
|
It is used to penalize new tokens based on their frequency in the
text so far. Defaults to |
None
|
logit_bias |
Optional[dict]
|
Used to modify the probability of specific tokens appearing in the
completion. Defaults to |
None
|
user |
Optional[str]
|
A unique identifier representing your end-user. This can help the LLM provider
to monitor and detect abuse. Defaults to |
None
|
metadata |
Optional[dict]
|
Pass in additional metadata to tag your completion calls - eg. prompt
version, details, etc. Defaults to |
None
|
api_base |
Optional[str]
|
Base URL for the API. Defaults to |
None
|
api_version |
Optional[str]
|
API version. Defaults to |
None
|
api_key |
Optional[str]
|
API key. Defaults to |
None
|
model_list |
Optional[list]
|
List of api base, version, keys. Defaults to |
None
|
mock_response |
Optional[str]
|
If provided, return a mock completion response for testing or debugging
purposes. Defaults to |
None
|
force_timeout |
Optional[int]
|
The maximum execution time in seconds for the completion request.
Defaults to |
600
|
custom_llm_provider |
Optional[str]
|
Used for Non-OpenAI LLMs, Example usage for bedrock, set(iterable)
model="amazon.titan-tg1-large" and custom_llm_provider="bedrock". 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/litellm.py
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|
load()
¶
Loads the acompletion
LiteLLM client to benefit from async requests.