OpenAILLM¶
OpenAILLM
¶
Bases: AsyncLLM
OpenAI LLM 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", "gpt-4", etc. Supported models can be found here. |
base_url |
Optional[RuntimeParameter[str]]
|
the base URL to use for the OpenAI API requests. Defaults to |
api_key |
Optional[RuntimeParameter[SecretStr]]
|
the API key to authenticate the requests to the OpenAI API. Defaults to
|
max_retries |
RuntimeParameter[int]
|
the maximum number of times to retry the request to the API before
failing. Defaults to |
timeout |
RuntimeParameter[int]
|
the maximum time in seconds to wait for a response from the API. Defaults
to |
structured_output |
Optional[RuntimeParameter[InstructorStructuredOutputType]]
|
a dictionary containing the structured output configuration configuration
using |
Runtime parameters
base_url
: the base URL to use for the OpenAI API requests. Defaults toNone
.api_key
: the API key to authenticate the requests to the OpenAI API. Defaults toNone
.max_retries
: the maximum number of times to retry the request to the API before failing. Defaults to6
.timeout
: the maximum time in seconds to wait for a response from the API. Defaults to120
.
Icon
:simple-openai:
Examples:
Generate text:
```python
from distilabel.llms import OpenAILLM
llm = OpenAILLM(model="gpt-4-turbo", api_key="api.key")
llm.load()
output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
```
Generate text from a custom endpoint following the OpenAI API:
```python
from distilabel.llms import OpenAILLM
llm = OpenAILLM(
model="prometheus-eval/prometheus-7b-v2.0",
base_url=r"http://localhost:8080/v1"
)
llm.load()
output = llm.generate(inputs=[[{"role": "user", "content": "Hello world!"}]])
```
Generate structured data:
```python
from pydantic import BaseModel
from distilabel.llms import OpenAILLM
class User(BaseModel):
name: str
last_name: str
id: int
llm = OpenAILLM(
model="gpt-4-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/openai.py
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|
model_name: str
property
¶
Returns the model name used for the LLM.
agenerate(input, num_generations=1, max_new_tokens=128, frequency_penalty=0.0, presence_penalty=0.0, temperature=1.0, top_p=1.0, stop=None, response_format=None)
async
¶
Generates num_generations
responses for the given input using the OpenAI 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
|
max_new_tokens |
int
|
the maximum number of new tokens that the model will generate.
Defaults to |
128
|
frequency_penalty |
float
|
the repetition penalty to use for the generation. Defaults
to |
0.0
|
presence_penalty |
float
|
the presence penalty to use for the generation. Defaults to
|
0.0
|
temperature |
float
|
the temperature to use for the generation. Defaults to |
1.0
|
top_p |
float
|
the top-p value to use for the generation. Defaults to |
1.0
|
stop |
Optional[Union[str, List[str]]]
|
a string or a list of strings to use as a stop sequence for the generation.
Defaults to |
None
|
response_format |
Optional[str]
|
the format of the response to return. Must be one of
"text" or "json". Read the documentation here
for more information on how to use the JSON model from OpenAI. Defaults to |
None
|
Note
If response_format
Returns:
Type | Description |
---|---|
GenerateOutput
|
A list of lists of strings containing the generated responses for each input. |
Source code in src/distilabel/llms/openai.py
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|
load()
¶
Loads the AsyncOpenAI
client to benefit from async requests.