LoRAX Python Client
Project description
LoRAX Python Client
LoRAX Python client provides a convenient way of interfacing with a
lorax
instance running in your environment.
Getting Started
Install
pip install lorax-client
Run
from lorax import Client
endpoint_url = "http://127.0.0.1:8080"
client = Client(endpoint_url)
text = client.generate("Why is the sky blue?", adapter_id="some/adapter").generated_text
print(text)
# ' Rayleigh scattering'
# Token Streaming
text = ""
for response in client.generate_stream("Why is the sky blue?", adapter_id="some/adapter"):
if not response.token.special:
text += response.token.text
print(text)
# ' Rayleigh scattering'
or with the asynchronous client:
from lorax import AsyncClient
endpoint_url = "http://127.0.0.1:8080"
client = AsyncClient(endpoint_url)
response = await client.generate("Why is the sky blue?", adapter_id="some/adapter")
print(response.generated_text)
# ' Rayleigh scattering'
# Token Streaming
text = ""
async for response in client.generate_stream("Why is the sky blue?", adapter_id="some/adapter"):
if not response.token.special:
text += response.token.text
print(text)
# ' Rayleigh scattering'
Predibase Inference Endpoints
The LoRAX client can also be used to connect to Predibase managed LoRAX endpoints (including Predibase's serverless endpoints).
You need only make the following changes to the above examples:
- Change the
endpoint_url
to match the endpoint of your Predibase LLM of choice. - Provide your Predibase API token in the
headers
provided to the client.
Example:
from lorax import Client
endpoint_url = f"https://api.app.predibase.com/v1/llms/{llm_deployment_name}"
headers = {
"Authorization": f"Bearer {api_token}"
}
client = Client(endpoint_url, headers=headers)
# same as above from here ...
response = client.generate("Why is the sky blue?", adapter_id=f"{model_repo}/{model_version}")
Note that by default Predibase will use its internal model repos as the default adapter_source
. To use an adapter from Huggingface:
response = client.generate("Why is the sky blue?", adapter_id="some/adapter", adapter_source="hub")
Types
# Request Parameters
class Parameters:
# The ID of the adapter to use
adapter_id: Optional[str]
# The source of the adapter to use
adapter_source: Optional[str]
# Activate logits sampling
do_sample: bool
# Maximum number of generated tokens
max_new_tokens: int
# The parameter for repetition penalty. 1.0 means no penalty.
# See [this paper](https://arxiv.org/pdf/1909.05858.pdf) for more details.
repetition_penalty: Optional[float]
# Whether to prepend the prompt to the generated text
return_full_text: bool
# Stop generating tokens if a member of `stop_sequences` is generated
stop: List[str]
# Random sampling seed
seed: Optional[int]
# The value used to module the logits distribution.
temperature: Optional[float]
# The number of highest probability vocabulary tokens to keep for top-k-filtering.
top_k: Optional[int]
# If set to < 1, only the smallest set of most probable tokens with probabilities that add up to `top_p` or
# higher are kept for generation.
top_p: Optional[float]
# truncate inputs tokens to the given size
truncate: Optional[int]
# Typical Decoding mass
# See [Typical Decoding for Natural Language Generation](https://arxiv.org/abs/2202.00666) for more information
typical_p: Optional[float]
# Generate best_of sequences and return the one if the highest token logprobs
best_of: Optional[int]
# Watermarking with [A Watermark for Large Language Models](https://arxiv.org/abs/2301.10226)
watermark: bool
# Get decoder input token logprobs and ids
decoder_input_details: bool
# The number of highest probability vocabulary tokens to return as alternative tokens in the generation result
return_k_alternatives: Optional[int]
# Decoder input tokens
class InputToken:
# Token ID from the model tokenizer
id: int
# Token text
text: str
# Logprob
# Optional since the logprob of the first token cannot be computed
logprob: Optional[float]
# Generated tokens
class Token:
# Token ID from the model tokenizer
id: int
# Token text
text: str
# Logprob
logprob: float
# Is the token a special token
# Can be used to ignore tokens when concatenating
special: bool
# Generation finish reason
class FinishReason(Enum):
# number of generated tokens == `max_new_tokens`
Length = "length"
# the model generated its end of sequence token
EndOfSequenceToken = "eos_token"
# the model generated a text included in `stop_sequences`
StopSequence = "stop_sequence"
# Additional sequences when using the `best_of` parameter
class BestOfSequence:
# Generated text
generated_text: str
# Generation finish reason
finish_reason: FinishReason
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int]
# Decoder input tokens, empty if decoder_input_details is False
prefill: List[InputToken]
# Generated tokens
tokens: List[Token]
# `generate` details
class Details:
# Generation finish reason
finish_reason: FinishReason
# Number of prompt tokens
prompt_tokens: int
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int]
# Decoder input tokens, empty if decoder_input_details is False
prefill: List[InputToken]
# Generated tokens
tokens: List[Token]
# Additional sequences when using the `best_of` parameter
best_of_sequences: Optional[List[BestOfSequence]]
# `generate` return value
class Response:
# Generated text
generated_text: str
# Generation details
details: Details
# `generate_stream` details
class StreamDetails:
# Generation finish reason
finish_reason: FinishReason
# Number of prompt tokens
prompt_tokens: int
# Number of generated tokens
generated_tokens: int
# Sampling seed if sampling was activated
seed: Optional[int]
# `generate_stream` return value
class StreamResponse:
# Generated token
token: Token
# Complete generated text
# Only available when the generation is finished
generated_text: Optional[str]
# Generation details
# Only available when the generation is finished
details: Optional[StreamDetails]
# Inference API currently deployed model
class DeployedModel:
model_id: str
sha: str
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