SamplingClient

SamplingClient for the Logits SDK.

SamplingClient Objects

class SamplingClient(TelemetryProvider, QueueStateObserver)

Client for text generation and inference from trained or base models.

The SamplingClient lets you generate text tokens from either a base model or from weights you've saved using a TrainingClient. You typically get one by calling service_client.create_sampling_client() or training_client.save_weights_and_get_sampling_client().

Key methods: - sample() - generate text completions with customizable parameters - compute_logprobs() - get log probabilities for prompt tokens

Create method parameters: - model_path: Path to saved model weights (tinker://... or logits://...) - base_model: Name of base model to use for inference (e.g., 'Qwen/Qwen3.5-4B') - retry_config: Configuration for retrying failed requests

Example:

sampling_client = service_client.create_sampling_client(base_model="Qwen/Qwen3.5-4B")
prompt = types.ModelInput.from_ints(tokenizer.encode("The weather today is"))
params = types.SamplingParams(max_tokens=20, temperature=0.7)
future = sampling_client.sample(prompt=prompt, sampling_params=params, num_samples=1)
result = future.result()

sample

def sample(
        prompt: types.ModelInput,
        num_samples: int,
        sampling_params: types.SamplingParams,
        include_prompt_logprobs: bool = False,
        topk_prompt_logprobs: int = 0
) -> ConcurrentFuture[types.SampleResponse]

Generate text completions from the model.

Args: - prompt: The input tokens as ModelInput - num_samples: Number of independent samples to generate - sampling_params: Parameters controlling generation (temperature, max_tokens, etc.) - include_prompt_logprobs: Whether to include log probabilities for prompt tokens - topk_prompt_logprobs: Number of top token log probabilities to return per position

Returns: - A Future containing the SampleResponse with generated text

Example:

prompt = types.ModelInput.from_ints(tokenizer.encode("The weather today is"))
params = types.SamplingParams(max_tokens=20, temperature=0.7)
future = sampling_client.sample(prompt=prompt, sampling_params=params, num_samples=1)
result = future.result()
for sample in result.sequences:
    print(tokenizer.decode(sample.tokens))

sample_async

async def sample_async(prompt: types.ModelInput,
                       num_samples: int,
                       sampling_params: types.SamplingParams,
                       include_prompt_logprobs: bool = False,
                       topk_prompt_logprobs: int = 0) -> types.SampleResponse

Async version of sample.

compute_logprobs

def compute_logprobs(
        prompt: types.ModelInput) -> ConcurrentFuture[list[float | None]]

Compute log probabilities for prompt tokens.

Args: - prompt: The input tokens as ModelInput

Returns: - A Future containing a list of log probabilities for each token in the prompt. None values indicate tokens where log probabilities couldn't be computed.

Example:

prompt = types.ModelInput.from_ints(tokenizer.encode("Hello world"))
future = sampling_client.compute_logprobs(prompt)
logprobs = future.result()
for i, logprob in enumerate(logprobs):
    if logprob is not None:
        print(f"Token {i}: logprob = {logprob:.4f}")

compute_logprobs_async

async def compute_logprobs_async(
        prompt: types.ModelInput) -> list[float | None]

Async version of compute_logprobs.