For the complete documentation index, see llms.txt. Markdown versions of documentation pages are available by appending .md to page URLs; this page is available as Markdown.

Parameters

Parasail supports all parameters that vLLM supports. These parameters control the randomness, diversity, and length of model outputs.

Sampling parameters

temperature (default: 1.0)

Controls randomness in token selection.

• Higher values (>1.0) increase randomness.
• Lower values (<1.0) make outputs more deterministic.
• A value of 0 forces greedy decoding.

top_p (Nucleus Sampling) (default: 1.0)

Controls the probability mass of token selection.

• Only considers tokens that sum up to top_p probability.
• Lower values (for example, 0.9) limit token choices to more likely options.
• Higher values (close to 1.0) allow a broader range of tokens.

top_k (default: -1, disabled)

Limits token selection to the top k most probable tokens.

• Lower values (for example, top_k=50) make output more deterministic.
• If -1, this setting is ignored.
• Since top_k is not in the OpenAI spec, pass it in the extra_body field.

max_tokens (default: None)

Sets the maximum number of tokens to generate. Helps prevent excessively long responses.

repetition_penalty (default: 1.0)

Penalizes repeated tokens to avoid looping responses. Common values: 1.1 to 1.2.

presence_penalty (default: 0.0)

Increases the likelihood of introducing new tokens. Useful for making outputs more diverse.

frequency_penalty (default: 0.0)

Penalizes tokens that have appeared frequently. Helps prevent excessive repetition of common words.

seed (default: None)

Sets a fixed seed for reproducible results. Useful for debugging or deterministic sampling.

How parameters work together

• Setting temperature=0 forces deterministic output (greedy decoding).
• Using top_p and top_k together balances diversity and coherence.
• repetition_penalty and presence_penalty help avoid repetitive loops.

Example

from openai import OpenAI

client = OpenAI(
    base_url="https://api.parasail.io/v1",
    api_key="<PARASAIL_API_KEY>"
)

response = client.chat.completions.create(
    model="parasail-deepseek-r1",
    messages=[{"role": "user", "content": "Write a creative story about a robot."}],
    max_completion_tokens=1000,
    temperature=0.7,
    top_p=0.1,
    repetition_penalty=1.1,
    extra_body={"top_k": 50}
)

print(response.choices[0].message.content)

Next steps

• Chat completions API—full chat completions API spec and endpoint compatibility matrix
• Structured output guide—constrain outputs to JSON
• Responses API—parameters for agentic, multi-turn calls