Groq

Using Groq for ultra-fast LLM inference with Upsonic

Overview

Groq provides ultra-fast inference through their Language Processing Unit (LPU) technology. Access open-source models with industry-leading speed and built-in web search capabilities.

Model Class: GroqModel

Authentication

export GROQ_API_KEY="gsk_..."

Examples

from upsonic import Agent, Task
from upsonic.models.groq import GroqModel

model = GroqModel(model_name="llama-3.3-70b-versatile")
agent = Agent(model=model)

task = Task("Hello, how are you?")
result = agent.do(task)
print(result)

Parameters

Parameter	Type	Description	Default	Source
max_tokens	int	Maximum tokens to generate	1024	Base
temperature	float	Sampling temperature (0.0-2.0)	1.0	Base
top_p	float	Nucleus sampling	1.0	Base
seed	int	Random seed	None	Base
stop_sequences	list[str]	Stop sequences	None	Base
presence_penalty	float	Token presence penalty	0.0	Base
frequency_penalty	float	Token frequency penalty	0.0	Base
parallel_tool_calls	bool	Allow parallel tools	True	Base
timeout	float	Request timeout (seconds)	600	Base
groq_reasoning_format	'hidden' | 'raw' | 'parsed'	Reasoning output format	None	Specific