We support ALL Groq models, just set model=groq/<any-model-on-groq> as a prefix when sending litellm requests

# env variable
os.environ['GROQ_API_KEY']

from litellm import completion
import os

os.environ['GROQ_API_KEY']=""
response = completion(
 model="groq/llama3-8b-8192",
 messages=[
{"role":"user","content":"hello from litellm"}
],
)
print(response)

from litellm import completion
import os

os.environ['GROQ_API_KEY']=""
response = completion(
 model="groq/llama3-8b-8192",
 messages=[
{"role":"user","content":"hello from litellm"}
],
 stream=True
)

for chunk in response:
print(chunk)

model_list:
-model_name: groq-llama3-8b-8192# Model Alias to use for requests
litellm_params:
model: groq/llama3-8b-8192
api_key:"os.environ/GROQ_API_KEY"# ensure you have `GROQ_API_KEY` in your .env

litellm --config config.yaml

# Example dummy function hard coded to return the current weather
import json
defget_current_weather(location, unit="fahrenheit"):
"""Get the current weather in a given location"""
if"tokyo"in location.lower():
return json.dumps({"location":"Tokyo","temperature":"10","unit":"celsius"})
elif"san francisco"in location.lower():
return json.dumps(
{"location":"San Francisco","temperature":"72","unit":"fahrenheit"}
)
elif"paris"in location.lower():
return json.dumps({"location":"Paris","temperature":"22","unit":"celsius"})
else:
return json.dumps({"location": location,"temperature":"unknown"})




# Step 1: send the conversation and available functions to the model
messages =[
{
"role":"system",
"content":"You are a function calling LLM that uses the data extracted from get_current_weather to answer questions about the weather in San Francisco.",
},
{
"role":"user",
"content":"What's the weather like in San Francisco?",
},
]
tools =[
{
"type":"function",
"function":{
"name":"get_current_weather",
"description":"Get the current weather in a given location",
"parameters":{
"type":"object",
"properties":{
"location":{
"type":"string",
"description":"The city and state, e.g. San Francisco, CA",
},
"unit":{
"type":"string",
"enum":["celsius","fahrenheit"],
},
},
"required":["location"],
},
},
}
]
response = litellm.completion(
 model="groq/llama3-8b-8192",
 messages=messages,
 tools=tools,
 tool_choice="auto",# auto is default, but we'll be explicit
)
print("Response\n", response)
response_message = response.choices[0].message
tool_calls = response_message.tool_calls


# Step 2: check if the model wanted to call a function
if tool_calls:
# Step 3: call the function
# Note: the JSON response may not always be valid; be sure to handle errors
 available_functions ={
"get_current_weather": get_current_weather,
}
 messages.append(
 response_message
)# extend conversation with assistant's reply
print("Response message\n", response_message)
# Step 4: send the info for each function call and function response to the model
for tool_call in tool_calls:
 function_name = tool_call.function.name
 function_to_call = available_functions[function_name]
 function_args = json.loads(tool_call.function.arguments)
 function_response = function_to_call(
 location=function_args.get("location"),
 unit=function_args.get("unit"),
)
 messages.append(
{
"tool_call_id": tool_call.id,
"role":"tool",
"name": function_name,
"content": function_response,
}
)# extend conversation with function response
print(f"messages: {messages}")
 second_response = litellm.completion(
 model="groq/llama3-8b-8192", messages=messages
)# get a new response from the model where it can see the function response
print("second response\n", second_response)

os.environ["GROQ_API_KEY"]=""
audio_file =open("/path/to/audio.mp3","rb")

transcript = litellm.transcription(
 model="groq/whisper-large-v3",
file=audio_file,
 prompt="Specify context or spelling",
 temperature=0,
 response_format="json"
)

print("response=", transcript)