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Fireworks AI

info

We support ALL Fireworks AI models, just set fireworks_ai/ as a prefix when sending completion requests

PropertyDetails
DescriptionThe fastest and most efficient inference engine to build production-ready, compound AI systems.
Provider Route on LiteLLMfireworks_ai/
Provider DocFireworks AI ↗
Supported OpenAI Endpoints/chat/completions, /embeddings, /completions, /audio/transcriptions

Overview​

This guide explains how to integrate LiteLLM with Fireworks AI. You can connect to Fireworks AI in three main ways:

  1. Using Fireworks AI serverless models – Easy connection to Fireworks-managed models.
  2. Connecting to a model in your own Fireworks account – Access models that are hosted within your Fireworks account.
  3. Connecting via a direct-route deployment – A more flexible, customizable connection to a specific Fireworks instance.

API Key​

# env variable
os.environ['FIREWORKS_AI_API_KEY']

Sample Usage - Serverless Models​

from litellm import completion
import os

os.environ['FIREWORKS_AI_API_KEY'] = ""
response = completion(
    model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
)
print(response)

Sample Usage - Serverless Models - Streaming​

from litellm import completion
import os

os.environ['FIREWORKS_AI_API_KEY'] = ""
response = completion(
    model="fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
    stream=True
)

for chunk in response:
    print(chunk)

Sample Usage - Models in Your Own Fireworks Account​

from litellm import completion
import os

os.environ['FIREWORKS_AI_API_KEY'] = ""
response = completion(
    model="fireworks_ai/accounts/fireworks/models/YOUR_MODEL_ID", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
)
print(response)

Sample Usage - Direct-Route Deployment​

from litellm import completion
import os

os.environ['FIREWORKS_AI_API_KEY'] = "YOUR_DIRECT_API_KEY"
response = completion(
    model="fireworks_ai/accounts/fireworks/models/qwen2p5-coder-7b#accounts/gitlab/deployments/2fb7764c", 
    messages=[
       {"role": "user", "content": "hello from litellm"}
   ],
   api_base="https://gitlab-2fb7764c.direct.fireworks.ai/v1"
)
print(response)

Note: The above is for the chat interface, if you want to use the text completion interface it's model="text-completion-openai/accounts/fireworks/models/qwen2p5-coder-7b#accounts/gitlab/deployments/2fb7764c"

Usage with LiteLLM Proxy​

1. Set Fireworks AI Models on config.yaml​

model_list:
  - model_name: fireworks-llama-v3-70b-instruct
    litellm_params:
      model: fireworks_ai/accounts/fireworks/models/llama-v3-70b-instruct
      api_key: "os.environ/FIREWORKS_AI_API_KEY"

2. Start Proxy​

litellm --config config.yaml

3. Test it​

curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
      "model": "fireworks-llama-v3-70b-instruct",
      "messages": [
        {
          "role": "user",
          "content": "what llm are you"
        }
      ]
    }
'

Supported Models - ALL Fireworks AI Models Supported!​

info

We support ALL Fireworks AI models, just set fireworks_ai/ as a prefix when sending completion requests

Model NameFunction Call
llama-v3p2-1b-instructcompletion(model="fireworks_ai/llama-v3p2-1b-instruct", messages)
llama-v3p2-3b-instructcompletion(model="fireworks_ai/llama-v3p2-3b-instruct", messages)
llama-v3p2-11b-vision-instructcompletion(model="fireworks_ai/llama-v3p2-11b-vision-instruct", messages)
llama-v3p2-90b-vision-instructcompletion(model="fireworks_ai/llama-v3p2-90b-vision-instruct", messages)
mixtral-8x7b-instructcompletion(model="fireworks_ai/mixtral-8x7b-instruct", messages)
firefunction-v1completion(model="fireworks_ai/firefunction-v1", messages)
llama-v2-70b-chatcompletion(model="fireworks_ai/llama-v2-70b-chat", messages)

Supported Embedding Models​

info

We support ALL Fireworks AI models, just set fireworks_ai/ as a prefix when sending embedding requests

Model NameFunction Call
fireworks_ai/nomic-ai/nomic-embed-text-v1.5response = litellm.embedding(model="fireworks_ai/nomic-ai/nomic-embed-text-v1.5", input=input_text)
fireworks_ai/nomic-ai/nomic-embed-text-v1response = litellm.embedding(model="fireworks_ai/nomic-ai/nomic-embed-text-v1", input=input_text)
fireworks_ai/WhereIsAI/UAE-Large-V1response = litellm.embedding(model="fireworks_ai/WhereIsAI/UAE-Large-V1", input=input_text)
fireworks_ai/thenlper/gte-largeresponse = litellm.embedding(model="fireworks_ai/thenlper/gte-large", input=input_text)
fireworks_ai/thenlper/gte-baseresponse = litellm.embedding(model="fireworks_ai/thenlper/gte-base", input=input_text)

Audio Transcription​

Quick Start​

from litellm import transcription
import os

os.environ["FIREWORKS_AI_API_KEY"] = "YOUR_API_KEY"
os.environ["FIREWORKS_AI_API_BASE"] = "https://audio-prod.us-virginia-1.direct.fireworks.ai/v1"

response = transcription(
    model="fireworks_ai/whisper-v3",
    audio=audio_file,
)

Pass API Key/API Base in .transcription