Bytez

LiteLLM supports all chat models on Bytez!

That also means multi-modal models are supported 🔥

Tasks supported: chat, image-text-to-text, audio-text-to-text, video-text-to-text

Usage​

import os
os.environ["BYTEZ_API_KEY"] = "YOUR_BYTEZ_KEY_GOES_HERE"

from litellm import completion
import os
## set ENV variables
os.environ["BYTEZ_API_KEY"] = "YOUR_BYTEZ_KEY_GOES_HERE"

response = completion(
    model="bytez/google/gemma-3-4b-it",
    messages = [{ "content": "Hello, how are you?","role": "user"}]
)

model_list:
  - model_name: gemma-3
    litellm_params:
      model: bytez/google/gemma-3-4b-it
      api_key: os.environ/BYTEZ_API_KEY

$ BYTEZ_API_KEY=YOUR_BYTEZ_API_KEY_HERE litellm --config /path/to/config.yaml --debug

import openai
client = openai.OpenAI(
    api_key="sk-1234",             # pass litellm proxy key, if you're using virtual keys
    base_url="http://0.0.0.0:4000" # litellm-proxy-base url
)

response = client.chat.completions.create(
    model="gemma-3",
    messages = [
      {
          "role": "system",
          "content": "Be a good human!"
      },
      {
          "role": "user",
          "content": "What do you know about earth?"
      }
  ]
)

print(response)

curl --location 'http://0.0.0.0:4000/chat/completions' \
    --header 'Authorization: Bearer sk-1234' \
    --header 'Content-Type: application/json' \
    --data '{
    "model": "gemma-3",
    "messages": [
      {
          "role": "system",
          "content": "Be a good human!"
      },
      {
          "role": "user",
          "content": "What do you know about earth?"
      }
      ],
}'

Automatic Prompt Template Handling​

All prompt formatting is handled automatically by our API when you send a messages list to it!

If you wish to use custom formatting, please let us know via either help@bytez.com or on our Discord and we will work to provide it!

Passing additional params - max_tokens, temperature​

See all litellm.completion supported params here

# !pip install litellm
from litellm import completion
import os
## set ENV variables
os.environ["BYTEZ_API_KEY"] = "YOUR_BYTEZ_KEY_HERE"

# bytez gemma-3 call
response = completion(
    model="bytez/google/gemma-3-4b-it",
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    max_tokens=20,
    temperature=0.5
)

proxy

model_list:
  - model_name: gemma-3
    litellm_params:
      model: bytez/google/gemma-3-4b-it
      api_key: os.environ/BYTEZ_API_KEY
      max_tokens: 20
      temperature: 0.5

Passing Bytez-specific params​

Any kwarg supported by huggingface we also support! (Provided the model supports it.)

Example repetition_penalty

# !pip install litellm
from litellm import completion
import os
## set ENV variables
os.environ["BYTEZ_API_KEY"] = "YOUR_BYTEZ_KEY_HERE"

# bytez llama3 call with additional params
response = completion(
    model="bytez/google/gemma-3-4b-it",
    messages = [{ "content": "Hello, how are you?","role": "user"}],
    repetition_penalty=1.2,
)

proxy

model_list:
  - model_name: gemma-3
    litellm_params:
      model: bytez/google/gemma-3-4b-it
      api_key: os.environ/BYTEZ_API_KEY
      repetition_penalty: 1.2