Weights & Biases Inference | liteLLM (original) (raw)

https://weave-docs.wandb.ai/quickstart-inference

tip

Litellm provides support to all models from W&B Inference service. To use a model, set model=wandb/<any-model-on-wandb-inference-dashboard> as a prefix for litellm requests. The full list of supported models is provided at https://docs.wandb.ai/guides/inference/models/

API Key​

You can get an API key for W&B Inference at - https://wandb.ai/authorize

import os
# env variable
os.environ['WANDB_API_KEY']

Sample Usage: Text Generation​

from litellm import completion
import os

os.environ['WANDB_API_KEY'] = "insert-your-wandb-api-key"
response = completion(
    model="wandb/Qwen/Qwen3-235B-A22B-Instruct-2507",
    messages=[
        {
            "role": "user",
            "content": "What character was Wall-e in love with?",
        }
    ],
    max_tokens=10,
    response_format={ "type": "json_object" },
    seed=123,
    temperature=0.6,  # either set temperature or `top_p`
    top_p=0.01,  # to get as deterministic results as possible
)
print(response)

Sample Usage - Streaming​

from litellm import completion
import os

os.environ['WANDB_API_KEY'] = ""
response = completion(
    model="wandb/Qwen/Qwen3-235B-A22B-Instruct-2507",
    messages=[
        {
            "role": "user",
            "content": "What character was Wall-e in love with?",
        }
    ],
    stream=True,
    max_tokens=10,
    response_format={ "type": "json_object" },
    seed=123,
    temperature=0.6,  # either set temperature or `top_p`
    top_p=0.01,  # to get as deterministic results as possible
)

for chunk in response:
    print(chunk)

Usage with LiteLLM Proxy Server​

Here's how to call a W&B Inference model with the LiteLLM Proxy Server

1. Modify the config.yaml

model_list:
  - model_name: my-model
    litellm_params:
      model: wandb/<your-model-name>  # add wandb/ prefix to use W&B Inference as provider
      api_key: api-key                 # api key to send your model

1. Start the proxy

$ litellm --config /path/to/config.yaml

1. Send Request to LiteLLM Proxy Server

• OpenAI Python v1.0.0+
• curl

import openai
client = openai.OpenAI(
    api_key="litellm-proxy-key",             # 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="my-model",
    messages = [
        {
            "role": "user",
            "content": "What character was Wall-e in love with?"
        }
    ],
)

print(response)

Supported Parameters​

The W&B Inference provider supports the following parameters:

Chat Completion Parameters​

Error Handling​

The integration uses the standard LiteLLM error handling. Further, here's a list of commonly encountered errors with the W&B Inference API -

Error 429: Personal entities unsupported​

The user is on a personal account, which doesn't have access to W&B Inference. If one isn't available, create a Team to create a non-personal account.

Once done, add the openai-project header to your request as shown below:

response = completion(
    model="...",
    extra_headers={"openai-project": "team_name/project_name"},
    ...

For more information, see Personal entities unsupported.

You can find more ways of using custom headers with LiteLLM here - https://docs.litellm.ai/docs/proxy/request_headers.