Shared Endpoints

info

Shared endpoints are available for free inference and are intended for experimentation and fast iteration since they're served on shared infrastructure and subject to capacity constraints.

For production-ready inference, use private serverless deployments.

Prompt Base Models

Predibase supports a variety of base models as shared endpoints.

When prompting using the SDK or REST API, we recommend including the model-specific instruction template, otherwise you may see less than stellar results. Prompting in the UI includes these templates by default.

Python SDK

# Specify the shared endpoint by name
lorax_client = pb.deployments.client("mistral-7b-instruct-v0-2")
print(lorax_client.generate("""<<SYS>>You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.<</SYS>>

[INST] What is the best pizza restaurant in New York? [/INST]""", max_new_tokens=100).generated_text)

REST

# Export environment variables
export PREDIBASE_API_TOKEN="<YOUR TOKEN HERE>" # Settings > My Profile > Generate API Token
export PREDIBASE_TENANT_ID="<YOUR TENANT ID>" # Settings > My Profile > Overview > Tenant ID
export PREDIBASE_DEPLOYMENT="llama-2-7b-chat"

# query the LLM deployment
curl -d '{"inputs": "What is your name?", "parameters": {"max_new_tokens": 256}}' \
    -H "Content-Type: application/json" \
    -X POST https://serving.app.predibase.com/$PREDIBASE_TENANT_ID/deployments/v2/llms/$PREDIBASE_DEPLOYMENT/generate \
    -H "Authorization: Bearer ${PREDIBASE_API_TOKEN}"

Prompt Fine-tuned Models (with LoRAX)

If the base model is one of the available shared endpoints, you can prompt your fine-tuned model immediately after training. If your base model is a custom model, you will need to use a private serverless deployment to prompt your fine-tuned model.

Python SDK

# Specify the shared endpoint of the base model which was fine-tuned
lorax_client = pb.deployments.client("mistral-7b-instruct-v0-2")

# Specify your adapter_id as "adapter-repo-name/adapter-version-number"
print(lorax_client.generate("""<<SYS>>You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature. If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.<</SYS>>

[INST] What is the best pizza restaurant in New York? [/INST]""", adapter_id="adapter-repo-name/1", max_new_tokens=100).generated_text)

REST

# Export environment variables
export PREDIBASE_API_TOKEN="<YOUR TOKEN HERE>" # Settings > My Profile > Generate API Token
export PREDIBASE_TENANT_ID="<YOUR TENANT ID>" # Settings > My Profile > Overview > Tenant ID
export PREDIBASE_DEPLOYMENT="llama-2-7b-chat"

# query the LLM deployment
curl -d '{"inputs": "What is your name?", "parameters": {"api_token": "${PREDIBASE_API_TOKEN}", "adapter_source": "pbase", "adapter_id": "<finetuned_model_repo_name>/<model_version>", "max_new_tokens": 256}}' \
    -H "Content-Type: application/json" \
    -X POST https://serving.app.predibase.com/$PREDIBASE_TENANT_ID/deployments/v2/llms/$PREDIBASE_DEPLOYMENT/generate \
    -H "Authorization: Bearer ${PREDIBASE_API_TOKEN}"

See REST API for more parameters.