model_kwargs={"temperature": 0.1})llm("How are you?")    'I am fine.'# Use `transform_input_fn` and `transform_output_fn` if the serving endpoint# expects a different input schema and does not return a JSON string,# respectively, or you want to apply a prompt template on top.def transform_input(**request):    full_prompt = f"""{request["prompt"]}    Be Concise.    """    request["prompt"] = full_prompt    return requestllm = Databricks(endpoint_name="dolly", transform_input_fn=transform_input)llm("How are you?")    'I‚Äôm Excellent. You?'Wrapping a cluster driver proxy app‚ÄãPrerequisites:An LLM loaded on a Databricks interactive cluster in "single user" or "no isolation shared" mode.A local HTTP server running on the driver node to serve the model at "/" using HTTP POST with JSON input/output.It uses a port number between [3000, 8000] and listens to the driver IP address or simply 0.0.0.0 instead of localhost only.You have "Can Attach To" permission to the cluster.The expected server schema (using JSON schema) is:inputs:{"type": "object", "properties": {    "prompt": {"type": "string"},     "stop": {"type": "array", "items": {"type": "string"}}},  "required": ["prompt"]}outputs: {"type": "string"}If the server schema is incompatible or you want to insert extra configs, you can use transform_input_fn and transform_output_fn accordingly.The following is a minimal example for running a driver proxy app to serve an LLM:from flask import Flask, request, jsonifyimport torchfrom transformers import pipeline, AutoTokenizer, StoppingCriteriamodel = "databricks/dolly-v2-3b"tokenizer = AutoTokenizer.from_pretrained(model, padding_side="left")dolly = pipeline(model=model, tokenizer=tokenizer, trust_remote_code=True, device_map="auto")device = dolly.deviceclass CheckStop(StoppingCriteria):    def __init__(self, stop=None):        super().__init__()        self.stop = stop or []        self.matched = ""        self.stop_ids = [tokenizer.encode(s, return_tensors='pt').to(device)
The Databricks Lakehouse Platform unifies data, analytics, and AI on one platform.
The Databricks Lakehouse Platform unifies data, analytics, and AI on one platform. ->: model_kwargs={"temperature": 0.1})llm("How are you?")    'I am fine.'# Use `transform_input_fn` and `transform_output_fn` if the serving endpoint# expects a different input schema and does not return a JSON string,# respectively, or you want to apply a prompt template on top.def transform_input(**request):    full_prompt = f"""{request["prompt"]}    Be Concise.    """    request["prompt"] = full_prompt    return requestllm = Databricks(endpoint_name="dolly", transform_input_fn=transform_input)llm("How are you?")    'I‚Äôm Excellent. You?'Wrapping a cluster driver proxy app‚ÄãPrerequisites:An LLM loaded on a Databricks interactive cluster in "single user" or "no isolation shared" mode.A local HTTP server running on the driver node to serve the model at "/" using HTTP POST with JSON input/output.It uses a port number between [3000, 8000] and listens to the driver IP address or simply 0.0.0.0 instead of localhost only.You have "Can Attach To" permission to the cluster.The expected server schema (using JSON schema) is:inputs:{"type": "object", "properties": {    "prompt": {"type": "string"},     "stop": {"type": "array", "items": {"type": "string"}}},  "required": ["prompt"]}outputs: {"type": "string"}If the server schema is incompatible or you want to insert extra configs, you can use transform_input_fn and transform_output_fn accordingly.The following is a minimal example for running a driver proxy app to serve an LLM:from flask import Flask, request, jsonifyimport torchfrom transformers import pipeline, AutoTokenizer, StoppingCriteriamodel = "databricks/dolly-v2-3b"tokenizer = AutoTokenizer.from_pretrained(model, padding_side="left")dolly = pipeline(model=model, tokenizer=tokenizer, trust_remote_code=True, device_map="auto")device = dolly.deviceclass CheckStop(StoppingCriteria):    def __init__(self, stop=None):        super().__init__()        self.stop = stop or []        self.matched = ""        self.stop_ids = [tokenizer.encode(s, return_tensors='pt').to(device)