text: 'ssh_private_key':'<path_to_key>'},#                  name='my-cluster')embeddings = SelfHostedHuggingFaceEmbeddings(hardware=gpu)text = "This is a test document."query_result = embeddings.embed_query(text)And similarly for SelfHostedHuggingFaceInstructEmbeddings:embeddings = SelfHostedHuggingFaceInstructEmbeddings(hardware=gpu)Now let's load an embedding model with a custom load function:def get_pipeline():    from transformers import (        AutoModelForCausalLM,        AutoTokenizer,        pipeline,    )  # Must be inside the function in notebooks    model_id = "facebook/bart-base"    tokenizer = AutoTokenizer.from_pretrained(model_id)    model = AutoModelForCausalLM.from_pretrained(model_id)    return pipeline("feature-extraction", model=model, tokenizer=tokenizer)def inference_fn(pipeline, prompt):    # Return last hidden state of the model    if isinstance(prompt, list):        return [emb[0][-1] for emb in pipeline(prompt)]    return pipeline(prompt)[0][-1]embeddings = SelfHostedEmbeddings(    model_load_fn=get_pipeline,    hardware=gpu,    model_reqs=["./", "torch", "transformers"],    inference_fn=inference_fn,)query_result = embeddings.embed_query(text)PreviousSageMaker Endpoint EmbeddingsNextSentence Transformers EmbeddingsCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â© 2023 LangChain, Inc.