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)
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Sentence Transformers
By Harrison Chase
    
      © Copyright 2023, Harrison Chase.
      
  Last updated on Jun 16, 2023.
rtdocs_stable/api.python.langchain.com/en/stable/modules/models/text_embedding/examples/self-hosted.html