Unknown model: davinci:ft-finetuned-2023-07-08-13-19-29. Please provide a valid OpenAI model name.Known models are: gpt-4, gpt-4-0314, gpt-4-32k, gpt-4-32k-0314, gpt-3.5-turbo, gpt-3.5-turbo-0301, text-ada-001, ada, text-babbage-001, babbage, text-curie-001, curie, davinci, text-davinci-003, text-davinci-002, code-davinci-002, code-davinci-001, code-cushman-002, code-cushman-001
modelname_to_contextsize from langchain OpenAI class. I'm using 0.6.15 for llamaIndex. I know the version is old. lol 😅 HuggingFaceLLMPredictor to use StabilityAI/stablelm-tuned-alpha-3b query_wrapper_prompt = SimpleInputPrompt(
"<|SYSTEM|>Below is an instruction that describes a task."
"Write a response that adequately completes the request.\n\n"
"<|USER|>{query_str}\n<|ASSISTANT|>"
)query_str here? Like providing context separately and then user query?HuggingFaceLLMPredictor then if I pass the text_qa_template while creating query_engine instance. Will it make any difference?ValueError: shapes (1536,) and (768,) not aligned: 1536 (dim 0) != 768 (dim 0)Tokensplitter with the following parameterstext_splitter = TokenTextSplitter(separator=" ", chunk_size=512, chunk_overlap=20)chunk_size and chunk_ovrlap chunk_size and overlap. Then why an extra doc is being created ?from langchain.chat_models import ChatOpenAI llm = LLMPredictor(llm=ChatOpenAI(openai_api_key=OPENAI_API_KEY,temperature=0, max_tokens=1024, model_name="gpt-3.5-turbo"))
service_context = ServiceContext.from_defaults(chunk_size_limit=512, llm=llm)
Token indices sequence length is longer than the specified maximum sequence length for this model (1043 > 512). Running this sequence through the model will result in indexing errorsprint(service_context.chunk_size_limit) Output: 1024
chat_engine = index.as_chat_engine()index.as_chat_engine() Had some doubts