Model path

Just check the path manually I think first, to decide on passing in the path or url

it should be by default no?

oh I see, we're trowing an error.

Yea you can follow with this

ok, I can check the /tmp/llama_index/models but wanted to save it on my own directory,

You can change the cache directory by setting $LLAMA_INDEX_CACHE_DIR in your environment variables

👍

WIll it work with open source llm models: https://gpt-index.readthedocs.io/en/latest/examples/finetuning/knowledge/finetune_retrieval_aug.html?
I don't have any information about the llamaindex's history or its connections with OpenAI, but it doesn't make sense to me that something called 'llama' wouldn't be compatible with other open-source LLMs like llama2 or similar models. The same applies to the following code snippet:I don't have any information about the llamaindex's history or its connections with OpenAI, but it doesn't make sense to me that something called 'llama' wouldn't be compatible with other open-source LLMs like llama2 or similar models.

The same applies to the following code snippet: agent = create_pandas_dataframe_agent(OpenAI(temperature=0, model_name='gpt-4'), [df], handle_parsing_errors=True, verbose=True)
I've noticed confusion on GitHub, and someone else asked me similar questions about evaluators. While evaluators work with other LLMs, I'd like to clarify why OpenAI is used in the examples. We wasted a lot of time trying to determine if it would work with other LLMs.

llama-index arrived a few weeks/days before the meta llama models 😅

the framework name itself has no relation to the models which the framework uses

so OpenAI always was the default one, since came firstly, and provide their API to anyone use

curiosity: before llama-index the name was gpt-index

Everyone wanted to spit like a llama 🙂 Thank you on explanation. And what do you think about finetunning with RAG (the link above)? Will it work with llama2, mistral or some other os model?

The concepts still the same, unfortunately you will need to find another way to fine-tune your llama, since this module on this doc is only for openai

That's a option: https://docs.llamaindex.ai/en/stable/examples/finetuning/gradient/gradient_text2sql.html#run-fine-tuning-with-gradient-ai