Open shivprasad94 opened 1 year ago
Hello @shivprasad94,
sorry for the late reply and thanks for reaching out!
TabLLM is LLM agnostic, so you can use whatever LLM you want. For instance, to use another HuggingFace model you could create a new json config in TabLLM/t-few/configs
(e.g. llama.json
) and use the model specifier for the original_model
parameter (e.g., "origin_model": "meta-llama/Llama-2-7b"
).
You can then use this model configuration in the run configuration few-shot-pretrained-100k.sh in line 18 as for model in 'llama'.
.
Let us know if you need any further help!
There seems to be something wrong with t-few when finetune since LLaMA is not an EncoderDecoder model
Hello @RyanJJP,
thanks for this additional comment. You are right, t-few might not work with LLaMA. However, other fine-tuning methods for LLaMA (e.g. QLoRA) should allow a similar functionality. This would require larger changes to the code basis but conceptually it should be similar.
I see from the code repo that we are using OpenAI APIs, how can we make this work for Open source models like Llama2? can someone give me detail on this and what steps I need to follow?