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Jacob Maximillian Miesner

miesnerjacob

miesnerjacob

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updated a Space 3 days ago

miesnerjacob/Multi-task-NLP

reacted to s3nh's post with 👍 about 1 year ago

GPU Poor POV: Dont be Afraid :D Sometimes we dont want to do something because of low self esteem, I ofter hear 'its to hard for me','i am not an expert','i do not know how to do it', etc. These words are never the truth, we should not be afraid and try to build something because there is no additive value without a failure. Same things comes in LLMs, there is a lot of fancy words happening, but whats is more important is that there are also people who are constantly building so other can build. Diving into finetuning LLMs is incredibly simple if we assume using axolotl library and pretrains stored on huggingface. All we need is an idea, our GPU Poor desktop or colab notebooks and these steps: ``` git clone https://github.com/OpenAccess-AI-Collective/axolotl cd axolotl pip3 install packaging pip3 install -e '.[flash-attn,deepspeed]' ``` After installation process we can go to examples, and modify configs to our own needs. Lets jump into ``` axolotl\examples\llama-2\qlora.yml ``` and change ``` base_model: NousResearch/Llama-2-7b-hf ``` to ``` base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0 ``` choose dataset from huge amounts of dataset that are possible to use from hf.co/datasets and tweak additional params like batch_size, number of epochs, how often do we want to save our model and many more (which I wont focus on rn). Then, ``` accelerate launch -m axolotl.cli.train examples/llama-2/qlora.yml ``` Will allow to start the finetuning process on structure defined strictly by you. After finetuning, model will be saved in path provided in config, and you can check out if it performs better than the base one. Or even you can put it on llm Leaderboard to check if we do not have new SOTA :) Have fun and have a great day <3

upvoted a paper over 1 year ago

Llama 2: Open Foundation and Fine-Tuned Chat Models

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miesnerjacob's activity

updated a Space 3 days ago

Multi Task NLP

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Easily analyze text for sentiment, keywords, emotion, and entities

reacted to s3nh's post with 👍 about 1 year ago

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git clone https://github.com/OpenAccess-AI-Collective/axolotl
cd axolotl

pip3 install packaging
pip3 install -e '.[flash-attn,deepspeed]'

After installation process we can go to examples, and modify configs to our own needs.
Lets jump into

axolotl\examples\llama-2\qlora.yml

and change

base_model: NousResearch/Llama-2-7b-hf

base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0

choose dataset from huge amounts of dataset that are possible to use from hf.co/datasets and tweak additional params like batch_size, number of epochs, how often do we want to save our model and many more (which I wont focus on rn).
Then,

accelerate launch -m axolotl.cli.train examples/llama-2/qlora.yml

Will allow to start the finetuning process on structure defined strictly by you. After finetuning, model will be saved in path provided in config, and you can check out if it performs better than the base one. Or even you can put it on llm Leaderboard to check if we do not have new SOTA :)
Have fun and have a great day <3