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GPU Poor POV: Dont be Afraid :D
GPU Poor POV: Dont be Afraid :D
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Sometimes we dont want to do something because of low self esteem,
Sometimes we dont want to do something because of low self esteem,
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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. 
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. 
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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. 
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. 
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All we need is an idea, our GPU Poor desktop or colab notebooks and these steps:
All we need is an idea, our GPU Poor desktop or colab notebooks and these steps:
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```
git clone https://github.com/OpenAccess-AI-Collective/axolotl
cd axolotl

pip3 install packaging
pip3 install -e '.[flash-attn,deepspeed]'
```
git clone https://github.com/OpenAccess-AI-Collective/axolotl
cd axolotl

pip3 install packaging
pip3 install -e '.[flash-attn,deepspeed]'
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After installation process we can go to examples, and modify configs to our own needs. 
After installation process we can go to examples, and modify configs to our own needs. 
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Lets jump into 
Lets jump into 
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```
axolotl\examples\llama-2\qlora.yml 
```
axolotl\examples\llama-2\qlora.yml 
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and change 
and change 
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```
base_model: NousResearch/Llama-2-7b-hf
```
base_model: NousResearch/Llama-2-7b-hf
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to 
to 
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```
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
```
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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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). 
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). 
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Then, 
Then, 
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```
accelerate launch -m axolotl.cli.train examples/llama-2/qlora.yml
```
accelerate launch -m axolotl.cli.train examples/llama-2/qlora.yml
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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 :)
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 :)
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Have fun and have a great day <3
Have fun and have a great day <3
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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

👍
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