| # llama.cpp/examples/training |
|
|
| This directory contains examples related to language model training using llama.cpp/GGML. |
| So far finetuning is technically functional (for FP32 models and limited hardware setups) but the code is very much WIP. |
| Finetuning of Stories 260K and LLaMA 3.2 1b seems to work with 24 GB of memory. |
| **For CPU training, compile llama.cpp without any additional backends such as CUDA.** |
| **For CUDA training, use the maximum number of GPU layers.** |
|
|
| Proof of concept: |
|
|
| ``` sh |
| export model_name=llama_3.2-1b && export quantization=f32 |
| ./build/bin/llama-finetune --file wikitext-2-raw/wiki.test.raw -ngl 999 --model models/${model_name}-${quantization}.gguf -c 512 -b 512 -ub 512 |
| ./build/bin/llama-perplexity --file wikitext-2-raw/wiki.test.raw -ngl 999 --model finetuned-model.gguf |
| ``` |
|
|
| The perplexity value of the finetuned model should be lower after training on the test set for 2 epochs. |
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|
