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@@ -19,15 +19,14 @@ trainer.train()
 ```
 
 ### Finetuning the `lm_head` and `embed_tokens` matrices:
-Don't forget to resize your embedding matrices if you added new tokens!
 ```python
 model = FastLanguageModel.get_peft_model(
     model,
     r = 16,
     target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
-                      "gate_proj", "up_proj", "down_proj",],
+                      "gate_proj", "up_proj", "down_proj",
+                      "lm_head", "embed_tokens",],
     lora_alpha = 16,
-    modules_to_save = ["lm_head", "embed_tokens",],
 )
 ```
 
@@ -128,11 +127,14 @@ model.save_pretrained_merged("merged_model", tokenizer, save_method = "merged_16
 ```
 Then use the terminal and do:
 ```bash
-git clone https://github.com/ggerganov/llama.cpp
-cd llama.cpp && make clean && LLAMA_CUBLAS=1 make all -j
+git clone --recursive https://github.com/ggerganov/llama.cpp
+make clean -C llama.cpp
+make all -j -C llama.cpp
 pip install gguf protobuf
+
+python llama.cpp/convert-hf-to-gguf.py FOLDER --outfile OUTPUT --outtype f16
 ```
-Then follow the steps at https://rentry.org/llama-cpp-conversions#merging-loras-into-a-model using the model name "merged_model" to merge to GGUF.
+Or follow the steps at https://rentry.org/llama-cpp-conversions#merging-loras-into-a-model using the model name "merged_model" to merge to GGUF.
 
 ### Evaluation Loop - also OOM or crashing.
 Set the trainer settings for evaluation to: