⚠️Errors/Troubleshoot

To fix any errors with your setup, see below:

Running in Unsloth works well, but after exporting & running on other platforms, the results are poor

You might sometimes encounter an issue where your model runs and produces good results on Unsloth, but when you use it on another platform like Ollama or vLLM, the results are poor or you might get gibberish, endless/infinite generations or repeated outputs.

• The most common cause of this error is using an incorrect chat template. It’s essential to use the SAME chat template that was used when training the model in Unsloth and later when you run it in another framework, such as llama.cpp or Ollama. When inferencing from a saved model, it's crucial to apply the correct template.

• It might also be because your inference engine adds an unnecessary "start of sequence" token (or the lack of thereof on the contrary) so ensure you check both hypotheses!

Saving to GGUF / vLLM 16bit crashes

You can try reducing the maximum GPU usage during saving by changing maximum_memory_usage.

The default is model.save_pretrained(..., maximum_memory_usage = 0.75). Reduce it to say 0.5 to use 50% of GPU peak memory or lower. This can reduce OOM crashes during saving.

Evaluation Loop - also OOM or crashing.

A common issue when you OOM is because you set your batch size too high. Set it lower than 3 to use less VRAM.

First split your training dataset into a train and test split. Set the trainer settings for evaluation to:

new_dataset = dataset.train_test_split(test_size = 0.01)
SFTTrainer(
    args = TrainingArguments(
        fp16_full_eval = True,
        per_device_eval_batch_size = 2,
        eval_accumulation_steps = 4,
        eval_strategy = "steps",
        eval_steps = 1,
    ),
    train_dataset = new_dataset["train"],
    eval_dataset = new_dataset["test"],

This will cause no OOMs and make it somewhat faster with no upcasting to float32. Validation set.

NotImplementedError: A UTF-8 locale is required. Got ANSI

See https://github.com/googlecolab/colabtools/issues/3409

In a new cell, run the below:

import locale
locale.getpreferredencoding = lambda: "UTF-8"