Additionally, please explain any advanced setup options available with Axolotl such as Docker or Conda/Pip venv.

Set up the environment:
Install Axolotl using pip or conda (see instructions in the documentation)
Create a new directory for your project and navigate into it
Clone the repository containing the pretrained model you want to fine-tune, or download its weights from Hugging Face Hub.
2. Preprocess dataset:
If necessary, convert raw data into a format that can be used by Axolotl (e.g., CSV, JSON).
Tokenize the text using the tokenizer provided with your pretrained model or create a custom one if needed.
Split the data into training and validation sets.
3. Create an Axolotl configuration file:
Define the input and output formats for your dataset (e.g., CSV, JSON).
Specify the path to your pretrained model weights or download them from Hugging Face Hub if they’re not already in your project directory.
Set up any additional options you want to use during training, such as learning rate scheduling, gradient accumulation steps, and batch size.
4. Run Axolotl:
Use the `accelerate launch` command to run your fine-tuning job on multiple GPUs (if available). This will automatically handle distributed training using DDP or FSDP depending on which option you choose in your configuration file.
5. Inference with Gradio:
Create a new Gradio interface for your model by defining the input and output formats, as well as any additional options you want to use during inference (e.g., maximum sequence length).
6. Advanced setup options:
Use Docker or Conda/Pip venv to create isolated environments for your project, which can help prevent conflicts with other packages installed on your system.
Enable debugging mode by setting the `debug` flag in your configuration file and running Axolotl using VSCode’s built-in debugger (see documentation). This will allow you to step through your code line by line, set breakpoints, and inspect variables as they change during execution.
Use the `preprocess` command to preprocess your dataset before fine-tuning, which can help improve training efficiency for large datasets.

7. Merge LORA to base:
After completing fine-tuning with Axolotl and generating a LORA adapter, you can merge it back into the original pretrained model using the `merge_lora` command provided by Axolotl. This will create a new merged model that combines both the pretrained weights and the LORA adapter’s parameters.
You can also try reducing other settings such as micro_batch_size or gradient_accumulation_steps if you encounter memory issues.
8. Common errors:
If you encounter a ‘Cuda out of memory’ error, it means your GPU ran out of memory during the training process. Try reducing any below micro_batch_size eval_batch_size gradient_accumulation_steps sequence_len to avoid running out of VRAM.
If you encounter a ‘RuntimeError: expected scalar type Float but found Half’ error, try setting fp16: true in your configuration file.
If you encounter an ‘No operator found for memory_efficient_attention_forward ‘ error, try turning off xformers by removing the `–use-xformers` flag from your command line or config file.
9. Debugging Axolotl:
For troubleshooting issues with Axolotl, you can use the debugging guide provided in the documentation to help identify and resolve common problems. This includes tips for setting up a VSCode environment for debugging, as well as instructions for running tests against your code using pytest.
10. Contributing:
If you’re interested in contributing to Axolotl or reporting bugs, please read the contributing guide provided in the documentation and follow the steps outlined therein. This includes creating a new issue if you encounter any problems with the software, as well as submitting pull requests for bug fixes or feature enhancements.