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I'm going to walk you through 10 quick tips for fine tuning. For each of those, I'll point you to one or two trellis videos on YouTube and also point you to the right branch if you're working out of the trellis advanced fine tuning repository. Tip number one is to start with a small model. I recommend starting with som... | 00:00:00.029 | 00:00:27.242 | |
If you start off with Lama 3 8 or 70B, it's going to take you much more time in order to test out what's working and what's not. You can always start small and scale up later. The video I recommend here is memorization. This one, I use a relatively small model as I do in many of my fine tuning tutorials, just because i... | 00:00:27.582 | 00:00:55.878 | |
First of all, LoRa and QLoRa allow you to start with fewer GPUs or a smaller GPU. That's going to make iteration faster. But for small datasets, the performance might even be better than full fine-tuning because full fine-tuning can tend to overfit. So I'd recommend even if you eventually want to do full fine-tuning, s... | 00:00:56.358 | 00:01:29.374 | |
And if you're working out of the Trellis repo, you can check out the Unsloth branch for the fastest fine-tuning on a single GPU using LoRa or QLoRa. Tip number three is to create 10 manual test questions. So you want to create 10 question answer pairs and use those to choose which base model is going to perform best. S... | 00:01:29.954 | 00:02:01.222 | |
and just evaluate whether the model is doing well. This gives you probably a better sense than solely looking at the eval and training loss during the fine-tuning process. This is what I do in this memorization video as well, which you can check out on YouTube, and you'll see in the memorization scripts how I allow you... | 00:02:01.662 | 00:02:31.010 | |
Yes, I know this is a bit of work, but I think it's underrated. When you manually curate a data set like I did for the trellis function calling data set, it lets you appreciate exactly which rows of data are needed to get the performance that you need. You can, of course, use Python and chat GPT to help automate some o... | 00:02:31.110 | 00:02:56.022 | |
Here, you can check out the function calling v3 branch and also the unslot and multi-GPU branches of the advanced fine-tuning repo. Tip number five is to start off training with a small number of rows. In fact, I always run training first with just one row of data to check that my training pipeline is working correctly... | 00:02:57.185 | 00:03:28.098 | |
If you do want to automate a little more how you generate synthetic data sets, you can check out this video here on data set preparation with LLMs. Tip number six is always use a validation data set. If you don't have one, you can just split off 10 to 20% of your training data set. You want to be checking your training... | 00:03:29.079 | 00:03:56.433 | |
You also want to check your validation loss, and this should be monotonically decreasing in a smooth way. If it's ever upticking, that means you might be overfitting and you're training for too many epochs, or you may not have enough data. Here, I recommend the Trellis repo branches of Unsloth or MultiGPU. They each al... | 00:03:57.153 | 00:04:26.050 | |
Tip number seven is to try to start training on just one GPU. Again, this allows you to iterate faster. If you really need to increase VRAM, say from what is on a T4 with Colab, then consider paying maybe 70 cents an hour for a 48 GB GPU like an A6000 or an A100 for 80 GB of VRAM. | 00:04:27.182 | 00:04:46.009 | |
I think you're better off to just fit it on one GPU, because when you move to multi GPU, you have data that's moving between them, the training becomes more complicated, it's easier to make mistakes, and it can be slower in some ways. Also, on one GPU, you can use unsloth, which gives you a 2x speed up. So that's quite... | 00:04:48.650 | 00:05:16.566 | |
Something I should mention as well is that you can waste a lot of time with installations and getting stuck in getting set up for fine tuning. One way around that is to use an image or a template that sets up your CUDA and PyTorch to a specific version. I've got a one-click template here for RunPod, and you can use tha... | 00:05:17.802 | 00:05:47.073 | |
This is a tool that allows you to track the losses and the rewards as you move through your training run. You can include this in a script with pip install wandb, then set the environment variable for wandb project to a project name. And this will create a folder basically within which you can have multiple runs of run... | 00:05:47.393 | 00:06:16.669 | |
And you also need to set up report to wand b weights and biases. This is supported in the Onslaught and the multi-GPU branches and also in many of the Jupyter notebooks that are throughout all the branches of the advanced fine-tuning repo. Before I move to tips 8 and 9, I want to comment on scaling up. So I've talked a... | 00:06:17.089 | 00:06:48.218 | |
If you really want to try full fine-tuning, test it out on a small model and see if it really improves performance. Then, as a very last step, you can think about moving to a larger model where it's going to take more time and money to get in that final result. There are two videos of relevance here. If you want to und... | 00:06:48.438 | 00:07:20.753 | |
Tip number nine is to use unsupervised fine-tuning. This can be useful if you have a large data set. I'm going to say larger than 10,000 rows of data. Here, you'll need to use Python scripts in order to clean up, say, repeated characters or too much new lines. You can also use language models in order to clean up the d... | 00:07:20.873 | 00:07:48.832 | |
Last of all, my tip number 10 is to do preference fine-tuning. This is where you have a data set with chosen, which are better or preferred responses, and rejected, which are the responses to the same prompts but are of lower quality. You might have a set of data like this if you have production data from customers or ... | 00:07:49.912 | 00:08:20.972 | |
That's going to be ideal as your chosen dataset. And you can always generate a rejected or lower quality dataset just by putting the same prompts into a language model and seeing what generic response it comes back with. So this approach here, called ORPO, or Odds Ratio Preference Optimization, it allows you to do both... | 00:08:21.593 | 00:08:43.308 | |
The preference fine-tuning will move your model to give responses more like your chosen answers and less like your rejected answers, which is useful if you want to do some fine-tuning for tone or style, or if you want to make some corrections where the model's giving a response you don't quite like. Here I recommend th... | 00:08:43.728 | 00:09:13.992 | |
And Orpo is supported as an option in the Multi-GPU branch too. So to recap these 10 tips, start with a small model, use LoRa or QLoRa, not full fine-tuning. Always create 10 manual test questions or maybe a few more. Remember that manual data sets are probably underrated. You can always get a little bit of help from P... | 00:09:14.433 | 00:09:47.199 | |
Make sure you know that the data type and the data set that you've set up is actually the right one. Number six, always use a validation set. Just split one off from a training set if you don't have one. Number seven, try to just start training on one GPU. Number eight, use weights and biases for tracking. And when you... | 00:09:47.559 | 00:10:18.710 | |
So LoRa is about four times QLoRa and full fine tuning is about four times. LoRa, or even more in some cases. And last of all, increase to a larger model size only at the very end of your training process when you think you have a pipeline that's working well. Then for advanced tips, consider doing unsupervised fine-tu... | 00:10:18.770 | 00:10:41.877 | |
And last of all, you can consider preference fine-tuning, in which case I'd recommend using ORPL, which will do supervised fine-tuning and odds ratio preference optimization. at the same time. Now, this approach here I've talked about for language models, but it also works for video and speech or images, multimodal mod... | 00:10:42.477 | 00:11:09.077 | |
And likewise, for this speech to text model, where I prepare a data set and bring it through fine tuning. There are specific repos for multimodal. That's the vision repository here. And there's a repo for transcription. And this LLMs repo is the advanced fine-tuning repo I've been talking to date in or up until now in ... | 00:11:09.157 | 00:11:40.804 | |
There's the LLM fine tuning playlist, which is all about fine tuning language models. Then there's a repo for that advanced fine tuning. There's the vision playlist, which is for multimodal models and repo link. There's a video on transcription and a repo link. And then there are many videos on server setup. | 00:11:41.205 | 00:11:59.581 | |
That's if you want to deploy your own custom model, either on a server that will sleep or start up when you need it to run, that's called serverless, or a server that's always on if you're using something like TGI or VLLM through a service like RunPod or Vast AI. And so here is the link for this. | 00:11:59.921 | 00:12:16.565 | |
I'll note as well that within this repo, there's some scripts that allow you to redact information, personally identifiable information like names, emails, or credit card numbers before you send the data to a third-party LLM. And there are also scripts on function calling inference and speed test too. I'll talk a littl... | 00:12:17.105 | 00:12:40.056 | |
Last of all, these repos, of which there are four, they're available for purchase individually, but you can also now buy a repo bundle, which will give you lifetime access to all four of these repositories, which includes any future updates made to those repos. You can purchase that all together now as a bundle. | 00:12:41.177 | 00:12:58.819 | |
This very last section of the video is for those who have purchased lifetime access to one of the Trellis repositories, but I'll just put it part of this public video because it will give a sense of what's in these repositories for those of you who might be interested to purchase lifetime membership later. The first re... | 00:12:59.943 | 00:13:27.216 | |
Now, a few of the branches that I'll highlight are, first of all, the Wikipedia branch, which is for unsupervised fine-tuning and data cleaning. If you do want to do ORPO, you have the ORPO branch here. And if you want to prepare data, you can do so with the help of a language model. This is done in the memorization br... | 00:13:27.756 | 00:13:50.269 | |
And likewise, if you go to the supervised fine tuning branch, there is also a script or multiple scripts for generating Q&A data from a base data set right there. Then there are two important branches here, unsloth and multi-GPU. The unsloth branch allows you to run fine tuning in either a notebook or as a Python scrip... | 00:13:50.870 | 00:14:21.609 | |
Now I'll briefly show you each of those two main branches. So here we'll go into the unsloth branch. The way that you run training in this Unslot branch is by setting up the configuration in a config file. I've also got a config file that you can use here if you want to do some function calling fine tuning. And once yo... | 00:14:22.370 | 00:14:52.930 | |
Then when you want to train your model, you simply run train.py, or you can run it step by step in a Python Jupyter notebook. Now, the notebook is recommended if you want to go through the training the first time, you can see step by step what's happening and easily print out things at intermediate points. But when you... | 00:14:53.590 | 00:15:21.205 | |
Just to give you a sense of how you configure the training and test setup, you'll set a model slug. You will then set some parameters, like whether you want to fine tune in 4-bit, what data type you want to use, depending on your GPU. You can then choose a data set, say for function calling, or if you want to memorize ... | 00:15:22.301 | 00:15:45.187 | |
You can decide to test either from a set of messages that you have prepared manually, or you can use the training, or you can use the validation split of a test set that's on Hugging Face by setting use data set to test equal to true right here. Next, you set up your training and validation splits. Here I've selected a... | 00:15:45.328 | 00:16:17.832 | |
Now, I spoke earlier about generating a validation set. You can either download from a split that's on Hugging Face called test or validation, but you can also generate a validation split from the train split. If you just set this to true, it will sequester 20% of the training data to use as validation. Next up is the ... | 00:16:19.229 | 00:16:47.113 | |
You can set LoRa or LoRa alpha and also rank stabilize LoRa, set that to true or false. Here you've got some Weights and Biases project configurations. You set the project name, and then for each run, you can use a different name here for running in Weights and Biases. You can set up your HuggingFace username. This wil... | 00:16:47.454 | 00:17:19.287 | |
And this can be useful if your answers are quite short and you don't want the loss on all of the prompts to kind of crowd out or cloud out the information or the signal that's coming from training on the response or the answer. So you set the completions to true here. Sometimes I use this for function calling, fine tun... | 00:17:19.647 | 00:17:52.328 | |
And then I think this is maybe a chat ML format. the answer will start after I am start assistant. So this allows the training loop to check within your prompt. It will check for where this start of the assistance answer is, and then it will only look at the loss after that point. After this, there are some standard pa... | 00:17:52.668 | 00:18:20.808 | |
the number of epochs, the learning rate, an output directory for your training model and results, whether you want to train with BrainFloat 16 or not. You can set your scheduler. You can decide whether to save the model at a certain number of steps of training. set your max sequence length, gradient checkpointing, and ... | 00:18:21.787 | 00:18:51.901 | |
If you're using ORPO, you need a column that's called chosen and one called rejected. and you can set your max prompt length. And then the beta, the beta basically weighs how much of the preference fine-tuning, what's the importance of that loss relative to the standard SFT loss. Remember, ORPL does two things in one. ... | 00:18:52.422 | 00:19:19.667 | |
Last of all, you can push to hub, so you can set a target model name if you want to push to hub. So very quickly, if we take a look at the test script, this will simply load the model. So it will load all of your configurations. It will load the model here, a fast language model using unsloth. | 00:19:20.867 | 00:19:39.559 | |
It will set up the tokenizer, set up the chat template, load the dataset, either from your manual data that's in the repo or from Hugging Face, and then it will run inference through all of those samples and print the results out to file. Just as an example, I can show you within test output, you'll see here a large nu... | 00:19:40.137 | 00:20:05.762 | |
So here is some fine-tuning on TouchRugby, and you'll see there is a prompt, a question, and it'll print out the correct response, and it'll also print out the generated response. And then you can just manually compare whether these answers are good or not. Now, just one other script I'll point out here, which is viewM... | 00:20:06.763 | 00:20:35.591 | |
And that's pretty much it for the unsloth branch, which is recommended if you're going to fine tune on one GPU. It does not work if you're fine tuning on multi GPU, which is why I have the multi GPU branch and the multi GPU branch is configured much in a similar way to unsloth, except that it allows you to run in fully... | 00:20:36.758 | 00:21:07.812 | |
And I'll just briefly show you the config file. So at the start here, you'll see this parameter that's not in the unsloth branch. If you set it to auto, it will just do standard training. You can train on multiple GPUs, but it will be pipeline parallel, so not quite as efficient. However, you can set this to DDP for di... | 00:21:09.133 | 00:21:37.410 | |
That can be done by running config, accelerate config, and you'll see the instructions if you head over to the multi-GPU branch for doing that. So this is the Advanced Fine Tuning repo, and you can find out more at trials.com forward slash advanced dash fine dash tuning. The next repo I'll briefly go through is the Adv... | 00:21:37.790 | 00:22:08.539 | |
Then you can fine-tune Lava, IdaFix, and Moondream models. You can do multimodal server setup with text generation inference. There's a one-click template for running an IdaFix server, including on a custom model. And last of all, there is a script for fine-tuning multimodal text plus video models. This is basically a ... | 00:22:08.920 | 00:22:38.829 | |
This allows you to set up a server so that you can hit an endpoint for a custom model. You can do so on RunPod, Vast AI, or using a Llama CPP type server. There's also the option to deploy serverlessly using RunPod. This means that you can put a server that will only turn on when it's being queried and will turn off af... | 00:22:39.250 | 00:23:01.108 | |
It's quite useful for batch jobs that are less time sensitive, because it means you're not paying for the server when it's not being used, and it will just turn on when you need it, which is going to save you cost. There are also a number of scripts for making API calls, simple API calls, OpenAI style or TGI style, fun... | 00:23:01.528 | 00:23:29.591 | |
Data extraction, if you want to extract JSON or YAML data from files, so you can input some text and have a language model extracted for you into JSON or YAML format. And there's also a more advanced technique called LLM-RAG, which is a version of RAG, but you retrieve the relevant snippets from a data set by using a l... | 00:23:30.112 | 00:23:54.874 | |
So the idea is to use a very fast and relatively small language model to pick out the right snippets and then include those snippets in the context of a more powerful model like, say, GPT-4. There's also a folder now on privacy, which allows you to basically hide information, like personal information on credit cards, ... | 00:23:55.274 | 00:24:24.271 | |
This one here allows you to generate data if you want to fine tune a whisper model and then do the fine tuning. And again, much of the 10 tips that I provided earlier are going to apply here for transcription. And that is it for my 10 tips on fine-tuning. If I've left anything out, please let me know below in the comme... | 00:24:24.652 | 00:24:56.711 | |
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