Training Large Language Models (LLMs)
One method for customizing and providing better context when using LLMs is to train the -LLM on your data. There are a number methods for fine-tuning LLMs including low-rank adaptation or LoRA1, -that allow you to fine-tune a small number of parameters of a LLM many parameters without a -large number of GPUs used for the initial training of these large models.
+model on your data. There are a number methods for fine-tuning LLMs including low-rank adaptation or LoRA1, +that allow you to fine-tune a small number of parameters of a model's many parameters without needing a +large number of GPUs.OpenAI allows you to train one of their models through their API and custom GPTs. Google Gemni offers fine-tuning through the Gemini API as does Anthropic as explained in their documentation.
Training LLaMA models locally on a personal computer is possible
-depending on the capacities of your local computer. Unfortunately, the process isn't
+depending on the resources of your local computer. Unfortunately, the process isn't
easy or straight-forward and require running Python code. If you
-have an Apple computer, the mlx-lm package,
-which uses Apple Silicon GPU, can be used for fine-tuning open-source models like LLaMA. Another
-possibility is using HuggingFace's peft package along
-with HuggingFace's transformers to fine-tune models.
Steps for Fine-tuning
-OpenAI provides directions for [fine-tuning]2 a ChatGPT model that are +
OpenAI provides directions for fine-tuning2 a ChatGPT model that are general enough for most fine-tuning tasks:
- Prepare and upload training data @@ -50,7 +50,7 @@
- Use your fine-tuned model
Steps for Fine-tuning
Create Inventory Instance Training Set
-We will create a training set 1,000 random records from the FOLIO community's Quesnelia Bugfest +
From the FOLIO community's Quesnelia Bugfest instance. The training set consists of the denormalize Instance along with a text prompt.
Depending on the model and service, you may need reformat the training set to match the expected inputs to the model.
diff --git a/exploring-llms/training-llms.md b/exploring-llms/training-llms.md index 985ff46..e78dc79 100644 --- a/exploring-llms/training-llms.md +++ b/exploring-llms/training-llms.md @@ -1,8 +1,8 @@ # Training Large Language Models (LLMs) One method for customizing and providing better context when using LLMs is to train the -LLM on your data. There are a number methods for fine-tuning LLMs including low-rank adaptation or LoRA[^LORA], -that allow you to fine-tune a small number of parameters of a LLM many parameters without a -large number of GPUs used for the initial training of these large models. +model on your data. There are a number methods for fine-tuning LLMs including low-rank adaptation or LoRA[^LORA], +that allow you to fine-tune a small number of parameters of a model's many parameters without needing a +large number of GPUs. [OpenAI][OPENAI] allows you to train one of their models through their API @@ -10,16 +10,15 @@ and custom GPTs. Google Gemni offers fine-tuning through the [Gemini API](https: as does Anthropic as explained in their [documentation](https://www.anthropic.com/news/fine-tune-claude-3-haiku). Training [LLaMA][LLAMA] models locally on a personal computer is possible -depending on the capacities of your local computer. Unfortunately, the process isn't +depending on the resources of your local computer. Unfortunately, the process isn't easy or straight-forward and require running Python code. If you -have an Apple computer, the [mlx-lm][MLX_LM] package, -which uses Apple Silicon GPU, can be used for fine-tuning open-source models like LLaMA. Another -possibility is using HuggingFace's [peft][PEFT] package along -with HuggingFace's transformers to fine-tune models. - +have an Apple computer, the [mlx-lm][MLX_LM] package, which uses Apple Silicon GPU, +can be used for fine-tuning open-source models like LLaMA. Another possibility is using +HuggingFace's [peft][PEFT] package that along with HuggingFace's transformers, can be used +to fine-tune models. ## Steps for Fine-tuning -OpenAI provides directions for [fine-tuning][^OPENAI_FINETUNE] a ChatGPT model that are +OpenAI provides directions for fine-tuning[^OPENAI_FINETUNE] a ChatGPT model that are general enough for most fine-tuning tasks: 1. Prepare and upload training data @@ -29,8 +28,7 @@ general enough for most fine-tuning tasks: ### Create Inventory Instance Training Set - -We will create a training set 1,000 random records from the FOLIO community's [Quesnelia Bugfest](https://bugfest-quesnelia.int.aws.folio.org/) +From the FOLIO community's [Quesnelia Bugfest](https://bugfest-quesnelia.int.aws.folio.org/) instance. The training set consists of the denormalize Instance along with a text prompt. Depending on the model and service, you may need reformat the training set to match the expected inputs