A Beginner's Guide to Using Hugging Face Models for AI Development

Hugging Face models are a powerful tool for AI development, and getting started can seem overwhelming. However, with the right guidance, you can unlock their full potential.

One of the first steps in using Hugging Face models is to choose the right model for your task. As mentioned in the article, there are over 2,000 pre-trained models available on the Hugging Face model hub, so it's essential to select one that fits your needs.

To get started with Hugging Face models, you'll need to install the Transformers library, which provides a simple and consistent interface to the Hugging Face models. This library is available in both Python and R, making it accessible to developers of all backgrounds.

The Hugging Face model hub is a vast repository of pre-trained models, and finding the right one can be a challenge. As explained in the article, you can filter models by their purpose, such as language translation or sentiment analysis, to narrow down your search.

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To get started with HuggingFace models, you can directly install Langchain with Huggingface using the command.

You don't need to install transformers in the latest update of Google Colab.

However, to use HuggingFace Models and embeddings, you will need to install transformers and sentence transformers.

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You can use Hugging Face models in various ways, including through pipelines and the Hugging Face Hub Inference API. Pipelines are high-level tools that package components required to perform different tasks, such as text generation and question-answering.

To use pipelines, you can specify a task and let it use the default settings for everything else. This approach is convenient, but it may be time-consuming if the model is enormous.

The Hugging Face Hub Inference API is a more efficient approach, especially for large models. To use it, you need to get a Hugging Face Access Token, which involves logging in to Hugging Face.co and generating a new access token with the "write" role.

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Here are some key points to keep in mind when using Hugging Face models:

Hugging Face is the cornerstone for developing AI and deep learning models. The extensive collection of open-source models in the Transformers repository by Hugging Face makes it a go-to choice for many practitioners.

Publicly accessible learning parameters characterize open-source large language models, such as LLaMA, Falcon, Mistral, etc. In contrast, closed-source large language models have private learning parameters.

Hugging Face is a top platform that offers pre-trained models and libraries for understanding natural language. It is well-known for its Transformers library, which includes a wide variety of pre-trained models that can be adjusted for different NLP tasks.

HuggingFace provided HuggingFace Hub, a platform with over 120k models, 20k datasets, and 50k spaces (demo AI applications). This is where HuggingFace comes in handy.

To run the GenAI applications on edge, Georgi Gerganov developed LLamaCPP. LLamaCPP implements the Meta’s LLaMa architecture in efficient C/C++.

You can use models packaged as .gguf files format that runs efficiently in CPU-only and mixed CPU/GPU environments using the llama.

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To use Hugging Face models, you'll need to meet some basic requirements. You'll need to have MLflow 2.3 installed.

For batch inference, any cluster with the Hugging Face transformers library installed can be used. This library comes preinstalled on Databricks Runtime 10.4 LTS ML and above.

Recent GPU hardware is recommended for best performance, especially if you're working with popular NLP models.

Hugging Face is a top platform that offers pre-trained models and libraries for understanding natural language. It is well-known for its Transformers library, which includes a wide variety of pre-trained models that can be adjusted for different NLP tasks.

You can use pre-trained models for general-purpose tasks, but they may not excel at any specific task. Fine-tuning is a process where you can further train these models on specific tasks to improve their performance.

To use pre-trained models, you can use Pandas UDFs to distribute model computation on a Spark cluster. This is particularly useful when experimenting with pre-trained models.

Hugging Face provides a platform with over 120k models, 20k datasets, and 50k spaces (demo AI applications). You can access these models through the HuggingFace Hub, a platform that offers pre-trained models and libraries for understanding natural language.

To get started with HuggingFace, you need to install the required libraries. HuggingFace is a cornerstone for developing AI and deep learning models, with an extensive collection of open-source models in the Transformers repository.

Pipelines are a great way to use models for inference. HuggingFace provides a pipeline wrapper class that can easily integrate tasks like text generation and summarization in just one line of code.

You can run pipelines by specifying a task and letting it use the default settings (for that task) for everything else. It's also possible to custom-build a pipeline by specifying the model, tokenizer, and other parameters.

Here are some examples of tasks that can be performed using HuggingFace pipelines:

Each pipeline requires a specific amount of GPU RAM to run. For example, the text generation pipeline requires 2 GB of GPU RAM.

You can use the HuggingFace Inference API to integrate HuggingFace with Langchain. This approach is particularly useful when working with large models, as it can save time by not having to download and load the weights.

Take a look at this: Fastapi Huggingface Gpu

To use the HuggingFace Inference API, you need to generate a HuggingFace Access Token. This can be done by logging in to HuggingFace.co and clicking on your profile icon at the top-right corner, then choosing “Settings” and navigating to “Access Token”.

Here is a summary of the steps to get started with HuggingFace models:

By following these steps, you can get started with using HuggingFace models for your NLP tasks.

Gpt-J is a class of models based on OpenAI's GPT architecture and is the successor to GPT Neo. It's a large model with 6 billion parameters.

To use Gpt-J in a pipeline, you'll need to import the required packages: `transformers`, `pipeline`, and `AutoTokenizer`. You'll also need to import `torch`.

Specify the model parameters with 16-bit weights by using the `revision="float16"` and `torch_dtype=torch.float16` arguments. This will reduce the memory footprint to around 12 GB of GPU RAM.

Initializing the tokenizer is straightforward: simply use the `AutoTokenizer.from_pretrained()` method with the model name "EleutherAI/gpt-j-6B".

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Here's a step-by-step guide to creating the pipeline:

For example, to generate text based on the prompt "Vultr is a cloud service provider", you can use the following code: `gen_gptj("Vultr is a cloud service provider")`. This will output the generated text.

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You'll need to pass a function to compute and report metrics to evaluate model performance during training. The 🤗 Evaluate library provides a simple accuracy function you can load with the evaluate.load function.

To calculate the accuracy of your predictions, call compute on the metric. You'll need to convert the logits to predictions first, as all 🤗 Transformers models return logits.

If you want to monitor your evaluation metrics during fine-tuning, specify the eval_strategy parameter in your training arguments. This will report the evaluation metric at the end of each epoch.

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To fine-tune a pretrained model, you need to download a dataset and prepare it for training. You can load the Yelp Reviews dataset and process it in one step using the 🤗 Datasets map method to apply a preprocessing function over the entire dataset.

You can also create a smaller subset of the full dataset to fine-tune on, which can reduce the time it takes. This is especially useful if you're working with a large dataset.

To avoid slowing down training, you can load your data as a tf.data.Dataset instead. There are two convenience methods for doing this: prepare_tf_dataset() and to_tf_dataset.

The prepare_tf_dataset() method is recommended in most cases, as it can automatically figure out which columns are usable as model inputs and discard the others. This makes a simpler and more performant dataset.

To use prepare_tf_dataset(), you need to add the tokenizer outputs to your dataset as columns. This will not inflate your memory usage, as Hugging Face datasets are stored on disk by default.

You can stream batches from the dataset and add padding to each batch, which greatly reduces the number of padding tokens compared to padding the entire dataset. This is especially useful if you're working with a large dataset.

Here are the two convenience methods for loading data as a tf.data.Dataset:

Once you've created a tf.data.Dataset, you can compile and fit the model as before.