5 Open Source Large Language Models APIs for Developers
Tags: python, ai, machinelearning, chatgpt
Published: 2024-05-01 20:41:56

## Introduction
Large language models (LLMs) have revolutionized the field of natural language processing (NLP) in recent years, enabling a wide range of new applications, such as writing services, translation, editing, and conversations with chatboxes. With the increasing popularity of LLMs, there is a growing demand for open-source APIs that allow developers to easily integrate these models into their projects.

From text generation and language translation to code synthesis and sentiment analysis, these APIs serve as essential tools for developers and businesses seeking to integrate cutting-edge language models seamlessly into their applications.

This comprehensive article aims to provide insights into the distinctive features, how they can be installed, and the usability of each open-source API. It delves into five of the most popular and widely utilized APIs for large language models, exploring the diverse functionalities they offer and their applications in the realm of NLP and offering a valuable resource for those navigating the ever-expanding landscape of language processing technologies.

## Open Source Large Language Models APIs for Developers
Here are 5 open-source APIs for large language models

### 1. BERT API
BERT, which stands for Bidirectional Encoder Representations from Transformers, is a Natural Language Processing (NLP) model developed by Google. It can be used for translation, text generation, and text summarization.

To make it more accessible to developers, Google released pre-trained models along with the TensorFlow library. These pre-trained models come in different sizes, and developers can choose the one that best fits their specific requirements and computational resources.

Also, developers can obtain the pre-trained BERT model from various sources, including the official BERT GitHub repository or model hubs like Hugging Face’s Transformers library. A library that provides interfaces for both TensorFlow and PyTorch.

#### How to install and use BERT API in Python
Using the BERT API involves several steps, from installing the necessary libraries to loading the pre-trained BERT model and generating text or performing other NLP tasks. Here’s a general overview of the process:

- Install the Transformers library using pip:

`pip install transformers`

This will install the BERT API along with other NLP models and tools.

- Import the necessary classes from the Transformers library:

`from transformers import AutoTokenizer, AutoModelForSequenceClassification`

These classes provide tokenization and classification capabilities for BERT models.

- Load the pre-trained BERT model you want to use:

`model_name = “bert-base-uncased”`

- Load the model from the Hugging Face model hub:

```
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

```
This will load the pre-trained BERT model and tokenizer for the specified model name.

- Prepare Input Text and encode the input text into tokens using the tokenizer:

```
`input_text = “This is text to be processed”
encoded_text = tokenizer(input_text, truncation=True, padding=True, return_tensors=”pt”)`

```
This will convert the input text into numerical representations the BERT model can understand.

- Perform a BERT Task. For example, text classification.

```
predictions = model(encoded_text)
predicted_label = predictions[0][0]

print(predicted_label)
```

This will classify the input text into a specified set of labels.

#### Some key features of BERT include:

1. BERT is pre-trained on a massive amount of text data, allowing it to learn general language representations. The pre-training involves predicting missing words in sentences (masked language model) and determining whether two sentences follow each other in the original text (next sentence prediction).

2. BERT has achieved state-of-the-art results on a wide range of NLP benchmarks and competitions. Its ability to capture intricate contextual relationships in language has made it a go-to choice for many NLP tasks.

3. BERT has been extended to support multiple languages, enabling its application in a global context for tasks like language translation and cross-lingual information retrieval.

4. BERT is designed to be fine-tuned for specific downstream tasks, such as text classification, named entity recognition, question answering, and more. This transfer learning approach allows BERT to leverage its pre-trained knowledge for various NLP applications.

### 2. ChatGPT API
Unarguably one of the biggest and powerful large language models and the most popular LLM in the world. ChatGPT (Chat Generative Pre-Trained Transformer) API attracts users in millions. The ChatGPTAPI is an interface created by OpenAI that allows developers to integrate GPT3 and GPT4 models into private, personal applications or programming languages like Python to build intelligent, conversational interfaces.

ChatGPT is renowned for its abilities to generate human-like quality text, translate languages, write programming code, write different kinds of creative content, and answer questions in an informative way.

Integrating ChatGPT into a programming language involves establishing a connection between the ChatGPT API and a chosen programming language. This allows you to leverage ChatGPT’s text-generation capabilities within your programming environment.

The specific steps and required libraries will vary depending on the programming language you’re using, but the general process involves the following steps:

- Create API Key. Generate a unique access code to enable communication and authentication with the API. You can generate an API key here.

- Install OpenAI API: Begin by installing the OpenAI API using your programming language’s package manager.
pip install openai

Once the OpenAI API is installed, import the classes and use the bindings and your secret key to run the following

```
from openai import OpenAI
client = OpenAI(
 api_key="Your_API_Key",
)
chat_completion = client.chat.completions.create(
 model=”gpt-3.5-turbo”,
 messages=[{“role”: “user”, “content”: “Hello world”}]
)
```

#### Key features of ChatGPT include:
ChatGPT is known for its following key features:

1. **Large and powerful Model:** ChatGPT is one of the largest open-source LLMs available. This means that it is capable of performing a wide range of tasks and generating high-quality output.

2. **Versatility:** ChatGPT can be used for a variety of NLP tasks, including text generation, translation, question answering, and code generation. This makes it a valuable tool for a wide range of applications.

**3. Pathways System:** ChatGPT is trained using the Pathways system, a novel training paradigm that enhances the model’s efficiency and adaptability. This system allows ChatGPT to learn from multiple sources of data and adapt to new tasks more effectively

**4. Chain-of-Thought Reasoning:** ChatGPT employs a chain-of-thought reasoning approach, allowing it to break down complex problems into smaller, more manageable steps. This capability enhances its ability to answer open-ended questions, provide explanations for its reasoning, and generate more coherent and relevant text.

**5. Dense Decoder-Only Transformer:** ChatGPT utilizes a dense decoder-only transformer architecture, which improves its fluency and coherence in generated text. This architecture enables the model to better capture long-range dependencies in language and produce more natural-sounding text.


### 3. LLAMA
LLAMA (Large Language Model Meta AI) is a large language model developed by Meta AI. Described “an easy-to-use API for LLAMA mocan dels. It is available in multiple sizes (7B, 13B, 33B, and 65B parameters). It is free for commercial and research purposes and it aims to democratize access to large language models by requiring less computing power and resources for training and deployment.

LLAMA is capable of performing many kinds of tasks, including text generation, translation, question answering, and code generation. The new LLAMA 2 model is even more impressive. It was trained on 40% more data than LLAMA 1 and has double the context length.

Available as an open-source API, it can be used with a variety of programming languages. There is also a pre-trained LLAMA model available, so you can get started right away.

To use LLAMA, you will need to:

- Install the LLAMA API: The LLAMA API can be installed using your programming language’s package manager.
For python, that would be

`pip install llama-cpp-python`

Or you can download the model from the official website.
Or load a pre-trained LLAMA model from a file or a remote repository.

Now, import the classes and specify the model path

```
from llama_cpp import Llama
llm = Llama(model_path=”./models/7B/ggml-model.bin”)
output = llm(“Q: Name the planets in the solar system? A: “, max_tokens=32, stop=[“Q:”, “\n”], echo=True)
print(output)
```

Process your input text: The LLAMA API can accept text input in a variety of formats, including plain text, HTML, and JSON.
Generate output text: The LLAMA API can generate text in a variety of formats, including plain text, HTML, and JSON.

#### Key features of LLAMA:

**1. Large and powerful:** LLAMA is one of the largest open-source LLMs available. This means that it is capable of performing a wide range of tasks and generating high-quality output.

**2. Versatile:** LLAMA can be used for a variety of NLP tasks, including text generation, translation, question answering, code generation, creative writing, and code completion. This makes it a valuable tool for a wide range of applications.

**3. Open-source:** LLAMA is an open-source model, which means that it is freely available for anyone to use. This makes it a great choice for developers who want to experiment with LLMs.

### 4. PaLM API
PaLM (Pathways Language Model) is a groundbreaking LLM introduced by Google AI in early 2022. The PaLM API and MakerSuite make it fast and easy for developers to use Google’s large language models to build innovative AI applications.

With a staggering 540 billion parameters, PaLM stands as one of the largest and most powerful language models ever created. Its immense capacity enables PaLM to excel in a wide range of natural language processing (NLP) tasks and allows it to process complex prompts quickly.

Installing the PaLM API is a straightforward process that involves setting up your environment and accessing the API through a package manager. Here’s a step-by-step guide with Python:

Get an API key. You can generate one with Google or MakerSuite.

```
Install using pip
pip install google-generativeai

Import the libraries and configure your API key
import google.generativeai as palm

palm.configure(api_key=API_KEY)

```
Using any of the models, you can prompt it to write a short story for your blog

```
model_name=”models/text-bison-001"
prompt=’’Write a short story of a girl and a little dinosaur. 
Summarize it to four paragraphs of 100 words each. ‘’’

completion=palm.generate_text(
 model=model_name,
 prompt=prompt,
 temperature=0.99,
 max_output_tokens=800,
)
print(completion.result)
```

You can also install PaLM on Google Cloud Platform (GCP) and use FireBase extensions to summarize text or perform other NLP tasks.

#### Key features of PaLM
**1. Multilingual Translation:** PaLM can accurately translate text between a wide range of languages, preserving the nuances and context of the original text. It can also adapt its translation style to different languages, considering cultural and linguistic differences.

**2. Domain-Specific Translation:** PaLM can be fine-tuned for specific domains, such as legal or medical translation, to improve accuracy and relevance.

**3. Reasoning and Inference:** PaLM can employ chain-of-thought reasoning and inference to understand the context and implications of questions, leading to more accurate and relevant answers.
Knowledge Base Access: PaLM can access and integrate information from external knowledge sources, such as Google Search, to provide more comprehensive and contextual answers.

**4. Code Optimization: **PaLM can generate code that adheres to established coding style conventions and best practices, making it easier to integrate into existing codebases.It can also optimize generated code for performance and efficiency, reducing computational overhead and resource utilization.

**5. Fine-Tuning:** PaLM can be fine-tuned for specific tasks or domains, tailoring its capabilities to address specialized requirements.

**6. Research and Development:** PaLM is an active area of research, with ongoing efforts to enhance its capabilities and expand its applications.

### 5. BLOOM API
The Bloom (BigScience Large Open-Science Open-Access Multilingual) Large Language Model API is a powerful tool for natural language processing (NLP) that utilizes a massive, 176-billion-parameter language model called BLOOM. It was developed by BigScience and built on transformers.

It’s the first major open-source project to authoritatively evaluate and rank human-level performance in a range of languages, with ground-breaking tools for evaluating them.

Installing the Bloom API (Bloom Large Language Model) involves setting up your environment and accessing the API through Hugging Face. Here’s a step-by-step guide:

**Prerequisites:**

- Python 3.7 or higher: Ensure you have Python 3.7 or higher installed on your system.

- Pip package manager: Install the pip package manager if you don’t already have it

- Hugging Face Transformers library: Install the Hugging Face - -

- Transformers library using pip. This library provides the necessary tools for interacting with the Bloom API.
pip install transformers

- Import the necessary classes from the Transformers library in your Python script. This will allow you to access the Bloom API functionality.
`from transformers import AutoTokenizer, AutoModelForSequenceClassification
`
Specify the pre-trained Bloom model you want to use. The Bloom API currently offers two models: Bloom 176B and Bloom 1.5B.
`model_name = “bigscience/bloom”`

- Download the pre-trained Bloom model from the Hugging Face model hub. This will download the model’s weights and configuration files.

```
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)

```

#### Key Features of Bloom LLM API

The Bloom LLM API stands out for its exceptional capabilities and versatile applications. Here are some of its key features:

**1. Large Model:** Bloom LLM boasts a massive parameter count of 176 billion, enabling it to capture intricate patterns and nuances in human language.

**2. Multilingual Support:** Bloom LLM can process and generate text in 46 natural languages, making it suitable for cross-lingual communication and content creation.

**3. Creative Text Generation:**
 Bloom LLM can generate creative text formats, including poems, code, scripts, musical pieces, emails, letters, and more.
Translation Accuracy: Bloom LLM excels at translation, accurately converting text between multiple languages while preserving context and meaning.

**4. Question Answering Prowess:** Bloom LLM provides comprehensive and informative answers to complex and open-ended questions, even when the questions are ambiguous or have multiple interpretations.

**5. Code Generation Versatility:** Bloom LLM can generate high-quality code in various programming languages, assisting developers in their coding tasks.

**6. Summarization Simplicity:** Bloom LLM effectively summarizes lengthy texts, capturing the essential information while maintaining clarity and coherence.

**7. Code Completion Efficiency:** Bloom LLM suggests relevant code snippets when provided with partial code, improving coding efficiency and reducing errors.

**8. Debugging Assistance:** Bloom LLM helps programmers identify and resolve errors in their code, facilitating efficient debugging and code optimization.

### Key features to consider when choosing an LLM API

When choosing an LLM API, there are several key factors to consider, including:

**Model size:** The size of the LLM model is a key factor to consider, as it will affect the model’s capabilities and performance. Larger models are generally more powerful and capable of performing a wider range of tasks, but they can also be slower to run and more expensive to use.

**Task support:** It is important to choose an LLM API that supports the specific tasks you need it for. Some APIs are specifically designed for certain tasks, such as text generation or translation, while others are more general-purpose.

**Pricing:** LLM APIs can vary widely in terms of pricing. Some APIs are free to use, while others charge a fee per request or month. It is important to choose an API that fits your budget.

**Documentation and support:** It is important to choose an LLM with good documentation and support. This will help you when you run into issues.