Are you a part of an organization that is open to adopting cutting-edge technology? Or do you wish to create a Large Language Model that you can customize as per your own needs? Definitely, you are at the right place. This blog elaborates on Lamini, a well-developed framework that helps users create their own LLM customized as per their requirements.
It has been recently made, but it is used to carry out a large number of tasks due to its efficiency. Some of the projects that are centered on lamini LLM are :
- Language translation
- Code generation using natural language inputs
- Training data generation from given data
- Human Resource model
- Train data consisting of text, code, and images.
- Generation of new data for datasets with limited labels.
You must be wondering why one should go for lamini when a plethora of LLMs are already available. This is because of a couple of reasons:
- Chatgpt’s model is accessible to a few Machine Learning experts only, but anyone can use Lamini. Anybody can develop the model from scratch.
- It is free of cost.
- It offers a very high speed. Plus, you can train the model on different machines altogether.
- It is highly flexible and helps one create a custom LLM.
Difference between lamini and ChatGPT
Let’s have a look at the differences between LAMINI and CHATGPT. The tabulated differences will help you gain better insights into the abilities of Lamini.
|Custom data is given for training purposes.||It is still in the development phase.|
|It holds fewer features.||It is not trained on given data.|
|It is an Open source LLM.||It is not an Open source LLM.|
|In certain cases, it can be less reliable.||It can be more reliable.|
|It holds more features as compared to ChatGPT.||It holds less features.|
|It offers more more flexibility.||It is less flexible.|
PEFT stands for Parameter-Efficient Fine-tuning. It is a modern-age method for fine-tuning LLMs. If you have a model that has already been trained, there is no need to retrain the model on the entire data. It takes a few minutes to fine-tune it. The traditional fine-tuning methods have a comparatively low speed. Moreover, it requires low speed. It works on limited resources only. Its speed is a billion times better than the traditional LLM’s
Techniques under PEFT
- parameter importance estimation
- parameter pruning
The first technique implies selecting the parameter with the highest importance. This is then fine-tuned. The LLM estimates the importance of all parameters in this case. As per the second technique, remove the parameter which doesn’t hold much importance. This method is called pruning.
Recommended Reading | LLM Chain Explained
Lamini Authentication Method
Before working on any of the Lamini models, it demands some sort of authentication. The GitHub code elaborates on the process of authentication. It is the primary and first step in order to start building a model. You should go through the following steps in order to authenticate your credentials:
- Select the “Sign Up” button available on the official website of lamini ai.
- You need to provide your name, email address, and password.
- Once you select the “Sign Up” button, you will obtain confirmation mail on the email ID that you have given.
- After clicking the link in your mail, you will be redirected to the site.
- Next, visit the “My Account” page and select your API key. The “API Key” tab enables you to create the key.
- Copy this key and save it somewhere.
Lamini Authentication Tips
For proper authentication, you should go through the following tips for sure.
- Change your key after a stipulated time period.
- You shouldn’t give the key to anyone else.
- Make sure that you have stored the key in a safe and secure place.
- Use the API key tab to change the key after regular time periods only.
At first, you need to install the library and load the required model. gpt-neo-125m model has been used here. Make a PEFT fine-tuner and load your data that consists of samples and labels depending on the type of dataset. You can specify epochs here, which means how many times you want to train the data. In this example, 100 epochs have been taken. Lastly, you need to save this using the save function and use it for any task of your choice.
import lamini # Load the LLM to be fine-tuned llm = lamini.LLM.load("gpt-neo-125m") # Create a PEFT fine-tuner peft = lamini.PEFT(llm) # Load the training data data =  with open("code_generation_data.txt", "r") as f: for line in f: natural_language_description, code = line.strip().split("\t") data.append((natural_language_description, code)) # Fine-tune the LLM peft.finetune(data, epochs=100) # Save the fine-tuned LLM peft.save("fine-tuned-code-generation-llm.pkl")
After saving the LLM, you can pass it to any self-created function that has been described naturally. You may use Pickle Library for the same. So, in the given example, the generate function accepts the prompt in the form of the natural_language_description variable.
import pickle # Load the fine-tuned LLM with open("fine-tuned-code-generation-llm.pkl", "rb") as f: llm = pickle.load(f) # Generate code natural_language_description = "Write a function that takes an integer as input and returns the factorial of that integer." code = llm.generate(natural_language_description) # Print the generated code print(code)
Thus you will get the Output as:
def factorial(n): if n == 0: return 1 else: return n * factorial(n - 1)
Models supported by lamini
The Lamini package supports a large number of models that users can use for different purposes. These are free to use. The models that are a part of the lamini library are:
In order to use any of the models, you need to specify the name of the model.
model = QuestionAnswerModel(model_name="YOUR_MODEL_NAME")
lamini hugging face
Hugging Face provides information about lamini. It discusses the documentation and how this large language model can be used by enterprises to curate their own custom LLM. It notifies users of the seven models and eleven datasets that are currently a part of lamini and can be used for fine-tuning new models too. It explains the libraries under lamini and how you may use this AI tool for deployment using REST APIs, gRPC, and TensorFlow Serving.
The lamini AI company doesn’t charge anything from users as of now. However, it may come up with a pricing model later. Its open dataset generator used for creating an interface similar to Chat GPT is also free of cost. As of now, you can enroll in the full training module of LLM. It includes extra features like cloud prem deployments. It is suitable for an enterprise.
If you require a small, efficient LLM, then you should definitely research Lamini-T5. It is Google AI’s T5 LLM.’s mini version. It holds multiple features like:
- Easy to train and deploy.
- It is capable of text generation, translation, and question-answering.
- Present in varied sizes from 248M parameters to 7.8B parameters
lamini vs. langchain
Let us now see how lamini is different from langchain.
|Primary focus||Training and deploying LLMs||Orchestrating and managing NLP workflows|
|Target audience||Developers||Data scientists and machine learning engineers|
|Key features||Variety of training methods, optimization techniques, and evaluation metrics||Process orchestration, chains, pipelines, and ready-made pipelines|
|Supported models||Hugging Face Transformers||Any NLP model|
|Use cases||Training and deploying LLMs||Automating NLP workflows, data augmentation, and model evaluation|
Speed, Cost, Flexibility, and Community support are the important factors that segregate lamini from other LLMs.
You may check the Lamini website or go through the official Lamini documentation.
This article covers lamini, which authors have developed recently. It is a tool that helps one in creating custom LLMs. It covers the advantages an enterprise might feel if they adapt to using lamini. The blog also covers its varied use cases and the concept of Lamini PEFT.