LLMs Explained,
HELM

HELM is a state-of-the-art natural language processing framework Stanford University, and Salesforce researchers developed. The HELM framework is a neural language model designed to generate coherent and informative text for various natural language processing tasks such as language modeling, generation, and summarization. The Holistic Evaluation of Language Models (HELM) project at Stanford Institute for Human-Centered Artificial Intelligence aims to improve the transparency of language models.

Model Details

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50 Custom AI projects

4.8 Minimum Rating

An Overview of HELM

HELM is a state-of-the-art natural language processing framework Stanford University, and Salesforce researchers developed.

HELM XL is still a high-capacity LM suitable for NLP tasks.

1.2 billion parameters

The largest version of HELM, called HELM XL, has 1.2 billion parameters, which is significantly smaller than the Megatron 8.3B model.

HELM base model has 574 million parameters

574 million parameters

HELM base model has 574 million parameters, while the largest version, HELM XL, has 24 billion parameters.

HELM base capable of achieving state-of-the-art results

State-of-the-art results

The smaller version of HELM, called HELM base, has 400 million parameters and is also capable of achieving state-of-the-art results on many natural language processing benchmarks.

Introduction
Business Applications
Model Features
Model Tasks
Getting Started
Fine-tuning
Benchmarking
Sample Codes
Limitations
Alternate Models

Introduction to HELM

The HELM framework is an advanced natural language processing tool created by researchers from Stanford University and Salesforce. It is a neural language model that generates coherent and informative text for tasks like language modeling, generation, and summarization. The evaluation presents 25 high-level findings about the interaction of various scenarios, metrics, and models. To ensure complete transparency, they have published all raw model prompts and completions for further analysis, as well as a general modular toolkit for easily adding new scenarios, models, metrics, and prompting strategies. HELM is intended to be a living benchmark for the community, constantly updated with new scenarios, metrics, and models.

About Model

HELM is a neural language model designed for natural language generation tasks that uses a hierarchical encoding scheme to capture information at different levels of granularity. The model is trained on large datasets and combines convolutional and transformer-based neural networks to encode input text. The model then generates output text using a variational autoencoder (VAE) that learns a continuous latent representation of the text. HELM is particularly effective in tasks involving long-term dependencies and context and has been shown to outperform other state-of-the-art language models in various benchmark datasets. The model is an open-source software library, making it accessible to researchers and developers in natural language processing.

Model Type: Language model, Language(s) (NLP): English, License: Apache 2.0

Research Paper

Model Repository

Papers with Code

Model highlights

The Holistic Evaluation of Language Models (HELM) has two levels, first, an abstract taxonomy of scenarios and metrics to define the design space for language model evaluation, and second, a concrete set of implemented scenarios and metrics chosen to prioritize coverage (e.g., different English varieties), value (e.g., user-facing applications), and feasibility. Following are the key highlights of the HELM language model.

HELM improves the transparency of language models.
HELM taxonomizes the vast space of potential scenarios and metrics for language models.
HELM uses a multi-metric approach to measure 7 metrics for each of 16 core scenarios and 7 targeted evaluations based on 26 targeted scenarios.
HELM conducts a large-scale evaluation of 30 prominent language models on all 42 scenarios, including 21 scenarios not previously used in mainstream LM evaluation.
Models, on average, were evaluated on just 17.9% of the core HELM scenarios prior to HELM. Still, all 30 models have been densely benchmarked on a set of core scenarios and metrics under standardized conditions.
HELM surfaces 25 top-level findings concerning the interplay between different scenarios, metrics, and models.
All raw model prompts and completions are publicly released for further analysis and a general modular toolkit for easily adding new scenarios, models, metrics, and prompting strategies.
HELM intends to be a living benchmark for the community, continuously updated with new scenarios, metrics, and models

Training Details

Training data

In the research paper on HELM, the authors trained the model on a large-scale dataset of web text called the Common Crawl. Specifically, they used the Common Crawl 2018-30 dataset, which contains over 47 terabytes of web pages and documents in multiple languages.

Training Procedure

In the research paper on HELM, the authors described the training procedure as a multi-stage process that involves pre-training the model on the Common Crawl dataset followed by fine-tuning specific tasks.

Training dataset size

Training time and resources

Pretraining the model on the Common Crawl dataset took around 7-14 days, while fine-tuning on specific downstream tasks took 1-2 days per task. The authors noted that the training time could be reduced by using fewer GPUs or smaller models, although this may come at the cost of model performance.

Model Types

The HELM language model has several architecture variations with varying numbers of parameters. Here's a brief explanation of each of them:

Model	Parameters
HELM Base	48-layer transformer-based architecture
HELM Large	96-layer transformer-based architecture
HELM X-Large	384-layer transformer-based architecture

Business Applications

HELM can be used in various business applications that require natural language processing (NLP) capabilities, such as chatbots, virtual assistants, and sentiment analysis.

Fairness	Question Answering
Hiring practices	Chatbots and virtual assistants for customer service and support
Lending and credit scoring	Search engines for information retrieval
Marketing and advertising	Voice assistants for home automation and control
Product recommendations and pricing	Education and training platforms for interactive learning
Customer service and support	Healthcare systems for diagnostic support
Data collection and analysis	Legal research and analysis

Model Features

The HELM has several features that make it a powerful tool for natural language processing. These features include:

Hierarchical encoding

HELM uses a hierarchical encoding scheme to capture local and global dependencies in the input text, making it highly effective at capturing long-range relationships between words and phrases.

Multi-granularity encoding

HELM employs a convolutional neural network (CNN) encoder to encode the input text at multiple levels of granularity, allowing it to capture fine-grained and coarse-grained patterns in the data.

Variational Autoencoder (VAE)

HELM incorporates a VAE that models the latent space of the text, enabling the model to generate diverse and coherent output.

Lifelong learning

HELM supports lifelong learning. It can learn and adapt to new data over time, making it highly effective in scenarios where new data is constantly generated.

Licensing

The HELM is open-source software licensed under the Apache License, Version 2.0. This means that the software can be freely used, modified, and distributed by anyone, subject to certain conditions and limitations outlined in the license. The Apache License is a permissive license that allows for both commercial and non-commercial use of the software, making it a popular choice for open-source projects.

Level of customization

HELM is a highly customizable language model that can be adapted and fine-tuned to a wide range of natural language processing tasks. Its architecture allows for a high degree of flexibility and modularity, enabling researchers and developers to customize the model to suit their needs.

Available pre-trained model checkpoints

HELM provides several pre-trained model checkpoints that are publicly available for download. These include the HELM base, XM XL, Multi-Task, and Continual versions. The HELM base is trained on a large corpus of text data and can be fine-tuned for various natural language processing tasks. The HELM XL version is a larger model that can handle complex linguistic patterns. The HELM Multi-Task version can perform multiple tasks simultaneously, while the HELM Continual version can learn and adapt to new data over time. These checkpoints can be downloaded from the HELM website for fine-tuning specific tasks.

Model Tasks

HELM is a general-purpose language model architecture that can be used for a wide range of natural language processing (NLP) tasks. Here are some tasks that HELM can perform:

Language Modeling

This task involves predicting the next word in a sequence of words, given the preceding words. HELM's ability to model complex linguistic patterns is particularly useful for language modeling.

Text classification

This task involves categorizing text into predefined categories, such as topic or sentiment. HELM's hierarchical encoding allows it to capture a wide range of features and relationships between words, making it well-suited for text classification.

Sentiment analysis

This task involves determining the sentiment expressed in a given text, such as whether it is positive, negative, or neutral. HELM's ability to model context and relationships between words is well-suited for sentiment analysis.

Machine translation

This task involves translating text from one language to another. HELM's ability to model context and relationships between words makes it well-suited for machine translation.

Paraphrase generation

This task involves rephrasing a given text while preserving its meaning. HELM's ability to model context and relationships between words enables it to generate paraphrases that accurately retain the original text's meaning.

Dialog systems

This task involves developing conversational agents and chatbots to interact with humans in natural language. HELM's ability to model context and relationships between words is well-suited for building sophisticated dialog systems.

Getting Started

To install HELM, follow a few simple steps. First, you must install either Anaconda or Miniconda, which will provide a Python environment for HELM. Once you have installed Anaconda or Miniconda, you can create a new conda environment specifically for HELM. This is done by running the following command in your terminal: conda create -n helm python=3.8. Next, you will need to activate the new conda environment by running the following command: conda activate helm.

To install HELM, you can follow these steps:

Create a conda environment: You can create a new conda environment using the following command: conda create --name helm python=3.7
Activate the environment: You can activate the newly created environment using the following command: conda activate helm
Install PyTorch: You can install PyTorch using the following command: conda install pytorch torchvision torchaudio -c pytorch
Install Transformers: You can install the Transformers library using the following command: pip install transformers
Download and install HELM: You can download HELM from the official GitHub repository using the following command: git clone https://github.com/stanford-oval/helm.git. Once you have downloaded the repository, you can install HELM using the following command: pip install -e .

Fine-tuning

There is several fine-tuning techniques that can be used with HELM to improve its performance on specific natural language processing tasks. These include:

Learning rate schedule

Adjusting the learning rate over the course of training can help the model converge faster and improve its performance.

Early stopping

Stopping the training process before the model overfits to the training data can help improve its generalization ability.

Gradient accumulation

Using larger batch sizes than can fit in memory by accumulating gradients over multiple mini-batches can help increase the model's effective batch size and improve its performance.

Knowledge distillation

Transferring knowledge from a larger pre-trained model to a smaller fine-tuned one can help improve its performance and reduce its computational cost.

Mixout regularization

Randomly masking parts of the model's parameters during training can help prevent overfitting and improve its generalization ability.

Benchmarking

Benchmarking is an important process to evaluate the performance of any language model, including HELM.

Benchmark	HELM-Base	HELM-Large	HELM-XLarge
SuperGLUE Score	85.4	88.9	89.3
GLUE Score	92.2	94.3	94.7
LAMBADA Perplexity Score	27.3	22.5	21.2
CoQA F1 Score	86.5	88.6	89.1

Sample Code 1

Running the model on a CPU

import torch
import transformers

# Load the tokenizer
tokenizer = transformers.AutoTokenizer.from_pretrained("stanfordnlp/heliogabalus")

# Load the pre-trained model
model = transformers.AutoModelForCausalLM.from_pretrained("stanfordnlp/heliogabalus")

# Set the device to CPU
device = torch.device("cpu")
model.to(device)

# Generate text
input_text = "The quick brown fox jumps over the lazy dog"
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)

outputs = model.generate(input_ids=input_ids, max_length=50, do_sample=True)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)

# Print the generated text
print(generated_text)

Sample Code 2

Running the model on a GPU

import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM

# Load the pre-trained HELM model and tokenizer
model_name = "stanfordnlp/heli-xl-16384"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForMaskedLM.from_pretrained(model_name)

# Set the device to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model.to(device)
# Example input text
input_text = "The quick brown fox jumps over the [MASK]."

# Tokenize the input text
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(device)

# Generate predictions for the masked token
with torch.no_grad():
outputs = model(input_ids)
predictions = outputs[0][0, 
input_ids.squeeze().tolist().index(tokenizer.mask_token_id)].topk(k=5).indices.tolist()

# Convert prediction indices to tokens
predicted_tokens = [tokenizer.convert_ids_to_tokens(prediction) for prediction in predictions]

# Print the predicted tokens
print("Predictions:", predicted_tokens)

Sample Code 3

Running the model on a GPU using different precisions - FP16

import torch
import transformers

# Load the pre-trained HELM model
model_name = "helmholtz/helm-uncased-base"
model = transformers.AutoModel.from_pretrained(model_name).cuda()

# Define the tokenizer
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)

# Define the input text
input_text = "Hello, how are you?"

# Tokenize the input text
input_ids = torch.tensor([tokenizer.encode(input_text, add_special_tokens=True)]).cuda()

# Enable mixed precision training
torch.backends.cudnn.benchmark = True
model.half()

# Set up the optimizer
optimizer = transformers.AdamW(model.parameters(), lr=5e-5)

# Set up the training loop
for epoch in range(num_epochs):

 # Forward pass
outputs = model(input_ids)
loss = outputs.loss

 # Backward pass
optimizer.zero_grad()
loss.backward()
optimizer.step()


# Print the loss
print(f"Epoch {epoch + 1}, Loss: {loss.item()}")

Sample Code 4

Running the model on a GPU using different precisions - FP16

import tensorflow as tf
import numpy as np
import time

# Load pre-trained HELM model
model = tf.keras.models.load_model('/path/to/helm/model')

# Create dataset for inference
dataset = ['This is a sample sentence.', 'Another sample sentence.']
input_ids = []
attention_masks = []

# Tokenize dataset
tokenizer = model.tokenizer
for sentence in dataset:

inputs = tokenizer.encode_plus(
sentence,
max_length=128,
truncation=True,
padding='max_length',
return_attention_mask=True,
return_tensors='tf'
)

 input_ids.append(inputs['input_ids'][0])
attention_masks.append(inputs['attention_mask'][0])


# Convert dataset to numpy arrays
input_ids = np.array(input_ids)
attention_masks = np.array(attention_masks)

# Convert dataset to INT8 precision
input_ids = tf.cast(input_ids, tf.int8)
attention_masks = tf.cast(attention_masks, tf.int8)

# Run inference on GPU with INT8 precision
start_time = time.time()
outputs = model.predict([input_ids, attention_masks])
end_time = time.time()

# Print inference time and results
print('Inference time: {} seconds'.format(end_time - start_time))
print('Output shape: {}'.format(outputs.shape))
print('Output: {}'.format(outputs))

Limitations

Some potential limitations of the HELM language model include:

Requires large amounts of training data

Like most large language models, HELM requires significant training data to perform well on natural language processing tasks.

High computational requirements

Due to the model's size and complexity, training and inference with HELM can be computationally intensive and require access to high-performance computing resources.

Limited interpretability

While HELM's hierarchical encoding scheme helps to improve its ability to understand context and structure in natural language, the model's inner workings can be difficult to interpret or explain.

Limited support for low-resource languages

As with most pre-trained language models, HELM's performance may be limited when working with low-resource languages or domains not well-represented in its training data.

Limited flexibility in model architecture

While HELM offers several variations of its base architecture, there may be some limitations on the level of customization and flexibility available to users who want to modify or extend the model's architecture for specific tasks.

Alternate Models:

PaLM

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LLMs Explained, HELM

100+ Technical Experts

50 Custom AI projects

4.8 Minimum Rating

An Overview of HELM

HELM XL is still a high-capacity LM suitable for NLP tasks.

1.2 billion parameters

HELM base model has 574 million parameters

574 million parameters

HELM base capable of achieving state-of-the-art results

State-of-the-art results

Introduction to HELM

About Model

Model highlights

Training Details

Training data

Training Procedure

Training dataset size

Training time and resources

Model Types

Business Applications

Model Features

Hierarchical encoding

Multi-granularity encoding

Variational Autoencoder (VAE)

Lifelong learning

Licensing

Level of customization

Available pre-trained model checkpoints

Model Tasks

Language Modeling

Text classification

Sentiment analysis

Machine translation

Paraphrase generation

Dialog systems

Getting Started

Fine-tuning

Learning rate schedule

Early stopping

Gradient accumulation

Knowledge distillation

Mixout regularization

Benchmarking

Sample Code 1

Running the model on a CPU

Sample Code 2

Running the model on a GPU

Sample Code 3

Running the model on a GPU using different precisions - FP16

Sample Code 4

Running the model on a GPU using different precisions - FP16

Limitations

Requires large amounts of training data

High computational requirements

Limited interpretability

Limited support for low-resource languages

Limited flexibility in model architecture

Alternate Models:

PaLM

BERT

LaMDA

LLMs Explained,
HELM