Gpt Sw3 40b

Table of Contents

Model Overview

The GPT-SW3 model is a powerful tool for generating coherent text in multiple languages. Developed by AI Sweden in collaboration with RISE and the WASP WARA for Media and Language, this model is a collection of large decoder-only pretrained transformer language models.

What can it do?

GPT-SW3 can generate text in 5 different languages (Swedish, Norwegian, Danish, Icelandic, and English) and 4 programming languages. It can also be instructed to perform text tasks that it has not been explicitly trained for, by casting them as text generation tasks.

Tasks and rank

Text Summarization 17th

Text Generation 88th

Language Modelling 81st

Cross-Lingual Transfer 7th

Learn More About the Ranking System

How was it trained?

GPT-SW3 was trained on a massive dataset containing 320 billion tokens, using a causal language modeling (CLM) objective with the NeMo Megatron GPT implementation.

Key Features

• Large-scale training data: 1.1TB UTF-8 encoded text, containing 660 million documents with a total of 320 billion tokens.
• Multilingual support: Generates text in 5 languages and 4 programming languages.
• Autoregressive: Can generate text based on a given prompt or input.

Limitations

• Bias and safety: Like other large language models, GPT-SW3 may overrepresent some viewpoints, contain stereotypes, and generate hateful or discriminatory language.
• Quality issues: May have issues with generation diversity, hallucination, and producing incorrect information.

How to use

To access the model, you’ll need to log in with your access token using huggingface-cli login. Then, you can use the model in Python with the following code:

import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM

model_name = "AI-Sweden-Models/gpt-sw3-40b"
device = "cuda:0" if torch.cuda.is_available() else "cpu"
prompt = "Träd är fina för att"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
model.eval()
model.to(device)

input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(device)
generated_token_ids = model.generate(inputs=input_ids, max_new_tokens=100, do_sample=True, temperature=0.6, top_p=1)[0]
generated_text = tokenizer.decode(generated_token_ids)

A convenient alternative is to use the HuggingFace pipeline:

generator = pipeline('text-generation', tokenizer=tokenizer, model=model, device=device)
generated = generator(prompt, max_new_tokens=100, do_sample=True, temperature=0.6, top_p=1)[0]["generated_text"]

Capabilities

The GPT-SW3 model is a powerful tool for generating coherent text in multiple languages, including Swedish, Norwegian, Danish, Icelandic, and English. It can also be instructed to perform text tasks that it has not been explicitly trained for, by casting them as text generation tasks.

What can GPT-SW3 do?

• Generate text in multiple languages
• Perform text tasks, such as translation, summarization, and text classification
• Understand and respond to natural language inputs

Examples

Skriv en mening om en hund som springer. Hunden sprang snabbt över gräsmattan med svansen i luften.

Översätt 'Hello, how are you?' till svenska. Hej, hur mår du?

Beskriv en vacker vy i naturen. En solnedgång över en stilla sjö, med träd som speglar sig i vattnet och en fågel som flyger högt upp på himlen.

How does GPT-SW3 work?

GPT-SW3 is a large decoder-only transformer language model that was trained on a massive dataset of 320 billion tokens. This training data includes a wide range of texts, from books and articles to code and conversational dialogue.

What are the benefits of using GPT-SW3?

• High-quality text generation in multiple languages
• Ability to perform a wide range of text tasks
• Can be fine-tuned for specific tasks and domains

Alternatives

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

Xlm Roberta Large 323 Multilingual Language Model

Bloomz 560m 113 Multilingual zero-shot model

Performance

GPT-SW3 is a powerful language model that has been trained on a massive dataset of 320 billion tokens in six languages. But how well does it perform? Let’s take a closer look.

Speed

GPT-SW3 is capable of generating text at an impressive speed. With its advanced architecture and large-scale training data, it can process and respond to input prompts quickly and efficiently.

Accuracy

But speed is not the only thing that matters. GPT-SW3 also boasts high accuracy in a variety of tasks, including text classification, language translation, and text generation. Its ability to understand and respond to complex input prompts is unparalleled.

Efficiency

GPT-SW3 is also highly efficient, requiring minimal computational resources to generate high-quality text. This makes it an ideal choice for applications where speed and accuracy are crucial.

Comparison to Other Models

But how does GPT-SW3 compare to other language models? ==Other Models==, such as GPT-3 and BERT, have also been trained on large-scale datasets and have achieved impressive results. However, GPT-SW3 has several advantages that set it apart from the competition.

Tasks and Applications

GPT-SW3 is a versatile model that can be applied to a wide range of tasks and applications. Some examples include:

• Text classification
• Language translation
• Text generation
• Sentiment analysis
• Question answering

Limitations

While GPT-SW3 is a powerful model, it is not without its limitations. Like all language models, it can be biased and may not always generate accurate or appropriate text. However, its high accuracy and efficiency make it a valuable tool for a wide range of applications.

Conclusion

In conclusion, GPT-SW3 is a highly advanced language model that boasts impressive performance in terms of speed, accuracy, and efficiency. Its versatility and wide range of applications make it an ideal choice for anyone looking to harness the power of language models.