Llama 3.1 Swallow 8B Instruct V0.1

Table of Contents

Model Overview

The Llama 3.1 Swallow model is a series of large language models that were built by continual pre-training on the Meta Llama 3.1 models. It’s designed to enhance Japanese language capabilities while retaining English language capabilities.

Key Features

• Language Support: Japanese and English
• Model Type: Large language model
• Library: Megatron-LM
• Tokenizer: Refer to Llama 3.1 blog for details

Tasks and rank

Text Generation 9856th

Response Generation 5282nd

Language Modelling 8584th

Dialogue Systems 3880th

Chatbot 6049th

Learn More About the Ranking System

Capabilities

The Llama 3.1 Swallow model is a powerful language model that excels in various tasks, including:

Japanese Tasks

• Question Answering: It can answer multiple-choice and open-ended questions with high accuracy.
• Text Summarization: It can summarize long pieces of text into concise and meaningful summaries.
• Machine Translation: It can translate text from Japanese to English and vice versa.
• Mathematical Reasoning: It can solve mathematical problems and reason about mathematical concepts.
• Code Generation: It can generate code in various programming languages.

English Tasks

• Question Answering: It can answer multiple-choice and open-ended questions with high accuracy.
• Text Summarization: It can summarize long pieces of text into concise and meaningful summaries.
• Machine Translation: It can translate text from English to Japanese and vice versa.
• Mathematical Reasoning: It can solve mathematical problems and reason about mathematical concepts.
• Code Generation: It can generate code in various programming languages.

Multi-Turn Dialogue

• It can engage in multi-turn dialogue, responding to user input and generating human-like responses.

Strengths

• Improved Japanese Language Capabilities: The model has been fine-tuned on a large Japanese web corpus, making it particularly strong in Japanese language tasks.
• Code Generation: The model can generate code in various programming languages, making it a valuable tool for developers.

Unique Features

• Instruction Tuning: The model has been fine-tuned on a dataset of instructions, making it capable of following instructions and generating text that is tailored to specific tasks.
• Continual Pre-Training: The model has been continually pre-trained on a large dataset, making it adaptable to new tasks and domains.

Performance

The Llama 3.1 Swallow model showcases remarkable performance in various tasks. Let’s dive into its capabilities.

Speed

The model’s speed is notable, especially when dealing with large datasets. Its ability to process information quickly makes it an excellent choice for applications where time is of the essence.

Accuracy

The model achieves high accuracy in multiple tasks, including:

• Japanese tasks:
  • Open-ended question answering (JEMHopQA)
  • Machine reading comprehension (JSQuAD)
  • Automatic summarization (XL-Sum)
• English tasks:
  • Multiple-choice question answering (OpenBookQA)
  • Open-ended question answering (TriviaQA)
  • Machine reading comprehension (SQuAD2)

Efficiency

The model’s efficiency is evident in its ability to handle a wide range of tasks with minimal computational resources. This makes it an excellent choice for applications where resources are limited.

Comparison to Other Models

The model outperforms other models in several tasks, including:

• Japanese tasks:
  • Outperforms RakutenAI-7B-chat in JEMHopQA and JSQuAD
  • Outperforms Qwen2-7B-Instruct in XL-Sum
• English tasks:
  • Outperforms Qwen2-7B-Instruct in OpenBookQA and TriviaQA
  • Outperforms ==Tanuki-8B-dpo-v1.0== in SQuAD2

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

BLOOMChat 176B V1 365 Multilingual chat model

SUS Chat 34B 121 Bilingual chat model

Qwen2.5 1.5B Instruct 51 Multilingual instruct model

Usage

You can use the model by installing the vllm library and importing the LLM and SamplingParams classes. Here’s an example:

pip install vllm
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams

model_name = "tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(model=model_name, tensor_parallel_size=1)
sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=512, stop="")

Limitations

The Llama 3.1 Swallow model is a powerful tool, but it has some limitations. Here are some of its weaknesses:

Lack of Human Intent Alignment

The model’s outputs may not always align with human intent and safety considerations. This is because the model is still in the early stages of research and development.

Limited Contextual Understanding

While the model can understand context to some extent, it may not always grasp the nuances of human communication. This can lead to misunderstandings or misinterpretations.

Dependence on Training Data

The model’s performance is heavily dependent on the quality and diversity of its training data. If the training data is biased or limited, the model’s outputs may reflect these biases.

Vulnerability to Adversarial Attacks

Like other AI models, the model may be vulnerable to adversarial attacks, which can manipulate the model’s outputs.

Limited Domain Knowledge

While the model has been trained on a wide range of topics, its knowledge in specific domains may be limited. This can lead to inaccuracies or outdated information.

Potential for Misuse

As with any powerful tool, the model can be misused if not handled responsibly. It’s essential to use the model in a way that aligns with human values and safety considerations.

Limited Transparency

The model’s decision-making process may not be entirely transparent, making it challenging to understand why it generates certain outputs.

Examples

Write a short story about a Tokyo park where the leaves have changed colors and the Tokyo Tower and skyscrapers are visible in the background. A swallow flies in the sky and a llama stands in the grass. On a crisp autumn day, the Tokyo park was ablaze with color. The leaves had turned brilliant shades of orange, red, and yellow, and the Tokyo Tower rose majestically in the background, its peak hiding in the clouds. The skyscrapers of the city seemed to stretch on forever, their peaks glinting in the sunlight. A swallow darted through the air, its iridescent feathers catching the sun's rays and sending shafts of light dancing through the trees. In the midst of this vibrant scene, a llama stood serenely in the grass, its soft fur a gentle contrast to the vibrant colors around it. The air was filled with the sweet scent of fallen leaves and the distant hum of the city, a soothing melody that seemed to match the rhythm of the swallow's flight.

Translate the sentence 'The quick brown fox jumps over the lazy dog' into Japanese.

Solve the math problem: 2x + 5 = 11 x = 3

Format

The Llama 3.1 Swallow model is a large language model that uses a transformer architecture. It accepts input in the form of tokenized text sequences, requiring a specific pre-processing step. The model supports both Japanese and English languages.

Input Format

The input format for the model is a tokenized text sequence. You can use the AutoTokenizer from the transformers library to tokenize your input text.

from transformers import AutoTokenizer

model_name = "tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1"
tokenizer = AutoTokenizer.from_pretrained(model_name)

Output Format

The output format for the model is a text sequence. The model generates text based on the input prompt and the sampling parameters.

output = llm.generate(prompt, sampling_params)
print(output[0].outputs[0].text)

Special Requirements

• The model requires a specific pre-processing step for input text, which involves tokenizing the text using the AutoTokenizer.
• The model supports both Japanese and English languages, but the input text should be in the correct language format.
• The model has a maximum input length of 512 tokens.