Meta Llama 3 70B Instruct GGUF

Table of Contents

Model Overview

The Meta-Llama-3-70B-Instruct model, developed by Meta, is a powerful tool for natural language processing tasks. It’s designed for commercial and research use in English, and is optimized for dialogue use cases. But what does that mean?

Tasks and rank

Text Generation 497th

Natural Language Understanding 406th

Language Modelling 411th

Dialogue Systems 202nd

Chatbot 312th

Learn More About the Ranking System

Capabilities

Capable of generating both text and code, this model outperforms many open-source chat models across common industry benchmarks.

Primary Tasks

The models are designed for:

• Assistant-like chat: They can engage in conversations and respond to user queries in a helpful and informative manner.
• Natural Language Generation: They can generate human-like text and code, making them suitable for a variety of applications.

Strengths

The Meta Llama 3 models have several strengths:

• Improved helpfulness: They are designed to be more helpful and informative than previous models.
• Better safety: They have undergone extensive red teaming exercises and adversarial evaluations to reduce residual risks.
• Less likely to refuse prompts: They are less likely to falsely refuse to answer prompts, making them more user-friendly.

Unique Features

The Meta Llama 3 models have several unique features:

• Grouped-Query Attention (GQA): They use an optimized transformer architecture that allows for improved inference scalability.
• Supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF): They have been fine-tuned using a combination of supervised and reinforcement learning techniques to align with human preferences for helpfulness and safety.
• Large pretraining dataset: They have been pretrained on over 15 trillion tokens of data from publicly available sources.

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

Roberta Base 399 Masked language model

BLOOMChat 176B V1 365 Multilingual chat model

SUS Chat 34B 121 Bilingual chat model

Performance

This model has shown remarkable performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.

Speed

• Fast inference: With the optimized transformer architecture and Grouped-Query Attention (GQA), this model can process input text quickly and efficiently.
• Scalability: The model can handle large-scale datasets with ease, making it suitable for commercial and research use cases.

Accuracy

• High accuracy: This model has achieved high accuracy in various benchmarks, including MMLU, AGIEval, and CommonSenseQA.
• Comparison to other models: The model outperforms other models, such as Llama 2 7B and Llama 2 13B, in many tasks.

Efficiency

• Low carbon footprint: The model was trained on a cumulative 7.7M GPU hours of computation, with estimated total emissions of 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
• Efficient training: The model was trained on a custom training library and Meta’s Research SuperCluster, making it an efficient choice for large-scale language model training.

Examples

Tell me a short story about a character who learns to appreciate the beauty of nature. Once upon a time, there was a young girl named Lily who lived in a bustling city. She spent most of her days cooped up indoors, staring at screens and rarely ventured outside. One day, her grandmother took her on a trip to the countryside, where they spent hours walking through rolling hills and dense forests. At first, Lily was bored and restless, but as they walked, she began to notice the intricate patterns on a butterfly's wings, the songs of the birds, and the vibrant colors of the wildflowers. She felt a sense of wonder and awe wash over her, and for the first time, she truly appreciated the beauty of nature. From that day on, Lily made it a point to spend more time outdoors, exploring the natural world and discovering its many wonders.

What is the definition of artificial intelligence? Artificial intelligence (AI) refers to the development of computer systems that can perform tasks that would typically require human intelligence, such as learning, problem-solving, and decision-making. AI systems use algorithms and data to make predictions, classify objects, and generate insights, often in ways that are similar to human thought processes.

Write a poem about the changing seasons. As summer's warmth begins to fade, a new season starts to unfold. The leaves turn golden, crimson, and gold, as autumn's palette is told. The air grows cool, the winds do blow, and nature's final dance begins to show. Then winter's chill begins to bite, and snowflakes swirl, and dance in sight. But even in the cold and darkest night, a promise of spring's new light.

Limitations

While this model is powerful, it’s not perfect. Let’s talk about some of its limitations.

Limited Context Understanding

• Context length: The model can only understand a limited amount of context, up to 8k tokens. This means it might struggle with very long conversations or complex topics that require a lot of background information.
• Lack of common sense: While the model is great at generating human-like text, it sometimes lacks common sense or real-world experience. This can lead to responses that are not practical or realistic.

Biased Training Data

• Data cutoff: The model was trained on data up to December 2023, which means it might not be aware of very recent events or developments.
• Limited diversity: The training data may not be diverse enough, which can result in biased or stereotypical responses.

Safety and Misuse

• Residual risks: Like any large language model, this model may still pose some risks, such as generating harmful or toxic content.
• Refusals: While the model is designed to be helpful, it may still refuse to answer certain prompts or provide incomplete information.

Technical Limitations

• Hardware requirements: The model requires significant computational resources, which can make it difficult to deploy on certain devices or platforms.
• Limited support for languages other than English: The model is primarily designed for English, and its performance may be limited when used with other languages.

Format

The model uses an optimized transformer architecture. It’s available in two sizes: 8B parameters and 70B parameters.

Architecture

The model is an auto-regressive language model that uses Grouped-Query Attention (GQA) for improved inference scalability.

Input

The model accepts input text only. You can use the following code example to preprocess your input:

import transformers
import torch

model_id = "meta-llama/Meta-Llama-3-70B-Instruct"
pipeline = transformers.pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device="cuda")

messages = [
    {"role": "system", "content": "You are a helpful, smart, kind, and efficient AI assistant."},
    {"role": "user", "content": "Hi! How are you?"}
]

prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)

Output

The model generates text and code only. You can use the following code example to get the output:

terminators = [tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("")]
outputs = pipeline(prompt, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9)
print(outputs[0]["generated_text"][len(prompt):])

Special Requirements

• You MUST follow the prompt template provided by Llama-3.
• The model is intended for commercial and research use in English.
• You should exercise discretion about how to weigh the benefits of alignment and helpfulness for your specific use case and audience.
• You should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for your use case.