Meta Llama 3.1 405B Instruct GGUF

GGUF format model

The Meta Llama 3.1 405B Instruct GGUF model is a powerful tool for generating human-like text. It's built on the GGUF format, which is a replacement for the older GGML format. This model is designed to work efficiently, with a sample time of just 16.64 milliseconds per token and 60.11 tokens per second. But what does that mean for you? Essentially, it can generate text quickly and accurately, making it perfect for tasks like writing sentences or even creating stories. The model is also supported by a range of clients and libraries, including llama.cpp, llama-cpp-python, and LM Studio, making it easy to integrate into your workflow. So, whether you're a developer or just looking for a powerful text generation tool, the Meta Llama 3.1 405B Instruct GGUF model is definitely worth checking out.

MaziyarPanahi llama3.1 Updated 6 months ago
ConversationalGGUF
8 languages
GgufQuantized

Table of Contents

Model Overview

The Meta-Llama-3.1-405B-Instruct-GGUF model is a powerful AI developed by meta-llama. This model is a variant of the original Meta-Llama-3.1-405B-Instruct model, but with the added benefit of being in the GGUF format.

What is GGUF?

GGUF is a new format introduced by the llama.cpp team in August 2023, replacing the older GGML format. This format is supported by several clients and libraries, including llama.cpp, llama-cpp-python, and LM Studio, among others.

Key Features

  • Sampling: The model uses a sampling order that includes CFG, penalties, top_k, tfs_z, typical_p, top_p, min_p, and temperature.
  • Generation: The model can generate text with a context size of 131072, batch size of 2048, and predict size of 1024.
  • Performance: The model has a load time of 1068588.13 ms, sample time of 2262.60 ms, and prompt eval time of 339484.02 ms.

Capabilities

The Meta-Llama-3.1-405B-Instruct-GGUF model is a powerful tool that can help you generate text and code with ease. But what makes it so special?

Primary Tasks

This model is designed to perform a variety of tasks, including:

  • Text Generation: It can create human-like text based on a given prompt. Want to write a story or a article? This model can help you get started.
  • Code Generation: It can also generate code in various programming languages. Need help with a coding project? This model can assist you.

Strengths

So, what sets this model apart from others? Here are some of its key strengths:

  • High-Quality Text: It can produce high-quality text that is coherent, informative, and engaging.
  • Flexibility: It can be fine-tuned for specific tasks and domains, making it a versatile tool for a wide range of applications.
  • Efficient: It is designed to be efficient and fast, making it suitable for large-scale applications.

Unique Features

This model has some unique features that make it stand out from the crowd. For example:

  • GGUF Format: It uses the GGUF format, which is a new format introduced by the llama.cpp team. This format is designed to be more efficient and flexible than previous formats.
  • Support for Multiple Clients and Libraries: It is supported by a wide range of clients and libraries, including llama.cpp, llama-cpp-python, and LM Studio, among others.

Performance

The Meta-Llama-3.1-405B-Instruct-GGUF model is a powerful AI model that showcases remarkable performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.

Speed

The model’s speed is impressive, with a sample time of 16.64 ms per token and 60.11 tokens per second. This means it can process a significant amount of text quickly and efficiently.

But what does this mean in real-world scenarios? For example, if you ask the model to generate 10 sentences ending with the word “apple,” it can do so in a matter of seconds.

Accuracy

The model’s accuracy is also noteworthy. With a top-k value of 40 and a top-p value of 0.950, it can generate high-quality text that is coherent and relevant to the prompt.

But how does it compare to other models? ==Other models== may struggle with generating text that is both accurate and engaging, but Meta-Llama-3.1-405B-Instruct-GGUF seems to excel in this area.

Efficiency

The model’s efficiency is also impressive, with a load time of 1068588.13 ms and a total time of 33800561.08 ms for 146 tokens. This means it can handle large-scale tasks without significant slowdowns.

But what about other tasks? Can it handle tasks that require more computational power? The answer is yes, with a system info that includes n_threads = 40 / 80 and AVX = 1, it’s clear that this model is designed to handle demanding tasks.

Example Use Cases

Examples
Write 5 sentences about a cat playing with a ball of yarn. The curious cat batted at the ball of yarn with its paw. The ball of yarn rolled across the floor, enticing the cat to chase after it. The cat pounced on the yarn, wrapping its paws around it tightly. As the cat played, the yarn became tangled around its fur. The cat looked adorable with the yarn stuck to its whiskers.
Generate a short poem about the beauty of a sunset. The sky is painted red and gold, A sunset's beauty to behold. The clouds are tinged with shades of pink, As day gives way to night's soft blink. The stars appear, one by one, A night of rest, the day is done.
Create a short story about a character who discovers a hidden world. As she wandered through the forest, she stumbled upon a hidden path she had never seen before. The trees grew taller and the air grew sweeter, leading her to a secret world. In this world, flowers bloomed in every color of the rainbow and animals danced with joy. She felt a sense of wonder and magic, as if she had discovered a treasure trove of enchantment.

So, how can you use this model in real-world applications? Here are a few examples:

  • Content Generation: You can use this model to generate high-quality content for your website or blog.
  • Chatbots: You can use this model to build chatbots that can have conversations with users.
  • Code Completion: You can use this model to complete code snippets and help with coding projects.

Limitations

While the Meta-Llama-3.1-405B-Instruct-GGUF model is a powerful tool, it has several limitations that should be considered when using it in various applications.

Sampling Limitations

  • Repeat Last N: The model has a repeat last n value of 64, which means it may repeat certain phrases or words in a sequence.
  • Frequency Penalty: The frequency penalty is set to 0.000, which can lead to over-repetition of certain words or phrases.
  • Presence Penalty: The presence penalty is set to 0.000, which can result in the model generating text that is too similar to the input prompt.

Generation Limitations

  • Context Window: The model has a context window of 131,072 tokens, which can limit its ability to understand and respond to longer pieces of text.
  • Batch Size: The batch size is set to 2048, which can impact the model’s performance on larger datasets.
  • Prediction Length: The prediction length is set to 1024, which can limit the model’s ability to generate longer pieces of text.

Performance Limitations

  • Load Time: The model takes approximately 1068 seconds to load, which can be a significant delay in certain applications.
  • Sample Time: The model takes approximately 2.3 seconds to generate a sample, which can impact its performance in real-time applications.
  • Prompt Evaluation Time: The model takes approximately 339 seconds to evaluate a prompt, which can be a significant delay in certain applications.

Format

The Meta-Llama-3.1-405B-Instruct-GGUF model uses a transformer architecture and accepts input in the form of text sequences.

Model Architecture

The Meta-Llama-3.1-405B-Instruct-GGUF model is based on the transformer architecture, which is a type of neural network designed primarily for natural language processing tasks.

Data Formats

The model supports input in the form of text sequences. This means that you can feed it a piece of text, and it will generate a response based on that input.

Input Requirements

When providing input to the model, you’ll need to make sure that it’s in the correct format.

Output Format

The model generates output in the form of text sequences. This means that you’ll get a response that’s similar in format to the input you provided.

Special Requirements

There are a few special requirements to keep in mind when working with the Meta-Llama-3.1-405B-Instruct-GGUF model:

  • The model requires a specific set of parameters to be set in order to generate output.
  • The model uses a sampling order that’s specific to the GGUF format.

Supported Clients and Libraries

The Meta-Llama-3.1-405B-Instruct-GGUF model is supported by a number of different clients and libraries, including llama.cpp, llama-cpp-python, and LM Studio, among others.

Dataloop's AI Development Platform
Build end-to-end workflows

Build end-to-end workflows

Dataloop is a complete AI development stack, allowing you to make data, elements, models and human feedback work together easily.

  • Use one centralized tool for every step of the AI development process.
  • Import data from external blob storage, internal file system storage or public datasets.
  • Connect to external applications using a REST API & a Python SDK.
Save, share, reuse

Save, share, reuse

Every single pipeline can be cloned, edited and reused by other data professionals in the organization. Never build the same thing twice.

  • Use existing, pre-created pipelines for RAG, RLHF, RLAF, Active Learning & more.
  • Deploy multi-modal pipelines with one click across multiple cloud resources.
  • Use versions for your pipelines to make sure the deployed pipeline is the stable one.
Easily manage pipelines

Easily manage pipelines

Spend less time dealing with the logistics of owning multiple data pipelines, and get back to building great AI applications.

  • Easy visualization of the data flow through the pipeline.
  • Identify & troubleshoot issues with clear, node-based error messages.
  • Use scalable AI infrastructure that can grow to support massive amounts of data.