Meta 405b Instruct Cpu Optimized Gguf

Table of Contents

Model Overview

The Meta-Llama-3.1-405B-Instruct model is a powerful language model designed for assistant-like chat and natural language generation tasks. With 405 billion parameters, it’s one of the largest language models available.

Key Features

• Supports 8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai
• Has a 128k context length, allowing it to understand and respond to longer pieces of text
• Uses Grouped-Query Attention (GQA) for improved inference scalability

Tasks and rank

Text Generation 29th

Natural Language Understanding 28th

Language Modelling 33rd

Learn More About the Ranking System

What can it do?

• Generate human-like text responses to questions and prompts
• Assist with language translation and understanding
• Help with writing and proofreading tasks
• Provide information on a wide range of topics

Quantizations

The model is available in several quantizations, including:

Quantization	Size
Q4_0_4_8 (CPU FMA-Optimized)	~246 GB
IQ4_XS (Fastest for CPU/GPU)	~212 GB
Q2K-Q8 Mixed quant with iMatrix	~154 GB
1-bit Custom per weight COHERENT quant	~103 GB
BF16	~811 GB
Q8_0	~406 GB

These quantizations allow the model to be run on different hardware configurations, making it more accessible to a wider range of users.

Performance

Current Model showcases excellent performance in various natural language processing tasks. Let’s dive into its speed, accuracy, and efficiency.

Speed

The model’s speed is impressive, thanks to its CPU-optimized quantizations. These quantizations enable the model to run efficiently on CPU hardware, making it suitable for a wide range of applications.

• The Q4_0_4_8 quantization, for example, has a size of approximately 246 GB and is optimized for CPU FMA (Fused Multiply-Add) instructions.
• The IQ4_XS quantization is the fastest for both CPU and GPU, with a size of around 212 GB.

Accuracy

The model’s accuracy is also noteworthy, with a high degree of precision in various tasks.

• The model supports 8 languages, including English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
• It has a 128k context length, allowing it to process long sequences of text.
• The model uses Grouped-Query Attention (GQA) for improved inference scalability.

Efficiency

The model’s efficiency is another key aspect of its performance.

• The 1-bit Custom Per Weight Quantization has a size of approximately 103 GB, making it a compact and efficient option.
• The Q2K-Q8 Mixed quantization has a size of around 154 GB and is designed for higher quality iMatrix.

Alternatives

NeMo SFT with Llama 2 10500 Fine-tuned language model

Bert Base Uncased 1831 Uncased BERT model

Roberta Base 399 Masked language model

Limitations

While Current Model is a powerful tool, it has some limitations. Here are a few things to keep in mind:

Language Support

While Current Model supports 8 languages, it may not perform equally well in all of them. If you’re working with a language other than English, you may encounter some issues.

Context Length

Current Model has a context length of 128k, which means it can only consider a certain amount of text when generating responses. If you need to work with longer texts, you may need to split them up or use a different model.

Inference Scalability

Current Model uses Grouped-Query Attention (GQA) to improve inference scalability, but it may still struggle with very large inputs or complex tasks.

Quantization

The quantized versions of Current Model may have slightly lower performance than the full-precision model. However, they are much smaller and more efficient, making them a good choice for deployment on devices with limited resources.

Examples

Adopt the persona of a NASA JPL mathematician and friendly helpful programmer. Can you explain the concept of orbit in simple terms? Hey there, space enthusiast! I'd be happy to explain orbits in a way that's easy to grasp. Think of an orbit like a big circle around a planet or moon. Imagine you're holding a ball on a string and spinning it around your head. The ball is moving in a curved path, right? That's basically what an orbit is - an object moving in a curved path around a larger body due to gravity. The shape of the orbit can be affected by things like the mass of the planet, the speed of the object, and even the gravity of other nearby objects. But that's the basic idea!

Write a short story about a character who discovers a hidden world within their own reflection. As she gazed into the mirror, Emily noticed something peculiar. Her reflection's eyes seemed to be staring back at her with an intensity she'd never seen before. Suddenly, the glassy surface began to ripple, like the gentle lapping of waves on a summer shore. Without warning, Emily felt herself being pulled into the mirror, sucked into a world that existed within her own reflection. She found herself standing in a lush forest, surrounded by towering trees that seemed to stretch up to the sky. A figure approached her - it was her own reflection, but with a mischievous glint in its eye. 'Welcome to the world within,' it said, with a sly smile. 'Here, anything is possible.'

Summarize the benefits of using a 1-bit custom per weight quantization model for natural language processing tasks. The 1-bit custom per weight quantization model offers several benefits for natural language processing tasks. Firstly, it significantly reduces the model's size, making it more efficient and requiring less computational resources. Secondly, it preserves the model's accuracy, ensuring that the performance is not compromised. Finally, it enables faster inference times, making it suitable for real-time applications. Overall, this model is an excellent choice for NLP tasks that require a balance between efficiency and accuracy.

Example Use Case

You can use the model with libraries like llama.cpp to generate text responses to questions and prompts. For example:

./llama-cli -t 32 --temp 0.4 -fa -m ~/meow/meta-405b-inst-cpu-optimized-q4048-00001-of-00006.gguf -b 512 -c 9000 -p "Adopt the persona of a NASA JPL mathematician and friendly helpful programmer." -cnv -co -i

This command would generate a response to the prompt “Adopt the persona of a NASA JPL mathematician and friendly helpful programmer.” using the meta-405b-inst-cpu-optimized-q4048-00001-of-00006.gguf model.

Format

Current Model utilizes a transformer architecture and accepts input in the form of tokenized text sequences.

Architecture

This model is based on the Meta-Llama-3.1-405B-Instruct model, which is an instruction-tuned version of the 405B parameter Llama 3.1 model. It’s designed for assistant-like chat and various natural language generation tasks.

Key Features

• 405 billion parameters
• Supports 8 languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai
• 128k context length
• Uses Grouped-Query Attention (GQA) for improved inference scalability

Data Formats

The model supports the following data formats:

Format	Description
Q4_0_4_8	CPU FMA-Optimized, ~246 GB
IQ4_XS	Fastest for CPU/GPU, ~212 GB
Q2K-Q8 Mixed	2bit 8bit, ~154 GB
Q2K-Q8 Mixed without iMat	2bit 8bit, ~165 GB
1-bit Custom per weight COHERENT	~103 GB
BF16	~811 GB (original model)
Q8_0	~406 GB (original model)

Input and Output

The model accepts input in the form of tokenized text sequences and produces output in the same format.

Here’s an example of how to use the model with the llama-cli library:

./llama-cli -t 32 --temp 0.4 -fa -m ~/meow/meta-405b-inst-cpu-optimized-q4048-00001-of-00006.gguf -b 512 -c 9000 -p "Adopt the persona of a NASA JPL mathmatician and firendly helpful programmer." -cnv -co -i

Note: Make sure to replace the ~/meow/meta-405b-inst-cpu-optimized-q4048-00001-of-00006.gguf with the actual path to the model file on your system.