Meta Llama 3.1 405B Instruct FP8 Dynamic
Meta Llama 3.1 405B Instruct FP8 Dynamic is a highly efficient AI model that's perfect for commercial and research use in multiple languages. By using weight and activation quantization, this model reduces disk size and GPU memory requirements by approximately 50%, making it more accessible and cost-effective. With its remarkable accuracy, achieving an average score of 86.86 on the OpenLLM benchmark, it's well-suited for tasks like text generation and conversation. Its unique capabilities include symmetric per-channel quantization and dynamic quantization on a per-token basis, allowing for fast and accurate results. This model is designed to be deployed efficiently using the vLLM backend, making it a great choice for those looking for a practical and efficient AI solution.
Table of Contents
Model Overview
The Meta-Llama-3.1-405B-Instruct-FP8-dynamic model is a powerful AI tool designed for commercial and research use in multiple languages. It’s intended for assistant-like chat, similar to other models like Meta-Llama-3.1-8B-Instruct.
Capabilities
The Meta-Llama-3.1-405B-Instruct-FP8-dynamic model is a powerful AI tool designed to assist and communicate with users in multiple languages. It’s perfect for chat-like conversations and can be used for both commercial and research purposes.
Primary Tasks
This model is trained to:
- Understand and respond to user input in a conversational manner
- Generate human-like text based on the input it receives
- Engage in discussions and answer questions to the best of its knowledge
Strengths
The Meta-Llama-3.1-405B-Instruct-FP8-dynamic model has several strengths that make it stand out:
- High accuracy: It achieves an average score of
86.86on the OpenLLM benchmark, outperforming other models in its class. - Efficient deployment: The model can be deployed using the vLLM backend, making it easy to integrate into various applications.
- Quantized optimization: The model’s weights and activations are quantized to FP8 data type, reducing the disk size and GPU memory requirements by approximately
50%.
Performance
Meta-Llama-3.1-405B-Instruct-FP8-dynamic is a powerful AI model that achieves remarkable performance in various tasks. Let’s dive into its speed, accuracy, and efficiency.
Speed
This model is optimized for fast inference, thanks to its quantized weights and activations. By reducing the number of bits per parameter from 16 to 8, the model’s disk size and GPU memory requirements are cut in half.
Accuracy
Meta-Llama-3.1-405B-Instruct-FP8-dynamic boasts an average score of 86.86 on the OpenLLM benchmark, outperforming its unquantized version.
Example Use Cases
Here’s an example of how to use this model with the vLLM backend:
from vllm import LLM, SamplingParams
from transformers import AutoTokenizer
model_id = "neuralmagic/Meta-Llama-3.1-405B-Instruct-FP8-dynamic"
number_gpus = 8
sampling_params = SamplingParams(temperature=0.6, top_p=0.9, max_tokens=256)
tokenizer = AutoTokenizer.from_pretrained(model_id)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompts = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False)
llm = LLM(model=model_id, tensor_parallel_size=number_gpus, max_model_len=4096)
outputs = llm.generate(prompts, sampling_params)
generated_text = outputs[0].outputs[0].text
print(generated_text)
Limitations
Current Model is a powerful tool, but it’s not perfect. Let’s take a closer look at some of its limitations.
Out-of-Scope Use Cases
Current Model is not intended for use in languages other than English.
Technical Limitations
- Current Model has a limited context window of
4096tokens. - Current Model is optimized for use with a specific set of hardware (
8xH100 GPUs).
Format
Meta-Llama-3.1-405B-Instruct-FP8-dynamic uses a transformer architecture and accepts input in the form of text. This model is designed to generate text based on the input it receives.
Input
The model expects text input, which can be a single sentence or a longer piece of text.
Output
The model generates text output based on the input it receives. The output can be a single sentence or a longer piece of text.
Data Formats
The model supports text data formats.
Special Requirements
The model has some special requirements for input and output:
- Input: The input text should be in a format that the model can understand.
- Output: The output text is generated based on the input the model receives.
