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How to Use Meta Llama 3 Large Language Model on Vultr Cloud GPU

Meta Llama 3Cloud GPULarge Language Model
MS
Updated on June 12, 2024
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Introduction

Llama 3 is a family of Large Language Models released by Meta, It is a collection of pre-trained and instruction-tuned generative text models. Llama 3 is pre-trained over 15T tokens collected from publically available sources, it is an auto-regressive family of models that uses an optimized transformer architecture. The instruction-tuned models use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). The Llama 3 models have a custom limited commercial license which means models can used for commercial and research purposes.

In this article, you are to run inference workloads on the Llama3 8B and Llama 3 8B Instruct models using Vultr's GPU Stack Marketplace Application. You also going to be exploring the model benchmarks to evaluate the model performance.

Prereqs

Infer Meta Llama 3 8B

In this section, you are to run inference workloads on the Llama 3 8B base model with 8 billion parameters.

  1. Install the required dependencies.

    python 
    !pip install transformer huggingface-hub

  2. Log in to the HuggingFace Hub.

    python 
    huggingfacehub-cli --login

    On the execution of this command, you will be prompted for an access token. Where you have to provide the token to access the Llama 3 Models.

  3. Import necessary modules.

    python 
    import transformers
import torch

  4. Declare the Model.

    python 
    model_id = "meta-llama/Meta-Llama-3-8B"

  5. Declare the model pipeline.

    python 
    pipeline = transformers.pipeline(
"text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"

    In the above code, you declared the model pipeline with the task-generation task. By default, Llama 3 8B is set to 16-bit precision.

  6. Provide a prompt.

    python 
    pipeline("Hey how are you doing today?")

Infer Meta Llama 3 8B Instruct

In this section, you are to run inference workloads on the Llama 3 8B Instruct model with 8 billion parameters. This model is fine-tuned for dialogue-based use cases.

  1. Import necessary modules.

    python 
    import transformers
import torch

  2. Declare the model.

    python 
    model_id = "meta-llama/Meta-Llama-3-8B-Instruct"

  3. Declare the model pipeline.

    python 
    pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="auto",
)

  4. Set roles for the model.

    python 
    messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]

    In the above code, you are setting the roles informing the chatbot that it is a pirate chatbot.

  5. Make a prompt template.

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

  6. Set the terminators for text generation.

    python 
    terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]

  7. Set the model parameters.

    python 
    outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)

    In the above code, you are setting up the model parameters that can be adjusted to improve the response generated by the model.

  8. Print the generated output.

    python 
    print(outputs[0]["generated_text"][len(prompt):])

Conclusion

In this article, you used Meta Llama 3 models on a Vultr Cloud GPU Server with Vultr's GPU Stack Marketplace Application. And ran inference workloads with the latest Llama 3 80B in 16-bit mode with its fine-tuned Instruct version also in the 16-bit mode.