Llama3

import transformers
import torch

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

pipeline = transformers.pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto")
pipeline("Hey how are you doing today?")

Overview

The Llama3 model was proposed in Introducing Meta Llama 3: The most capable openly available LLM to date by the meta AI team.

The abstract from the blogpost is the following:

Today, we’re excited to share the first two models of the next generation of Llama, Meta Llama 3, available for broad use. This release features pretrained and instruction-fine-tuned language models with 8B and 70B parameters that can support a broad range of use cases. This next generation of Llama demonstrates state-of-the-art performance on a wide range of industry benchmarks and offers new capabilities, including improved reasoning. We believe these are the best open source models of their class, period. In support of our longstanding open approach, we’re putting Llama 3 in the hands of the community. We want to kickstart the next wave of innovation in AI across the stack—from applications to developer tools to evals to inference optimizations and more. We can’t wait to see what you build and look forward to your feedback.

Checkout all Llama3 model checkpoints here. The original code of the authors can be found here.

Usage tips

The Llama3 models were trained using bfloat16, but the original inference uses float16. The checkpoints uploaded on the Hub use torch_dtype = 'float16', which will be used by the AutoModel API to cast the checkpoints from torch.float32 to torch.float16.

The dtype of the online weights is mostly irrelevant unless you are using torch_dtype="auto" when initializing a model using model = AutoModelForCausalLM.from_pretrained("path", torch_dtype = "auto"). The reason is that the model will first be downloaded ( using the dtype of the checkpoints online), then it will be casted to the default dtype of torch (becomes torch.float32), and finally, if there is a torch_dtype provided in the config, it will be used.

Training the model in float16 is not recommended and is known to produce nan; as such, the model should be trained in bfloat16.

Tips:

Resources

A ton of cool resources are already available on the documentation page of Llama2, inviting contributors to add new resources curated for Llama3 here! 🤗

< > Update on GitHub