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.
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:
"hugging"
, instead of having the smallest units, like ["hug","ging"] form 2 tokens, if
“hugging”` is part of the vocab, it will be automatically returned as a token.pad_id = -1
which means that there is no padding token. We can’t have the same logic, make sure to add a padding token using tokenizer.add_special_tokens({"pad_token":"<pad>"})
and resize the token embedding accordingly. You should also set the model.config.pad_token_id
. The embed_tokens
layer of the model is initialized with self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.config.padding_idx)
, which makes sure that encoding the padding token will output zeros, so passing it when initializing is recommended.python src/transformers/models/llama/convert_llama_weights_to_hf.py \ --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/path --llama_version 3
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("/output/path")
model = AutoModelForCausalLM.from_pretrained("/output/path")
Note that executing the script requires enough CPU RAM to host the whole model in float16 precision (even if the biggest versions come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM). For the 75B model, it’s thus 145GB of RAM needed.
attn_implementation="flash_attention_2"
, don’t pass torch_dtype
to the from_pretrained
class method and use Automatic Mixed-Precision training. When using Trainer
, it is simply specifying either fp16
or bf16
to True
. Otherwise, make sure you are using torch.autocast
. This is required because the Flash Attention only support fp16
and bf16
data type.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?")
A ton of cool resources are already available on the documentation page of [~llama2], inviting contributors to add new recourses curated for Llama3 here! 🤗
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