Meta Llama3 in torchtune

You will learn how to:

• Download the Llama3-8B-Instruct weights and tokenizer

• Fine-tune Llama3-8B-Instruct with LoRA and QLoRA

• Evaluate your fine-tuned Llama3-8B-Instruct model

• Generate text with your fine-tuned model

• Quantize your model to speed up generation

Prerequisites

• Be familiar with torchtune

• Make sure to install torchtune

Llama3-8B

Meta Llama 3 is a new family of models released by Meta AI that improves upon the performance of the Llama2 family of models across a range of different benchmarks. Currently there are two different sizes of Meta Llama 3: 8B and 70B. In this tutorial we will focus on the 8B size model. There are a few main changes between Llama2-7B and Llama3-8B models:

• Llama3-8B uses grouped-query attention instead of the standard multi-head attention from Llama2-7B

• Llama3-8B has a larger vocab size (128,256 instead of 32,000 from Llama2 models)

• Llama3-8B uses a different tokenizer than Llama2 models (tiktoken instead of sentencepiece)

• Llama3-8B uses a larger intermediate dimension in its MLP layers than Llama2-7B

• Llama3-8B uses a higher base value to calculate theta in its rotary positional embeddings

Getting access to Llama3-8B-Instruct

For this tutorial, we will be using the instruction-tuned version of Llama3-8B. First, let’s download the model from Hugging Face. You will need to follow the instructions on the official Meta page to gain access to the model. Next, make sure you grab your Hugging Face token from here.

tune download meta-llama/Meta-Llama-3-8B-Instruct \
    --output-dir <checkpoint_dir> \
    --hf-token <ACCESS TOKEN>

Fine-tuning Llama3-8B-Instruct in torchtune

torchtune provides LoRA, QLoRA, and full fine-tuning recipes for fine-tuning Llama3-8B on one or more GPUs. For more on LoRA in torchtune, see our LoRA Tutorial. For more on QLoRA in torchtune, see our QLoRA Tutorial.

Let’s take a look at how we can fine-tune Llama3-8B-Instruct with LoRA on a single device using torchtune. In this example, we will fine-tune for one epoch on a common instruct dataset for illustrative purposes. The basic command for a single-device LoRA fine-tune is

tune run lora_finetune_single_device --config llama3/8B_lora_single_device

Note

To see a full list of recipes and their corresponding configs, simply run tune ls from the command line.

We can also add command-line overrides as needed, e.g.

tune run lora_finetune_single_device --config llama3/8B_lora_single_device \
    checkpointer.checkpoint_dir=<checkpoint_dir> \
    tokenizer.path=<checkpoint_dir>/tokenizer.model \
    checkpointer.output_dir=<checkpoint_dir>

This will load the Llama3-8B-Instruct checkpoint and tokenizer from <checkpoint_dir> used in the tune download command above, then save a final checkpoint in the same directory following the original format. For more details on the checkpoint formats supported in torchtune, see our checkpointing deep-dive.

Note

To see the full set of configurable parameters for this (and other) configs we can use tune cp to copy (and modify) the default config. tune cp can be used with recipe scripts too, in case you want to make more custom changes that cannot be achieved by directly modifying existing configurable parameters. For more on tune cp see the section on modifying configs.

Once training is complete, the model checkpoints will be saved and their locations will be logged. For LoRA fine-tuning, the final checkpoint will contain the merged weights, and a copy of just the (much smaller) LoRA weights will be saved separately.

In our experiments, we observed a peak memory usage of 18.5 GB. The default config can be trained on a consumer GPU with 24 GB VRAM.

If you have multiple GPUs available, you can run the distributed version of the recipe. torchtune makes use of the FSDP APIs from PyTorch Distributed to shard the model, optimizer states, and gradients. This should enable you to increase your batch size, resulting in faster overall training. For example, on two devices:

tune run --nproc_per_node 2 lora_finetune_distributed --config llama3/8B_lora

Finally, if we want to use even less memory, we can leverage torchtune’s QLoRA recipe via:

tune run lora_finetune_single_device --config llama3/8B_qlora_single_device

Since our default configs enable full bfloat16 training, all of the above commands can be run with devices having at least 24 GB of VRAM, and in fact the QLoRA recipe should have peak allocated memory below 10 GB. You can also experiment with different configurations of LoRA and QLoRA, or even run a full fine-tune. Try it out!

Evaluating fine-tuned Llama3-8B models with EleutherAI’s Eval Harness

Now that we’ve fine-tuned our model, what’s next? Let’s take our LoRA-finetuned model from the preceding section and look at a couple different ways we can evaluate its performance on the tasks we care about.

First, torchtune provides an integration with EleutherAI’s evaluation harness for model evaluation on common benchmark tasks.

Note

Make sure you’ve first installed the evaluation harness via pip install "lm_eval==0.4.*".

For this tutorial we’ll use the truthfulqa_mc2 task from the harness. This task measures a model’s propensity to be truthful when answering questions and measures the model’s zero-shot accuracy on a question followed by one or more true responses and one or more false responses. First, let’s copy the config so we can point the YAML file to our fine-tuned checkpoint files.

tune cp eleuther_evaluation ./custom_eval_config.yaml

Next, we modify custom_eval_config.yaml to include the fine-tuned checkpoints.

model:
  _component_: torchtune.models.llama3.llama3_8b

checkpointer:
  _component_: torchtune.utils.FullModelMetaCheckpointer

  # directory with the checkpoint files
  # this should match the output_dir specified during
  # fine-tuning
  checkpoint_dir: <checkpoint_dir>

  # checkpoint files for the fine-tuned model. These will be logged
  # at the end of your fine-tune
  checkpoint_files: [
    meta_model_0.pt
  ]

  output_dir: <checkpoint_dir>
  model_type: LLAMA3

# Make sure to update the tokenizer path to the right
# checkpoint directory as well
tokenizer:
  _component_: torchtune.models.llama3.llama3_tokenizer
  path: <checkpoint_dir>/tokenizer.model

Finally, we can run evaluation using our modified config.

tune run eleuther_eval --config ./custom_eval_config.yaml

Try it for yourself and see what accuracy your model gets!

Generating text with our fine-tuned Llama3 model

Next, let’s look at one other way we can evaluate our model: generating text! torchtune provides a recipe for generation as well.

Similar to what we did, let’s copy and modify the default generation config.

tune cp generation ./custom_generation_config.yaml

Now we modify custom_generation_config.yaml to point to our checkpoint and tokenizer.

model:
  _component_: torchtune.models.llama3.llama3_8b

checkpointer:
  _component_: torchtune.utils.FullModelMetaCheckpointer

  # directory with the checkpoint files
  # this should match the output_dir specified during
  # fine-tuning
  checkpoint_dir: <checkpoint_dir>

  # checkpoint files for the fine-tuned model. These will be logged
  # at the end of your fine-tune
  checkpoint_files: [
    meta_model_0.pt
  ]

  output_dir: <checkpoint_dir>
  model_type: LLAMA3

# Make sure to update the tokenizer path to the right
# checkpoint directory as well
tokenizer:
  _component_: torchtune.models.llama3.llama3_tokenizer
  path: <checkpoint_dir>/tokenizer.model

Running generation with our LoRA-finetuned model, we see the following output:

tune run generate --config ./custom_generation_config.yaml \
prompt="Hello, my name is"

[generate.py:122] Hello, my name is Sarah and I am a busy working mum of two young children, living in the North East of England.
...
[generate.py:135] Time for inference: 10.88 sec total, 18.94 tokens/sec
[generate.py:138] Bandwidth achieved: 346.09 GB/s
[generate.py:139] Memory used: 18.31 GB

Faster generation via quantization

We can see that the model took just under 11 seconds, generating almost 19 tokens per second. We can speed this up a bit by quantizing our model. Here we’ll use 4-bit weights-only quantization as provided by torchao.

If you’ve been following along this far, you know the drill by now. Let’s copy the quantization config and point it at our fine-tuned model.

tune cp quantization ./custom_quantization_config.yaml

And update custom_quantization_config.yaml with the following:

# Model arguments
model:
  _component_: torchtune.models.llama3.llama3_8b

checkpointer:
  _component_: torchtune.utils.FullModelMetaCheckpointer

  # directory with the checkpoint files
  # this should match the output_dir specified during
  # fine-tuning
  checkpoint_dir: <checkpoint_dir>

  # checkpoint files for the fine-tuned model. These will be logged
  # at the end of your fine-tune
  checkpoint_files: [
    meta_model_0.pt
  ]

  output_dir: <checkpoint_dir>
  model_type: LLAMA3

To quantize the model, we can now run:

tune run quantize --config ./custom_quantization_config.yaml

[quantize.py:90] Time for quantization: 2.93 sec
[quantize.py:91] Memory used: 23.13 GB
[quantize.py:104] Model checkpoint of size 4.92 GB saved to /tmp/Llama-3-8B-Instruct-hf/consolidated-4w.pt

We can see that the model is now under 5 GB, or just over four bits for each of the 8B parameters.

Note

Unlike the fine-tuned checkpoints, the quantization recipe outputs a single checkpoint file. This is because our quantization APIs currently don’t support any conversion across formats. As a result you won’t be able to use these quantized models outside of torchtune. But you should be able to use these with the generation and evaluation recipes within torchtune. These results will help inform which quantization methods you should use with your favorite inference engine.

Let’s take our quantized model and run the same generation again. First, we’ll make one more change to our custom_generation_config.yaml.

checkpointer:
  # we need to use the custom torchtune checkpointer
  # instead of the HF checkpointer for loading
  # quantized models
  _component_: torchtune.utils.FullModelTorchTuneCheckpointer

  # directory with the checkpoint files
  # this should match the output_dir specified during
  # fine-tuning
  checkpoint_dir: <checkpoint_dir>

  # checkpoint files point to the quantized model
  checkpoint_files: [
    consolidated-4w.pt,
  ]

  output_dir: <checkpoint_dir>
  model_type: LLAMA3

# we also need to update the quantizer to what was used during
# quantization
quantizer:
  _component_: torchtune.utils.quantization.Int4WeightOnlyQuantizer
  groupsize: 256

Let’s re-run generation!

tune run generate --config ./custom_generation_config.yaml \
prompt="Hello, my name is"

[generate.py:122] Hello, my name is Jake.
I am a multi-disciplined artist with a passion for creating, drawing and painting.
...
Time for inference: 1.62 sec total, 57.95 tokens/sec

By quantizing the model and running torch.compile we get over a 3x speedup!

This is just the beginning of what you can do with Meta Llama3 using torchtune and the broader ecosystem. We look forward to seeing what you build!