v0.2.0

What's Changed

• Bump actions/download-artifact from 4.1.7 to 4.1.8 by @dependabot in #127
• Fix ckpt save to include architecture for inference runtime consumption by @Maxusmusti in #129
• Reducing deepspeed timeout to 10mins by @Maxusmusti in #132
• Logging updates by @Maxusmusti in #134

Full Changelog: v0.1.0...v0.2.0

v0.1.0

What's Changed

• Reduce Unnecessary Multiprocessing by @Maxusmusti in #79
• Flash Attention Disable Toggle (Take 2) by @Maxusmusti in #118
• 🐛: fix optimizer selection logic so that FusedAdam is never loaded when CPU offloading is enabled by @RobotSail in #122
• Add wheel to requirements by @Maxusmusti in #123

Full Changelog: v0.0.5.1...v0.1.0

Updates Lazy Import Logic

This release includes PR #121 to overcome an issue where our way of lazily importing the run_training function is being picked up as an error by pylint.

v0.0.5

Minor bugfixes and updates.

Minor bugfixes and updates

Minor bugfixes and updates

Minor bugfixes and updates.

Minor bugfixes and updates.

instructlab-training initial release take 2

This introduces the instructlab library as a package in the instructlab package namespace.

To install it:
pip install instructlab-training

And to install it with flash-attn and other CUDA-dependent packages, you can use
pip install instructlab-training[cuda]

Here's how to use it:

from instructlab.training.config import TorchrunArgs, TrainingArgs, run_training

torchrun_args = TorchrunArgs(
    nproc_per_node = 1,  # 1 GPU
    nnodes = 1,  # only 1 overall machine in the system
    node_rank = 0,  # rank of the current machine
    rdzv_id = 123,  # what ID other nodes will join on
    rdzv_endpoint = '0.0.0.0:12345'  # address where other nodes will join
)

training_args = TrainingArgs(
    # specify training args here
)


run_training(torch_args = torchrun_args, train_args = training_args)

instructlab-training initial release

This introduces the instructlab library as a package in the instructlab package namespace.

To install it:
pip install instructlab-training

And to install it with flash-attn and other CUDA-dependent packages, you can use
pip install instructlab-training[cuda]

Here's how to use it:

from instructlab.training.config import TorchrunArgs, TrainingArgs, run_training

torchrun_args = TorchrunArgs(
    nproc_per_node = 1,  # 1 GPU
    nnodes = 1,  # only 1 overall machine in the system
    node_rank = 0,  # rank of the current machine
    rdzv_id = 123,  # what ID other nodes will join on
    rdzv_endpoint = '0.0.0.0:12345'  # address where other nodes will join
)

training_args = TrainingArgs(
    # specify training args here
)


run_training(torch_args = torchrun_args, train_args = training_args)