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* feat: move to uv first * fix: update doc to uv first * fix: merge dev/tests into uv pyproject * fix: update docker docs to match current config * fix: migrate examples to readme * fix: add llmcompressor to conflict * feat: rec uv sync with lockfile for dev/ci * fix: update docker docs to clarify how to use uv images * chore: docs * fix: use system python, no venv * fix: set backend cpu * fix: only set for installing pytorch step * fix: remove unsloth kernel and installs * fix: remove U in tests * fix: set backend in deps too * chore: test * chore: comments * fix: attempt to lock torch * fix: workaround torch cuda and not upgraded * fix: forgot to push * fix: missed source * fix: nightly upstream loralinear config * fix: nightly phi3 long rope not work * fix: forgot commit * fix: test phi3 template change * fix: no more requirements * fix: carry over changes from new requirements to pyproject * chore: remove lockfile per discussion * fix: set match-runtime * fix: remove unneeded hf hub buildtime * fix: duplicate cache delete on nightly * fix: torchvision being overridden * fix: migrate to uv images * fix: leftover from merge * fix: simplify base readme * fix: update assertion message to be clearer * chore: docs * fix: change fallback for cicd script * fix: match against main exactly * fix: peft 0.19.1 change * fix: e2e test * fix: ci * fix: e2e test
1.7 KiB
1.7 KiB
Finetune ByteDance's Seed-OSS with Axolotl
Seed-OSS are a series of 36B parameter open source models trained by ByteDance's Seed Team.
This guide shows how to fine-tune it with Axolotl with multi-turn conversations and proper masking.
Getting started
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Install Axolotl following the installation guide.
Here is an example of how to install from pip:
# Ensure you have a compatible version of Pytorch installed uv pip install --no-build-isolation 'axolotl[flash-attn]>=0.12.0' # Install Cut Cross Entropy python scripts/cutcrossentropy_install.py | sh -
Run the finetuning example:
axolotl train examples/seed-oss/seed-oss-36b-qlora.yaml
This config uses about 27.7 GiB VRAM.
Let us know how it goes. Happy finetuning! 🚀
TIPS
- For inference, the official Seed Team recommends
top_p=0.95andtemperature=1.1. - You can run a full finetuning by removing the
adapter: qloraandload_in_4bit: truefrom the config. - Read more on how to load your own dataset at docs.
- The dataset format follows the OpenAI Messages format as seen here.
Optimization Guides
Please check the Optimizations doc.