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feat: move to uv first (#3545)

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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
This commit is contained in:
NanoCode012
2026-04-21 21:16:03 +07:00
committed by GitHub
parent 323da791eb
commit 9de5b76336
58 changed files with 496 additions and 1520 deletions
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README.md
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@@ -95,14 +95,11 @@ Features:
### Installation
#### Using uv (recommended)
```bash
# install uv if you don't already have it installed
# install uv if you don't already have it installed (restart shell after)
curl -LsSf https://astral.sh/uv/install.sh | sh
source $HOME/.local/bin/env
# CUDA 12.8.1 tends to have better package compatibility
# change depending on system
export UV_TORCH_BACKEND=cu128
# create a new virtual environment
@@ -112,23 +109,6 @@ source .venv/bin/activate
uv pip install torch==2.10.0 torchvision
uv pip install --no-build-isolation axolotl[deepspeed]
# recommended - install cut-cross-entropy
uv pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@main"
# (optional) - prefetch flash-attn2 and causal-conv1d kernels
uv run --python 3.12 python -c "from kernels import get_kernel; get_kernel('kernels-community/flash-attn2'); get_kernel('kernels-community/causal-conv1d')"
# Download example axolotl configs, deepspeed configs
axolotl fetch examples
axolotl fetch deepspeed_configs # OPTIONAL
```
#### Using pip
```bash
pip3 install -U packaging==26.0 setuptools==75.8.0 wheel ninja
pip3 install --no-build-isolation axolotl[flash-attn,deepspeed]
# Download example axolotl configs, deepspeed configs
axolotl fetch examples
axolotl fetch deepspeed_configs # OPTIONAL
@@ -138,7 +118,7 @@ axolotl fetch deepspeed_configs # OPTIONAL
Installing with Docker can be less error prone than installing in your own environment.
```bash
docker run --gpus '"all"' --rm -it axolotlai/axolotl:main-latest
docker run --gpus '"all"' --ipc=host --rm -it axolotlai/axolotl:main-latest
```
Other installation approaches are described [here](https://docs.axolotl.ai/docs/installation.html).