From 35e6a7e228d3978d310afbcfe69e5f1178e1a7eb Mon Sep 17 00:00:00 2001 From: Quarto GHA Workflow Runner Date: Tue, 3 Mar 2026 14:34:09 +0000 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- docs/models/qwen3-next.html | 27 +-- search.json | 2 +- sitemap.xml | 472 ++++++++++++++++++------------------ 4 files changed, 244 insertions(+), 259 deletions(-) diff --git a/.nojekyll b/.nojekyll index c73b28016..d717f3928 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -ec7962f3 \ No newline at end of file +590c8a6b \ No newline at end of file diff --git a/docs/models/qwen3-next.html b/docs/models/qwen3-next.html index 890a1d492..4916ede73 100644 --- a/docs/models/qwen3-next.html +++ b/docs/models/qwen3-next.html @@ -784,31 +784,16 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});

Getting started

    -

  1. Install Axolotl following the installation guide. You need to install from main as Qwen3-Next is only on nightly or use our latest Docker images.

    -

    Here is an example of how to install from main for pip:

    2. +

  2. Install Axolotl following the installation guide.

    3. +

  3. Install Cut Cross Entropy to reduce training VRAM usage.

    4. +

  4. Install FLA for improved performance

-
# Ensure you have Pytorch installed (Pytorch 2.6.0 min)
-git clone https://github.com/axolotl-ai-cloud/axolotl.git
-cd axolotl
-
-pip3 install packaging==26.0 setuptools==75.8.0 wheel ninja
-pip3 install --no-build-isolation -e '.[flash-attn]'
-
-# Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy
-python scripts/cutcrossentropy_install.py | sh
-
    -
  1. Install Qwen3-Next transformers commit
    2. -
-
pip3 uninstall -y transformers && pip3 install "git+https://github.com/huggingface/transformers.git@b9282355bea846b54ed850a066901496b19da654"
-
    -
  1. Install FLA for improved performance
    2. -
-
pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.3.2
+
pip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.4.1
  1. Run the finetuning example:
-
axolotl train examples/qwen3-next/qwen3-next-80b-a3b-qlora.yaml
-

This config uses about 45.62 GiB VRAM.

+
axolotl train examples/qwen3-next/qwen3-next-80b-a3b-qlora.yaml
+

This config uses about ~47 GiB (no target experts) and ~71GiB (target experts) VRAM.

Let us know how it goes. Happy finetuning! 🚀

TIPS

diff --git a/search.json b/search.json index 93c849e42..021b35870 100644 --- a/search.json +++ b/search.json @@ -5572,7 +5572,7 @@ "href": "docs/models/qwen3-next.html#getting-started", "title": "Qwen 3 Next", "section": "Getting started", - "text": "Getting started\n\nInstall Axolotl following the installation guide. You need to install from main as Qwen3-Next is only on nightly or use our latest Docker images.\nHere is an example of how to install from main for pip:\n\n# Ensure you have Pytorch installed (Pytorch 2.6.0 min)\ngit clone https://github.com/axolotl-ai-cloud/axolotl.git\ncd axolotl\n\npip3 install packaging==26.0 setuptools==75.8.0 wheel ninja\npip3 install --no-build-isolation -e '.[flash-attn]'\n\n# Install CCE https://docs.axolotl.ai/docs/custom_integrations.html#cut-cross-entropy\npython scripts/cutcrossentropy_install.py | sh\n\nInstall Qwen3-Next transformers commit\n\npip3 uninstall -y transformers && pip3 install \"git+https://github.com/huggingface/transformers.git@b9282355bea846b54ed850a066901496b19da654\"\n\nInstall FLA for improved performance\n\npip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.3.2\n\nRun the finetuning example:\n\naxolotl train examples/qwen3-next/qwen3-next-80b-a3b-qlora.yaml\nThis config uses about 45.62 GiB VRAM.\nLet us know how it goes. Happy finetuning! 🚀\n\nTIPS\n\nFor inference, you can experiment with temperature: 0.7, top_p: 0.8, top_k: 20, and min_p: 0.\nYou can run a full finetuning by removing the adapter: qlora and load_in_4bit: true from the config. See Multi-GPU section below.\nRead more on how to load your own dataset at docs.\nThe dataset format follows the OpenAI Messages format as seen here.", + "text": "Getting started\n\nInstall Axolotl following the installation guide.\nInstall Cut Cross Entropy to reduce training VRAM usage.\nInstall FLA for improved performance\n\npip3 uninstall -y causal-conv1d && pip3 install flash-linear-attention==0.4.1\n\nRun the finetuning example:\n\naxolotl train examples/qwen3-next/qwen3-next-80b-a3b-qlora.yaml\nThis config uses about ~47 GiB (no target experts) and ~71GiB (target experts) VRAM.\nLet us know how it goes. Happy finetuning! 🚀\n\nTIPS\n\nFor inference, you can experiment with temperature: 0.7, top_p: 0.8, top_k: 20, and min_p: 0.\nYou can run a full finetuning by removing the adapter: qlora and load_in_4bit: true from the config. See Multi-GPU section below.\nRead more on how to load your own dataset at docs.\nThe dataset format follows the OpenAI Messages format as seen here.", "crumbs": [ "Getting Started", "Model Guides", diff --git a/sitemap.xml b/sitemap.xml index 7a3b0a69f..87cc2676e 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,946 +2,946 @@ https://docs.axolotl.ai/examples/colab-notebooks/colab-axolotl-example.html - 2026-03-02T21:40:48.654Z + 2026-03-03T14:27:57.076Z https://docs.axolotl.ai/src/axolotl/integrations/cut_cross_entropy/ACKNOWLEDGEMENTS.html - 2026-03-02T21:40:48.676Z + 2026-03-03T14:27:57.103Z https://docs.axolotl.ai/docs/inference.html - 2026-03-02T21:40:48.647Z + 2026-03-03T14:27:57.066Z https://docs.axolotl.ai/docs/debugging.html - 2026-03-02T21:40:48.645Z + 2026-03-03T14:27:57.062Z https://docs.axolotl.ai/docs/models/ministral3/vision.html - 2026-03-02T21:44:45.694Z + 2026-03-03T14:31:49.671Z https://docs.axolotl.ai/docs/models/ministral.html - 2026-03-02T21:44:45.696Z + 2026-03-03T14:31:49.672Z https://docs.axolotl.ai/docs/models/qwen3.html - 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