From 1a2b198299ad444b028414b64112fe751d3cd54c Mon Sep 17 00:00:00 2001 From: Quarto GHA Workflow Runner Date: Wed, 30 Jul 2025 09:02:40 +0000 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- docs/api/utils.dict.html | 28 ++ docs/custom_integrations.html | 2 +- .../colab-axolotl-example.html | 2 +- index.html | 1 + search.json | 13 +- sitemap.xml | 382 +++++++++--------- 7 files changed, 233 insertions(+), 197 deletions(-) diff --git a/.nojekyll b/.nojekyll index df2d1bce5..a7e23ccee 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -64183d6f \ No newline at end of file +314ef855 \ No newline at end of file diff --git a/docs/api/utils.dict.html b/docs/api/utils.dict.html index d71c1f889..5514987af 100644 --- a/docs/api/utils.dict.html +++ b/docs/api/utils.dict.html @@ -488,6 +488,10 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true}); +
  • Functions +
    • @@ -522,6 +526,30 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});

    DictDefault

    utils.dict.DictDefault()

    A Dict that returns None instead of returning empty Dict for missing keys.

    + + +
    +

    Functions

    + + + + + + + + + + + + + +
    NameDescription
    remove_none_valuesRemove null from a dictionary-like obj or list.
    +
    +

    remove_none_values

    +
    utils.dict.remove_none_values(obj)
    +

    Remove null from a dictionary-like obj or list. +These can appear due to Dataset loading causing schema merge. +See https://github.com/axolotl-ai-cloud/axolotl/pull/2909

    diff --git a/docs/custom_integrations.html b/docs/custom_integrations.html index a6194dac2..2c480d38e 100644 --- a/docs/custom_integrations.html +++ b/docs/custom_integrations.html @@ -574,7 +574,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true}); -
    pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@631d646"
    +
    pip3 uninstall -y cut-cross-entropy && pip3 install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@010c3ac3f1e725098961832830303eeb4142dd88"

    Usage

    diff --git a/examples/colab-notebooks/colab-axolotl-example.html b/examples/colab-notebooks/colab-axolotl-example.html index 6e94b0ed5..5f4329b66 100644 --- a/examples/colab-notebooks/colab-axolotl-example.html +++ b/examples/colab-notebooks/colab-axolotl-example.html @@ -538,7 +538,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true}); 
    %%capture
 # This step can take ~5-10 minutes to install dependencies
 !pip install --no-build-isolation axolotl[flash-attn]>=0.9.1
-!pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@631d646"
    +!pip install "cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@010c3ac3f1e725098961832830303eeb4142dd88"

    Demo: Talk Like a Pirate

    diff --git a/index.html b/index.html index 4ed7603b4..424de4cca 100644 --- a/index.html +++ b/index.html @@ -528,6 +528,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});

    🎉 Latest Updates

      +
    • 2025/07: Voxtral with mistral-common tokenizer support has been integrated in Axolotl. Read the docs!
    • 2025/07: TiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See examples for using ALST with Axolotl!
    • 2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See examples to start training your own Magistral models with Axolotl!
    • 2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the docs to learn more!
      • diff --git a/search.json b/search.json index d3f20c606..212ce1243 100644 --- a/search.json +++ b/search.json @@ -18,7 +18,7 @@ "href": "index.html#latest-updates", "title": "Axolotl", "section": "🎉 Latest Updates", - "text": "🎉 Latest Updates\n\n2025/07: TiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See examples for using ALST with Axolotl!\n2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See examples to start training your own Magistral models with Axolotl!\n2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the docs to learn more!\n2025/04: Llama 4 support has been added in Axolotl. See examples to start training your own Llama 4 models with Axolotl’s linearized version!\n2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the blog and docs to learn how to scale your context length when fine-tuning.\n2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the docs to fine-tune your own!\n2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the docs to give it a try.\n2025/02: Axolotl has added GRPO support. Dive into our blog and GRPO example and have some fun!\n2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See docs.", + "text": "🎉 Latest Updates\n\n2025/07: Voxtral with mistral-common tokenizer support has been integrated in Axolotl. Read the docs!\n2025/07: TiledMLP support for single-GPU to multi-GPU training with DDP, DeepSpeed and FSDP support has been added to support Arctic Long Sequence Training. (ALST). See examples for using ALST with Axolotl!\n2025/06: Magistral with mistral-common tokenizer support has been added to Axolotl. See examples to start training your own Magistral models with Axolotl!\n2025/05: Quantization Aware Training (QAT) support has been added to Axolotl. Explore the docs to learn more!\n2025/04: Llama 4 support has been added in Axolotl. See examples to start training your own Llama 4 models with Axolotl’s linearized version!\n2025/03: Axolotl has implemented Sequence Parallelism (SP) support. Read the blog and docs to learn how to scale your context length when fine-tuning.\n2025/03: (Beta) Fine-tuning Multimodal models is now supported in Axolotl. Check out the docs to fine-tune your own!\n2025/02: Axolotl has added LoRA optimizations to reduce memory usage and improve training speed for LoRA and QLoRA in single GPU and multi-GPU training (DDP and DeepSpeed). Jump into the docs to give it a try.\n2025/02: Axolotl has added GRPO support. Dive into our blog and GRPO example and have some fun!\n2025/01: Axolotl has added Reward Modelling / Process Reward Modelling fine-tuning support. See docs.", "crumbs": [ "Home" ] @@ -1153,7 +1153,7 @@ "href": "docs/custom_integrations.html#cut-cross-entropy", "title": "Custom Integrations", "section": "Cut Cross Entropy", - "text": "Cut Cross Entropy\nCut Cross Entropy (CCE) reduces VRAM usage through optimization on the cross-entropy operation during loss calculation.\nSee https://github.com/apple/ml-cross-entropy\n\nRequirements\n\nPyTorch 2.4.0 or higher\n\n\n\nInstallation\nRun the following command to install cut_cross_entropy[transformers] if you don’t have it already.\n\nIf you are in dev environment\n\npython scripts/cutcrossentropy_install.py | sh\n\nIf you are installing from pip\n\npip3 uninstall -y cut-cross-entropy && pip3 install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@631d646\"\n\n\nUsage\nplugins:\n - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n\n\nSupported Models\n\ncohere\ncohere2\ngemma\ngemma2\ngemma3\ngemma3_text\ngemma3n\ngemma3n_text\nglm\nglm4\nllama\nllama4\nllama4_text\nmistral\nmistral3\nmllama\nphi\nphi3\nphi4_multimodal\nqwen2\nqwen2_vl\nqwen2_moe\nqwen2_5_vl\nqwen3\nqwen3_moe\n\n\n\nCitation\n@article{wijmans2024cut,\n author = {Erik Wijmans and\n Brody Huval and\n Alexander Hertzberg and\n Vladlen Koltun and\n Philipp Kr\\\"ahenb\\\"uhl},\n title = {Cut Your Losses in Large-Vocabulary Language Models},\n journal = {arXiv},\n year = {2024},\n url = {https://arxiv.org/abs/2411.09009},\n}\nPlease see reference here", + "text": "Cut Cross Entropy\nCut Cross Entropy (CCE) reduces VRAM usage through optimization on the cross-entropy operation during loss calculation.\nSee https://github.com/apple/ml-cross-entropy\n\nRequirements\n\nPyTorch 2.4.0 or higher\n\n\n\nInstallation\nRun the following command to install cut_cross_entropy[transformers] if you don’t have it already.\n\nIf you are in dev environment\n\npython scripts/cutcrossentropy_install.py | sh\n\nIf you are installing from pip\n\npip3 uninstall -y cut-cross-entropy && pip3 install \"cut-cross-entropy[transformers] @ git+https://github.com/axolotl-ai-cloud/ml-cross-entropy.git@010c3ac3f1e725098961832830303eeb4142dd88\"\n\n\nUsage\nplugins:\n - axolotl.integrations.cut_cross_entropy.CutCrossEntropyPlugin\n\n\nSupported Models\n\ncohere\ncohere2\ngemma\ngemma2\ngemma3\ngemma3_text\ngemma3n\ngemma3n_text\nglm\nglm4\nllama\nllama4\nllama4_text\nmistral\nmistral3\nmllama\nphi\nphi3\nphi4_multimodal\nqwen2\nqwen2_vl\nqwen2_moe\nqwen2_5_vl\nqwen3\nqwen3_moe\n\n\n\nCitation\n@article{wijmans2024cut,\n author = {Erik Wijmans and\n Brody Huval and\n Alexander Hertzberg and\n Vladlen Koltun and\n Philipp Kr\\\"ahenb\\\"uhl},\n title = {Cut Your Losses in Large-Vocabulary Language Models},\n journal = {arXiv},\n year = {2024},\n url = {https://arxiv.org/abs/2411.09009},\n}\nPlease see reference here", "crumbs": [ "Advanced Features", "Custom Integrations" @@ -3539,7 +3539,7 @@ "href": "docs/api/utils.dict.html", "title": "utils.dict", "section": "", - "text": "utils.dict\nModule containing the DictDefault class\n\n\n\n\n\nName\nDescription\n\n\n\n\nDictDefault\nA Dict that returns None instead of returning empty Dict for missing keys.\n\n\n\n\n\nutils.dict.DictDefault()\nA Dict that returns None instead of returning empty Dict for missing keys." + "text": "utils.dict\nModule containing the DictDefault class\n\n\n\n\n\nName\nDescription\n\n\n\n\nDictDefault\nA Dict that returns None instead of returning empty Dict for missing keys.\n\n\n\n\n\nutils.dict.DictDefault()\nA Dict that returns None instead of returning empty Dict for missing keys.\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nremove_none_values\nRemove null from a dictionary-like obj or list.\n\n\n\n\n\nutils.dict.remove_none_values(obj)\nRemove null from a dictionary-like obj or list.\nThese can appear due to Dataset loading causing schema merge.\nSee https://github.com/axolotl-ai-cloud/axolotl/pull/2909" }, { "objectID": "docs/api/utils.dict.html#classes", @@ -3548,6 +3548,13 @@ "section": "", "text": "Name\nDescription\n\n\n\n\nDictDefault\nA Dict that returns None instead of returning empty Dict for missing keys.\n\n\n\n\n\nutils.dict.DictDefault()\nA Dict that returns None instead of returning empty Dict for missing keys." }, + { + "objectID": "docs/api/utils.dict.html#functions", + "href": "docs/api/utils.dict.html#functions", + "title": "utils.dict", + "section": "", + "text": "Name\nDescription\n\n\n\n\nremove_none_values\nRemove null from a dictionary-like obj or list.\n\n\n\n\n\nutils.dict.remove_none_values(obj)\nRemove null from a dictionary-like obj or list.\nThese can appear due to Dataset loading causing schema merge.\nSee https://github.com/axolotl-ai-cloud/axolotl/pull/2909" + }, { "objectID": "docs/api/core.builders.rl.html", "href": "docs/api/core.builders.rl.html", diff --git a/sitemap.xml b/sitemap.xml index 2ac2fd115..1d55d6b64 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,766 +2,766 @@ https://docs.axolotl.ai/TODO.html - 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