From c6274d05824c88a99f7e4843baa3933280e1174a Mon Sep 17 00:00:00 2001 From: Quarto GHA Workflow Runner Date: Thu, 1 May 2025 16:27:24 +0000 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- docs/custom_integrations.html | 104 ++++++++++- search.json | 11 ++ sitemap.xml | 340 +++++++++++++++++----------------- 4 files changed, 283 insertions(+), 174 deletions(-) diff --git a/.nojekyll b/.nojekyll index 4ac9fe5fe..6ebce2657 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -7eacb345 \ No newline at end of file +98a26bb0 \ No newline at end of file diff --git a/docs/custom_integrations.html b/docs/custom_integrations.html index 489ac5e86..08262a8ae 100644 --- a/docs/custom_integrations.html +++ b/docs/custom_integrations.html @@ -480,6 +480,15 @@ pre > code.sourceCode > span > a:first-child::before { text-decoration: underlin
  • Usage
  • Citation
    • +
  • LLMCompressor +
  • Adding a new integration
    • @@ -742,6 +751,95 @@ By identifying the top n% of layers with the highest SNR, you can optimize train

    Please see reference here

    +
    +

    LLMCompressor

    +

    Fine-tune sparsified models in Axolotl using Neural Magic’s LLMCompressor.

    +

    This integration enables fine-tuning of models sparsified using LLMCompressor within the Axolotl training framework. By combining LLMCompressor’s model compression capabilities with Axolotl’s distributed training pipelines, users can efficiently fine-tune sparse models at scale.

    +

    It uses Axolotl’s plugin system to hook into the fine-tuning flows while maintaining sparsity throughout training.

    +
    +
    +

    Requirements

    +
      +

    • Axolotl with llmcompressor extras:

      +
      pip install "axolotl[llmcompressor]"
      • +

    • Requires llmcompressor >= 0.5.1

      • +
    +

    This will install all necessary dependencies to fine-tune sparsified models using the integration.

    +
    +
    +
    +

    Usage

    +

    To enable sparse fine-tuning with this integration, include the plugin in your Axolotl config:

    +
    plugins:
+  - axolotl.integrations.llm_compressor.LLMCompressorPlugin
+
+llmcompressor:
+  recipe:
+    finetuning_stage:
+      finetuning_modifiers:
+        ConstantPruningModifier:
+          targets: [
+            're:.*q_proj.weight',
+            're:.*k_proj.weight',
+            're:.*v_proj.weight',
+            're:.*o_proj.weight',
+            're:.*gate_proj.weight',
+            're:.*up_proj.weight',
+            're:.*down_proj.weight',
+          ]
+          start: 0
+  save_compressed: true
    +

    This plugin does not apply pruning or sparsification itself — it is intended for fine-tuning models that have already been sparsified.

    +

    Pre-sparsified checkpoints can be: +- Generated using LLMCompressor +- Downloaded from Neural Magic’s Hugging Face page +- Any custom LLM with compatible sparsity patterns that you’ve created yourself

    +

    To learn more about writing and customizing LLMCompressor recipes, refer to the official documentation: +https://github.com/vllm-project/llm-compressor/blob/main/README.md

    +
    +
    +

    Storage Optimization with save_compressed

    +

    Setting save_compressed: true in your configuration enables saving models in a compressed format, which: +- Reduces disk space usage by approximately 40% +- Maintains compatibility with vLLM for accelerated inference +- Maintains compatibility with llmcompressor for further optimization (example: quantization)

    +

    This option is highly recommended when working with sparse models to maximize the benefits of model compression.

    +
    +
    +

    Example Config

    +

    See examples/llama-3/sparse-finetuning.yaml for a complete example.

    +
    +
    +
    +

    Inference with vLLM

    +

    After fine-tuning your sparse model, you can leverage vLLM for efficient inference. +You can also use LLMCompressor to apply additional quantization to your fine-tuned +sparse model before inference for even greater performance benefits.:

    +
    from vllm import LLM, SamplingParams
+
+prompts = [
+    "Hello, my name is",
+    "The president of the United States is",
+    "The capital of France is",
+    "The future of AI is",
+]
+sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
+llm = LLM("path/to/your/sparse/model")
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    prompt = output.prompt
+    generated_text = output.outputs[0].text
+    print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
    +

    For more details on vLLM’s capabilities and advanced configuration options, see the official vLLM documentation.

    +
    +
    +

    Learn More

    +

    For details on available sparsity and quantization schemes, fine-tuning recipes, and usage examples, visit the official LLMCompressor repository:

    +

    https://github.com/vllm-project/llm-compressor

    +

    Please see reference here

    +
    +

    Adding a new integration

    Plugins can be used to customize the behavior of the training pipeline through hooks. See axolotl.integrations.BasePlugin for the possible hooks.

    @@ -782,10 +880,10 @@ Warning

    If you could not load your integration, please ensure you are pip installing in editable mode.

    -
    pip install -e .
    +
    pip install -e .

    and correctly spelled the integration name in the config file.

    -
    plugins:
-  - axolotl.integrations.your_integration_name.YourIntegrationPlugin
    +
    plugins:
+  - axolotl.integrations.your_integration_name.YourIntegrationPlugin
    diff --git a/search.json b/search.json index eb5088008..24639c31a 100644 --- a/search.json +++ b/search.json @@ -1622,6 +1622,17 @@ "Custom Integrations" ] }, + { + "objectID": "docs/custom_integrations.html#llmcompressor", + "href": "docs/custom_integrations.html#llmcompressor", + "title": "Custom Integrations", + "section": "LLMCompressor", + "text": "LLMCompressor\nFine-tune sparsified models in Axolotl using Neural Magic’s LLMCompressor.\nThis integration enables fine-tuning of models sparsified using LLMCompressor within the Axolotl training framework. By combining LLMCompressor’s model compression capabilities with Axolotl’s distributed training pipelines, users can efficiently fine-tune sparse models at scale.\nIt uses Axolotl’s plugin system to hook into the fine-tuning flows while maintaining sparsity throughout training.\n\n\nRequirements\n\nAxolotl with llmcompressor extras:\npip install \"axolotl[llmcompressor]\"\nRequires llmcompressor >= 0.5.1\n\nThis will install all necessary dependencies to fine-tune sparsified models using the integration.\n\n\n\nUsage\nTo enable sparse fine-tuning with this integration, include the plugin in your Axolotl config:\nplugins:\n - axolotl.integrations.llm_compressor.LLMCompressorPlugin\n\nllmcompressor:\n recipe:\n finetuning_stage:\n finetuning_modifiers:\n ConstantPruningModifier:\n targets: [\n 're:.*q_proj.weight',\n 're:.*k_proj.weight',\n 're:.*v_proj.weight',\n 're:.*o_proj.weight',\n 're:.*gate_proj.weight',\n 're:.*up_proj.weight',\n 're:.*down_proj.weight',\n ]\n start: 0\n save_compressed: true\nThis plugin does not apply pruning or sparsification itself — it is intended for fine-tuning models that have already been sparsified.\nPre-sparsified checkpoints can be:\n- Generated using LLMCompressor\n- Downloaded from Neural Magic’s Hugging Face page\n- Any custom LLM with compatible sparsity patterns that you’ve created yourself\nTo learn more about writing and customizing LLMCompressor recipes, refer to the official documentation:\nhttps://github.com/vllm-project/llm-compressor/blob/main/README.md\n\n\nStorage Optimization with save_compressed\nSetting save_compressed: true in your configuration enables saving models in a compressed format, which:\n- Reduces disk space usage by approximately 40%\n- Maintains compatibility with vLLM for accelerated inference\n- Maintains compatibility with llmcompressor for further optimization (example: quantization)\nThis option is highly recommended when working with sparse models to maximize the benefits of model compression.\n\n\nExample Config\nSee examples/llama-3/sparse-finetuning.yaml for a complete example.\n\n\n\nInference with vLLM\nAfter fine-tuning your sparse model, you can leverage vLLM for efficient inference.\nYou can also use LLMCompressor to apply additional quantization to your fine-tuned\nsparse model before inference for even greater performance benefits.:\nfrom vllm import LLM, SamplingParams\n\nprompts = [\n \"Hello, my name is\",\n \"The president of the United States is\",\n \"The capital of France is\",\n \"The future of AI is\",\n]\nsampling_params = SamplingParams(temperature=0.8, top_p=0.95)\nllm = LLM(\"path/to/your/sparse/model\")\noutputs = llm.generate(prompts, sampling_params)\n\nfor output in outputs:\n prompt = output.prompt\n generated_text = output.outputs[0].text\n print(f\"Prompt: {prompt!r}, Generated text: {generated_text!r}\")\nFor more details on vLLM’s capabilities and advanced configuration options, see the official vLLM documentation.\n\n\nLearn More\nFor details on available sparsity and quantization schemes, fine-tuning recipes, and usage examples, visit the official LLMCompressor repository:\nhttps://github.com/vllm-project/llm-compressor\nPlease see reference here", + "crumbs": [ + "Advanced Features", + "Custom Integrations" + ] + }, { "objectID": "docs/custom_integrations.html#adding-a-new-integration", "href": "docs/custom_integrations.html#adding-a-new-integration", diff --git a/sitemap.xml b/sitemap.xml index abb1a26a7..9ab4ef458 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,682 +2,682 @@ https://docs.axolotl.ai/examples/colab-notebooks/colab-axolotl-example.html - 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