From d7f9f4e61f6525a95adb7161239cf1fbffcccdb4 Mon Sep 17 00:00:00 2001 From: Quarto GHA Workflow Runner Date: Mon, 7 Jul 2025 21:10:36 +0000 Subject: [PATCH] Built site for gh-pages --- .nojekyll | 2 +- docs/custom_integrations.html | 409 +++++++++++++++++----------------- search.json | 35 ++- sitemap.xml | 378 +++++++++++++++---------------- 4 files changed, 422 insertions(+), 402 deletions(-) diff --git a/.nojekyll b/.nojekyll index adb0eac46..ce3b02b59 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -6fea3a42 \ No newline at end of file +2aa75668 \ No newline at end of file diff --git a/docs/custom_integrations.html b/docs/custom_integrations.html index 11dfc0712..44c037289 100644 --- a/docs/custom_integrations.html +++ b/docs/custom_integrations.html @@ -475,6 +475,7 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});
  • Supported Models
  • Citation
    • +
  • DenseMixer
  • Grokfast @@ -609,25 +610,33 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});

    Please see reference here

    +
    +

    DenseMixer

    +

    See DenseMixer

    +

    Simply add the following to your axolotl YAML config:

    +
    plugins:
+  - axolotl.integrations.densemixer.DenseMixerPlugin
    +

    Please see reference here

    +

    Grokfast

    See https://github.com/ironjr/grokfast

    Usage

    -
    plugins:
-  - axolotl.integrations.grokfast.GrokfastPlugin
-
-grokfast_alpha: 2.0
-grokfast_lamb: 0.98
    +
    plugins:
+  - axolotl.integrations.grokfast.GrokfastPlugin
+
+grokfast_alpha: 2.0
+grokfast_lamb: 0.98

    Citation

    -
    @article{lee2024grokfast,
-    title={{Grokfast}: Accelerated Grokking by Amplifying Slow Gradients},
-    author={Lee, Jaerin and Kang, Bong Gyun and Kim, Kihoon and Lee, Kyoung Mu},
-    journal={arXiv preprint arXiv:2405.20233},
-    year={2024}
-}
    +
    @article{lee2024grokfast,
+    title={{Grokfast}: Accelerated Grokking by Amplifying Slow Gradients},
+    author={Lee, Jaerin and Kang, Bong Gyun and Kim, Kihoon and Lee, Kyoung Mu},
+    journal={arXiv preprint arXiv:2405.20233},
+    year={2024}
+}

    Please see reference here

    @@ -635,145 +644,25 @@ gtag('config', 'G-9KYCVJBNMQ', { 'anonymize_ip': true});

    Knowledge Distillation (KD)

    Usage

    -
    plugins:
-  - "axolotl.integrations.kd.KDPlugin"
-
-kd_trainer: True
-kd_ce_alpha: 0.1
-kd_alpha: 0.9
-kd_temperature: 1.0
-
-torch_compile: True  # torch>=2.5.1, recommended to reduce vram
-
-datasets:
-  - path: ...
-    type: "axolotl.integrations.kd.chat_template"
-    field_messages: "messages_combined"
-    logprobs_field: "llm_text_generation_vllm_logprobs"  # for kd only, field of logprobs
    +
    plugins:
+  - "axolotl.integrations.kd.KDPlugin"
+
+kd_trainer: True
+kd_ce_alpha: 0.1
+kd_alpha: 0.9
+kd_temperature: 1.0
+
+torch_compile: True  # torch>=2.5.1, recommended to reduce vram
+
+datasets:
+  - path: ...
+    type: "axolotl.integrations.kd.chat_template"
+    field_messages: "messages_combined"
+    logprobs_field: "llm_text_generation_vllm_logprobs"  # for kd only, field of logprobs

    An example dataset can be found at axolotl-ai-co/evolkit-logprobs-pipeline-75k-v2-sample

    Please see reference here

    -
    -

    Liger Kernels

    -

    Liger Kernel provides efficient Triton kernels for LLM training, offering:

    -
      -
    • 20% increase in multi-GPU training throughput
      • -
    • 60% reduction in memory usage
      • -
    • Compatibility with both FSDP and DeepSpeed
      • -
    -

    See https://github.com/linkedin/Liger-Kernel

    -
    -

    Usage

    -
    plugins:
-  - axolotl.integrations.liger.LigerPlugin
-liger_rope: true
-liger_rms_norm: true
-liger_glu_activation: true
-liger_layer_norm: true
-liger_fused_linear_cross_entropy: true
    -
    -
    -

    Supported Models

    -
      -
    • deepseek_v2
      • -
    • gemma
      • -
    • gemma2
      • -
    • gemma3
      • -
    • granite
      • -
    • jamba
      • -
    • llama
      • -
    • mistral
      • -
    • mixtral
      • -
    • mllama
      • -
    • mllama_text_model
      • -
    • olmo2
      • -
    • paligemma
      • -
    • phi3
      • -
    • qwen2
      • -
    • qwen2_5_vl
      • -
    • qwen2_vl
      • -
    -
    -
    -

    Citation

    -
    @article{hsu2024ligerkernelefficienttriton,
-      title={Liger Kernel: Efficient Triton Kernels for LLM Training},
-      author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},
-      year={2024},
-      eprint={2410.10989},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG},
-      url={https://arxiv.org/abs/2410.10989},
-      journal={arXiv preprint arXiv:2410.10989},
-}
    -

    Please see reference here

    -
    -
    -
    -

    Language Model Evaluation Harness (LM Eval)

    -

    Run evaluation on model using the popular lm-evaluation-harness library.

    -

    See https://github.com/EleutherAI/lm-evaluation-harness

    -
    -

    Usage

    -
    plugins:
-  - axolotl.integrations.lm_eval.LMEvalPlugin
-
-lm_eval_tasks:
-  - gsm8k
-  - hellaswag
-  - arc_easy
-
-lm_eval_batch_size: # Batch size for evaluation
-output_dir: # Directory to save evaluation results
    -
    -
    -

    Citation

    -
    @misc{eval-harness,
-  author       = {Gao, Leo and Tow, Jonathan and Abbasi, Baber and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and Le Noac'h, Alain and Li, Haonan and McDonell, Kyle and Muennighoff, Niklas and Ociepa, Chris and Phang, Jason and Reynolds, Laria and Schoelkopf, Hailey and Skowron, Aviya and Sutawika, Lintang and Tang, Eric and Thite, Anish and Wang, Ben and Wang, Kevin and Zou, Andy},
-  title        = {A framework for few-shot language model evaluation},
-  month        = 07,
-  year         = 2024,
-  publisher    = {Zenodo},
-  version      = {v0.4.3},
-  doi          = {10.5281/zenodo.12608602},
-  url          = {https://zenodo.org/records/12608602}
-}
    -

    Please see reference here

    -
    -
    -
    -

    Spectrum

    -

    by Eric Hartford, Lucas Atkins, Fernando Fernandes, David Golchinfar

    -

    This plugin contains code to freeze the bottom fraction of modules in a model, based on the Signal-to-Noise Ratio (SNR).

    -

    See https://github.com/cognitivecomputations/spectrum

    -
    -

    Overview

    -

    Spectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models. -By identifying the top n% of layers with the highest SNR, you can optimize training efficiency.

    -
    -
    -

    Usage

    -
    plugins:
-  - axolotl.integrations.spectrum.SpectrumPlugin
-
-spectrum_top_fraction: 0.5
-spectrum_model_name: meta-llama/Meta-Llama-3.1-8B
    -
    -
    -

    Citation

    -
    @misc{hartford2024spectrumtargetedtrainingsignal,
-      title={Spectrum: Targeted Training on Signal to Noise Ratio},
-      author={Eric Hartford and Lucas Atkins and Fernando Fernandes Neto and David Golchinfar},
-      year={2024},
-      eprint={2406.06623},
-      archivePrefix={arXiv},
-      primaryClass={cs.LG},
-      url={https://arxiv.org/abs/2406.06623},
-}
    -

    Please see reference here

    -
    -

    LLMCompressor

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

    @@ -784,34 +673,34 @@ By identifying the top n% of layers with the highest SNR, you can optimize train

    Requirements

    • Axolotl with llmcompressor extras:

      -
      pip install "axolotl[llmcompressor]"
      • +
      pip install "axolotl[llmcompressor]"

    • Requires llmcompressor >= 0.5.1

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

    -
    -

    Usage

    +
    +

    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
    +
    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 @@ -838,22 +727,22 @@ By identifying the top n% of layers with the highest SNR, you can optimize train

    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}")
    +
    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.

    @@ -863,6 +752,126 @@ sparse model before inference for even greater performance benefits.:

    Please see reference here

    +
    +

    Language Model Evaluation Harness (LM Eval)

    +

    Run evaluation on model using the popular lm-evaluation-harness library.

    +

    See https://github.com/EleutherAI/lm-evaluation-harness

    +
    +

    Usage

    +
    plugins:
+  - axolotl.integrations.lm_eval.LMEvalPlugin
+
+lm_eval_tasks:
+  - gsm8k
+  - hellaswag
+  - arc_easy
+
+lm_eval_batch_size: # Batch size for evaluation
+output_dir: # Directory to save evaluation results
    +
    +
    +

    Citation

    +
    @misc{eval-harness,
+  author       = {Gao, Leo and Tow, Jonathan and Abbasi, Baber and Biderman, Stella and Black, Sid and DiPofi, Anthony and Foster, Charles and Golding, Laurence and Hsu, Jeffrey and Le Noac'h, Alain and Li, Haonan and McDonell, Kyle and Muennighoff, Niklas and Ociepa, Chris and Phang, Jason and Reynolds, Laria and Schoelkopf, Hailey and Skowron, Aviya and Sutawika, Lintang and Tang, Eric and Thite, Anish and Wang, Ben and Wang, Kevin and Zou, Andy},
+  title        = {A framework for few-shot language model evaluation},
+  month        = 07,
+  year         = 2024,
+  publisher    = {Zenodo},
+  version      = {v0.4.3},
+  doi          = {10.5281/zenodo.12608602},
+  url          = {https://zenodo.org/records/12608602}
+}
    +

    Please see reference here

    +
    +
    +
    +

    Liger Kernels

    +

    Liger Kernel provides efficient Triton kernels for LLM training, offering:

    +
      +
    • 20% increase in multi-GPU training throughput
      • +
    • 60% reduction in memory usage
      • +
    • Compatibility with both FSDP and DeepSpeed
      • +
    +

    See https://github.com/linkedin/Liger-Kernel

    +
    +

    Usage

    +
    plugins:
+  - axolotl.integrations.liger.LigerPlugin
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_layer_norm: true
+liger_fused_linear_cross_entropy: true
    +
    +
    +

    Supported Models

    +
      +
    • deepseek_v2
      • +
    • gemma
      • +
    • gemma2
      • +
    • gemma3
      • +
    • granite
      • +
    • jamba
      • +
    • llama
      • +
    • mistral
      • +
    • mixtral
      • +
    • mllama
      • +
    • mllama_text_model
      • +
    • olmo2
      • +
    • paligemma
      • +
    • phi3
      • +
    • qwen2
      • +
    • qwen2_5_vl
      • +
    • qwen2_vl
      • +
    +
    +
    +

    Citation

    +
    @article{hsu2024ligerkernelefficienttriton,
+      title={Liger Kernel: Efficient Triton Kernels for LLM Training},
+      author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},
+      year={2024},
+      eprint={2410.10989},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2410.10989},
+      journal={arXiv preprint arXiv:2410.10989},
+}
    +

    Please see reference here

    +
    +
    +
    +

    Spectrum

    +

    by Eric Hartford, Lucas Atkins, Fernando Fernandes, David Golchinfar

    +

    This plugin contains code to freeze the bottom fraction of modules in a model, based on the Signal-to-Noise Ratio (SNR).

    +

    See https://github.com/cognitivecomputations/spectrum

    +
    +

    Overview

    +

    Spectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models. +By identifying the top n% of layers with the highest SNR, you can optimize training efficiency.

    +
    +
    +

    Usage

    +
    plugins:
+  - axolotl.integrations.spectrum.SpectrumPlugin
+
+spectrum_top_fraction: 0.5
+spectrum_model_name: meta-llama/Meta-Llama-3.1-8B
    +
    +
    +

    Citation

    +
    @misc{hartford2024spectrumtargetedtrainingsignal,
+      title={Spectrum: Targeted Training on Signal to Noise Ratio},
+      author={Eric Hartford and Lucas Atkins and Fernando Fernandes Neto and David Golchinfar},
+      year={2024},
+      eprint={2406.06623},
+      archivePrefix={arXiv},
+      primaryClass={cs.LG},
+      url={https://arxiv.org/abs/2406.06623},
+}
    +

    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.

    @@ -903,10 +912,10 @@ 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 67fbd84a4..4335f4077 100644 --- a/search.json +++ b/search.json @@ -3070,6 +3070,17 @@ "Custom Integrations" ] }, + { + "objectID": "docs/custom_integrations.html#densemixer", + "href": "docs/custom_integrations.html#densemixer", + "title": "Custom Integrations", + "section": "DenseMixer", + "text": "DenseMixer\nSee DenseMixer\nSimply add the following to your axolotl YAML config:\nplugins:\n - axolotl.integrations.densemixer.DenseMixerPlugin\nPlease see reference here", + "crumbs": [ + "Advanced Features", + "Custom Integrations" + ] + }, { "objectID": "docs/custom_integrations.html#grokfast", "href": "docs/custom_integrations.html#grokfast", @@ -3093,11 +3104,11 @@ ] }, { - "objectID": "docs/custom_integrations.html#liger-kernels", - "href": "docs/custom_integrations.html#liger-kernels", + "objectID": "docs/custom_integrations.html#llmcompressor", + "href": "docs/custom_integrations.html#llmcompressor", "title": "Custom Integrations", - "section": "Liger Kernels", - "text": "Liger Kernels\nLiger Kernel provides efficient Triton kernels for LLM training, offering:\n\n20% increase in multi-GPU training throughput\n60% reduction in memory usage\nCompatibility with both FSDP and DeepSpeed\n\nSee https://github.com/linkedin/Liger-Kernel\n\nUsage\nplugins:\n - axolotl.integrations.liger.LigerPlugin\nliger_rope: true\nliger_rms_norm: true\nliger_glu_activation: true\nliger_layer_norm: true\nliger_fused_linear_cross_entropy: true\n\n\nSupported Models\n\ndeepseek_v2\ngemma\ngemma2\ngemma3\ngranite\njamba\nllama\nmistral\nmixtral\nmllama\nmllama_text_model\nolmo2\npaligemma\nphi3\nqwen2\nqwen2_5_vl\nqwen2_vl\n\n\n\nCitation\n@article{hsu2024ligerkernelefficienttriton,\n title={Liger Kernel: Efficient Triton Kernels for LLM Training},\n author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},\n year={2024},\n eprint={2410.10989},\n archivePrefix={arXiv},\n primaryClass={cs.LG},\n url={https://arxiv.org/abs/2410.10989},\n journal={arXiv preprint arXiv:2410.10989},\n}\nPlease see reference here", + "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" @@ -3115,22 +3126,22 @@ ] }, { - "objectID": "docs/custom_integrations.html#spectrum", - "href": "docs/custom_integrations.html#spectrum", + "objectID": "docs/custom_integrations.html#liger-kernels", + "href": "docs/custom_integrations.html#liger-kernels", "title": "Custom Integrations", - "section": "Spectrum", - "text": "Spectrum\nby Eric Hartford, Lucas Atkins, Fernando Fernandes, David Golchinfar\nThis plugin contains code to freeze the bottom fraction of modules in a model, based on the Signal-to-Noise Ratio (SNR).\nSee https://github.com/cognitivecomputations/spectrum\n\nOverview\nSpectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models.\nBy identifying the top n% of layers with the highest SNR, you can optimize training efficiency.\n\n\nUsage\nplugins:\n - axolotl.integrations.spectrum.SpectrumPlugin\n\nspectrum_top_fraction: 0.5\nspectrum_model_name: meta-llama/Meta-Llama-3.1-8B\n\n\nCitation\n@misc{hartford2024spectrumtargetedtrainingsignal,\n title={Spectrum: Targeted Training on Signal to Noise Ratio},\n author={Eric Hartford and Lucas Atkins and Fernando Fernandes Neto and David Golchinfar},\n year={2024},\n eprint={2406.06623},\n archivePrefix={arXiv},\n primaryClass={cs.LG},\n url={https://arxiv.org/abs/2406.06623},\n}\nPlease see reference here", + "section": "Liger Kernels", + "text": "Liger Kernels\nLiger Kernel provides efficient Triton kernels for LLM training, offering:\n\n20% increase in multi-GPU training throughput\n60% reduction in memory usage\nCompatibility with both FSDP and DeepSpeed\n\nSee https://github.com/linkedin/Liger-Kernel\n\nUsage\nplugins:\n - axolotl.integrations.liger.LigerPlugin\nliger_rope: true\nliger_rms_norm: true\nliger_glu_activation: true\nliger_layer_norm: true\nliger_fused_linear_cross_entropy: true\n\n\nSupported Models\n\ndeepseek_v2\ngemma\ngemma2\ngemma3\ngranite\njamba\nllama\nmistral\nmixtral\nmllama\nmllama_text_model\nolmo2\npaligemma\nphi3\nqwen2\nqwen2_5_vl\nqwen2_vl\n\n\n\nCitation\n@article{hsu2024ligerkernelefficienttriton,\n title={Liger Kernel: Efficient Triton Kernels for LLM Training},\n author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},\n year={2024},\n eprint={2410.10989},\n archivePrefix={arXiv},\n primaryClass={cs.LG},\n url={https://arxiv.org/abs/2410.10989},\n journal={arXiv preprint arXiv:2410.10989},\n}\nPlease see reference here", "crumbs": [ "Advanced Features", "Custom Integrations" ] }, { - "objectID": "docs/custom_integrations.html#llmcompressor", - "href": "docs/custom_integrations.html#llmcompressor", + "objectID": "docs/custom_integrations.html#spectrum", + "href": "docs/custom_integrations.html#spectrum", "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", + "section": "Spectrum", + "text": "Spectrum\nby Eric Hartford, Lucas Atkins, Fernando Fernandes, David Golchinfar\nThis plugin contains code to freeze the bottom fraction of modules in a model, based on the Signal-to-Noise Ratio (SNR).\nSee https://github.com/cognitivecomputations/spectrum\n\nOverview\nSpectrum is a tool for scanning and evaluating the Signal-to-Noise Ratio (SNR) of layers in large language models.\nBy identifying the top n% of layers with the highest SNR, you can optimize training efficiency.\n\n\nUsage\nplugins:\n - 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