diff --git a/.nojekyll b/.nojekyll index 7009b12ee..303544c0a 100644 --- a/.nojekyll +++ b/.nojekyll @@ -1 +1 @@ -604c0b1b \ No newline at end of file +3b828c6a \ No newline at end of file diff --git a/docs/api/utils.samplers.multipack.html b/docs/api/utils.samplers.multipack.html index b33d68e06..680542123 100644 --- a/docs/api/utils.samplers.multipack.html +++ b/docs/api/utils.samplers.multipack.html @@ -920,16 +920,17 @@ Returns the minimum number of batches available on any rank

bin_size, num_processes=None, safe_mode=True, -) + mp_start_method='spawn', +)

Pack sequences into bins using parallel processing

Parameters

----++++ @@ -976,32 +977,16 @@ Returns the minimum number of batches available on any rank

- -
If True, use a more conservative packing approach True
-
-
-

Returns

- ----- - - - - - - - - - - - + + + +
NameTypeDescription
List of bins, where each bin contains indices of sequences assigned to itmp_start_methodstr | NoneMultiprocessing start method (‘fork’, ‘spawn’, ‘forkserver’). ‘spawn’ is often safer with Numba/PyTorch. Set to None to use system default.'spawn'
+

Returns: +List of bins, where each bin contains indices of sequences assigned to it

diff --git a/search.json b/search.json index e70865c97..395d015a5 100644 --- a/search.json +++ b/search.json @@ -644,7 +644,7 @@ "href": "docs/api/utils.samplers.multipack.html", "title": "utils.samplers.multipack", "section": "", - "text": "utils.samplers.multipack\nMultipack Batch Sampler - An efficient batch sampler for packing variable-length sequences\ninto fixed-capacity batches to optimize memory usage and training throughput.\n\n\n\n\n\nName\nDescription\n\n\n\n\nMultipackBatchSampler\nBatch sampler class for efficient packing of variable-length sequences\n\n\n\n\n\nutils.samplers.multipack.MultipackBatchSampler(\n self,\n sampler,\n batch_size,\n batch_max_len,\n lengths,\n packing_efficiency_estimate=1.0,\n drop_last=False,\n num_count_samples=16,\n sequential=False,\n group_size=100000,\n bin_size=200,\n num_processes=None,\n safe_mode=True,\n **kwargs,\n)\nBatch sampler class for efficient packing of variable-length sequences\nThis sampler packs sequences into fixed-capacity bins (batches) to maximize\nGPU memory utilization and training throughput by reducing padding.\nIt supports both parallel packing (using FFD algorithm) and\nsequential packing (preserving original sequence order).\n\n\n\n\n\nName\nDescription\n\n\n\n\nefficiency\nCalculate the packing efficiency (ratio of tokens used to total token slots)\n\n\ngather_efficiency\nGather and synchronize packing efficiency estimates across all distributed ranks\n\n\ngather_len_batches\nGather and synchronize batch counts across all distributed ranks\n\n\ngenerate_batches\nGenerate packed batches for training\n\n\nset_epoch\nSet the epoch number, used for reproducible shuffling across epochs\n\n\n\n\n\nutils.samplers.multipack.MultipackBatchSampler.efficiency()\nCalculate the packing efficiency (ratio of tokens used to total token slots)\nHigher is better - 1.0 would mean perfect packing with no wasted space\n\n\n\nutils.samplers.multipack.MultipackBatchSampler.gather_efficiency()\nGather and synchronize packing efficiency estimates across all distributed ranks\nReturns a conservative efficiency estimate based on the measurements\n\n\n\nutils.samplers.multipack.MultipackBatchSampler.gather_len_batches(num)\nGather and synchronize batch counts across all distributed ranks\nReturns the minimum number of batches available on any rank\n\n\n\nutils.samplers.multipack.MultipackBatchSampler.generate_batches(set_stats=False)\nGenerate packed batches for training\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nset_stats\n\nWhether to update efficiency statistics\nFalse\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\n\nList of batches, where each batch contains multiple bins,\n\n\n\n\nand each bin contains multiple sequence indices\n\n\n\n\n\n\n\nutils.samplers.multipack.MultipackBatchSampler.set_epoch(epoch)\nSet the epoch number, used for reproducible shuffling across epochs\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nallocate_sequentially\nSequential allocator that preserves example order\n\n\nffd_check\nFirst-fit-decreasing bin packing algorithm check\n\n\npack_group\nPack a group of sequences into bins using First-Fit Decreasing algorithm\n\n\npack_parallel\nPack sequences into bins using parallel processing\n\n\n\n\n\nutils.samplers.multipack.allocate_sequentially(\n sequence_lengths,\n rank,\n bin_capacity,\n num_ranks,\n)\nSequential allocator that preserves example order\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nThe lengths of all examples\nrequired\n\n\nrank\nint\nThe current rank (for distributed training)\nrequired\n\n\nbin_capacity\nint\nThe capacity of each bin (maximum sequence length)\nrequired\n\n\nnum_ranks\nint\nNumber of ranks (processes/GPUs)\nrequired\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\nrank_batches\n\nList of batches for the current rank\n\n\ntotal_tokens_used\n\nNumber of actual example tokens\n\n\ntotal_token_slots\n\nMaximum theoretical number of example tokens (number of bins * bin capacity)\n\n\n\n\n\n\n\nutils.samplers.multipack.ffd_check(sequence_lengths, bin_capacity, num_bins)\nFirst-fit-decreasing bin packing algorithm check\nChecks if sequences with the given lengths could fit in the specified number of bins\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nArray of sequence lengths\nrequired\n\n\nbin_capacity\nint\nMaximum capacity of each bin\nrequired\n\n\nnum_bins\nint\nNumber of bins available\nrequired\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\n\nTrue if all sequences can be packed, False otherwise\n\n\n\n\n\n\n\nutils.samplers.multipack.pack_group(\n sequence_lengths,\n group_offset,\n bin_capacity,\n max_bins,\n bin_size,\n safe_mode=True,\n)\nPack a group of sequences into bins using First-Fit Decreasing algorithm\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nArray of sequence lengths\nrequired\n\n\ngroup_offset\nint\nOffset to apply to indices when returning results\nrequired\n\n\nbin_capacity\nint\nMaximum capacity of each bin\nrequired\n\n\nmax_bins\nint\nMaximum number of bins to use\nrequired\n\n\nbin_size\nint\nMaximum number of sequences per bin\nrequired\n\n\nsafe_mode\nbool\nIf True, use a more conservative packing approach\nTrue\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\n\nList of bins, where each bin contains indices of sequences assigned to it\n\n\n\n\n\n\n\nutils.samplers.multipack.pack_parallel(\n sequence_lengths,\n bin_capacity,\n group_size,\n bin_size,\n num_processes=None,\n safe_mode=True,\n)\nPack sequences into bins using parallel processing\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nArray of sequence lengths\nrequired\n\n\nbin_capacity\nint\nMaximum capacity of each bin as total number of tokens\nrequired\n\n\ngroup_size\nint\nNumber of sequences to process in each group\nrequired\n\n\nbin_size\nint\nMaximum number of bins to use\nrequired\n\n\nnum_processes\nint | None\nNumber of parallel processes to use\nNone\n\n\nsafe_mode\nbool\nIf True, use a more conservative packing approach\nTrue\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\n\nList of bins, where each bin contains indices of sequences assigned to it" + "text": "utils.samplers.multipack\nMultipack Batch Sampler - An efficient batch sampler for packing variable-length sequences\ninto fixed-capacity batches to optimize memory usage and training throughput.\n\n\n\n\n\nName\nDescription\n\n\n\n\nMultipackBatchSampler\nBatch sampler class for efficient packing of variable-length sequences\n\n\n\n\n\nutils.samplers.multipack.MultipackBatchSampler(\n self,\n sampler,\n batch_size,\n batch_max_len,\n lengths,\n packing_efficiency_estimate=1.0,\n drop_last=False,\n num_count_samples=16,\n sequential=False,\n group_size=100000,\n bin_size=200,\n num_processes=None,\n safe_mode=True,\n **kwargs,\n)\nBatch sampler class for efficient packing of variable-length sequences\nThis sampler packs sequences into fixed-capacity bins (batches) to maximize\nGPU memory utilization and training throughput by reducing padding.\nIt supports both parallel packing (using FFD algorithm) and\nsequential packing (preserving original sequence order).\n\n\n\n\n\nName\nDescription\n\n\n\n\nefficiency\nCalculate the packing efficiency (ratio of tokens used to total token slots)\n\n\ngather_efficiency\nGather and synchronize packing efficiency estimates across all distributed ranks\n\n\ngather_len_batches\nGather and synchronize batch counts across all distributed ranks\n\n\ngenerate_batches\nGenerate packed batches for training\n\n\nset_epoch\nSet the epoch number, used for reproducible shuffling across epochs\n\n\n\n\n\nutils.samplers.multipack.MultipackBatchSampler.efficiency()\nCalculate the packing efficiency (ratio of tokens used to total token slots)\nHigher is better - 1.0 would mean perfect packing with no wasted space\n\n\n\nutils.samplers.multipack.MultipackBatchSampler.gather_efficiency()\nGather and synchronize packing efficiency estimates across all distributed ranks\nReturns a conservative efficiency estimate based on the measurements\n\n\n\nutils.samplers.multipack.MultipackBatchSampler.gather_len_batches(num)\nGather and synchronize batch counts across all distributed ranks\nReturns the minimum number of batches available on any rank\n\n\n\nutils.samplers.multipack.MultipackBatchSampler.generate_batches(set_stats=False)\nGenerate packed batches for training\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nset_stats\n\nWhether to update efficiency statistics\nFalse\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\n\nList of batches, where each batch contains multiple bins,\n\n\n\n\nand each bin contains multiple sequence indices\n\n\n\n\n\n\n\nutils.samplers.multipack.MultipackBatchSampler.set_epoch(epoch)\nSet the epoch number, used for reproducible shuffling across epochs\n\n\n\n\n\n\n\n\n\nName\nDescription\n\n\n\n\nallocate_sequentially\nSequential allocator that preserves example order\n\n\nffd_check\nFirst-fit-decreasing bin packing algorithm check\n\n\npack_group\nPack a group of sequences into bins using First-Fit Decreasing algorithm\n\n\npack_parallel\nPack sequences into bins using parallel processing\n\n\n\n\n\nutils.samplers.multipack.allocate_sequentially(\n sequence_lengths,\n rank,\n bin_capacity,\n num_ranks,\n)\nSequential allocator that preserves example order\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nThe lengths of all examples\nrequired\n\n\nrank\nint\nThe current rank (for distributed training)\nrequired\n\n\nbin_capacity\nint\nThe capacity of each bin (maximum sequence length)\nrequired\n\n\nnum_ranks\nint\nNumber of ranks (processes/GPUs)\nrequired\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\nrank_batches\n\nList of batches for the current rank\n\n\ntotal_tokens_used\n\nNumber of actual example tokens\n\n\ntotal_token_slots\n\nMaximum theoretical number of example tokens (number of bins * bin capacity)\n\n\n\n\n\n\n\nutils.samplers.multipack.ffd_check(sequence_lengths, bin_capacity, num_bins)\nFirst-fit-decreasing bin packing algorithm check\nChecks if sequences with the given lengths could fit in the specified number of bins\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nArray of sequence lengths\nrequired\n\n\nbin_capacity\nint\nMaximum capacity of each bin\nrequired\n\n\nnum_bins\nint\nNumber of bins available\nrequired\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\n\nTrue if all sequences can be packed, False otherwise\n\n\n\n\n\n\n\nutils.samplers.multipack.pack_group(\n sequence_lengths,\n group_offset,\n bin_capacity,\n max_bins,\n bin_size,\n safe_mode=True,\n)\nPack a group of sequences into bins using First-Fit Decreasing algorithm\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nArray of sequence lengths\nrequired\n\n\ngroup_offset\nint\nOffset to apply to indices when returning results\nrequired\n\n\nbin_capacity\nint\nMaximum capacity of each bin\nrequired\n\n\nmax_bins\nint\nMaximum number of bins to use\nrequired\n\n\nbin_size\nint\nMaximum number of sequences per bin\nrequired\n\n\nsafe_mode\nbool\nIf True, use a more conservative packing approach\nTrue\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\n\nList of bins, where each bin contains indices of sequences assigned to it\n\n\n\n\n\n\n\nutils.samplers.multipack.pack_parallel(\n sequence_lengths,\n bin_capacity,\n group_size,\n bin_size,\n num_processes=None,\n safe_mode=True,\n mp_start_method='spawn',\n)\nPack sequences into bins using parallel processing\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nArray of sequence lengths\nrequired\n\n\nbin_capacity\nint\nMaximum capacity of each bin as total number of tokens\nrequired\n\n\ngroup_size\nint\nNumber of sequences to process in each group\nrequired\n\n\nbin_size\nint\nMaximum number of bins to use\nrequired\n\n\nnum_processes\nint | None\nNumber of parallel processes to use\nNone\n\n\nsafe_mode\nbool\nIf True, use a more conservative packing approach\nTrue\n\n\nmp_start_method\nstr | None\nMultiprocessing start method (‘fork’, ‘spawn’, ‘forkserver’). ‘spawn’ is often safer with Numba/PyTorch. Set to None to use system default.\n'spawn'\n\n\n\nReturns:\nList of bins, where each bin contains indices of sequences assigned to it" }, { "objectID": "docs/api/utils.samplers.multipack.html#classes", @@ -658,7 +658,7 @@ "href": "docs/api/utils.samplers.multipack.html#functions", "title": "utils.samplers.multipack", "section": "", - "text": "Name\nDescription\n\n\n\n\nallocate_sequentially\nSequential allocator that preserves example order\n\n\nffd_check\nFirst-fit-decreasing bin packing algorithm check\n\n\npack_group\nPack a group of sequences into bins using First-Fit Decreasing algorithm\n\n\npack_parallel\nPack sequences into bins using parallel processing\n\n\n\n\n\nutils.samplers.multipack.allocate_sequentially(\n sequence_lengths,\n rank,\n bin_capacity,\n num_ranks,\n)\nSequential allocator that preserves example order\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nThe lengths of all examples\nrequired\n\n\nrank\nint\nThe current rank (for distributed training)\nrequired\n\n\nbin_capacity\nint\nThe capacity of each bin (maximum sequence length)\nrequired\n\n\nnum_ranks\nint\nNumber of ranks (processes/GPUs)\nrequired\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\nrank_batches\n\nList of batches for the current rank\n\n\ntotal_tokens_used\n\nNumber of actual example tokens\n\n\ntotal_token_slots\n\nMaximum theoretical number of example tokens (number of bins * bin capacity)\n\n\n\n\n\n\n\nutils.samplers.multipack.ffd_check(sequence_lengths, bin_capacity, num_bins)\nFirst-fit-decreasing bin packing algorithm check\nChecks if sequences with the given lengths could fit in the specified number of bins\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nArray of sequence lengths\nrequired\n\n\nbin_capacity\nint\nMaximum capacity of each bin\nrequired\n\n\nnum_bins\nint\nNumber of bins available\nrequired\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\n\nTrue if all sequences can be packed, False otherwise\n\n\n\n\n\n\n\nutils.samplers.multipack.pack_group(\n sequence_lengths,\n group_offset,\n bin_capacity,\n max_bins,\n bin_size,\n safe_mode=True,\n)\nPack a group of sequences into bins using First-Fit Decreasing algorithm\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nArray of sequence lengths\nrequired\n\n\ngroup_offset\nint\nOffset to apply to indices when returning results\nrequired\n\n\nbin_capacity\nint\nMaximum capacity of each bin\nrequired\n\n\nmax_bins\nint\nMaximum number of bins to use\nrequired\n\n\nbin_size\nint\nMaximum number of sequences per bin\nrequired\n\n\nsafe_mode\nbool\nIf True, use a more conservative packing approach\nTrue\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\n\nList of bins, where each bin contains indices of sequences assigned to it\n\n\n\n\n\n\n\nutils.samplers.multipack.pack_parallel(\n sequence_lengths,\n bin_capacity,\n group_size,\n bin_size,\n num_processes=None,\n safe_mode=True,\n)\nPack sequences into bins using parallel processing\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nArray of sequence lengths\nrequired\n\n\nbin_capacity\nint\nMaximum capacity of each bin as total number of tokens\nrequired\n\n\ngroup_size\nint\nNumber of sequences to process in each group\nrequired\n\n\nbin_size\nint\nMaximum number of bins to use\nrequired\n\n\nnum_processes\nint | None\nNumber of parallel processes to use\nNone\n\n\nsafe_mode\nbool\nIf True, use a more conservative packing approach\nTrue\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\n\nList of bins, where each bin contains indices of sequences assigned to it" + "text": "Name\nDescription\n\n\n\n\nallocate_sequentially\nSequential allocator that preserves example order\n\n\nffd_check\nFirst-fit-decreasing bin packing algorithm check\n\n\npack_group\nPack a group of sequences into bins using First-Fit Decreasing algorithm\n\n\npack_parallel\nPack sequences into bins using parallel processing\n\n\n\n\n\nutils.samplers.multipack.allocate_sequentially(\n sequence_lengths,\n rank,\n bin_capacity,\n num_ranks,\n)\nSequential allocator that preserves example order\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nThe lengths of all examples\nrequired\n\n\nrank\nint\nThe current rank (for distributed training)\nrequired\n\n\nbin_capacity\nint\nThe capacity of each bin (maximum sequence length)\nrequired\n\n\nnum_ranks\nint\nNumber of ranks (processes/GPUs)\nrequired\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\nrank_batches\n\nList of batches for the current rank\n\n\ntotal_tokens_used\n\nNumber of actual example tokens\n\n\ntotal_token_slots\n\nMaximum theoretical number of example tokens (number of bins * bin capacity)\n\n\n\n\n\n\n\nutils.samplers.multipack.ffd_check(sequence_lengths, bin_capacity, num_bins)\nFirst-fit-decreasing bin packing algorithm check\nChecks if sequences with the given lengths could fit in the specified number of bins\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nArray of sequence lengths\nrequired\n\n\nbin_capacity\nint\nMaximum capacity of each bin\nrequired\n\n\nnum_bins\nint\nNumber of bins available\nrequired\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\n\nTrue if all sequences can be packed, False otherwise\n\n\n\n\n\n\n\nutils.samplers.multipack.pack_group(\n sequence_lengths,\n group_offset,\n bin_capacity,\n max_bins,\n bin_size,\n safe_mode=True,\n)\nPack a group of sequences into bins using First-Fit Decreasing algorithm\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nArray of sequence lengths\nrequired\n\n\ngroup_offset\nint\nOffset to apply to indices when returning results\nrequired\n\n\nbin_capacity\nint\nMaximum capacity of each bin\nrequired\n\n\nmax_bins\nint\nMaximum number of bins to use\nrequired\n\n\nbin_size\nint\nMaximum number of sequences per bin\nrequired\n\n\nsafe_mode\nbool\nIf True, use a more conservative packing approach\nTrue\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\n\n\n\n\n\n\nList of bins, where each bin contains indices of sequences assigned to it\n\n\n\n\n\n\n\nutils.samplers.multipack.pack_parallel(\n sequence_lengths,\n bin_capacity,\n group_size,\n bin_size,\n num_processes=None,\n safe_mode=True,\n mp_start_method='spawn',\n)\nPack sequences into bins using parallel processing\n\n\n\n\n\n\n\n\n\n\n\nName\nType\nDescription\nDefault\n\n\n\n\nsequence_lengths\nnp.ndarray\nArray of sequence lengths\nrequired\n\n\nbin_capacity\nint\nMaximum capacity of each bin as total number of tokens\nrequired\n\n\ngroup_size\nint\nNumber of sequences to process in each group\nrequired\n\n\nbin_size\nint\nMaximum number of bins to use\nrequired\n\n\nnum_processes\nint | None\nNumber of parallel processes to use\nNone\n\n\nsafe_mode\nbool\nIf True, use a more conservative packing approach\nTrue\n\n\nmp_start_method\nstr | None\nMultiprocessing start method (‘fork’, ‘spawn’, ‘forkserver’). ‘spawn’ is often safer with Numba/PyTorch. Set to None to use system default.\n'spawn'\n\n\n\nReturns:\nList of bins, where each bin contains indices of sequences assigned to it" }, { "objectID": "docs/api/utils.callbacks.comet_.html", diff --git a/sitemap.xml b/sitemap.xml index a6b2031f1..d4bf416cc 100644 --- a/sitemap.xml +++ b/sitemap.xml @@ -2,682 +2,682 @@ https://docs.axolotl.ai/FAQS.html - 2025-05-10T00:29:11.331Z + 2025-05-12T14:51:27.715Z https://docs.axolotl.ai/src/axolotl/integrations/cut_cross_entropy/ACKNOWLEDGEMENTS.html - 2025-05-10T00:29:11.353Z + 2025-05-12T14:51:27.737Z https://docs.axolotl.ai/TODO.html - 2025-05-10T00:29:11.331Z + 2025-05-12T14:51:27.715Z https://docs.axolotl.ai/docs/multimodal.html - 2025-05-10T00:29:11.336Z + 2025-05-12T14:51:27.720Z https://docs.axolotl.ai/docs/multi-node.html - 2025-05-10T00:29:11.336Z + 2025-05-12T14:51:27.720Z https://docs.axolotl.ai/docs/lora_optims.html - 2025-05-10T00:29:11.336Z + 2025-05-12T14:51:27.720Z https://docs.axolotl.ai/docs/api/prompt_strategies.stepwise_supervised.html - 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