alexgenovese / train-sdxl-lora
Train on RealVisXL 4.0 (Realistic Vision XL 4) | Mixed precision bf16 any LoRA
- Public
- 598 runs
-
A100 (80GB)
- GitHub
Prediction
alexgenovese/train-sdxl-lora:533e1248263c06db92aea251ca69c3115f3ffbd19c86346b46e52eda14e8fd11IDrsan62rbf65msxaftuoqcuvvnmStatusSucceededSourceWebHardwareA100 (40GB)Total durationCreatedInput
- seed
- 82
- ti_lr
- 0.0001
- is_lora
- lora_lr
- 0.0004
- verbose
- lora_rank
- 32
- resolution
- 1024
- class_token
- blazer
- input_images
- image_dataset_1024.zip
- lr_scheduler
- constant
- optimization
- AdamW
- token_string
- siduhc
- caption_prefix
- a photo of siduhc blazer
- lora_rank_alpha
- 32
- lr_warmup_steps
- 0
- num_train_epochs
- 100
- train_batch_size
- 2
- unet_learning_rate
- 0.0001
- checkpointing_steps
- 999999
- clipseg_temperature
- 1
- input_images_filetype
- infer
- crop_based_on_salience
- use_face_detection_instead
{ "seed": 82, "ti_lr": 0.0001, "is_lora": true, "lora_lr": 0.0004, "verbose": true, "lora_rank": 32, "resolution": 1024, "class_token": "blazer", "input_images": "https://replicate.delivery/pbxt/Jai9c4k3MPpZHVP6Ypro2J8viBe9wgzQ7hgvK5YE1cWm2yKS/image_dataset_1024.zip", "lr_scheduler": "constant", "optimization": "AdamW", "token_string": "siduhc", "caption_prefix": "a photo of siduhc blazer", "lora_rank_alpha": 32, "lr_warmup_steps": 0, "num_train_epochs": 100, "train_batch_size": 2, "unet_learning_rate": 0.0001, "checkpointing_steps": 999999, "clipseg_temperature": 1, "input_images_filetype": "infer", "crop_based_on_salience": false, "use_face_detection_instead": false }Install Replicate’s Node.js client library:npm install replicateImport and set up the client:import Replicate from "replicate"; const replicate = new Replicate({ auth: process.env.REPLICATE_API_TOKEN, });Run alexgenovese/train-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
const output = await replicate.run( "alexgenovese/train-sdxl-lora:533e1248263c06db92aea251ca69c3115f3ffbd19c86346b46e52eda14e8fd11", { input: { seed: 82, ti_lr: 0.0001, is_lora: true, lora_lr: 0.0004, verbose: true, lora_rank: 32, resolution: 1024, class_token: "blazer", input_images: "https://replicate.delivery/pbxt/Jai9c4k3MPpZHVP6Ypro2J8viBe9wgzQ7hgvK5YE1cWm2yKS/image_dataset_1024.zip", lr_scheduler: "constant", optimization: "AdamW", token_string: "siduhc", caption_prefix: "a photo of siduhc blazer", lora_rank_alpha: 32, lr_warmup_steps: 0, num_train_epochs: 100, train_batch_size: 2, unet_learning_rate: 0.0001, checkpointing_steps: 999999, clipseg_temperature: 1, input_images_filetype: "infer", crop_based_on_salience: false, use_face_detection_instead: false } } ); // To access the file URL: console.log(output.url()); //=> "http://example.com" // To write the file to disk: fs.writeFile("my-image.png", output);To learn more, take a look at the guide on getting started with Node.js.
Install Replicate’s Python client library:pip install replicateImport the client:import replicateRun alexgenovese/train-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
output = replicate.run( "alexgenovese/train-sdxl-lora:533e1248263c06db92aea251ca69c3115f3ffbd19c86346b46e52eda14e8fd11", input={ "seed": 82, "ti_lr": 0.0001, "is_lora": True, "lora_lr": 0.0004, "verbose": True, "lora_rank": 32, "resolution": 1024, "class_token": "blazer", "input_images": "https://replicate.delivery/pbxt/Jai9c4k3MPpZHVP6Ypro2J8viBe9wgzQ7hgvK5YE1cWm2yKS/image_dataset_1024.zip", "lr_scheduler": "constant", "optimization": "AdamW", "token_string": "siduhc", "caption_prefix": "a photo of siduhc blazer", "lora_rank_alpha": 32, "lr_warmup_steps": 0, "num_train_epochs": 100, "train_batch_size": 2, "unet_learning_rate": 0.0001, "checkpointing_steps": 999999, "clipseg_temperature": 1, "input_images_filetype": "infer", "crop_based_on_salience": False, "use_face_detection_instead": False } ) print(output)To learn more, take a look at the guide on getting started with Python.
Run alexgenovese/train-sdxl-lora using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.
curl -s -X POST \ -H "Authorization: Bearer $REPLICATE_API_TOKEN" \ -H "Content-Type: application/json" \ -H "Prefer: wait" \ -d $'{ "version": "alexgenovese/train-sdxl-lora:533e1248263c06db92aea251ca69c3115f3ffbd19c86346b46e52eda14e8fd11", "input": { "seed": 82, "ti_lr": 0.0001, "is_lora": true, "lora_lr": 0.0004, "verbose": true, "lora_rank": 32, "resolution": 1024, "class_token": "blazer", "input_images": "https://replicate.delivery/pbxt/Jai9c4k3MPpZHVP6Ypro2J8viBe9wgzQ7hgvK5YE1cWm2yKS/image_dataset_1024.zip", "lr_scheduler": "constant", "optimization": "AdamW", "token_string": "siduhc", "caption_prefix": "a photo of siduhc blazer", "lora_rank_alpha": 32, "lr_warmup_steps": 0, "num_train_epochs": 100, "train_batch_size": 2, "unet_learning_rate": 0.0001, "checkpointing_steps": 999999, "clipseg_temperature": 1, "input_images_filetype": "infer", "crop_based_on_salience": false, "use_face_detection_instead": false } }' \ https://api.replicate.com/v1/predictionsTo learn more, take a look at Replicate’s HTTP API reference docs.
Output
{ "completed_at": "2023-09-25T20:30:01.545310Z", "created_at": "2023-09-25T20:22:53.668149Z", "data_removed": false, "error": null, "id": "rsan62rbf65msxaftuoqcuvvnm", "input": { "seed": 82, "ti_lr": 0.0001, "is_lora": true, "lora_lr": 0.0004, "verbose": true, "lora_rank": 32, "resolution": 1024, "class_token": "blazer", "input_images": "https://replicate.delivery/pbxt/Jai9c4k3MPpZHVP6Ypro2J8viBe9wgzQ7hgvK5YE1cWm2yKS/image_dataset_1024.zip", "lr_scheduler": "constant", "optimization": "AdamW", "token_string": "siduhc", "caption_prefix": "a photo of siduhc blazer", "lora_rank_alpha": 32, "lr_warmup_steps": 0, "num_train_epochs": 100, "train_batch_size": 2, "unet_learning_rate": 0.0001, "checkpointing_steps": 999999, "clipseg_temperature": 1, "input_images_filetype": "infer", "crop_based_on_salience": false, "use_face_detection_instead": false }, "logs": "['./temp_in/1.jpeg', './temp_in/10.jpeg', './temp_in/2.jpeg', './temp_in/3.jpeg', './temp_in/4.jpeg', './temp_in/5.jpeg', './temp_in/6.jpeg', './temp_in/7.jpeg', './temp_in/8.jpeg', './temp_in/9.jpeg']\nGenerating 10 captions...\nInput captioning text: a photo of siduhc blazer\n 0%| | 0/10 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/transformers/generation/utils.py:1369: UserWarning: Using `max_length`'s default (20) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation.\nwarnings.warn(\nsiduhc\nsiduhc\n 10%|█ | 1/10 [00:00<00:04, 2.24it/s]\nsiduhc\n 20%|██ | 2/10 [00:00<00:03, 2.18it/s]\nsiduhc\n 30%|███ | 3/10 [00:01<00:03, 2.09it/s]\nsiduhc\n 40%|████ | 4/10 [00:01<00:02, 2.22it/s]\nsiduhc\n 50%|█████ | 5/10 [00:02<00:02, 2.19it/s]\nsiduhc\n 60%|██████ | 6/10 [00:02<00:01, 2.29it/s]\nsiduhc\n 70%|███████ | 7/10 [00:03<00:01, 2.35it/s]\nsiduhc\n 80%|████████ | 8/10 [00:03<00:00, 2.28it/s]\nsiduhc\nGenerated captions ['a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ']\n 90%|█████████ | 9/10 [00:04<00:00, 2.26it/s]\n100%|██████████| 10/10 [00:04<00:00, 2.16it/s]\n100%|██████████| 10/10 [00:04<00:00, 2.21it/s]\nGenerating 10 masks...\nWarning: only one target prompt \"\" was given, so it will be used for all images\nInstead of {class_token} is using {class_token}\n0it [00:00, ?it/s]\n1it [00:00, 9.45it/s]\n2it [00:00, 9.03it/s]\n4it [00:00, 9.70it/s]\n5it [00:00, 9.64it/s]\n6it [00:00, 9.57it/s]\n8it [00:00, 9.76it/s]\n9it [00:00, 9.56it/s]\n10it [00:01, 9.25it/s]\n10it [00:01, 9.45it/s]\nUpscaling 10 images...\n0%| | 0/10 [00:00<?, ?it/s]\n100%|██████████| 10/10 [00:00<00:00, 143150.31it/s]\nUsing seed 82\nYou are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors.\nYou are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors.\n# PTI : Loaded models\n0 text encodedr's std_token_embedding: 0.015380859375\ntorch.Size([49410])\n1 text encodedr's std_token_embedding: 0.014404296875\ntorch.Size([49410])\ntext_model.embeddings.token_embedding.weight\ntext_model.embeddings.token_embedding.weight\n# PTI : Loading dataset, do_cache True\nCaptions to train on:\na photo of <s0><s1> blazer\na photo of <s0><s1> blazer\na photo of <s0><s1> blazer\na photo of <s0><s1> blazer\na photo of <s0><s1> blazer\na photo of <s0><s1> blazer\na photo of <s0><s1> blazer\na photo of <s0><s1> blazer\na photo of <s0><s1> blazer\na photo of <s0><s1> blazer\n# PTI : Loaded dataset\n# PTI : Running training\n# PTI : Num examples = 10\n# PTI : Num batches each epoch = 5\n# PTI : Num Epochs = 100\n# PTI : Instantaneous batch size per device = 2\nTotal train batch size (w. parallel, distributed & accumulation) = 2\n# PTI : Gradient Accumulation steps = 1\n# PTI : Total 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[03:31<02:42, 1.31it/s]\n# PTI :step: 287, epoch: 57: 58%|█████▊ | 288/500 [03:31<02:42, 1.31it/s]\n# PTI :step: 287, epoch: 57: 58%|█████▊ | 289/500 [03:31<02:42, 1.30it/s]\n# PTI :step: 288, epoch: 57: 58%|█████▊ | 289/500 [03:31<02:42, 1.30it/s]\n# PTI :step: 288, epoch: 57: 58%|█████▊ | 290/500 [03:32<02:42, 1.29it/s]\n# PTI :step: 289, epoch: 57: 58%|█████▊ | 290/500 [03:32<02:42, 1.29it/s]\n# PTI :step: 289, epoch: 57: 58%|█████▊ | 291/500 [03:33<02:44, 1.27it/s]\n# PTI :step: 290, epoch: 58: 58%|█████▊ | 291/500 [03:33<02:44, 1.27it/s]\n# PTI :step: 290, epoch: 58: 58%|█████▊ | 292/500 [03:34<02:45, 1.26it/s]\n# PTI :step: 291, epoch: 58: 58%|█████▊ | 292/500 [03:34<02:45, 1.26it/s]\n# PTI :step: 291, epoch: 58: 59%|█████▊ | 293/500 [03:35<02:45, 1.25it/s]\n# PTI :step: 292, epoch: 58: 59%|█████▊ | 293/500 [03:35<02:45, 1.25it/s]\n# PTI :step: 292, epoch: 58: 59%|█████▉ | 294/500 [03:35<02:44, 1.25it/s]\n# PTI :step: 293, epoch: 58: 59%|█████▉ | 294/500 [03:35<02:44, 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[05:54<00:12, 1.48it/s]\n# PTI :step: 481, epoch: 96: 96%|█████████▋| 482/500 [05:54<00:12, 1.48it/s]\n# PTI :step: 481, epoch: 96: 97%|█████████▋| 483/500 [05:55<00:11, 1.49it/s]\n# PTI :step: 482, epoch: 96: 97%|█████████▋| 483/500 [05:55<00:11, 1.49it/s]\n# PTI :step: 482, epoch: 96: 97%|█████████▋| 484/500 [05:56<00:10, 1.48it/s]\n# PTI :step: 483, epoch: 96: 97%|█████████▋| 484/500 [05:56<00:10, 1.48it/s]\n# PTI :step: 483, epoch: 96: 97%|█████████▋| 485/500 [05:56<00:10, 1.49it/s]\n# PTI :step: 484, epoch: 96: 97%|█████████▋| 485/500 [05:56<00:10, 1.49it/s]\n# PTI :step: 484, epoch: 96: 97%|█████████▋| 486/500 [05:57<00:09, 1.50it/s]\n# PTI :step: 485, epoch: 97: 97%|█████████▋| 486/500 [05:57<00:09, 1.50it/s]\n# PTI :step: 485, epoch: 97: 97%|█████████▋| 487/500 [05:58<00:08, 1.48it/s]\n# PTI :step: 486, epoch: 97: 97%|█████████▋| 487/500 [05:58<00:08, 1.48it/s]\n# PTI :step: 486, epoch: 97: 98%|█████████▊| 488/500 [05:58<00:08, 1.46it/s]\n# PTI :step: 487, epoch: 97: 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100%|██████████| 500/500 [06:09<00:00, 1.35it/s]\ntraining_out/lora.safetensors\ntraining_out/embeddings.pti\ntraining_out/special_params.json", "metrics": { "predict_time": 427.932906, "total_time": 427.877161 }, "output": "https://pbxt.replicate.delivery/EnE55SO5IuKlCpTWAyKpveolsXkaIGHNSkGNedspS8dGfzPjA/trained_model.tar", "started_at": "2023-09-25T20:22:53.612404Z", "status": "succeeded", "urls": { "get": "https://api.replicate.com/v1/predictions/rsan62rbf65msxaftuoqcuvvnm", "cancel": "https://api.replicate.com/v1/predictions/rsan62rbf65msxaftuoqcuvvnm/cancel" }, "version": "533e1248263c06db92aea251ca69c3115f3ffbd19c86346b46e52eda14e8fd11" }Generated in['./temp_in/1.jpeg', './temp_in/10.jpeg', './temp_in/2.jpeg', './temp_in/3.jpeg', './temp_in/4.jpeg', './temp_in/5.jpeg', './temp_in/6.jpeg', './temp_in/7.jpeg', './temp_in/8.jpeg', './temp_in/9.jpeg'] Generating 10 captions... Input captioning text: a photo of siduhc blazer 0%| | 0/10 [00:00<?, ?it/s]/root/.pyenv/versions/3.9.18/lib/python3.9/site-packages/transformers/generation/utils.py:1369: UserWarning: Using `max_length`'s default (20) to control the generation length. This behaviour is deprecated and will be removed from the config in v5 of Transformers -- we recommend using `max_new_tokens` to control the maximum length of the generation. warnings.warn( siduhc siduhc 10%|█ | 1/10 [00:00<00:04, 2.24it/s] siduhc 20%|██ | 2/10 [00:00<00:03, 2.18it/s] siduhc 30%|███ | 3/10 [00:01<00:03, 2.09it/s] siduhc 40%|████ | 4/10 [00:01<00:02, 2.22it/s] siduhc 50%|█████ | 5/10 [00:02<00:02, 2.19it/s] siduhc 60%|██████ | 6/10 [00:02<00:01, 2.29it/s] siduhc 70%|███████ | 7/10 [00:03<00:01, 2.35it/s] siduhc 80%|████████ | 8/10 [00:03<00:00, 2.28it/s] siduhc Generated captions ['a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer ', 'a photo of siduhc blazer '] 90%|█████████ | 9/10 [00:04<00:00, 2.26it/s] 100%|██████████| 10/10 [00:04<00:00, 2.16it/s] 100%|██████████| 10/10 [00:04<00:00, 2.21it/s] Generating 10 masks... Warning: only one target prompt "" was given, so it will be used for all images Instead of {class_token} is using {class_token} 0it [00:00, ?it/s] 1it [00:00, 9.45it/s] 2it [00:00, 9.03it/s] 4it [00:00, 9.70it/s] 5it [00:00, 9.64it/s] 6it [00:00, 9.57it/s] 8it [00:00, 9.76it/s] 9it [00:00, 9.56it/s] 10it [00:01, 9.25it/s] 10it [00:01, 9.45it/s] Upscaling 10 images... 0%| | 0/10 [00:00<?, ?it/s] 100%|██████████| 10/10 [00:00<00:00, 143150.31it/s] Using seed 82 You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. You are using a model of type clip_text_model to instantiate a model of type . This is not supported for all configurations of models and can yield errors. # PTI : Loaded models 0 text encodedr's std_token_embedding: 0.015380859375 torch.Size([49410]) 1 text encodedr's std_token_embedding: 0.014404296875 torch.Size([49410]) text_model.embeddings.token_embedding.weight text_model.embeddings.token_embedding.weight # PTI : Loading dataset, do_cache True Captions to train on: a photo of <s0><s1> blazer a photo of <s0><s1> blazer a photo of <s0><s1> blazer a photo of <s0><s1> blazer a photo of <s0><s1> blazer a photo of <s0><s1> blazer a photo of <s0><s1> blazer a photo of <s0><s1> blazer a photo of <s0><s1> blazer a photo of <s0><s1> blazer # PTI : Loaded dataset # PTI : Running training # PTI : Num examples = 10 # PTI : Num batches each epoch = 5 # PTI : Num Epochs = 100 # PTI : Instantaneous batch size per device = 2 Total train batch size (w. parallel, distributed & accumulation) = 2 # PTI : Gradient Accumulation steps = 1 # PTI : Total optimization steps = 500 0%| | 0/500 [00:00<?, ?it/s] # PTI :step: 0, epoch: 0: 0%| | 1/500 [00:00<00:11, 43.23it/s] # PTI :step: 0, epoch: 0: 0%| | 2/500 [00:01<04:26, 1.87it/s] # PTI :step: 1, epoch: 0: 0%| | 2/500 [00:01<04:26, 1.87it/s] # PTI :step: 1, epoch: 0: 1%| | 3/500 [00:01<05:01, 1.65it/s] # PTI :step: 2, epoch: 0: 1%| | 3/500 [00:01<05:01, 1.65it/s] # PTI :step: 2, epoch: 0: 1%| | 4/500 [00:02<05:21, 1.54it/s] # PTI :step: 3, epoch: 0: 1%| | 4/500 [00:02<05:21, 1.54it/s] # PTI :step: 3, epoch: 0: 1%| | 5/500 [00:03<05:36, 1.47it/s] # PTI :step: 4, epoch: 0: 1%| | 5/500 [00:03<05:36, 1.47it/s] # PTI :step: 4, epoch: 0: 1%| | 6/500 [00:04<05:51, 1.40it/s] # PTI :step: 5, epoch: 1: 1%| | 6/500 [00:04<05:51, 1.40it/s] # PTI :step: 5, epoch: 1: 1%|▏ | 7/500 [00:04<05:50, 1.41it/s] # PTI :step: 6, epoch: 1: 1%|▏ | 7/500 [00:04<05:50, 1.41it/s] # PTI :step: 6, epoch: 1: 2%|▏ | 8/500 [00:05<05:48, 1.41it/s] # PTI :step: 7, epoch: 1: 2%|▏ | 8/500 [00:05<05:48, 1.41it/s] # PTI :step: 7, epoch: 1: 2%|▏ | 9/500 [00:06<05:49, 1.41it/s] # PTI :step: 8, epoch: 1: 2%|▏ | 9/500 [00:06<05:49, 1.41it/s] # 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PTI :step: 24, epoch: 4: 5%|▌ | 26/500 [00:18<05:39, 1.39it/s] # PTI :step: 25, epoch: 5: 5%|▌ | 26/500 [00:18<05:39, 1.39it/s] # PTI :step: 25, epoch: 5: 5%|▌ | 27/500 [00:19<05:41, 1.39it/s] # PTI :step: 26, epoch: 5: 5%|▌ | 27/500 [00:19<05:41, 1.39it/s] # PTI :step: 26, epoch: 5: 6%|▌ | 28/500 [00:20<05:48, 1.35it/s] # PTI :step: 27, epoch: 5: 6%|▌ | 28/500 [00:20<05:48, 1.35it/s] # PTI :step: 27, epoch: 5: 6%|▌ | 29/500 [00:20<05:55, 1.33it/s] # PTI :step: 28, epoch: 5: 6%|▌ | 29/500 [00:20<05:55, 1.33it/s] # PTI :step: 28, epoch: 5: 6%|▌ | 30/500 [00:21<05:48, 1.35it/s] # PTI :step: 29, epoch: 5: 6%|▌ | 30/500 [00:21<05:48, 1.35it/s] # PTI :step: 29, epoch: 5: 6%|▌ | 31/500 [00:22<05:53, 1.33it/s] # PTI :step: 30, epoch: 6: 6%|▌ | 31/500 [00:22<05:53, 1.33it/s] # PTI :step: 30, epoch: 6: 6%|▋ | 32/500 [00:23<05:53, 1.32it/s] # PTI :step: 31, epoch: 6: 6%|▋ | 32/500 [00:23<05:53, 1.32it/s] # PTI :step: 31, epoch: 6: 7%|▋ | 33/500 [00:23<05:45, 1.35it/s] # PTI :step: 32, epoch: 6: 7%|▋ | 33/500 [00:23<05:45, 1.35it/s] # 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PTI :step: 477, epoch: 95: 96%|█████████▌| 478/500 [05:52<00:16, 1.37it/s] # PTI :step: 477, epoch: 95: 96%|█████████▌| 479/500 [05:52<00:14, 1.41it/s] # PTI :step: 478, epoch: 95: 96%|█████████▌| 479/500 [05:52<00:14, 1.41it/s] # PTI :step: 478, epoch: 95: 96%|█████████▌| 480/500 [05:53<00:13, 1.45it/s] # PTI :step: 479, epoch: 95: 96%|█████████▌| 480/500 [05:53<00:13, 1.45it/s] # PTI :step: 479, epoch: 95: 96%|█████████▌| 481/500 [05:54<00:12, 1.47it/s] # PTI :step: 480, epoch: 96: 96%|█████████▌| 481/500 [05:54<00:12, 1.47it/s] # PTI :step: 480, epoch: 96: 96%|█████████▋| 482/500 [05:54<00:12, 1.48it/s] # PTI :step: 481, epoch: 96: 96%|█████████▋| 482/500 [05:54<00:12, 1.48it/s] # PTI :step: 481, epoch: 96: 97%|█████████▋| 483/500 [05:55<00:11, 1.49it/s] # PTI :step: 482, epoch: 96: 97%|█████████▋| 483/500 [05:55<00:11, 1.49it/s] # PTI :step: 482, epoch: 96: 97%|█████████▋| 484/500 [05:56<00:10, 1.48it/s] # PTI :step: 483, epoch: 96: 97%|█████████▋| 484/500 [05:56<00:10, 1.48it/s] # PTI :step: 483, epoch: 96: 97%|█████████▋| 485/500 [05:56<00:10, 1.49it/s] # PTI :step: 484, epoch: 96: 97%|█████████▋| 485/500 [05:56<00:10, 1.49it/s] # PTI :step: 484, epoch: 96: 97%|█████████▋| 486/500 [05:57<00:09, 1.50it/s] # PTI :step: 485, epoch: 97: 97%|█████████▋| 486/500 [05:57<00:09, 1.50it/s] # PTI :step: 485, epoch: 97: 97%|█████████▋| 487/500 [05:58<00:08, 1.48it/s] # PTI :step: 486, epoch: 97: 97%|█████████▋| 487/500 [05:58<00:08, 1.48it/s] # PTI :step: 486, epoch: 97: 98%|█████████▊| 488/500 [05:58<00:08, 1.46it/s] # PTI :step: 487, epoch: 97: 98%|█████████▊| 488/500 [05:58<00:08, 1.46it/s] # PTI :step: 487, epoch: 97: 98%|█████████▊| 489/500 [05:59<00:07, 1.40it/s] # PTI :step: 488, epoch: 97: 98%|█████████▊| 489/500 [05:59<00:07, 1.40it/s] # PTI :step: 488, epoch: 97: 98%|█████████▊| 490/500 [06:00<00:08, 1.17it/s] # PTI :step: 489, epoch: 97: 98%|█████████▊| 490/500 [06:00<00:08, 1.17it/s] # PTI :step: 489, epoch: 97: 98%|█████████▊| 491/500 [06:02<00:10, 1.17s/it] # PTI :step: 490, epoch: 98: 98%|█████████▊| 491/500 [06:02<00:10, 1.17s/it] # PTI :step: 490, epoch: 98: 98%|█████████▊| 492/500 [06:03<00:08, 1.10s/it] # PTI :step: 491, epoch: 98: 98%|█████████▊| 492/500 [06:03<00:08, 1.10s/it] # PTI :step: 491, epoch: 98: 99%|█████████▊| 493/500 [06:04<00:06, 1.04it/s] # PTI :step: 492, epoch: 98: 99%|█████████▊| 493/500 [06:04<00:06, 1.04it/s] # PTI :step: 492, epoch: 98: 99%|█████████▉| 494/500 [06:05<00:05, 1.15it/s] # PTI :step: 493, epoch: 98: 99%|█████████▉| 494/500 [06:05<00:05, 1.15it/s] # PTI :step: 493, epoch: 98: 99%|█████████▉| 495/500 [06:05<00:03, 1.25it/s] # PTI :step: 494, epoch: 98: 99%|█████████▉| 495/500 [06:05<00:03, 1.25it/s] # PTI :step: 494, epoch: 98: 99%|█████████▉| 496/500 [06:06<00:03, 1.32it/s] # PTI :step: 495, epoch: 99: 99%|█████████▉| 496/500 [06:06<00:03, 1.32it/s] # PTI :step: 495, epoch: 99: 99%|█████████▉| 497/500 [06:06<00:02, 1.38it/s] # PTI :step: 496, epoch: 99: 99%|█████████▉| 497/500 [06:06<00:02, 1.38it/s] # PTI :step: 496, epoch: 99: 100%|█████████▉| 498/500 [06:07<00:01, 1.43it/s] # PTI :step: 497, epoch: 99: 100%|█████████▉| 498/500 [06:07<00:01, 1.43it/s] # PTI :step: 497, epoch: 99: 100%|█████████▉| 499/500 [06:08<00:00, 1.46it/s] # PTI :step: 498, epoch: 99: 100%|█████████▉| 499/500 [06:08<00:00, 1.46it/s] # PTI :step: 498, epoch: 99: 100%|██████████| 500/500 [06:08<00:00, 1.48it/s] Saving final model for return # PTI :step: 499, epoch: 99: 100%|██████████| 500/500 [06:08<00:00, 1.48it/s] # PTI :step: 499, epoch: 99: 100%|██████████| 500/500 [06:09<00:00, 1.35it/s] training_out/lora.safetensors training_out/embeddings.pti training_out/special_params.json
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