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48490757da
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fixes
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2024-11-10 20:37:50 -06:00 |
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9cb0b6901b
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unified nar.py into ar_nar.py
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2024-11-10 12:19:48 -06:00 |
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e108c54daf
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new NAR-len training paradigm......
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2024-11-07 11:32:11 -06:00 |
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4049f51ba9
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added option to load lora directly from the model file itself with --lora
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2024-10-26 00:13:10 -05:00 |
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ccf71dc1b6
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added option to load from a model state dict directly instead of a yaml (to-do: do this for LoRAs too), automatically download the default model if none is provided
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2024-10-25 22:15:15 -05:00 |
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c8d4716a9f
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ugh
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2024-09-18 21:40:57 -05:00 |
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413097f5f7
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fixes
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2024-09-05 21:42:59 -05:00 |
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685f4faec0
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ugh
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2024-08-30 10:46:26 -05:00 |
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32287710a2
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moved prints to use logger, edited readme (fused_attn doesnt seem stable for training)
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2024-08-29 13:27:16 -05:00 |
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b7b99a25f1
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added ability to specify attention backend for CLI and webui (because im tired of editing the yaml)
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2024-08-26 19:33:51 -05:00 |
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d19f93a2c0
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documentation update
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2024-08-04 00:14:49 -05:00 |
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2cb465018b
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implicitly load either normal pickled weights or safetensors on loading the model
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2024-08-03 23:34:18 -05:00 |
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c09133d00f
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added safetensors support (with metadata) and feed whatever torch.load/torch.save into it
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2024-08-03 23:15:20 -05:00 |
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6a733eb2ed
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changed torch.Tensor().to(device, dtype) to just torch.tensor(..., device, dtype) because it's been bothering my autism that I'm creating tensors then converting rather than creating with the right device/dtype, some 'optimization' to compile the model but it doesnt seem to do anything useful
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2024-08-03 22:10:21 -05:00 |
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66407e5bdb
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tweaks for the NAR-len model, maybe
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2024-08-03 08:40:39 -05:00 |
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7a77978096
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oversight with using resize_modules
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2024-08-02 20:28:49 -05:00 |
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b4c895114c
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naive model offloading support (handles automatically splitting parts of the model to requested device per memory constraints, either inferred or requested in the yaml, input tensors are automatically migrated to the right device, it SEEMS to work for training under the test trainer when split between GPU and CPU) (this was specifically only because that Flux imagegen model released so I can test it there)
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2024-08-01 20:12:06 -05:00 |
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387358bc8a
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fixes for the NAR-len model, and documentation some config options, and a better way to handle resizing modules on state_dict load
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2024-07-31 20:35:09 -05:00 |
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d7c6be6f78
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fix weird regression in handling checkpoints when backend is local, but deepspeed checkpoints are in (it was handled with LoRA loading but not real loading...)
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2024-07-30 22:15:56 -05:00 |
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188d116222
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some weird fixes for an equally weird regression with LoRA loading
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2024-07-22 20:47:24 -05:00 |
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d53038a9e4
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actually have split classifiers working
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2024-07-19 15:33:31 -05:00 |
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fe0f235335
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mechanism to store the model config inside the weights and load them, some other things to allow LoRA training on the RetNet (gradient checkpointing will gripe about inputs not having require_grad and nothing seems to remedy it)
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2024-07-16 18:23:13 -05:00 |
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3acc54df22
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allow loading a different model within the web ui (apparently I did not have the web UI in the documentation)
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2024-07-15 19:59:48 -05:00 |
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c4dd523b6f
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change from chunk-slicing paths for distributed dataloader to instead interleave
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2024-06-29 10:10:35 -05:00 |
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dd40463803
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limit eval size because the training batch size seems to be used for the eval dataloader, somehow (bandaid)
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2024-06-29 09:11:28 -05:00 |
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8fffb94964
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backport fix from tortoise_tts with local trainer + loading state when training lora
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2024-06-25 13:41:29 -05:00 |
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8a986eb480
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load exported LoRA weights if exists (to-do: make a better LoRA loading mechanism)
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2024-06-18 21:45:46 -05:00 |
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7cfb78fa64
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enable LoRA for targetted RVQ levels (to experiment with, seems to help)
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2024-06-17 21:45:03 -05:00 |
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7047fcc6e2
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actually make deepspeed work with LoRAs
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2024-06-17 13:55:37 -05:00 |
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726a4b613f
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naive, rudimentary DeepSpeed support (just live with the LoRA weights living with the original weights, they can be split later)
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2024-06-17 13:17:24 -05:00 |
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45a39fb79f
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very rudimentary lora support (no deepspeed support, tested training and saving but not loading yet)
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2024-06-17 00:09:16 -05:00 |
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19410a919e
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ugh
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2024-06-15 12:29:03 -05:00 |
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132a02c48b
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sanity cleanup, backup config yaml for each log file
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2024-06-09 11:22:52 -05:00 |
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8d92dac829
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forgot I renamed this
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2024-06-09 11:12:30 -05:00 |
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fcac9503e2
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cleanup
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2024-06-06 13:08:02 -05:00 |
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b2194b859a
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re-added loading multiple models because I'm now entertaining having split AR/NAR models again (and need a way to load both at once)
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2024-06-06 09:48:43 -05:00 |
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b05a905b95
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ugh
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2024-06-05 21:02:05 -05:00 |
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934672252b
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feverish cleanup
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2024-06-03 21:28:49 -05:00 |
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c22a177cf8
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forgot to pass warmup to schedule free
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2024-05-09 22:18:49 -05:00 |
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0d5d545a40
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crammed in DAdaptation (doesn't seem worth it) and ScheduleFree (forgot I wanted to weeks ago, seems promising), optimization wrapper cleanup, test trainer changes, etc.
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2024-05-09 20:28:20 -05:00 |
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c494894261
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simple DDP wrapper (for my NVlink test)
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2024-05-04 11:48:26 -05:00 |
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a7b43b98b5
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renamed cfg.bitsandbytes to cfg.optimizations (and having it serve as cfg.optimizations.bitsandbytes)
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2024-05-02 20:08:59 -05:00 |
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545162195b
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deprecate sole AR/NAR model by only keeping the AR+NAR (the beauty of no one using this is that I can break compat as much as I want), add tone token for when I classify my dataset with tone/emotion in the future, some other things
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2024-04-15 19:54:32 -05:00 |
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f0c4baeb25
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added Adagrad (experimenting with it), added 'extended' model size (16 layers instead of 12, experimenting with it)
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2024-04-09 22:04:01 -05:00 |
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4d75ee066c
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actually do the Linear replacement with TE's Linear
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2024-04-09 14:41:13 -05:00 |
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7075c2a5f0
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added an option to allow injecting embeddings from another model, because it dawned upon me how valuable embeddings from a good model can be for subsequent trainings (defined under cfg.models._embeddings as a relative path to the yaml)
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2024-04-04 19:11:49 -05:00 |
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47435207f7
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Added cfg.bitsandbytes.replace as a less intrusive alternative to cfg.bitsandbytes.inject to replace all Linear modules in a model
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2024-03-01 19:20:10 -06:00 |
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3da1518ace
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added Mistral (non-Mixtral) backend, useless optimization when not training, proper adjustment of the LR for Prodigyopt through d_coeff (maybe), recurrent sampling for LLaMA/Mistral/Mixtral backends (again, doesn't actually work)
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2024-01-31 21:48:36 -06:00 |
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c690aa509d
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fixes and compat (MoE-fying an existing model and retraining from there just ruins it after a second of audio...)
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2023-12-25 21:20:32 -06:00 |
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32d4271ca8
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fixed issue with training from scratch (oops)
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2023-10-21 09:55:38 -05:00 |
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