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e094bd23ec
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web UI support for HF ZeroGPU
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2024-11-05 21:22:29 -06:00 |
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e58a9469a3
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move layerskip to experimental settings.......
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2024-11-05 20:37:06 -06:00 |
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d5aa8186f0
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more doc
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2024-11-05 16:53:00 -06:00 |
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9901c4f8ca
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documentation under ./docs/
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2024-11-05 16:11:01 -06:00 |
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bbc2de3713
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ugh
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2024-11-05 11:50:05 -06:00 |
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9e65e05e83
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more windows specific fixes, limit gradio to <5.0.0 on linux (it works on windows, but not on my linux machine tm)
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2024-11-04 18:00:33 -06:00 |
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c83670c38c
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Windows specific fixes (to-do: find libespeak-ng.dll automatically because it cannot be trusted to do it by default)
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2024-11-03 19:19:15 -06:00 |
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d229725c76
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more adjustments (adjustments of early-exit entropy/varentropy thresholds, default rep pen being 1.5, experimental refine-on-stop, etc.)
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2024-11-03 18:31:28 -06:00 |
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aee08b7307
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changed layerskip float16 training warning (since it didnt seem to fry on my 4xV100 system)
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2024-11-03 09:58:29 -06:00 |
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3826f9bae4
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saner mask creation? (it doesnt matter, kv cache wont work)
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2024-11-02 21:00:21 -05:00 |
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ded746e157
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very, very naive layerskip speculative sampling (it just checks if the current layer's state is good enough)
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2024-11-02 11:49:05 -05:00 |
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62fe5b0943
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ughh
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2024-11-01 22:36:48 -05:00 |
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ec79230965
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shuffled web UI options hidden by cfg.experimental to its own tab, expose early exit selection to inferencing (it kinda works naively, still need to implement self-speculation)
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2024-11-01 21:30:06 -05:00 |
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ef1c17430f
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skip step on nan loss (ironically I have not had a nan loss after adding this), throw exception with invalid cfg.dataset.sample_type and sample_order combination (because I was tricked by this in my yaml and had inconsistent vram usage)
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2024-11-01 20:54:53 -05:00 |
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fb8faa295b
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actually float16(+AMP) and layerskip is bad and will kill the model......
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2024-11-01 18:36:44 -05:00 |
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edf1e66bf9
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layerskip_r=6 fries the model so hard the loss is sub-1...
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2024-11-01 17:06:07 -05:00 |
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9b6c57bc57
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third time's the charm (for some reason it escaped me that I should treat early exit loss as an aux_loss to be used with the normal loss, as if I was training a MoE's router)
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2024-11-01 12:50:37 -05:00 |
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76ebef45dc
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off-by-one...
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2024-10-31 13:24:48 -05:00 |
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b63293cbbe
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ugh
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2024-10-30 22:49:11 -05:00 |
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a22534e8f4
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layer skip training implemented (need to gut the inferencing from the repo, and to actually see if the model can benefit from this)
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2024-10-30 20:05:45 -05:00 |
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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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023c3af331
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updated readme to reflect changes
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2024-10-25 22:17:05 -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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a96f5aee32
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adjusted how i want to pass eval kwargs
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2024-10-25 20:38:09 -05:00 |
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92e6bff6dc
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actually ar temp 0.5 with rep pen 1.125 seems to have the benefits of better outputs without it degrading some of the time but not all the time
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2024-10-23 00:03:35 -05:00 |
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8920e5e86b
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actually have beam_width in the webUI work
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2024-10-22 22:06:22 -05:00 |
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910571ad34
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too brainlet to diagnose why low temp / greedy sampling is randomly unstable some of the time
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2024-10-22 20:13:54 -05:00 |
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8eb9a4056b
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modified default arguments (ar temp = 0 and rep pen = 1.125 seems to be stable, at least given the few things i tested), do not pass top k/top p/min p to NAR even though technically none of those things should matter when greedy sampling
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2024-10-22 18:12:39 -05:00 |
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1a02cd5bce
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modify demo template to say F5 instead of YourTTS, swap LoRA comparison around to make the lora'd the base file, and the no-lora the suffix'd file
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2024-10-21 19:52:02 -05:00 |
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02dfc60ac3
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ugh
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2024-10-18 17:23:22 -05:00 |
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71731ed785
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added prefixing with silence (was to test something, currently hidden under cfg.experimental=True)
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2024-10-18 17:19:52 -05:00 |
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6b04c13c56
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print warning if audio promtpless inferencing with low AR temp (it really doesn't like low temps / greedy sampling)
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2024-10-18 17:01:40 -05:00 |
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c8f31db1de
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default to greedy sample AR (i should probably test this more but it seems to pass my harvard sentences and tongue twisters)
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2024-10-18 16:58:56 -05:00 |
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fc8dfd8617
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made greedy AR sampling viable (and preferable), with caveats (per comment in vall_e.models.ar_nar)
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2024-10-18 16:55:00 -05:00 |
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07f4935a75
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more tweaks
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2024-10-18 13:19:36 -05:00 |
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0dfab973e7
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oops
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2024-10-18 09:40:06 -05:00 |
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75b90be325
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cleaned up unused config flags, allow less strict yaml by pruning missing keys, renamed some dataset configs to be more unified
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2024-10-17 17:06:48 -05:00 |
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8b6095f681
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saner defaults, maybe
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2024-10-17 14:37:21 -05:00 |
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f88097ccf6
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add config option to set the rate of sampling randomly vs similar speakers during training
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2024-10-16 14:27:58 -05:00 |
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48461833c2
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ugh
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2024-10-15 19:30:43 -05:00 |
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eea70f5698
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kludge fix for an oversight in the model when trying to train for longer input prompt durations......
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2024-10-15 19:25:03 -05:00 |
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84005c5b00
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entropix apparently processes the entire sequence of logits but it falls apart when doing that
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2024-10-13 12:01:12 -05:00 |
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c800d28bb8
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respect attention defined in the yaml for web UI (which might explain why theres been a discrepancy in outputs for me)
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2024-10-13 11:02:24 -05:00 |
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ed6b7a690f
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ugh.........
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2024-10-13 00:26:46 -05:00 |
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d405f243d4
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at wits end in trying to output the right attention scores
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2024-10-12 23:53:13 -05:00 |
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70cf694cfd
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output attention scores for SDPA/flash, since naive attention seems broken
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2024-10-12 12:09:17 -05:00 |
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541e45263c
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ugh
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2024-10-12 11:29:16 -05:00 |
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04e983b86b
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modified demo page to be more modular with demoing comparisons, actually provide a path to use modified naive attention, entropix sampling is not tied to an experimental yaml flag now
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2024-10-12 11:27:55 -05:00 |
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666e8038fb
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ugh
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2024-10-12 10:41:35 -05:00 |
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3d6ef9666b
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overridden naive llama attention to get the right score values that entropix needs
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2024-10-12 10:05:47 -05:00 |
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