SAVRN
Search Contact SAVRN

Open-weight model · Text to audio

Ovis-Omni-Embedding-3B-gptq-mixed-w4-w4-w8

by Hung Cheung Chan pt810/Ovis-Omni-Embedding-3B-gptq-mixed-w4-w4-w8

Ovis-Omni-Embedding-3B-gptq-mixed-w4-w4-w8 is an open-weight model for text to audio from Hung Cheung Chan. It has 2,935 parameters. At 16-bit it needs about 0 GB of GPU memory, which fits on 1x MI300X from $1.85 an hour, at the lowest prices in the SAVRN Index.

Experimental calibration-based weight-only GPTQ variant of The Thinker transformer uses 4-bit weights for layers 0–26 and 8-bit weights for layer 27. Audio, vision, talker, token2wav, embeddings, and norms remain in the original precision.

Parameters2,935
Context—
Weights7.9 GB
License—
AccessOpen weights
Monthly Downloads—

Runs On

What it takes to serve Ovis-Omni-Embedding-3B-gptq-mixed-w4-w4-w8 (2,935 parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them.

PrecisionWeightsMemory neededCheapest setupPer hourAlso fits
16-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
8-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00
4-bit 0.0 GB 0.0 GB 1x MI300X (192 GB)
Vultr
$1.85 1x H100 $1.99 · 1x MI325X $2.00

Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the SAVRN Index, read Oct 9, 2026.

Ovis-Omni-Embedding-3B-gptq-mixed-w4-w4-w8 on every accelerator the SAVRN Index prices, at every precision

Model Card

Experimental calibration-based weight-only GPTQ variant of The Thinker transformer uses 4-bit weights for layers 0–26 and 8-bit weights for layer 27. Audio, vision, talker, token2wav, embeddings, and norms remain in the original precision. Calibration used eight short text samples with LLM Compressor 0.14.0 and compressed-tensors 0.19.0. This checkpoint includes a packaging repair: the compressor export contained invalid group scales, so scales were recomputed from the original BF16 weights per group before the vLLM test. Treat this as an experimental GPTQ-derived checkpoint and benchmark retrieval quality before production use. Tested with vLLM 0.30.0 on an 8-GiB RTX 3080 Laptop GPU: The…

Excerpt from the card by Hung Cheung Chan.

Configuration

Architecture
Qwen2_5OmniForConditionalGeneration
Hidden size
2,048
Model type
qwen2_5_omni
Quantization
compressed-tensors

Identity and Version

Repository
pt810/Ovis-Omni-Embedding-3B-gptq-mixed-w4-w4-w8
Publisher
Hung Cheung Chan
Task
Text to audio
Modality
Other
Library
transformers
Parameters
2,935 parameters
Languages
Not stated by the source
Revision
667b6a7a0058b0c652342618fd7f18111448beb1
First published
2026-09-24
Last updated
2026-09-24

Files and Weights

257 files, 8.0 GB in total. The weights are 232 files totalling 7.9 GB in bin, pt, pth, safetensors.

Weights232 files · 7.9 GB
Configuration8 files · 438.6 KB
Tokenizer2 files · 11.4 MB
Documentation2 files · 13.5 KB
Other12 files · 69.9 MB
Repository1 file · 1.9 KB
Every file
FileTypeSizeSHA-256
gptq-noscale-part-00001.safetensorsWeights622.3 MB 18e3b3c10b04
gptq-noscale-part-00002.safetensorsWeights22.5 MB 9faaa35b1719
gptq-noscale-part-00003.safetensorsWeights27.3 MB 914234460360
gptq-noscale-part-00004.safetensorsWeights27.3 MB 9da1c6b28176
gptq-noscale-part-00005.safetensorsWeights22.5 MB 71f61323f5b4
gptq-noscale-part-00006.safetensorsWeights27.3 MB 281e0b97df4b
gptq-noscale-part-00007.safetensorsWeights27.3 MB f4fdbbac908e
gptq-noscale-part-00008.safetensorsWeights22.5 MB 4d483b3eb7f2
gptq-noscale-part-00009.safetensorsWeights27.3 MB a79ab4a4f2a9
gptq-noscale-part-00010.safetensorsWeights27.3 MB 597761192896
gptq-noscale-part-00011.safetensorsWeights22.5 MB 26b7bf622cda
gptq-noscale-part-00012.safetensorsWeights27.3 MB ac183259a828
gptq-noscale-part-00013.safetensorsWeights27.3 MB 861ace164f58
gptq-noscale-part-00014.safetensorsWeights22.5 MB f604893088b6
gptq-noscale-part-00015.safetensorsWeights27.3 MB facf92bb8a53
gptq-noscale-part-00016.safetensorsWeights27.3 MB b7705719dad4
gptq-noscale-part-00017.safetensorsWeights22.5 MB bc1c70f6e6c4
gptq-noscale-part-00018.safetensorsWeights27.3 MB 56bd379167f3
gptq-noscale-part-00019.safetensorsWeights27.3 MB 872ab109fb5a
gptq-noscale-part-00020.safetensorsWeights22.5 MB e2be4acebe6c
gptq-noscale-part-00021.safetensorsWeights27.3 MB 9492e34cbe18
gptq-noscale-part-00022.safetensorsWeights27.3 MB 1cb36de02be2
gptq-noscale-part-00023.safetensorsWeights22.5 MB 7f50fd3a242f
gptq-noscale-part-00024.safetensorsWeights27.3 MB 437d622906cc
gptq-noscale-part-00025.safetensorsWeights27.3 MB e74b34c3a2f8
gptq-noscale-part-00026.safetensorsWeights22.5 MB b472844d2e97
gptq-noscale-part-00027.safetensorsWeights27.3 MB e567059d1c70
gptq-noscale-part-00028.safetensorsWeights27.3 MB e470e66c4e29
gptq-noscale-part-00029.safetensorsWeights22.5 MB fe43932a9f62
gptq-noscale-part-00030.safetensorsWeights27.3 MB 99d7969ae15a
gptq-noscale-part-00031.safetensorsWeights27.3 MB 6a211813a6ba
gptq-noscale-part-00032.safetensorsWeights22.5 MB eef8c672ebfb
gptq-noscale-part-00033.safetensorsWeights22.5 MB 23a2f6880d63
gptq-noscale-part-00034.safetensorsWeights32.0 MB 7d4ea732b63b
gptq-noscale-part-00035.safetensorsWeights45.1 MB 603b164c37d8
gptq-noscale-part-00036.safetensorsWeights45.1 MB 715082875d7a
gptq-noscale-part-00037.safetensorsWeights45.1 MB e5bae4c8db49
gptq-noscale-part-00038.safetensorsWeights18.9 MB 9f5943516290
gptq-noscale-part-00039.safetensorsWeights45.1 MB 43afa938a2cd
gptq-noscale-part-00040.safetensorsWeights45.1 MB c988aad20794
gptq-noscale-part-00041.safetensorsWeights45.1 MB ab6edf352b8d
gptq-noscale-part-00042.safetensorsWeights30.2 MB 70eef0ab56c6
gptq-noscale-part-00043.safetensorsWeights27.3 MB 5fe72d8b2cdf
gptq-noscale-part-00044.safetensorsWeights45.1 MB 4c50be882345
gptq-noscale-part-00045.safetensorsWeights45.1 MB f6047f068350
gptq-noscale-part-00046.safetensorsWeights45.1 MB 9317316a0fe6
gptq-noscale-part-00047.safetensorsWeights18.9 MB fba5ee18dade
gptq-noscale-part-00048.safetensorsWeights45.1 MB dce8e4815b92
gptq-noscale-part-00049.safetensorsWeights45.1 MB 7fb6ba530825
gptq-noscale-part-00050.safetensorsWeights45.1 MB 53d6ef525a32
gptq-noscale-part-00051.safetensorsWeights18.9 MB 91b51e4f9993
gptq-noscale-part-00052.safetensorsWeights45.1 MB 7ede65d676cc
gptq-noscale-part-00053.safetensorsWeights45.1 MB e2fa62a78c59
gptq-noscale-part-00054.safetensorsWeights45.1 MB cd93aff371bd
gptq-noscale-part-00055.safetensorsWeights18.9 MB e3aa0622f278
gptq-noscale-part-00056.safetensorsWeights45.1 MB 9662916121dd
gptq-noscale-part-00057.safetensorsWeights45.1 MB e06657b37aa5
gptq-noscale-part-00058.safetensorsWeights45.1 MB 210b7701cdc6
gptq-noscale-part-00059.safetensorsWeights18.9 MB cf4bce48db61
gptq-noscale-part-00060.safetensorsWeights45.1 MB 14915382f2b2
gptq-noscale-part-00061.safetensorsWeights45.1 MB d5db4e6eabb8
gptq-noscale-part-00062.safetensorsWeights45.1 MB 15b0f23e4b44
gptq-noscale-part-00063.safetensorsWeights18.9 MB 53999c46b372
gptq-noscale-part-00064.safetensorsWeights45.1 MB ea534282439c
gptq-noscale-part-00065.safetensorsWeights45.1 MB 21dd12bd0154
gptq-noscale-part-00066.safetensorsWeights45.1 MB 9247dc62ccff
gptq-noscale-part-00067.safetensorsWeights30.2 MB 86712ba7267a
gptq-noscale-part-00068.safetensorsWeights27.3 MB 4e9e2548a137
gptq-noscale-part-00069.safetensorsWeights22.5 MB e7eb172fcf40
gptq-noscale-part-00070.safetensorsWeights27.3 MB 912e87ce96d6
gptq-noscale-part-00071.safetensorsWeights27.3 MB 3fb1b988030f
gptq-noscale-part-00072.safetensorsWeights22.5 MB 7a7484abd8f0
gptq-noscale-part-00073.safetensorsWeights27.3 MB 7bcde5fb5d84
gptq-noscale-part-00074.safetensorsWeights27.3 MB fc2b8f7868c7
gptq-noscale-part-00075.safetensorsWeights22.5 MB dc54627530aa
gptq-noscale-part-00076.safetensorsWeights16.0 MB aa0dca939a13
gptq-repaired-scales.safetensorsWeights33.7 MB e549f8216c8a
orig-1-part-00001.safetensorsWeights15.1 MB 97ea623c6e64
orig-1-part-00002.safetensorsWeights34.6 MB ee2c9cca465a
orig-1-part-00003.safetensorsWeights29.8 MB ffba8993f637
orig-1-part-00004.safetensorsWeights29.8 MB 556d18d09c70
orig-1-part-00005.safetensorsWeights29.8 MB a0e93080691b
orig-1-part-00006.safetensorsWeights29.8 MB 94ab920503ac
orig-1-part-00007.safetensorsWeights29.8 MB 3de6c27c9c2c
orig-1-part-00008.safetensorsWeights29.8 MB 772e48eacbc8
orig-1-part-00009.safetensorsWeights29.8 MB 68a35817b81a
orig-1-part-00010.safetensorsWeights29.8 MB 27a9089d4266
orig-1-part-00011.safetensorsWeights29.8 MB 64a681dfa415
orig-1-part-00012.safetensorsWeights29.8 MB 35ae3cd3494a
orig-1-part-00013.safetensorsWeights29.8 MB f7b9dda8d51b
orig-1-part-00014.safetensorsWeights29.8 MB 1839d1af699d
orig-1-part-00015.safetensorsWeights29.8 MB 09d8d44ecce8
orig-1-part-00016.safetensorsWeights29.8 MB 4a293b505b21
orig-1-part-00017.safetensorsWeights29.8 MB 491918d98c0a
orig-1-part-00018.safetensorsWeights29.8 MB 7e7a7942f83a
orig-1-part-00019.safetensorsWeights29.8 MB d53449b60343
orig-1-part-00020.safetensorsWeights29.8 MB c7fdd7509c00
orig-1-part-00021.safetensorsWeights29.8 MB 349e771520b3
orig-1-part-00022.safetensorsWeights29.8 MB d473493531a6
orig-1-part-00023.safetensorsWeights29.8 MB 4ebd559dc7fc
orig-1-part-00024.safetensorsWeights29.8 MB 316f253530cf
orig-1-part-00025.safetensorsWeights29.8 MB e0967bb66672
orig-1-part-00026.safetensorsWeights33.5 MB e046a3d2c3ec
orig-1-part-00027.safetensorsWeights23.9 MB 194510a670f9
orig-1-part-00028.safetensorsWeights26.2 MB 0c2834b5ac28
orig-1-part-00029.safetensorsWeights32.8 MB f87e968c5c26
orig-1-part-00030.safetensorsWeights32.8 MB 5dc7f9aa72c2
orig-1-part-00031.safetensorsWeights26.2 MB e5ad85262944
orig-1-part-00032.safetensorsWeights26.2 MB 6e6821df278c
orig-1-part-00033.safetensorsWeights32.8 MB 62c7def02ed4
orig-1-part-00034.safetensorsWeights32.8 MB c82fc91b9d4e
orig-1-part-00035.safetensorsWeights26.2 MB c36fd5dc2337
orig-1-part-00036.safetensorsWeights26.2 MB ba9d699e88d4
orig-1-part-00037.safetensorsWeights32.8 MB 586fcee4e85a
orig-1-part-00038.safetensorsWeights32.8 MB bd6226e448fc
orig-1-part-00039.safetensorsWeights26.2 MB c47e1612743b
orig-1-part-00040.safetensorsWeights26.2 MB ad48b7c17e6d
orig-1-part-00041.safetensorsWeights32.8 MB 436ffe6c0c09
orig-1-part-00042.safetensorsWeights32.8 MB 359e6dec9d7a
orig-1-part-00043.safetensorsWeights26.2 MB 74f1f73d4518
orig-1-part-00044.safetensorsWeights26.2 MB 015e83f081be
orig-1-part-00045.safetensorsWeights32.8 MB d3dc5ebf8d42
orig-1-part-00046.safetensorsWeights32.8 MB 79546275c76e
orig-1-part-00047.safetensorsWeights26.2 MB c79d4e3b8273
orig-1-part-00048.safetensorsWeights26.2 MB 255c317e34e5
orig-1-part-00049.safetensorsWeights32.8 MB 0252d67663dd
orig-1-part-00050.safetensorsWeights32.8 MB 0918927446c9
orig-1-part-00051.safetensorsWeights26.2 MB 0dd3166ba927
orig-1-part-00052.safetensorsWeights26.2 MB 1726bb174e8f
orig-1-part-00053.safetensorsWeights32.8 MB 1cdad91c71b8
orig-1-part-00054.safetensorsWeights32.8 MB d409411f3b17
orig-1-part-00055.safetensorsWeights26.2 MB 01541461aeff
orig-1-part-00056.safetensorsWeights26.2 MB 72c04ae152fe
orig-1-part-00057.safetensorsWeights32.8 MB fe4de7256e58
orig-1-part-00058.safetensorsWeights32.8 MB d17f79d91600
orig-1-part-00059.safetensorsWeights26.2 MB 8449892eac68
orig-1-part-00060.safetensorsWeights26.2 MB 82e0205ea49d
orig-1-part-00061.safetensorsWeights32.8 MB 2608100ef2dd
orig-1-part-00062.safetensorsWeights32.8 MB 28896af2b1a8
orig-1-part-00063.safetensorsWeights26.2 MB 2b6f7a5a4aab
orig-1-part-00064.safetensorsWeights26.2 MB 20323b60898d
orig-1-part-00065.safetensorsWeights32.8 MB bde71feb5d24
orig-1-part-00066.safetensorsWeights32.8 MB 2191032350a0
orig-1-part-00067.safetensorsWeights26.2 MB 56c68760873b
orig-1-part-00068.safetensorsWeights26.2 MB 0f950f4c8fae
orig-1-part-00069.safetensorsWeights32.8 MB b7918c01d6a2
orig-1-part-00070.safetensorsWeights11.8 MB 853f44dad306
orig-1-part-00071.safetensorsWeights622.3 MB 053dddc78ee2
orig-2-part-00001.safetensorsWeights30.6 MB 3f840b599146
orig-2-part-00002.safetensorsWeights30.6 MB 70151986fc71
orig-2-part-00003.safetensorsWeights30.6 MB 989a4ce1cfd5
orig-2-part-00004.safetensorsWeights32.8 MB 4a85ba273866
orig-2-part-00005.safetensorsWeights32.8 MB ddf259cea1c7
orig-2-part-00006.safetensorsWeights30.6 MB 6f5d98b417cc
orig-2-part-00007.safetensorsWeights30.6 MB 8dba9579e591
orig-2-part-00008.safetensorsWeights30.6 MB d1214df2d5eb
orig-2-part-00009.safetensorsWeights32.8 MB 7dbb55b374a4
orig-2-part-00010.safetensorsWeights32.8 MB 280e2c6631a4
orig-2-part-00011.safetensorsWeights30.6 MB c3784d0eee0a
orig-2-part-00012.safetensorsWeights30.6 MB a81c4ed68e07
orig-2-part-00013.safetensorsWeights30.6 MB c13139dabae5
orig-2-part-00014.safetensorsWeights32.8 MB dea03e69cc56
orig-2-part-00015.safetensorsWeights32.8 MB 55599c12ddbe
orig-2-part-00016.safetensorsWeights30.6 MB 5c836cc4cb58
orig-2-part-00017.safetensorsWeights30.6 MB 89da203dd46f
orig-2-part-00018.safetensorsWeights30.6 MB d3a09325762c
orig-2-part-00019.safetensorsWeights32.8 MB 9809b881a39f
orig-2-part-00020.safetensorsWeights32.8 MB 9137a4eed957
orig-2-part-00021.safetensorsWeights30.6 MB f5998be5287f
orig-2-part-00022.safetensorsWeights30.6 MB 0da834fd84b7
orig-2-part-00023.safetensorsWeights30.6 MB 89b597352fd7
orig-2-part-00024.safetensorsWeights32.8 MB a1c21c9dbfed
orig-2-part-00025.safetensorsWeights32.8 MB e0bc7c0c35b5
orig-2-part-00026.safetensorsWeights30.6 MB f41d32226684
orig-2-part-00027.safetensorsWeights30.6 MB 6421ec668f77
orig-2-part-00028.safetensorsWeights30.6 MB 2fa8a94017a6
orig-2-part-00029.safetensorsWeights32.8 MB a99e833eaa06
orig-2-part-00030.safetensorsWeights27.4 MB 8f73d67bb4e7
orig-2-part-00031.safetensorsWeights30.6 MB 08d9861ac06e
orig-2-part-00032.safetensorsWeights30.6 MB a240351082be
orig-2-part-00033.safetensorsWeights32.8 MB 56e04178fd7c
orig-2-part-00034.safetensorsWeights32.8 MB a0a8bc8aae2b
orig-2-part-00035.safetensorsWeights30.6 MB f73475797e70
orig-2-part-00036.safetensorsWeights30.6 MB dd1fca0cba13
orig-2-part-00037.safetensorsWeights17.5 MB c934947d4cc6
orig-3-part-00001.safetensorsWeights27.4 MB dfa66d4dce3a
orig-3-part-00002.safetensorsWeights30.6 MB ff9d5c90cbcc
orig-3-part-00003.safetensorsWeights30.6 MB 302c6fd43307
orig-3-part-00004.safetensorsWeights26.3 MB 98f44d141639
orig-3-part-00005.safetensorsWeights52.4 MB ff8a9b594ecb
orig-3-part-00006.safetensorsWeights32.8 MB 0aaa54ff2d8d
orig-3-part-00007.safetensorsWeights30.7 MB 9dc454320c80
orig-3-part-00008.safetensorsWeights33.6 MB bf67a98d500b
orig-3-part-00009.safetensorsWeights28.2 MB 13896baf33f4
orig-3-part-00010.safetensorsWeights26.0 MB 63b7db35cd82
orig-3-part-00011.safetensorsWeights33.4 MB aab31ba06b9a
orig-3-part-00012.safetensorsWeights33.2 MB 9c8ef6dbe454
orig-3-part-00013.safetensorsWeights32.2 MB 1c1924147f9e
orig-3-part-00014.safetensorsWeights31.9 MB ee1b3a0031d2
orig-3-part-00015.safetensorsWeights31.5 MB 3028472d32f6
orig-3-part-00016.safetensorsWeights29.4 MB ae660d6e3c3c
orig-3-part-00017.safetensorsWeights29.4 MB 44db5d504dde
orig-3-part-00018.safetensorsWeights29.4 MB b97bed2cdd84
orig-3-part-00019.safetensorsWeights29.4 MB dc4a04925edb
orig-3-part-00020.safetensorsWeights29.4 MB 273523e5523c
orig-3-part-00021.safetensorsWeights29.4 MB cbd3c0cc08ca
orig-3-part-00022.safetensorsWeights29.4 MB 4828bcb45ed3
orig-3-part-00023.safetensorsWeights29.4 MB 72e0584e7a2f
orig-3-part-00024.safetensorsWeights29.4 MB 6ad465c5d7e4
orig-3-part-00025.safetensorsWeights29.4 MB 265b3d244dda
orig-3-part-00026.safetensorsWeights29.4 MB c5ce6debe6c6
orig-3-part-00027.safetensorsWeights29.4 MB 3be6359a4d21
orig-3-part-00028.safetensorsWeights29.4 MB 83d1b0c23cbf
orig-3-part-00029.safetensorsWeights29.4 MB 00e95b6e0655
orig-3-part-00030.safetensorsWeights29.4 MB f4c7dfb193a7
orig-3-part-00031.safetensorsWeights29.4 MB 7fe5d86688e0
orig-3-part-00032.safetensorsWeights29.4 MB 67c993c6db63
orig-3-part-00033.safetensorsWeights29.4 MB 48bf5444f80e
orig-3-part-00034.safetensorsWeights29.4 MB 5e04579b8051
orig-3-part-00035.safetensorsWeights29.4 MB 194e4ade4393
orig-3-part-00036.safetensorsWeights21.0 MB f00f30fc829b
rng_state_0.pthWeights16.4 KB 478b41e9f26d
rng_state_1.pthWeights16.4 KB ce29a8767a7d
rng_state_2.pthWeights16.4 KB 61a48db01164
rng_state_3.pthWeights16.4 KB b9562ee82247
rng_state_4.pthWeights16.4 KB e7d2767d83c3
rng_state_5.pthWeights16.4 KB 76816358d4e5
rng_state_6.pthWeights16.4 KB 1562e7520c97
rng_state_7.pthWeights16.4 KB a6b6cabaed04
scheduler.ptWeights1.5 KB 2947d2764fc5
spk_dict.ptWeights259.5 KB 6a05609b28f5
training_args.binWeights8.7 KB efe2b1b63155
args.jsonConfiguration96 B —
config.jsonConfiguration16.0 KB —
generation_config.jsonConfiguration154 B —
model.safetensors.index.jsonConfiguration278.9 KB —
preprocessor_config.jsonConfiguration667 B —
processor_config.jsonConfiguration2.8 KB —
trainer_state.jsonConfiguration106.8 KB —
zero_to_fp32.pyConfiguration33.3 KB —
LICENSEDocumentation11.5 KB —
README.mdDocumentation2.0 KB —
chat_template.jinjaOther1.3 KB —
latestOther15 B —
model-00001-of-00003.safetensors.download.logOther73.5 KB —
model-00002-of-00003.safetensors.download.logOther72.9 KB —
model-00003-of-00003.safetensors.download.logOther73.4 KB —
ovis_blog_table1.pdfOther484.4 KB ae5707e6bc8e
ovis_blog_table1.pngOther436.4 KB e6cdde834fdb
ovis_embedding_data_centric.pngOther729.9 KB 0ad28dd7a765
ovis_embedding_model_architecture.pngOther401.5 KB da256408d299
ovis_embedding_train_inference.pngOther414.1 KB 49353acc8881
ovis_logo.pngOther85.5 KB —
test.partOther67.1 MB f4c42971269d
.gitattributesRepository1.9 KB —
tokenizer.jsonTokenizer11.4 MB 1ab7a851e5c6
tokenizer_config.jsonTokenizer939 B —

License and Download

License
Not stated by the source
Access
Open weights, no gate
Download size
7.9 GB
Download from Hung Cheung Chan

Released by Hung Cheung Chan through its official repository on Hugging Face.

Built From

Memory Requirements

PrecisionWeights in memory
As published7.9 GB
16-bit0.0 GB
8-bit0.0 GB
4-bit0.0 GB

Weights only, from the published parameter count; the key-value cache and runtime add to this.

Questions About Ovis-Omni-Embedding-3B-gptq-mixed-w4-w4-w8

How much GPU memory does Ovis-Omni-Embedding-3B-gptq-mixed-w4-w4-w8 need?

About 0 GB at 16-bit and 0 GB at 4-bit: the weights (2,935 parameters) plus a working margin. A long context needs more.

What is the cheapest GPU to run Ovis-Omni-Embedding-3B-gptq-mixed-w4-w4-w8 on?

At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists.

Similar Models

Model · Text to audio

musicgen-medium

AI at Meta

MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. MusicGen was published in Simple and Controllable Music Generation by Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant…

Open weights cc-by-nc-4.0 transformers

Model · Text to audio

MiniMax-Music3-GGUF

Audio.cpp

GGUF package for MiniMax Music 3 for audio.cpp. Star our repo so you don't miss important updates! https://github.com/0xShug0/audio.cpp Upstream license: https://huggingface.co/MiniMaxAI/MiniMax-Music3/blob/main/LICENSE - The implementation is available on the main branch and release 0.6.1. - The current runtime uses model-local resource loading instead of treating the v1 spec as the runtime contract. This keeps component selection flexible while the package layout and option surface settle. - The default component mix favors Q40 for the large language model and flow transformer, with Q80 for the RVQ depth decoder. - BF16, Q80, and Q40 component variants are included for quality/performance…

Open weights other audio.cpp

Model · Text to audio

Yue2-3B-GGUF

Audio.cpp

This repository contains audio.cpp-native GGUF weights for Yue2-3B. The model has been merged into the main branch. LoRA support added in release 0.8.1. The demos below were generated with yue2-3b-bf16.gguf and yue2-vae-f32.gguf. The q8.wav comparison files were generated with yue2-3b-q80.gguf and yue2-vae-f16.gguf using the same prompts and seeds. The q40.wav comparison files were generated with yue2-3b-q40.gguf and yue2-vae-f16.gguf. Replace with your audio.cpp checkout and with this downloaded GGUF repo directory. To generate the Q40 versions, use the same commands and replace --session-option yue2.modelgguf=yue2-3b-bf16.gguf with --session-option yue2.modelgguf=yue2-3b-q40.gguf, and use…

Open weights cc-by-nc-4.0 audio.cpp

Four artist-style LoRAs that push YuE2-3B into modern militant roots reggae: dark raspy male patois vocals, steppers and one-drop grooves, deep sub bass, bubbling Hammond, nyabinghi drums, horn stabs, dub sirens and spring reverb. Conscious, apocalyptic, anthemic. Each file patches both halves of YuE2 in one go: the autoregressive planner (writes the score, decides the arrangement and the vocal lines) and the flow-matching decoder (the sound). Trigger word for all three: mltnt. All demos use the same original lyric, seed 7, 32 steps dpm2 / sgmuniform, no post-processing. MLTNT Frontline — baseline recipe, prompt prompts/steppersbaseline.txt, dense lyric (verses written at ~17 words per…

Open weights cc-by-nc-4.0

Model · Text to audio

igbo-mms-tts

Omeziri Zion

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Open weights 36M parameters transformers

Model · Text to audio

akan-twi-mms

Abdul Rashid Dickson

This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

Open weights cc-by-nc-4.0 83M parameters transformers