modelId
stringlengths 4
122
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
74.7M
| likes
int64 0
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| library_name
stringlengths 2
84
⌀ | tags
list | pipeline_tag
stringlengths 5
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⌀ | createdAt
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| embedding
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tkcho/commerce-clf-kr-sku-brand-c8929a27772164363ef67bdca3268e2f
|
tkcho
| 2023-11-18T06:57:28Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T06:57:00Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-cabe15c50d339f71c32e0f1db282a0ca
|
tkcho
| 2023-11-19T10:05:54Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-19T10:05:27Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-ae48a6bea18359a171960ead05c40edf
|
tkcho
| 2023-11-19T13:41:03Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-19T13:40:35Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-44a2347d3a6c5bc3c8b456d2fb39ef59
|
tkcho
| 2023-11-20T01:10:38Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T01:10:12Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-2d5a05d9e87e6d10b9a2d720653717ba
|
tkcho
| 2023-11-20T17:07:01Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T17:06:33Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-24bea0c57490150918a184a134a4264a
|
tkcho
| 2023-11-20T18:37:05Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T18:36:35Z |
Entry not found
|
[
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] |
tkcho/commerce-clf-kr-sku-brand-a5735943f3d5b90acefd1f23c4e5c93b
|
tkcho
| 2023-11-21T04:26:08Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T04:25:38Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-cff0169d4a7b72f8a864860fe6709986
|
tkcho
| 2023-11-21T08:56:00Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T08:55:14Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-76be4e31b22d9f606be51ed62ba61afd
|
tkcho
| 2023-11-22T17:15:59Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-22T17:15:26Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-486efd8485783ee5abb44a1c17e0c36a
|
tkcho
| 2023-11-22T19:10:12Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-22T19:09:38Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-609d5ac5a5b395ec486b58f39f257c54
|
tkcho
| 2023-11-23T06:22:54Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-23T06:22:13Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-ca5301ee79300189671509cb45cf14ea
|
tkcho
| 2023-11-23T08:46:35Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-23T08:46:05Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-3826245b400ad1e29f23874455e334fd
|
tkcho
| 2023-11-24T02:33:55Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T02:33:27Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-b222509c00feab9fc411aca416f2434d
|
tkcho
| 2023-11-24T13:29:32Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T13:28:58Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-65658556654df2e600248ddb58699438
|
tkcho
| 2023-11-24T21:22:36Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T21:22:09Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-91def386e162319f336f160d46cad6ff
|
tkcho
| 2023-11-24T22:52:35Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T22:52:05Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-3092c4898fa052c74146f59967481057
|
tkcho
| 2023-11-25T05:16:02Z | 4,424 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-25T05:15:33Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-4ed51aa34ef15572a997977a528bb2bc
|
tkcho
| 2023-11-14T14:56:25Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-14T14:55:58Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-2c26e30a2ecf003be02506319144eca2
|
tkcho
| 2023-11-17T09:59:31Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T09:58:59Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-1b05eb4e14ef8eff14c9cb51ab17a2bf
|
tkcho
| 2023-11-18T06:24:38Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T06:24:08Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-f4f71b793373bcf809fda1535b09feff
|
tkcho
| 2023-11-18T14:31:16Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T14:30:49Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-a9fa6e8bcd0a7fdc6f1c37519885cada
|
tkcho
| 2023-11-20T01:39:07Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T01:38:46Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-bcc3a26c0a2db3ef36818c452ff369b9
|
tkcho
| 2023-11-20T01:39:50Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T01:39:28Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-c83ac5ee37c40f06e82876babb9990bd
|
tkcho
| 2023-11-20T07:36:02Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T07:35:22Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-3827675683dda6161381fb839d7beb7f
|
tkcho
| 2023-11-22T10:43:58Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-22T10:43:28Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-8a17683ab4ff582dec5613b6e8253701
|
tkcho
| 2023-11-23T07:34:21Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-23T07:33:53Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-b6195316813443c62fb987def6ba71e2
|
tkcho
| 2023-11-24T08:49:10Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T08:48:41Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-02427283f53eae73844ddf2e5f6e693d
|
tkcho
| 2023-11-24T08:55:01Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T08:54:35Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-b89976e011860f43a0fbb98fe03ebd40
|
tkcho
| 2023-11-24T12:24:23Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T12:23:41Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-2fa7dfbc037d639a25d910291d8b7193
|
tkcho
| 2023-11-24T17:49:32Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T17:49:00Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-40b5cd359b01673d9f645224529042f5
|
tkcho
| 2023-11-24T22:46:40Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T22:46:15Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-14d19f8512e5280bb15e1d4b602974b1
|
tkcho
| 2023-11-25T01:25:11Z | 4,423 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-25T01:24:41Z |
Entry not found
|
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] |
Fredithefish/ReasonixPajama-3B-HF
|
Fredithefish
| 2023-09-01T11:01:51Z | 4,422 | 3 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt_neox",
"text-generation",
"en",
"dataset:Fredithefish/Reasonix",
"license:apache-2.0",
"has_space",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-06-07T17:20:44Z |
---
license: apache-2.0
datasets:
- Fredithefish/Reasonix
language:
- en
library_name: transformers
inference: false
---
# ReasonixPajama
|
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] |
tkcho/commerce-clf-kr-sku-brand-a46916fa803bea3cb193ec0be93fefa6
|
tkcho
| 2023-11-12T15:22:19Z | 4,422 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-12T15:21:38Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-477161f0a79b9df1cc830f65cffa5cf5
|
tkcho
| 2023-11-15T21:14:36Z | 4,422 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-15T21:14:07Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-0008cc5ffdcc5add5f7af284fe5cfc31
|
tkcho
| 2023-11-16T03:40:21Z | 4,422 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-16T03:39:54Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-9e0ebb41239900e3410863fc11a63e64
|
tkcho
| 2023-11-27T15:05:56Z | 4,422 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T20:19:01Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-4e5f46373def269a8bcc39576895d713
|
tkcho
| 2023-11-20T01:35:38Z | 4,422 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T01:35:18Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-ec3298ccbbe83255ef244583f0b098f4
|
tkcho
| 2023-11-20T01:37:54Z | 4,422 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T01:37:35Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-dc00ec97868c956988fc6d87167a95ec
|
tkcho
| 2023-11-20T06:16:43Z | 4,422 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T06:16:11Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-57a559a6070aaf7ba418d13cf75c6496
|
tkcho
| 2023-11-20T15:42:25Z | 4,422 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T15:41:55Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-566c6c486c0c488420746ed4bd8f45c0
|
tkcho
| 2023-11-21T02:27:05Z | 4,422 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T02:26:27Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-1f388763dbddc9891892c0a6fef77e76
|
tkcho
| 2023-11-21T07:28:04Z | 4,422 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T07:27:38Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-f33e251827d4b4094475dc55e80b8738
|
tkcho
| 2023-11-23T04:35:00Z | 4,422 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-23T04:34:31Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-63d3038664aa455ba140ed70f26ada4c
|
tkcho
| 2023-11-24T02:28:02Z | 4,422 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T02:27:29Z |
Entry not found
|
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timm/mobilenetv3_small_050.lamb_in1k
|
timm
| 2023-04-27T22:49:29Z | 4,421 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2110.00476",
"arxiv:1905.02244",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2022-12-16T05:38:23Z |
---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for mobilenetv3_small_050.lamb_in1k
A MobileNet-v3 image classification model. Trained on ImageNet-1k in `timm` using recipe template described below.
Recipe details:
* A LAMB optimizer recipe that is similar to [ResNet Strikes Back](https://arxiv.org/abs/2110.00476) `A2` but 50% longer with EMA weight averaging, no CutMix
* RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging
* Step (exponential decay w/ staircase) LR schedule with warmup
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 1.6
- GMACs: 0.0
- Activations (M): 0.9
- Image size: 224 x 224
- **Papers:**
- Searching for MobileNetV3: https://arxiv.org/abs/1905.02244
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/huggingface/pytorch-image-models
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('mobilenetv3_small_050.lamb_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'mobilenetv3_small_050.lamb_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 16, 112, 112])
# torch.Size([1, 8, 56, 56])
# torch.Size([1, 16, 28, 28])
# torch.Size([1, 24, 14, 14])
# torch.Size([1, 288, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'mobilenetv3_small_050.lamb_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 288, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@inproceedings{howard2019searching,
title={Searching for mobilenetv3},
author={Howard, Andrew and Sandler, Mark and Chu, Grace and Chen, Liang-Chieh and Chen, Bo and Tan, Mingxing and Wang, Weijun and Zhu, Yukun and Pang, Ruoming and Vasudevan, Vijay and others},
booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
pages={1314--1324},
year={2019}
}
```
|
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] |
vicgalle/gpt2-alpaca-gpt4
|
vicgalle
| 2023-11-30T10:37:59Z | 4,421 | 14 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"safetensors",
"gpt2",
"text-generation",
"instruction-tuning",
"alpaca",
"gpt4",
"en",
"dataset:vicgalle/alpaca-gpt4",
"doi:10.57967/hf/1408",
"license:mit",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-04-08T20:05:16Z |
---
license: mit
datasets:
- vicgalle/alpaca-gpt4
language:
- en
tags:
- instruction-tuning
- alpaca
- gpt4
---
# GPT-2 finetuned with Alpaca-GPT4
### Examples
The following samples were generated with `{"temperature" : 0.7, "top_p" : 0.92, "top_k" : 0}`:
##### **Give three tips for a good meal**
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Give three tips for a good meal.
### Response:
1. **Serve fresh, locally grown produce:** Fresh produce is a great way to eat healthy and to enjoy a healthy meal. Try to choose a variety of fruits, vegetables, and whole grains, and experiment with the variety to find the perfect balance. Make sure to bring plenty of fresh fruits, vegetables, and whole grains throughout the meal.
2. **Strive for a quick and easy meal:** The key is to make a meal that is quick and easy to prepare. Take a break from the food, grab some snacks, and enjoy a delicious meal. Make sure to cook the ingredients beforehand, and avoid overcooking or filling the food up.
3. **Determine the time and place:** If you're eating a lot of food, it can be tempting to make a time and place for it. This will help you stay organized and get your ingredients in the right order. Make sure to set up a timer and avoid rushing to the grocery store. If possible, make sure to follow the recipe for your specific dish and avoid leaving the refrigerator unattended for too long.
```
##### **Write a poem about a delicious night**
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Write a poem about a delicious night.
### Response:
As the sky above me fluttered with the gentle breeze,
The moon was in its soft, gentle embrace,
The night was full of cheer and joy,
A perfect night's night.
The sun shone bright, the stars twinkled,
The colors were pure and true,
As if in the presence of a master.
But the night was not without its troubles.
A stormy night, it rained heavily,
A dark and stormy night, it raged with fire.
But one thing was certain, it was in the air,
The scent of the sweet scent of a sweet sweet night.
The night was filled with joy, with a sense of peace and satisfaction.
The stars twinkled, the moon was in its soft, gentle embrace,
The night was full of cheer and joy,
A perfect night's night.
The night was full of laughter, with the gentle breeze,
The colors were pure and true,
As if in the presence of a master.
But the night was full of worries, with the fierce tempest.
But the night was filled with hope, with a sense of hope that could not be ignored.
```
##### **Write a tweet describing your capabilities**
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
Write a tweet describing your capabilities.
### Response:
"Empowering your creativity, skills, and abilities through my technology-driven approach. My team of professionals will help you achieve your goals, stay ahead of the curve, and contribute to your success. #Diversity"
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_vicgalle__gpt2-alpaca-gpt4)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 24.98 |
| ARC (25-shot) | 22.61 |
| HellaSwag (10-shot) | 31.17 |
| MMLU (5-shot) | 25.76 |
| TruthfulQA (0-shot) | 38.04 |
| Winogrande (5-shot) | 52.17 |
| GSM8K (5-shot) | 0.3 |
| DROP (3-shot) | 4.83 |
|
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JosephusCheung/Qwen-LLaMAfied-7B-Chat
|
JosephusCheung
| 2023-10-22T18:39:39Z | 4,421 | 100 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"qwen",
"llama-2",
"en",
"zh",
"license:gpl-3.0",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-08-04T08:43:39Z |
---
language:
- en
- zh
tags:
- qwen
- llama
- llama-2
license: gpl-3.0
---
NEW VERSIONS: [https://huggingface.co/CausalLM/14B](https://huggingface.co/CausalLM/14B)
This is the LLaMAfied replica of [Qwen/Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat) (Original Version before 25.09.2023), recalibrated to fit the original LLaMA/LLaMA-2-like model structure.
You can use LlamaForCausalLM for model inference, which is the same as LLaMA/LLaMA-2 models (using GPT2Tokenizer converted from the original tiktoken, by [vonjack](https://huggingface.co/vonjack)).
The model has been edited to be white-labelled, meaning the model will no longer call itself a Qwen.
Up until now, the model has undergone numerical alignment of weights and preliminary reinforcement learning in order to align with the original model. Some errors and outdated knowledge have been addressed through model editing methods. This model remains completely equivalent to the original version, without having any dedicated supervised finetuning on downstream tasks or other extensive conversation datasets.
PROMPT FORMAT: [chatml](https://github.com/openai/openai-python/blob/main/chatml.md)
CURRENT MMLU: 53.48
CURRENT CEval (val): 54.13
```
MMLU - stem ACC: 46.40 Humanities ACC: 47.61 other ACC: 61.31 social ACC: 61.78 AVERAGE ACC:53.48
CEval (val) - STEM acc: 45.28 Social Science acc: 66.19 Humanities acc: 58.76 Other acc: 54.62 Hard acc:28.64 AVERAGE acc:54.13
```
Issue: Compared to the original Qwen-7B-Chat scoring 53.90 in MMLU and 54.18 in CEval (val), the our scores dropped slightly [-0.42 in MMLU, -0.05 in CEval (val)] due to insufficient realignment.
这是 [通义千问 Qwen/Qwen-7B-Chat](https://huggingface.co/Qwen/Qwen-7B-Chat) (在 25.09.2023 之前的原始版本) 的 LLaMA 化版本，经过重新校准以适应原始的类似 LLaMA/LLaMA-2 的模型结构。
您可以使用 LlamaCausalLM 进行模型推理，和 LLaMA/LLaMA-2 保持一致（使用由 [vonjack](https://huggingface.co/vonjack) 从原始 tiktoken 转换而来的 GPT2Tokenizer 分词器）。
模型已经被编辑实现白标化，不再自称通义千问。
到目前为止，该模型已经进行了权重的数值对齐和初步的强化学习，以与原始模型保持一致。 一些错误和过时的知识已通过模型编辑方法得到解决。 该模型与原始版本完全等效，尚未对下游任务或其他广泛的对话数据集进行任何专门的监督微调。
PROMPT 格式: [chatml](https://github.com/openai/openai-python/blob/main/chatml.md)
当前的 MMLU: 53.48
当前的 CEval (val): 54.13
```
MMLU - stem ACC: 46.40 Humanities ACC: 47.61 other ACC: 61.31 social ACC: 61.78 AVERAGE ACC:53.48
CEval (val) - STEM acc: 45.28 Social Science acc: 66.19 Humanities acc: 58.76 Other acc: 54.62 Hard acc:28.64 AVERAGE acc:54.13
```
问题：相比原本的 Qwen-7B-Chat 的 MMLU 分数 53.90 和 CEval (val) 分数 54.18，由于不够充分的重新对齐，分数都略有下降 [-0.42 in MMLU, -0.05 in CEval (val)]。
|
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] |
tkcho/commerce-clf-kr-sku-brand-5ea0829945d3650c142a762d2404cb5f
|
tkcho
| 2023-11-12T12:04:05Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-12T12:03:22Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-38540c7e422fa948a88ff0be31a2df83
|
tkcho
| 2023-11-14T22:46:17Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-14T22:45:48Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-d6f818942ce1f0d073691c0730621d18
|
tkcho
| 2023-11-15T19:30:53Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-15T19:30:21Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-18699b4241ac408cfa0827e00f6c1bfd
|
tkcho
| 2023-11-16T04:11:56Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-16T04:11:28Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-39301a8bd19be0d938b440315cb6c06c
|
tkcho
| 2023-11-17T06:46:58Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T06:46:33Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-b6d8ba395cdb57db392ef6874d10bab0
|
tkcho
| 2023-11-18T06:13:46Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T06:13:18Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-72e41cb47618c2830a86dbdb93ac9134
|
tkcho
| 2023-11-18T18:06:38Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T18:06:05Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-4f281febe0b8236745cfaa70154c7d9a
|
tkcho
| 2023-11-19T02:29:58Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-19T02:29:30Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-ef4355493aeb46d598dcdd7fa18840a3
|
tkcho
| 2023-11-20T01:09:58Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T01:09:38Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-dc26fe0a858790ad51b6996c08c191e5
|
tkcho
| 2023-11-20T08:32:55Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T08:32:21Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-f363288ea75adad41cfa9952871b0940
|
tkcho
| 2023-11-21T00:22:37Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T00:22:08Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-000af009f905e73eaa2353000c75cb4d
|
tkcho
| 2023-11-21T04:49:54Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T04:49:21Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-984e2ac06e903e043498d50e30dea850
|
tkcho
| 2023-11-22T07:53:25Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-22T07:52:53Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-0ec698ba8c3f573af7455910addd0844
|
tkcho
| 2023-11-23T17:47:20Z | 4,421 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-23T17:46:53Z |
Entry not found
|
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] |
ehartford/WizardLM-33B-V1.0-Uncensored
|
ehartford
| 2023-11-18T03:52:36Z | 4,420 | 50 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split",
"license:other",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-06-24T11:08:38Z |
---
license: other
datasets:
- ehartford/WizardLM_evol_instruct_V2_196k_unfiltered_merged_split
language:
- en
---
This is a retraining of https://huggingface.co/WizardLM/WizardLM-30B-V1.0 with a filtered dataset, intended to reduce refusals, avoidance, and bias.
Note that LLaMA itself has inherent ethical beliefs, so there's no such thing as a "truly uncensored" model. But this model will be more compliant than WizardLM/WizardLM-7B-V1.0.
Shout out to the open source AI/ML community, and everyone who helped me out.
Note: An uncensored model has no guardrails. You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car. Publishing anything this model generates is the same as publishing it yourself. You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it.
Like WizardLM/WizardLM-30B-V1.0, this model is trained with Vicuna-1.1 style prompts.
```
You are a helpful AI assistant.
USER: <prompt>
ASSISTANT:
```
Thank you [chirper.ai](https://chirper.ai) for sponsoring some of my compute!
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_ehartford__WizardLM-33B-V1.0-Uncensored)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 54.41 |
| ARC (25-shot) | 63.65 |
| HellaSwag (10-shot) | 83.84 |
| MMLU (5-shot) | 59.36 |
| TruthfulQA (0-shot) | 56.8 |
| Winogrande (5-shot) | 77.66 |
| GSM8K (5-shot) | 18.65 |
| DROP (3-shot) | 20.89 |
|
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CHIH-HUNG/llama-2-13b-dolphin_20w
|
CHIH-HUNG
| 2023-09-06T04:55:19Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:ehartford/dolphin",
"license:llama2",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-08-29T00:58:57Z |
---
license: llama2
datasets:
- ehartford/dolphin
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
在llama-2-13b上使用dolphin前20萬筆資料集進行訓練
# Fine-Tuning Information
- **GPU:** RTX4090 (single core / 24564MiB)
- **model:** meta-llama/Llama-2-13b-hf
- **dataset:** ehartford/dolphin (取前20w筆訓練集)
- **peft_type:** LoRA
- **lora_rank:** 8
- **lora_target:** q_proj, v_proj
- **per_device_train_batch_size:** 8
- **gradient_accumulation_steps:** 8
- **learning_rate :** 5e-5
- **epoch:** 1
- **precision:** bf16
- **quantization:** load_in_4bit
# Fine-Tuning Detail
- **train_loss:** 0.8354
- **train_runtime:** 28:42:18 (use deepspeed)
# Evaluation
- 評估結果來自**HuggingFaceH4/open_llm_leaderboard**
- 與Llama-2-13b和其他使用dolphin的模型比較4種Benchmark
- Benchmark包含**ARC**、**HellaSwag**、**MMLU**、**TruthfulQA**
- **注意**：ehartford/dolphin-llama-13b使用的是llama-1
| Model |Average| ARC |HellaSwag| MMLU | TruthfulQA |
|----------------------------------|-------|-------|---------|-------|------------|
|meta-llama/Llama-2-13b-hf | 56.9 | 58.11 | 80.97 | 54.34 | 34.17 |
|meta-llama/Llama-2-13b-chat-hf | 59.93 | 59.04 | 81.94 | 54.64 | 44.12 |
|ehartford/dolphin-llama-13b | 59.26 | 55.55 | 77.11 | 52.16 | 52.23 |
|CHIH-HUNG/llama-2-13b-dolphin_5w | 61 | 60.67 | 82.69 | 56.23 | 44.41 |
|CHIH-HUNG/llama-2-13b-dolphin_20w | 60.17 | 59.56 | 82.55 | 55.89 | 42.67 |
# How to convert dataset to json
- 在**load_dataset**中輸入資料集名稱，並且在**take**中輸入要取前幾筆資料
- 觀察該資料集的欄位名稱，填入**example**欄位中(例如instruction、input、output)
- 最後指定json檔儲存位置 (**json_filename**)
```py
import json
from datasets import load_dataset
# 讀取數據集，take可以取得該數據集前n筆資料
dataset = load_dataset("ehartford/dolphin", split="train", streaming=True).take(200000)
# 提取所需欄位並建立新的字典列表
extracted_data = []
for example in dataset:
extracted_example = {
### dolphin
"instruction": example["instruction"],
"input": example["input"],
"output": example["output"]
}
extracted_data.append(extracted_example)
# 指定 JSON 文件名稱
json_filename = "dolphin.json"
# 寫入 JSON 文件
with open(json_filename, "w") as json_file:
json.dump(extracted_data, json_file, indent=4)
print(f"數據已提取並保存為 {json_filename}")
```
|
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] |
tkcho/commerce-clf-kr-sku-brand-758525bfedd14b460e725b08ebde4d65
|
tkcho
| 2023-11-12T13:23:16Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-12T13:22:33Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-d60b0d66177ba3bafaf6cba3731e73d2
|
tkcho
| 2023-11-15T01:18:57Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-15T01:18:27Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-a8ecbabae151abacba7dbde04f761c37
|
tkcho
| 2023-11-16T02:06:15Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-16T02:05:45Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-847c69e012af7536e76de97ca5b19c5e
|
tkcho
| 2023-11-17T19:36:03Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T19:35:34Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-0f29b1df4cbcd53f1e782ab554286401
|
tkcho
| 2023-11-18T00:11:29Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T00:10:59Z |
Entry not found
|
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0.31015971302986145,
-1.0262157917022705
] |
tkcho/commerce-clf-kr-sku-brand-a6532ebe20d6405ed4bd43b83873d6ca
|
tkcho
| 2023-11-18T19:11:20Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T19:10:51Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-d5df30994c830574bad73bd29af40d51
|
tkcho
| 2023-11-22T03:40:21Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T01:26:32Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-ab0f7d119d14ec8d1c2073eb9acac1d4
|
tkcho
| 2023-11-20T05:02:41Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T05:02:11Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-4b90b64207a3bca1eca1409a3bc43823
|
tkcho
| 2023-11-20T14:50:31Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T14:50:02Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-6255824c55ae1d92f858e1dcde5b75da
|
tkcho
| 2023-11-21T03:38:34Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T03:38:06Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-62a4cbda25df61729ba38d6c4ac46d20
|
tkcho
| 2023-11-24T05:20:39Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T05:19:53Z |
Entry not found
|
[
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] |
tkcho/commerce-clf-kr-sku-brand-467d614a68364699134716af151166f2
|
tkcho
| 2023-11-24T07:55:25Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T07:54:55Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-49c134050c2590809636cc44dd820cf8
|
tkcho
| 2023-11-24T18:07:06Z | 4,420 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T18:06:37Z |
Entry not found
|
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TheBloke/medalpaca-13B-GPTQ
|
TheBloke
| 2023-09-27T12:43:29Z | 4,419 | 28 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"medical",
"en",
"arxiv:2303.14070",
"base_model:medalpaca/medalpaca-13b",
"license:other",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-04-22T20:46:56Z |
---
language:
- en
license: other
library_name: transformers
tags:
- medical
model_name: Medalpaca 13B
base_model: medalpaca/medalpaca-13b
inference: false
model_creator: medalpaca
model_type: llama
pipeline_tag: text-generation
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Medalpaca 13B - GPTQ
- Model creator: [medalpaca](https://huggingface.co/medalpaca)
- Original model: [Medalpaca 13B](https://huggingface.co/medalpaca/medalpaca-13b)
<!-- description start -->
## Description
This repo contains GPTQ model files for [medalpaca's Medalpaca 13B](https://huggingface.co/medalpaca/medalpaca-13b).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/medalpaca-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/medalpaca-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/medalpaca-13B-GGUF)
* [medalpaca's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/medalpaca/medalpaca-13b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
All recent GPTQ files are made with AutoGPTQ, and all files in non-main branches are made with AutoGPTQ. Files in the `main` branch which were uploaded before August 2023 were made with GPTQ-for-LLaMa.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/medalpaca-13B-GPTQ/tree/main) | 4 | 128 | No | 0.01 | [c4](https://huggingface.co/datasets/allenai/c4) | 2048 | 7.26 GB | Yes | 4-bit, without Act Order and group size 128g. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download from branches
- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/medalpaca-13B-GPTQ:main`
- With Git, you can clone a branch with:
```
git clone --single-branch --branch main https://huggingface.co/TheBloke/medalpaca-13B-GPTQ
```
- In Python Transformers code, the branch is the `revision` parameter; see below.
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/medalpaca-13B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/medalpaca-13B-GPTQ:main`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `medalpaca-13B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.32.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers>=4.32.0 optimum>=1.12.0
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip3 install .
```
### For CodeLlama models only: you must use Transformers 4.33.0 or later.
If 4.33.0 is not yet released when you read this, you will need to install Transformers from source:
```shell
pip3 uninstall -y transformers
pip3 install git+https://github.com/huggingface/transformers.git
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/medalpaca-13B-GPTQ"
# To use a different branch, change revision
# For example: revision="main"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ, both via Transformers and using AutoGPTQ directly. They should also work with [Occ4m's GPTQ-for-LLaMa fork](https://github.com/0cc4m/KoboldAI).
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
[Huggingface Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) is compatible with all GPTQ models.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: medalpaca's Medalpaca 13B
# MedAlpaca 13b
## Table of Contents
[Model Description](#model-description)
- [Architecture](#architecture)
- [Training Data](#trainig-data)
[Model Usage](#model-usage)
[Limitations](#limitations)
## Model Description
### Architecture
`medalpaca-13b` is a large language model specifically fine-tuned for medical domain tasks.
It is based on LLaMA (Large Language Model Meta AI) and contains 13 billion parameters.
The primary goal of this model is to improve question-answering and medical dialogue tasks.
### Training Data
The training data for this project was sourced from various resources.
Firstly, we used Anki flashcards to automatically generate questions,
from the front of the cards and anwers from the back of the card.
Secondly, we generated medical question-answer pairs from [Wikidoc](https://www.wikidoc.org/index.php/Main_Page).
We extracted paragraphs with relevant headings, and used Chat-GPT 3.5
to generate questions from the headings and using the corresponding paragraphs
as answers. This dataset is still under development and we believe
that approximately 70% of these question answer pairs are factual correct.
Thirdly, we used StackExchange to extract question-answer pairs, taking the
top-rated question from five categories: Academia, Bioinformatics, Biology,
Fitness, and Health. Additionally, we used a dataset from [ChatDoctor](https://arxiv.org/abs/2303.14070)
consisting of 200,000 question-answer pairs, available at https://github.com/Kent0n-Li/ChatDoctor.
| Source | n items |
|------------------------------|--------|
| ChatDoc large | 200000 |
| wikidoc | 67704 |
| Stackexchange academia | 40865 |
| Anki flashcards | 33955 |
| Stackexchange biology | 27887 |
| Stackexchange fitness | 9833 |
| Stackexchange health | 7721 |
| Wikidoc patient information | 5942 |
| Stackexchange bioinformatics | 5407 |
## Model Usage
To evaluate the performance of the model on a specific dataset, you can use the Hugging Face Transformers library's built-in evaluation scripts. Please refer to the evaluation guide for more information.
Inference
You can use the model for inference tasks like question-answering and medical dialogues using the Hugging Face Transformers library. Here's an example of how to use the model for a question-answering task:
```python
from transformers import pipeline
pl = pipeline("text-generation", model="medalpaca/medalpaca-13b", tokenizer="medalpaca/medalpaca-13b")
question = "What are the symptoms of diabetes?"
context = "Diabetes is a metabolic disease that causes high blood sugar. The symptoms include increased thirst, frequent urination, and unexplained weight loss."
answer = pl(f"Context: {context}\n\nQuestion: {question}\n\nAnswer: ")
print(answer)
```
## Limitations
The model may not perform effectively outside the scope of the medical domain.
The training data primarily targets the knowledge level of medical students,
which may result in limitations when addressing the needs of board-certified physicians.
The model has not been tested in real-world applications, so its efficacy and accuracy are currently unknown.
It should never be used as a substitute for a doctor's opinion and must be treated as a research tool only.
|
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] |
Writer/InstructPalmyra-20b
|
Writer
| 2023-08-31T17:01:39Z | 4,419 | 37 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"InstructGPT",
"hf",
"palmyra",
"en",
"dataset:Writer/palmyra-data-index",
"license:apache-2.0",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-04-28T15:52:03Z |
---
license: apache-2.0
language:
- en
tags:
- InstructGPT
- hf
- palmyra
datasets:
- Writer/palmyra-data-index
---
# InstructPalmyra-20b
- **Developed by:** [https://writer.com/](https://writer.com/);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English;
- **License:** Apache 2.0;
- **Finetuned from model:** [Palmyra-20B](https://huggingface.co/Writer/palmyra-large).
<style>
img {
display: inline;
}
</style>
## Model Description
Introducing InstructPalmyra-20b, a state-of-the-art instruction-following 20b language model designed to deliver exceptional performance and versatility. Derived from the foundational architecture of [Palmyra-20b](https://huggingface.co/Writer/palmyra-large), InstructPalmyra-20b is specifically tailored to address the growing demand for advanced natural language processing and comprehension capabilities.
The InstructPalmyra-20b model is meticulously trained on an extensive dataset of approximately 70,000 instruction-response records. These records are generated by our dedicated Writer Linguist team, who possess considerable expertise in language modeling and fine-tuning techniques. By leveraging their skills and knowledge, the InstructPalmyra-20b model is primed to offer unparalleled proficiency in understanding and executing language-based instructions.
One of the key differentiators of InstructPalmyra-20b lies in its ability to process complex instructions and generate accurate, contextually appropriate responses. This makes it an ideal choice for a wide range of applications, including virtual assistants, customer support, content generation, and more. Additionally, the model's comprehensive training enables it to adapt and perform well under varying conditions and contexts, further expanding its potential use cases.
## Usage :
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "Writer/InstructPalmyra-20b"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
torch_dtype=torch.float16
)
instruction = "Describe a futuristic device that revolutionizes space travel."
PROMPT_DICT = {
"prompt_input": (
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request\n\n"
"### Instruction:\n{instruction}\n\n### Input:\n{input}\n\n### Response:"
),
"prompt_no_input": (
"Below is an instruction that describes a task. "
"Write a response that appropriately completes the request.\n\n"
"### Instruction:\n{instruction}\n\n### Response:"
),
}
text = (
PROMPT_DICT["prompt_no_input"].format(instruction=instruction)
if not input
else PROMPT_DICT["prompt_input"].format(instruction=instruction, input=input)
)
model_inputs = tokenizer(text, return_tensors="pt").to("cuda")
output_ids = model.generate(
**model_inputs,
max_length=256,
)
output_text = tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0]
clean_output = output_text.split("### Response:")[1].strip()
print(clean_output)
```
It can also be used with text-generation-inference
```sh
model=Writer/InstructPalmyra-20b
volume=$PWD/data
docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference --model-id $model
```
### Limitations and Biases
InstructPalmyra's core functionality is to take a string of text and predict the next token. While language models are widely used for other tasks, there are many unknowns in this work. When prompting InstructPalmyra, keep in mind that the next statistically likely token is not always the token that produces the most "accurate" text. Never rely on InstructPalmyra to produce factually correct results.
InstructPalmyra was trained on Writer’s custom data. As with all language models, it is difficult to predict how InstructPalmyra will respond to specific prompts, and offensive content may appear unexpectedly. We recommend that the outputs be curated or filtered by humans before they are released, both to censor undesirable content and to improve the quality of the results.
## Uses
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
InstructPalmyra-20b is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of InstructPalmyra-20b to develop guardrails and to take appropriate precautions for any production use.
## Citation and Related Information
To cite this model:
```
@misc{InstructPalmyra,
author = {Writer Engineering team},
title = {{InstructPalmyra-20b : Instruct tuned Palmyra-Large model}},
howpublished = {\url{https://dev.writer.com}},
year = 2023,
month = Augest
}
```
[](#model-architecture)|[](#model-architecture)|[](#datasets)|
|
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tkcho/commerce-clf-kr-sku-brand-8e5dfbba30fb3b6aba4e8f2d0b04c6fd
|
tkcho
| 2023-11-15T17:50:40Z | 4,419 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-15T17:50:12Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-d6f591a6f867c74e4bc7d9360a1c453a
|
tkcho
| 2023-11-16T20:27:40Z | 4,419 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-16T20:27:09Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-81f152312ae7420a40f8de2bf334e11e
|
tkcho
| 2023-11-16T22:53:17Z | 4,419 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-16T22:52:49Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-136d24fff330601d153d13700b3e79e7
|
tkcho
| 2023-11-17T00:52:58Z | 4,419 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T00:52:28Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-e2f839f0aedd37de4e2020d27046b41e
|
tkcho
| 2023-11-18T22:56:12Z | 4,419 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T22:55:47Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-d3a055ea7480b140c2a38b603f9f777a
|
tkcho
| 2023-11-20T01:37:22Z | 4,419 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T01:37:01Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-e3185031cd1b46dd36e687056b024fee
|
tkcho
| 2023-11-20T10:45:22Z | 4,419 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T10:44:42Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-1a0a2f82d5c08ec3d9532887b922c9e9
|
tkcho
| 2023-11-20T15:59:05Z | 4,419 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T15:58:39Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-ce51fc3c4546798d0b361479a2bb914e
|
tkcho
| 2023-11-20T22:35:57Z | 4,419 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T22:35:25Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-3a95ef7b24ff269b10846f05886d1f56
|
tkcho
| 2023-11-21T12:25:04Z | 4,419 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-21T12:24:38Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-836da7312e502f80a34c2ba5782d5f60
|
tkcho
| 2023-11-22T19:22:07Z | 4,419 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-22T19:21:35Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-fddd80fe82c43d1c26fa0774e92ee218
|
tkcho
| 2023-11-23T16:09:02Z | 4,419 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-23T16:08:32Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-5ae4f52b8cf733b799b201348c85dba9
|
tkcho
| 2023-11-24T19:18:03Z | 4,419 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-24T19:17:30Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-29ba2140c485474adb94bd88d70fcca5
|
tkcho
| 2023-11-14T21:23:30Z | 4,418 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-14T21:23:00Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-389bd5bb28c20881b27ff17a94157680
|
tkcho
| 2023-11-17T16:41:42Z | 4,418 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T16:41:14Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-520fd3279a4d77669047165b97e22ed5
|
tkcho
| 2023-11-17T17:25:23Z | 4,418 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-17T17:24:42Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-70221e1fd04dc1c093fd063292c6bc53
|
tkcho
| 2023-11-18T04:34:39Z | 4,418 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T04:34:09Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-746e873a27eaac260289879408a370b2
|
tkcho
| 2023-11-18T09:59:52Z | 4,418 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T09:59:26Z |
Entry not found
|
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] |
tkcho/commerce-clf-kr-sku-brand-95bf658c6ef90e741b0b5a889c369e87
|
tkcho
| 2023-11-18T12:31:17Z | 4,418 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-18T12:30:51Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-e9641a6dbeec4f7bf0efadc87e6fcbd6
|
tkcho
| 2023-11-28T14:18:34Z | 4,418 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T08:15:34Z |
Entry not found
|
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tkcho/commerce-clf-kr-sku-brand-c6a6f3b5adfd358b7ff5e1ec7946f525
|
tkcho
| 2023-11-20T10:56:39Z | 4,418 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-20T10:56:15Z |
Entry not found
|
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