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seatond/NEW_badprompt_rank16_lr1.5e-05_target8_epochs1_laplha32_wuratio0.125_wdecay0.25
|
seatond
| 2023-12-13T15:27:40Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:TheBloke/Mistral-7B-v0.1-GPTQ",
"region:us"
] | null | 2023-12-13T15:11:32Z |
---
library_name: peft
base_model: TheBloke/Mistral-7B-v0.1-GPTQ
---
# Model Card for Model ID
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## Model Details
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### Framework versions
- PEFT 0.7.1.dev0
|
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situty/situ
|
situty
| 2023-12-13T15:12:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T15:12:06Z |
Entry not found
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] |
Realgon/N_distilbert_agnews_padding70model
|
Realgon
| 2023-12-13T16:48:19Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T15:13:54Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_distilbert_agnews_padding70model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.945
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# N_distilbert_agnews_padding70model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6396
- Accuracy: 0.945
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1861 | 1.0 | 7500 | 0.1881 | 0.9424 |
| 0.1359 | 2.0 | 15000 | 0.1967 | 0.9451 |
| 0.1194 | 3.0 | 22500 | 0.2221 | 0.9429 |
| 0.0881 | 4.0 | 30000 | 0.2671 | 0.9451 |
| 0.0577 | 5.0 | 37500 | 0.3216 | 0.9367 |
| 0.0503 | 6.0 | 45000 | 0.3547 | 0.9420 |
| 0.0402 | 7.0 | 52500 | 0.4013 | 0.9433 |
| 0.0279 | 8.0 | 60000 | 0.4334 | 0.9438 |
| 0.0169 | 9.0 | 67500 | 0.5243 | 0.9371 |
| 0.0104 | 10.0 | 75000 | 0.4979 | 0.9425 |
| 0.0149 | 11.0 | 82500 | 0.5179 | 0.9436 |
| 0.0129 | 12.0 | 90000 | 0.5020 | 0.9413 |
| 0.0054 | 13.0 | 97500 | 0.5674 | 0.9420 |
| 0.01 | 14.0 | 105000 | 0.5720 | 0.9433 |
| 0.0087 | 15.0 | 112500 | 0.5746 | 0.9409 |
| 0.0022 | 16.0 | 120000 | 0.5759 | 0.9436 |
| 0.0012 | 17.0 | 127500 | 0.5933 | 0.9436 |
| 0.0005 | 18.0 | 135000 | 0.6308 | 0.9429 |
| 0.0021 | 19.0 | 142500 | 0.6259 | 0.9445 |
| 0.0017 | 20.0 | 150000 | 0.6396 | 0.945 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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simoHamlili/results
|
simoHamlili
| 2023-12-13T15:14:59Z | 0 | 0 | null |
[
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2023-12-13T15:14:08Z |
---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- generated_from_trainer
model-index:
- name: chatbot413
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# chatbot413
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.09
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.13.3
|
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sid220/asl-now-fingerspelling
|
sid220
| 2023-12-13T15:14:41Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-13T15:14:41Z |
---
license: mit
---
|
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FranzderPapst/MA
|
FranzderPapst
| 2023-12-13T15:19:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T15:19:06Z |
Entry not found
|
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ADISH007/Aws_donut_10k_incremental_1_Epoch_11
|
ADISH007
| 2023-12-13T15:20:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | null | 2023-12-13T15:20:35Z |
Entry not found
|
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] |
fayegairbus/ukraine-invasion_propaganda-detection-EN
|
fayegairbus
| 2023-12-13T15:31:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T15:24:04Z |
Entry not found
|
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] |
Tachi67/CoderFlowModule
|
Tachi67
| 2023-12-15T12:46:36Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-13T15:24:08Z |
---
license: mit
---
|
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] |
Pray123/BERTlm
|
Pray123
| 2023-12-13T15:24:31Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T15:24:31Z |
---
license: apache-2.0
---
|
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Janhaci/bert-based
|
Janhaci
| 2023-12-13T15:26:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T15:26:27Z |
Entry not found
|
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] |
tanooki426/OliviaNewtonJohn
|
tanooki426
| 2023-12-13T15:29:52Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T15:27:07Z |
---
license: openrail
---
|
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sandorscog/judicial_brazil
|
sandorscog
| 2023-12-14T02:16:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T15:28:25Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: judicial_brazil
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# judicial_brazil
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3551
- Accuracy: 0.8564
- Precision: 0.8611
- Recall: 0.8564
- F1: 0.8529
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| No log | 1.0 | 405 | 0.4573 | 0.8267 | 0.8318 | 0.8267 | 0.8230 |
| 0.7643 | 2.0 | 810 | 0.3391 | 0.8540 | 0.8612 | 0.8540 | 0.8481 |
| 0.3575 | 3.0 | 1215 | 0.3551 | 0.8564 | 0.8611 | 0.8564 | 0.8529 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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seatond/NEW_REMn_rank16_lr2e-05_target8_epochs2_laplha32_wuratio0.125_wdecay0.25
|
seatond
| 2023-12-13T15:44:42Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:TheBloke/Mistral-7B-v0.1-GPTQ",
"region:us"
] | null | 2023-12-13T15:30:28Z |
---
library_name: peft
base_model: TheBloke/Mistral-7B-v0.1-GPTQ
---
# Model Card for Model ID
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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### Framework versions
- PEFT 0.7.1.dev0
|
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lixtic/rare-puppers
|
lixtic
| 2023-12-13T15:31:19Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T15:31:13Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9850746393203735
---
# rare-puppers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Cat
#### Dog
#### Horse
#### lion
|
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jitu028/jitu028-ai-lab
|
jitu028
| 2023-12-13T15:33:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T15:31:44Z |
Entry not found
|
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] |
askomorokhov/hai-OpenOrca-Mistral-7b-v0.8-merged
|
askomorokhov
| 2023-12-13T15:48:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T15:36:48Z |
Entry not found
|
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] |
hkivancoral/smids_3x_beit_base_sgd_001_fold5
|
hkivancoral
| 2023-12-14T01:40:55Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T15:39:24Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_sgd_001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8783333333333333
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_3x_beit_base_sgd_001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3105
- Accuracy: 0.8783
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8516 | 1.0 | 225 | 0.8297 | 0.6267 |
| 0.6679 | 2.0 | 450 | 0.6103 | 0.7567 |
| 0.57 | 3.0 | 675 | 0.5223 | 0.7883 |
| 0.4959 | 4.0 | 900 | 0.4753 | 0.8083 |
| 0.4424 | 5.0 | 1125 | 0.4319 | 0.8233 |
| 0.4261 | 6.0 | 1350 | 0.4129 | 0.8283 |
| 0.4396 | 7.0 | 1575 | 0.4075 | 0.8167 |
| 0.4595 | 8.0 | 1800 | 0.3942 | 0.8267 |
| 0.4172 | 9.0 | 2025 | 0.3692 | 0.8367 |
| 0.3688 | 10.0 | 2250 | 0.3605 | 0.8583 |
| 0.4132 | 11.0 | 2475 | 0.3610 | 0.8417 |
| 0.369 | 12.0 | 2700 | 0.3465 | 0.8567 |
| 0.3672 | 13.0 | 2925 | 0.3443 | 0.8517 |
| 0.3409 | 14.0 | 3150 | 0.3437 | 0.855 |
| 0.2695 | 15.0 | 3375 | 0.3370 | 0.8567 |
| 0.311 | 16.0 | 3600 | 0.3373 | 0.8533 |
| 0.3177 | 17.0 | 3825 | 0.3325 | 0.8567 |
| 0.3059 | 18.0 | 4050 | 0.3310 | 0.8567 |
| 0.3295 | 19.0 | 4275 | 0.3271 | 0.8583 |
| 0.3201 | 20.0 | 4500 | 0.3301 | 0.8667 |
| 0.2645 | 21.0 | 4725 | 0.3242 | 0.8683 |
| 0.2497 | 22.0 | 4950 | 0.3240 | 0.8633 |
| 0.2626 | 23.0 | 5175 | 0.3196 | 0.8617 |
| 0.267 | 24.0 | 5400 | 0.3185 | 0.8733 |
| 0.2637 | 25.0 | 5625 | 0.3155 | 0.8733 |
| 0.3416 | 26.0 | 5850 | 0.3155 | 0.8783 |
| 0.3255 | 27.0 | 6075 | 0.3159 | 0.8767 |
| 0.3021 | 28.0 | 6300 | 0.3189 | 0.875 |
| 0.2292 | 29.0 | 6525 | 0.3137 | 0.8783 |
| 0.2207 | 30.0 | 6750 | 0.3185 | 0.8733 |
| 0.2158 | 31.0 | 6975 | 0.3173 | 0.8683 |
| 0.2149 | 32.0 | 7200 | 0.3154 | 0.87 |
| 0.248 | 33.0 | 7425 | 0.3134 | 0.8767 |
| 0.2339 | 34.0 | 7650 | 0.3133 | 0.875 |
| 0.2585 | 35.0 | 7875 | 0.3147 | 0.8767 |
| 0.2565 | 36.0 | 8100 | 0.3120 | 0.875 |
| 0.269 | 37.0 | 8325 | 0.3111 | 0.8783 |
| 0.2546 | 38.0 | 8550 | 0.3139 | 0.8733 |
| 0.2114 | 39.0 | 8775 | 0.3110 | 0.8767 |
| 0.2032 | 40.0 | 9000 | 0.3108 | 0.8767 |
| 0.2376 | 41.0 | 9225 | 0.3108 | 0.8783 |
| 0.2558 | 42.0 | 9450 | 0.3092 | 0.8767 |
| 0.2753 | 43.0 | 9675 | 0.3113 | 0.875 |
| 0.2795 | 44.0 | 9900 | 0.3109 | 0.8767 |
| 0.2412 | 45.0 | 10125 | 0.3113 | 0.8783 |
| 0.2003 | 46.0 | 10350 | 0.3105 | 0.88 |
| 0.2528 | 47.0 | 10575 | 0.3109 | 0.88 |
| 0.2265 | 48.0 | 10800 | 0.3109 | 0.8783 |
| 0.2494 | 49.0 | 11025 | 0.3106 | 0.8783 |
| 0.2763 | 50.0 | 11250 | 0.3105 | 0.8783 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
Realgon/N_bert_imdb_padding40model
|
Realgon
| 2023-12-13T18:05:23Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:bert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T15:41:17Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: N_bert_imdb_padding40model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.939
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# N_bert_imdb_padding40model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6742
- Accuracy: 0.939
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2234 | 1.0 | 1563 | 0.2483 | 0.9251 |
| 0.1543 | 2.0 | 3126 | 0.2148 | 0.9323 |
| 0.0957 | 3.0 | 4689 | 0.2969 | 0.9329 |
| 0.0674 | 4.0 | 6252 | 0.3085 | 0.9369 |
| 0.035 | 5.0 | 7815 | 0.3765 | 0.9367 |
| 0.0398 | 6.0 | 9378 | 0.4149 | 0.9368 |
| 0.0215 | 7.0 | 10941 | 0.4424 | 0.9376 |
| 0.0162 | 8.0 | 12504 | 0.4885 | 0.9352 |
| 0.0113 | 9.0 | 14067 | 0.4668 | 0.935 |
| 0.0168 | 10.0 | 15630 | 0.5267 | 0.9367 |
| 0.0077 | 11.0 | 17193 | 0.5049 | 0.9378 |
| 0.0082 | 12.0 | 18756 | 0.5595 | 0.9374 |
| 0.0055 | 13.0 | 20319 | 0.5650 | 0.9341 |
| 0.0035 | 14.0 | 21882 | 0.6518 | 0.9356 |
| 0.0017 | 15.0 | 23445 | 0.6662 | 0.9385 |
| 0.0036 | 16.0 | 25008 | 0.6536 | 0.9369 |
| 0.0 | 17.0 | 26571 | 0.7483 | 0.9354 |
| 0.0003 | 18.0 | 28134 | 0.7027 | 0.9368 |
| 0.0034 | 19.0 | 29697 | 0.6818 | 0.9384 |
| 0.0008 | 20.0 | 31260 | 0.6742 | 0.939 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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] |
hkivancoral/smids_3x_beit_base_rms_0001_fold5
|
hkivancoral
| 2023-12-13T16:30:22Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T15:43:03Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_rms_0001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8133333333333334
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_3x_beit_base_rms_0001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9458
- Accuracy: 0.8133
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.8091 | 1.0 | 225 | 0.7888 | 0.5967 |
| 0.6825 | 2.0 | 450 | 0.6663 | 0.7 |
| 0.6331 | 3.0 | 675 | 0.6179 | 0.7233 |
| 0.545 | 4.0 | 900 | 0.5461 | 0.7667 |
| 0.4511 | 5.0 | 1125 | 0.5082 | 0.785 |
| 0.5168 | 6.0 | 1350 | 0.4960 | 0.7867 |
| 0.498 | 7.0 | 1575 | 0.4691 | 0.8133 |
| 0.4445 | 8.0 | 1800 | 0.5042 | 0.7883 |
| 0.3476 | 9.0 | 2025 | 0.5424 | 0.7883 |
| 0.3599 | 10.0 | 2250 | 0.4586 | 0.8183 |
| 0.2707 | 11.0 | 2475 | 0.5539 | 0.8133 |
| 0.3169 | 12.0 | 2700 | 0.5413 | 0.805 |
| 0.2566 | 13.0 | 2925 | 0.6318 | 0.7933 |
| 0.1731 | 14.0 | 3150 | 0.6551 | 0.805 |
| 0.1033 | 15.0 | 3375 | 0.7315 | 0.7967 |
| 0.1418 | 16.0 | 3600 | 0.6489 | 0.8217 |
| 0.1165 | 17.0 | 3825 | 0.8690 | 0.815 |
| 0.1219 | 18.0 | 4050 | 0.8038 | 0.7883 |
| 0.1084 | 19.0 | 4275 | 0.9593 | 0.795 |
| 0.0893 | 20.0 | 4500 | 0.9469 | 0.8 |
| 0.0691 | 21.0 | 4725 | 1.0155 | 0.8183 |
| 0.155 | 22.0 | 4950 | 1.0808 | 0.8033 |
| 0.068 | 23.0 | 5175 | 1.2932 | 0.8067 |
| 0.0871 | 24.0 | 5400 | 1.0549 | 0.7933 |
| 0.0614 | 25.0 | 5625 | 1.2073 | 0.8183 |
| 0.0426 | 26.0 | 5850 | 1.1147 | 0.8167 |
| 0.0501 | 27.0 | 6075 | 1.1794 | 0.8067 |
| 0.0496 | 28.0 | 6300 | 1.2384 | 0.8033 |
| 0.0951 | 29.0 | 6525 | 1.1319 | 0.795 |
| 0.0433 | 30.0 | 6750 | 1.1451 | 0.8217 |
| 0.0293 | 31.0 | 6975 | 1.3635 | 0.815 |
| 0.0095 | 32.0 | 7200 | 1.4313 | 0.8067 |
| 0.0383 | 33.0 | 7425 | 1.2822 | 0.8217 |
| 0.0173 | 34.0 | 7650 | 1.4012 | 0.8217 |
| 0.0213 | 35.0 | 7875 | 1.5178 | 0.8117 |
| 0.0022 | 36.0 | 8100 | 1.6408 | 0.8283 |
| 0.0311 | 37.0 | 8325 | 1.7017 | 0.8117 |
| 0.0285 | 38.0 | 8550 | 1.5903 | 0.8183 |
| 0.0027 | 39.0 | 8775 | 1.6460 | 0.815 |
| 0.0036 | 40.0 | 9000 | 1.6806 | 0.7983 |
| 0.0136 | 41.0 | 9225 | 1.5975 | 0.8067 |
| 0.0063 | 42.0 | 9450 | 1.8013 | 0.8217 |
| 0.0155 | 43.0 | 9675 | 1.6944 | 0.8083 |
| 0.0003 | 44.0 | 9900 | 1.5360 | 0.8283 |
| 0.0001 | 45.0 | 10125 | 1.7589 | 0.8283 |
| 0.0203 | 46.0 | 10350 | 1.7247 | 0.8267 |
| 0.0004 | 47.0 | 10575 | 1.7343 | 0.8217 |
| 0.0069 | 48.0 | 10800 | 1.8685 | 0.8183 |
| 0.0001 | 49.0 | 11025 | 1.9172 | 0.8133 |
| 0.0 | 50.0 | 11250 | 1.9458 | 0.8133 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
miweru/ochat3-5_schwurpus
|
miweru
| 2023-12-16T10:16:17Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openchat/openchat_3.5",
"region:us"
] | null | 2023-12-13T15:43:25Z |
---
library_name: peft
base_model: openchat/openchat_3.5
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct Use
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.7.0
|
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] |
tanooki426/VanessaHudgens
|
tanooki426
| 2023-12-13T15:46:37Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T15:44:13Z |
---
license: openrail
---
|
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shidowake/stablelm-zephyr-3b-GGUF
|
shidowake
| 2023-12-13T15:54:56Z | 0 | 0 | null |
[
"gguf",
"region:us"
] | null | 2023-12-13T15:48:03Z |
Entry not found
|
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] |
suncy13/graysacleAugmentedFoot
|
suncy13
| 2023-12-17T17:14:43Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"dinov2",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T15:48:28Z |
Entry not found
|
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] |
sandorscog/judicial_swiss
|
sandorscog
| 2023-12-13T23:20:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T15:52:55Z |
Entry not found
|
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] |
sanghyo/FinalAssginment_model1
|
sanghyo
| 2023-12-14T14:35:52Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-13T15:53:17Z |
Entry not found
|
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] |
fayegairbus/ukraine-invasion_propaganda-detection-FR
|
fayegairbus
| 2023-12-13T16:13:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"camembert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T15:55:22Z |
Entry not found
|
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hamxea/Mistral-7B-v0.1-activity-fine-tuned-adapters-v4
|
hamxea
| 2023-12-13T15:59:31Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2023-12-13T15:58:15Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# Model Card for Model ID
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## Model Details
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### Framework versions
- PEFT 0.7.2.dev0
|
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] |
tanooki426/JeffConaway
|
tanooki426
| 2023-12-13T16:01:43Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T15:59:11Z |
---
license: openrail
---
|
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] |
sh-zheng/pegasus-samsum
|
sh-zheng
| 2023-12-14T00:42:12Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"base_model:google/pegasus-cnn_dailymail",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-12-13T16:00:14Z |
---
base_model: google/pegasus-cnn_dailymail
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4861
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6566 | 0.54 | 500 | 1.4861 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Hasnain12/CostAccounting
|
Hasnain12
| 2023-12-13T16:01:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:01:25Z |
Entry not found
|
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sofiapecora/wav2vec2-large-robust-12-ft-emotion-msp-dim-finetuned-gtzan
|
sofiapecora
| 2023-12-13T19:04:01Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-12-13T16:02:26Z |
Entry not found
|
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] |
AleRams/app_prova
|
AleRams
| 2023-12-14T17:50:19Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T16:04:09Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: app_prova
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# app_prova
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.9808
- eval_precision: 0.3115
- eval_recall: 0.3273
- eval_f1: 0.3076
- eval_runtime: 7.2594
- eval_samples_per_second: 137.753
- eval_steps_per_second: 17.219
- epoch: 0.4
- step: 50
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
SharonTudi/DIALOGUE
|
SharonTudi
| 2023-12-15T10:15:08Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T16:04:21Z |
---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: DIALOGUE
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# DIALOGUE
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4043
- Precision: 0.6659
- Recall: 0.6150
- F1: 0.6275
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| 0.3304 | 0.91 | 50 | 0.4934 | 0.6659 | 0.6150 | 0.6275 |
| 0.3675 | 1.82 | 100 | 0.4452 | 0.6659 | 0.6150 | 0.6275 |
| 0.2974 | 2.73 | 150 | 0.4165 | 0.6659 | 0.6150 | 0.6275 |
| 0.3194 | 3.64 | 200 | 0.4034 | 0.6659 | 0.6150 | 0.6275 |
| 0.1905 | 4.55 | 250 | 0.4043 | 0.6659 | 0.6150 | 0.6275 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
marco27/Joe-station
|
marco27
| 2023-12-13T16:04:42Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T16:04:42Z |
---
license: apache-2.0
---
|
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LoneStriker/SOLAR-10.7B-Instruct-v1.0-3.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-13T16:10:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T16:07:44Z |
---
license: apache-2.0
---
# **Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!**
**(This model is [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) fine-tuned version for single-turn conversation. Detailed description to be added.)**
# **Introduction**
We introduce the first 10.7 billion (B) parameter model, SOLAR-10.7B. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B.
We developed the Depth Up-Scaling technique. Built on the Llama2 architecture, SOLAR-10.7B incorporates the innovative Upstage Depth Up-Scaling. We then integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.
Depth-Upscaled SOLAR-10.7B has remarkable performance. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model. For detailed information, please refer to the experimental table ([link to be updated soon]).
Solar 10.7B is an ideal choice for fine-tuning. SOLAR-10.7B offers robustness and adaptability for your fine-tuning needs. Our simple instruction fine-tuning using the SOLAR-10.7B pre-trained model yields significant performance improvements. [[link to be updated soon]]
# **Usage Instructions**
This model has been fine-tuned primarily for single-turn conversation, making it less suitable for multi-turn conversations such as chat.
### **Version**
Make sure you have the correct version of the transformers library installed:
```sh
pip install transformers==4.35.2
```
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Upstage/SOLAR-10.7B-Instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained(
"Upstage/SOLAR-10.7B-Instruct-v1.0",
device_map="auto",
torch_dtype=torch.float16,
)
```
### **Conducting Single-Turn Conversation**
```python
conversation = [ {'role': 'user', 'content': 'Hello?'} ]
prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, use_cache=True, max_length=4096) output_text = tokenizer.decode(outputs[0])
print(output_text)
```
Below is an example of the output.
```
<s> ### User:
Hello?
### Assistant:
Hello, how can I assist you today? Please feel free to ask any questions or request help with a specific task.</s>
```
### **The Upstage AI Team** ###
Upstage is creating the best LLM and DocAI. Please find more information at https://upstage.ai
### **Contact Us** ###
Any questions and suggestions, please use the discussion tab. If you want to contact us directly, drop an email to [[email protected]](mailto:[email protected])
|
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jane91/LLama2
|
jane91
| 2023-12-13T16:09:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:09:28Z |
Entry not found
|
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] |
bio-datasets/Mistral-7B-Instruct-v0.1-icy-dawn-191-epoch-0
|
bio-datasets
| 2023-12-13T16:42:11Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"region:us"
] | null | 2023-12-13T16:14:39Z |
---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.5.0
|
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mojuss/llama2
|
mojuss
| 2023-12-13T16:16:57Z | 0 | 0 | null |
[
"license:llama2",
"region:us"
] | null | 2023-12-13T16:16:57Z |
---
license: llama2
---
|
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] |
DFJordan/EmailPurchaseClassifier
|
DFJordan
| 2023-12-13T16:18:08Z | 0 | 0 |
sklearn
|
[
"sklearn",
"skops",
"text-classification",
"region:us"
] |
text-classification
| 2023-12-13T16:18:06Z |
---
library_name: sklearn
tags:
- sklearn
- skops
- text-classification
model_format: pickle
model_file: logistic_regression_model_GOOD_2.pkl
---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
[More Information Needed]
### Hyperparameters
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|-------------------|---------------|
| C | 1.0 |
| class_weight | {0: 1, 1: 10} |
| dual | False |
| fit_intercept | True |
| intercept_scaling | 1 |
| l1_ratio | |
| max_iter | 100 |
| multi_class | auto |
| n_jobs | |
| penalty | l2 |
| random_state | |
| solver | lbfgs |
| tol | 0.0001 |
| verbose | 0 |
| warm_start | False |
</details>
### Model Plot
<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container" style="overflow: auto;"><div class="sk-text-repr-fallback"><pre>LogisticRegression(class_weight={0: 1, 1: 10})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(class_weight={0: 1, 1: 10})</pre></div></div></div></div></div>
## Evaluation Results
[More Information Needed]
# How to Get Started with the Model
[More Information Needed]
# Model Card Authors
This model card is written by following authors:
[More Information Needed]
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
```
# model_description
Classifies an email as a purchase or not
# model_card_authors
Jordan Howlett
# limitations
Literally none
|
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LoneStriker/SOLAR-10.7B-Instruct-v1.0-4.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-13T16:21:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T16:18:28Z |
---
license: apache-2.0
---
# **Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!**
**(This model is [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) fine-tuned version for single-turn conversation. Detailed description to be added.)**
# **Introduction**
We introduce the first 10.7 billion (B) parameter model, SOLAR-10.7B. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B.
We developed the Depth Up-Scaling technique. Built on the Llama2 architecture, SOLAR-10.7B incorporates the innovative Upstage Depth Up-Scaling. We then integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.
Depth-Upscaled SOLAR-10.7B has remarkable performance. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model. For detailed information, please refer to the experimental table ([link to be updated soon]).
Solar 10.7B is an ideal choice for fine-tuning. SOLAR-10.7B offers robustness and adaptability for your fine-tuning needs. Our simple instruction fine-tuning using the SOLAR-10.7B pre-trained model yields significant performance improvements. [[link to be updated soon]]
# **Usage Instructions**
This model has been fine-tuned primarily for single-turn conversation, making it less suitable for multi-turn conversations such as chat.
### **Version**
Make sure you have the correct version of the transformers library installed:
```sh
pip install transformers==4.35.2
```
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Upstage/SOLAR-10.7B-Instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained(
"Upstage/SOLAR-10.7B-Instruct-v1.0",
device_map="auto",
torch_dtype=torch.float16,
)
```
### **Conducting Single-Turn Conversation**
```python
conversation = [ {'role': 'user', 'content': 'Hello?'} ]
prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, use_cache=True, max_length=4096) output_text = tokenizer.decode(outputs[0])
print(output_text)
```
Below is an example of the output.
```
<s> ### User:
Hello?
### Assistant:
Hello, how can I assist you today? Please feel free to ask any questions or request help with a specific task.</s>
```
### **The Upstage AI Team** ###
Upstage is creating the best LLM and DocAI. Please find more information at https://upstage.ai
### **Contact Us** ###
Any questions and suggestions, please use the discussion tab. If you want to contact us directly, drop an email to [[email protected]](mailto:[email protected])
|
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ineoApp/LayoutLMv3_5_entities_filtred_22
|
ineoApp
| 2023-12-13T16:21:58Z | 0 | 0 | null |
[
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christopheparisse/my_awesome_model
|
christopheparisse
| 2023-12-14T03:18:52Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T16:23:50Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: christopheparisse/my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# christopheparisse/my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0637
- Validation Loss: 0.2249
- Train Accuracy: 0.9328
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7810, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2546 | 0.2058 | 0.9199 | 0 |
| 0.1362 | 0.1698 | 0.9361 | 1 |
| 0.0637 | 0.2249 | 0.9328 | 2 |
### Framework versions
- Transformers 4.36.0
- TensorFlow 2.15.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Thekingbalxd/Mymodels
|
Thekingbalxd
| 2023-12-13T16:25:51Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:25:51Z |
Entry not found
|
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mdesousa/output_dir
|
mdesousa
| 2023-12-13T16:27:43Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:27:43Z |
Entry not found
|
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cwiz/art-kris
|
cwiz
| 2023-12-13T16:28:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:28:22Z |
Entry not found
|
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cwiz/art_kris
|
cwiz
| 2023-12-13T16:29:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:29:22Z |
Entry not found
|
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LoneStriker/SOLAR-10.7B-Instruct-v1.0-5.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-13T16:33:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T16:29:32Z |
---
license: apache-2.0
---
# **Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!**
**(This model is [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) fine-tuned version for single-turn conversation. Detailed description to be added.)**
# **Introduction**
We introduce the first 10.7 billion (B) parameter model, SOLAR-10.7B. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B.
We developed the Depth Up-Scaling technique. Built on the Llama2 architecture, SOLAR-10.7B incorporates the innovative Upstage Depth Up-Scaling. We then integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.
Depth-Upscaled SOLAR-10.7B has remarkable performance. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model. For detailed information, please refer to the experimental table ([link to be updated soon]).
Solar 10.7B is an ideal choice for fine-tuning. SOLAR-10.7B offers robustness and adaptability for your fine-tuning needs. Our simple instruction fine-tuning using the SOLAR-10.7B pre-trained model yields significant performance improvements. [[link to be updated soon]]
# **Usage Instructions**
This model has been fine-tuned primarily for single-turn conversation, making it less suitable for multi-turn conversations such as chat.
### **Version**
Make sure you have the correct version of the transformers library installed:
```sh
pip install transformers==4.35.2
```
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Upstage/SOLAR-10.7B-Instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained(
"Upstage/SOLAR-10.7B-Instruct-v1.0",
device_map="auto",
torch_dtype=torch.float16,
)
```
### **Conducting Single-Turn Conversation**
```python
conversation = [ {'role': 'user', 'content': 'Hello?'} ]
prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, use_cache=True, max_length=4096) output_text = tokenizer.decode(outputs[0])
print(output_text)
```
Below is an example of the output.
```
<s> ### User:
Hello?
### Assistant:
Hello, how can I assist you today? Please feel free to ask any questions or request help with a specific task.</s>
```
### **The Upstage AI Team** ###
Upstage is creating the best LLM and DocAI. Please find more information at https://upstage.ai
### **Contact Us** ###
Any questions and suggestions, please use the discussion tab. If you want to contact us directly, drop an email to [[email protected]](mailto:[email protected])
|
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] |
EthanRhys/Princess-Daisy
|
EthanRhys
| 2023-12-13T16:31:12Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T16:29:58Z |
---
license: openrail
---
|
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BauyrjanQ/wav2vec2-large-mms-1b-kazakh-ksc2-2b-5ep_2nd
|
BauyrjanQ
| 2023-12-15T13:36:25Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-13T16:30:05Z |
Entry not found
|
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] |
stvhuang/rcr-run-g6knz4zq-v2-61961-master-0_20231212T101450-ep05
|
stvhuang
| 2023-12-13T16:33:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-12-13T16:30:42Z |
Entry not found
|
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] |
VishalMysore/cookgpt
|
VishalMysore
| 2023-12-13T16:33:34Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T16:33:34Z |
---
license: apache-2.0
---
|
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] |
bansilp/xyz
|
bansilp
| 2023-12-15T04:20:18Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T16:35:12Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: xyz
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9018518518518519
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xyz
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4588
- Accuracy: 0.9019
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 48
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1739 | 1.11 | 100 | 0.5771 | 0.8407 |
| 0.0677 | 2.22 | 200 | 0.5907 | 0.8519 |
| 0.0699 | 3.33 | 300 | 0.4160 | 0.8870 |
| 0.0598 | 4.44 | 400 | 0.7336 | 0.8380 |
| 0.0108 | 5.56 | 500 | 0.5133 | 0.8898 |
| 0.0082 | 6.67 | 600 | 0.4786 | 0.8981 |
| 0.0031 | 7.78 | 700 | 0.4624 | 0.9009 |
| 0.0046 | 8.89 | 800 | 0.4594 | 0.9 |
| 0.0052 | 10.0 | 900 | 0.4588 | 0.9019 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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EliteTestoRX/EliteTestoRXGummies
|
EliteTestoRX
| 2023-12-13T16:36:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:35:53Z |
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|
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Ubaid000/game
|
Ubaid000
| 2023-12-13T16:38:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:38:06Z |
Entry not found
|
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] |
hkivancoral/smids_3x_beit_base_sgd_0001_fold1
|
hkivancoral
| 2023-12-13T17:27:20Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T16:40:05Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_sgd_0001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7512520868113522
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_3x_beit_base_sgd_0001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6006
- Accuracy: 0.7513
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2428 | 1.0 | 226 | 1.2900 | 0.3139 |
| 1.1165 | 2.0 | 452 | 1.2280 | 0.3389 |
| 1.085 | 3.0 | 678 | 1.1717 | 0.3606 |
| 1.0873 | 4.0 | 904 | 1.1174 | 0.3973 |
| 1.0209 | 5.0 | 1130 | 1.0648 | 0.4207 |
| 0.9387 | 6.0 | 1356 | 1.0163 | 0.4741 |
| 0.9347 | 7.0 | 1582 | 0.9719 | 0.5175 |
| 0.8727 | 8.0 | 1808 | 0.9312 | 0.5626 |
| 0.8169 | 9.0 | 2034 | 0.8951 | 0.5993 |
| 0.861 | 10.0 | 2260 | 0.8623 | 0.6160 |
| 0.8138 | 11.0 | 2486 | 0.8344 | 0.6327 |
| 0.7635 | 12.0 | 2712 | 0.8096 | 0.6444 |
| 0.7469 | 13.0 | 2938 | 0.7879 | 0.6477 |
| 0.7457 | 14.0 | 3164 | 0.7697 | 0.6561 |
| 0.6958 | 15.0 | 3390 | 0.7527 | 0.6728 |
| 0.6961 | 16.0 | 3616 | 0.7374 | 0.6795 |
| 0.6436 | 17.0 | 3842 | 0.7245 | 0.6878 |
| 0.6513 | 18.0 | 4068 | 0.7127 | 0.6912 |
| 0.6672 | 19.0 | 4294 | 0.7016 | 0.6962 |
| 0.6558 | 20.0 | 4520 | 0.6918 | 0.7012 |
| 0.6466 | 21.0 | 4746 | 0.6834 | 0.7028 |
| 0.6561 | 22.0 | 4972 | 0.6751 | 0.7045 |
| 0.6208 | 23.0 | 5198 | 0.6670 | 0.7145 |
| 0.6499 | 24.0 | 5424 | 0.6602 | 0.7162 |
| 0.6316 | 25.0 | 5650 | 0.6537 | 0.7179 |
| 0.6488 | 26.0 | 5876 | 0.6486 | 0.7245 |
| 0.6013 | 27.0 | 6102 | 0.6431 | 0.7229 |
| 0.6349 | 28.0 | 6328 | 0.6385 | 0.7295 |
| 0.5571 | 29.0 | 6554 | 0.6343 | 0.7312 |
| 0.6883 | 30.0 | 6780 | 0.6303 | 0.7329 |
| 0.5874 | 31.0 | 7006 | 0.6269 | 0.7362 |
| 0.5957 | 32.0 | 7232 | 0.6236 | 0.7412 |
| 0.5454 | 33.0 | 7458 | 0.6209 | 0.7446 |
| 0.5392 | 34.0 | 7684 | 0.6182 | 0.7446 |
| 0.6014 | 35.0 | 7910 | 0.6160 | 0.7462 |
| 0.5394 | 36.0 | 8136 | 0.6140 | 0.7462 |
| 0.5557 | 37.0 | 8362 | 0.6119 | 0.7479 |
| 0.5868 | 38.0 | 8588 | 0.6101 | 0.7479 |
| 0.5673 | 39.0 | 8814 | 0.6084 | 0.7479 |
| 0.5576 | 40.0 | 9040 | 0.6071 | 0.7479 |
| 0.5598 | 41.0 | 9266 | 0.6057 | 0.7479 |
| 0.5493 | 42.0 | 9492 | 0.6045 | 0.7496 |
| 0.573 | 43.0 | 9718 | 0.6035 | 0.7513 |
| 0.5428 | 44.0 | 9944 | 0.6027 | 0.7513 |
| 0.6174 | 45.0 | 10170 | 0.6020 | 0.7513 |
| 0.5654 | 46.0 | 10396 | 0.6015 | 0.7513 |
| 0.5911 | 47.0 | 10622 | 0.6010 | 0.7513 |
| 0.5644 | 48.0 | 10848 | 0.6008 | 0.7513 |
| 0.5284 | 49.0 | 11074 | 0.6007 | 0.7513 |
| 0.5888 | 50.0 | 11300 | 0.6006 | 0.7513 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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LoneStriker/SOLAR-10.7B-Instruct-v1.0-6.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-13T16:46:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T16:40:58Z |
---
license: apache-2.0
---
# **Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!**
**(This model is [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0) fine-tuned version for single-turn conversation. Detailed description to be added.)**
# **Introduction**
We introduce the first 10.7 billion (B) parameter model, SOLAR-10.7B. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B.
We developed the Depth Up-Scaling technique. Built on the Llama2 architecture, SOLAR-10.7B incorporates the innovative Upstage Depth Up-Scaling. We then integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.
Depth-Upscaled SOLAR-10.7B has remarkable performance. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model. For detailed information, please refer to the experimental table ([link to be updated soon]).
Solar 10.7B is an ideal choice for fine-tuning. SOLAR-10.7B offers robustness and adaptability for your fine-tuning needs. Our simple instruction fine-tuning using the SOLAR-10.7B pre-trained model yields significant performance improvements. [[link to be updated soon]]
# **Usage Instructions**
This model has been fine-tuned primarily for single-turn conversation, making it less suitable for multi-turn conversations such as chat.
### **Version**
Make sure you have the correct version of the transformers library installed:
```sh
pip install transformers==4.35.2
```
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Upstage/SOLAR-10.7B-Instruct-v1.0")
model = AutoModelForCausalLM.from_pretrained(
"Upstage/SOLAR-10.7B-Instruct-v1.0",
device_map="auto",
torch_dtype=torch.float16,
)
```
### **Conducting Single-Turn Conversation**
```python
conversation = [ {'role': 'user', 'content': 'Hello?'} ]
prompt = tokenizer.apply_chat_template(conversation, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, use_cache=True, max_length=4096) output_text = tokenizer.decode(outputs[0])
print(output_text)
```
Below is an example of the output.
```
<s> ### User:
Hello?
### Assistant:
Hello, how can I assist you today? Please feel free to ask any questions or request help with a specific task.</s>
```
### **The Upstage AI Team** ###
Upstage is creating the best LLM and DocAI. Please find more information at https://upstage.ai
### **Contact Us** ###
Any questions and suggestions, please use the discussion tab. If you want to contact us directly, drop an email to [[email protected]](mailto:[email protected])
|
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] |
hkivancoral/smids_3x_beit_base_rms_001_fold1
|
hkivancoral
| 2023-12-13T17:29:40Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T16:41:12Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_rms_001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7579298831385642
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_3x_beit_base_rms_001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7807
- Accuracy: 0.7579
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1042 | 1.0 | 226 | 1.0981 | 0.3456 |
| 0.942 | 2.0 | 452 | 0.9022 | 0.5275 |
| 0.8328 | 3.0 | 678 | 0.9600 | 0.4691 |
| 0.8702 | 4.0 | 904 | 0.9083 | 0.5543 |
| 0.8313 | 5.0 | 1130 | 0.8171 | 0.5760 |
| 0.8558 | 6.0 | 1356 | 0.8467 | 0.5342 |
| 0.7514 | 7.0 | 1582 | 0.7612 | 0.6277 |
| 0.7839 | 8.0 | 1808 | 0.7968 | 0.5659 |
| 0.7602 | 9.0 | 2034 | 0.7655 | 0.6210 |
| 0.7694 | 10.0 | 2260 | 0.7429 | 0.6060 |
| 0.7166 | 11.0 | 2486 | 0.7968 | 0.5626 |
| 0.7048 | 12.0 | 2712 | 0.8272 | 0.6077 |
| 0.6745 | 13.0 | 2938 | 0.8054 | 0.5993 |
| 0.7185 | 14.0 | 3164 | 0.7867 | 0.6194 |
| 0.7264 | 15.0 | 3390 | 0.7701 | 0.6377 |
| 0.6767 | 16.0 | 3616 | 0.7383 | 0.6144 |
| 0.6006 | 17.0 | 3842 | 0.8677 | 0.6077 |
| 0.6721 | 18.0 | 4068 | 0.7460 | 0.6361 |
| 0.6352 | 19.0 | 4294 | 0.7492 | 0.6127 |
| 0.642 | 20.0 | 4520 | 0.7712 | 0.6160 |
| 0.6647 | 21.0 | 4746 | 0.7257 | 0.6544 |
| 0.6408 | 22.0 | 4972 | 0.7629 | 0.6611 |
| 0.7655 | 23.0 | 5198 | 0.7723 | 0.6127 |
| 0.7074 | 24.0 | 5424 | 0.6879 | 0.6928 |
| 0.6919 | 25.0 | 5650 | 0.6962 | 0.6828 |
| 0.698 | 26.0 | 5876 | 0.7479 | 0.6361 |
| 0.641 | 27.0 | 6102 | 0.7653 | 0.6644 |
| 0.6417 | 28.0 | 6328 | 0.7791 | 0.6594 |
| 0.6123 | 29.0 | 6554 | 0.7195 | 0.6761 |
| 0.5918 | 30.0 | 6780 | 0.6991 | 0.6995 |
| 0.5562 | 31.0 | 7006 | 0.6938 | 0.6978 |
| 0.6293 | 32.0 | 7232 | 0.6564 | 0.7145 |
| 0.5615 | 33.0 | 7458 | 0.7421 | 0.6878 |
| 0.5411 | 34.0 | 7684 | 0.6688 | 0.7145 |
| 0.4483 | 35.0 | 7910 | 0.7701 | 0.6962 |
| 0.4776 | 36.0 | 8136 | 0.6349 | 0.7412 |
| 0.4775 | 37.0 | 8362 | 0.6430 | 0.7262 |
| 0.4854 | 38.0 | 8588 | 0.7095 | 0.7078 |
| 0.4208 | 39.0 | 8814 | 0.6254 | 0.7412 |
| 0.3846 | 40.0 | 9040 | 0.6645 | 0.7396 |
| 0.3663 | 41.0 | 9266 | 0.6430 | 0.7563 |
| 0.3616 | 42.0 | 9492 | 0.6767 | 0.7462 |
| 0.3863 | 43.0 | 9718 | 0.6432 | 0.7596 |
| 0.2608 | 44.0 | 9944 | 0.6472 | 0.7563 |
| 0.4159 | 45.0 | 10170 | 0.6400 | 0.7663 |
| 0.333 | 46.0 | 10396 | 0.6911 | 0.7613 |
| 0.2718 | 47.0 | 10622 | 0.7119 | 0.7696 |
| 0.2639 | 48.0 | 10848 | 0.7421 | 0.7596 |
| 0.236 | 49.0 | 11074 | 0.7543 | 0.7613 |
| 0.2672 | 50.0 | 11300 | 0.7807 | 0.7579 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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AnveshakR/Reddit-NFL-FineTuned-Model
|
AnveshakR
| 2023-12-13T16:55:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:44:03Z |
Entry not found
|
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] |
MidPrepAdobe/test_1
|
MidPrepAdobe
| 2023-12-13T16:56:38Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T16:44:47Z |
---
license: apache-2.0
---
|
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] |
aaalby/asean
|
aaalby
| 2023-12-13T16:46:58Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T16:45:42Z |
---
license: openrail
---
|
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dvshah13/q-FrozenLake-v1-4x4-noSlippery
|
dvshah13
| 2023-12-13T16:47:24Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-13T16:47:22Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="dvshah13/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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bodam/sd-model-finetuned-dreambooth-lora
|
bodam
| 2023-12-13T16:47:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:47:36Z |
Entry not found
|
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] |
Realgon/N_distilbert_agnews_padding80model
|
Realgon
| 2023-12-13T18:26:31Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-13T16:48:40Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: N_distilbert_agnews_padding80model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9467105263157894
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# N_distilbert_agnews_padding80model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6373
- Accuracy: 0.9467
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1867 | 1.0 | 7500 | 0.1871 | 0.9422 |
| 0.1412 | 2.0 | 15000 | 0.1944 | 0.9457 |
| 0.1163 | 3.0 | 22500 | 0.2140 | 0.945 |
| 0.0838 | 4.0 | 30000 | 0.2455 | 0.9476 |
| 0.0572 | 5.0 | 37500 | 0.2839 | 0.9436 |
| 0.0433 | 6.0 | 45000 | 0.3780 | 0.9411 |
| 0.035 | 7.0 | 52500 | 0.4167 | 0.9384 |
| 0.0303 | 8.0 | 60000 | 0.4762 | 0.9401 |
| 0.0198 | 9.0 | 67500 | 0.4682 | 0.9407 |
| 0.0186 | 10.0 | 75000 | 0.4855 | 0.9429 |
| 0.0166 | 11.0 | 82500 | 0.5062 | 0.9416 |
| 0.0125 | 12.0 | 90000 | 0.5277 | 0.9418 |
| 0.0061 | 13.0 | 97500 | 0.5436 | 0.9442 |
| 0.0028 | 14.0 | 105000 | 0.5485 | 0.9420 |
| 0.0034 | 15.0 | 112500 | 0.5601 | 0.9441 |
| 0.0029 | 16.0 | 120000 | 0.5588 | 0.9459 |
| 0.0032 | 17.0 | 127500 | 0.6078 | 0.9436 |
| 0.0046 | 18.0 | 135000 | 0.6353 | 0.9429 |
| 0.0003 | 19.0 | 142500 | 0.6454 | 0.9458 |
| 0.0011 | 20.0 | 150000 | 0.6373 | 0.9467 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
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DJ7/DJ14
|
DJ7
| 2023-12-13T16:49:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:49:27Z |
Entry not found
|
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] |
hkivancoral/smids_5x_deit_tiny_adamax_001_fold1
|
hkivancoral
| 2023-12-17T02:43:13Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T16:51:13Z |
---
license: apache-2.0
base_model: facebook/deit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_adamax_001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8898163606010017
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_tiny_adamax_001_fold1
This model is a fine-tuned version of [facebook/deit-small-patch16-224](https://huggingface.co/facebook/deit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9489
- Accuracy: 0.8898
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3549 | 1.0 | 376 | 0.4844 | 0.8264 |
| 0.2678 | 2.0 | 752 | 0.3259 | 0.8798 |
| 0.3098 | 3.0 | 1128 | 0.3469 | 0.8548 |
| 0.2057 | 4.0 | 1504 | 0.3089 | 0.8831 |
| 0.15 | 5.0 | 1880 | 0.4280 | 0.8748 |
| 0.0947 | 6.0 | 2256 | 0.5773 | 0.8581 |
| 0.1544 | 7.0 | 2632 | 0.3805 | 0.8881 |
| 0.1085 | 8.0 | 3008 | 0.4878 | 0.8731 |
| 0.0399 | 9.0 | 3384 | 0.4495 | 0.8965 |
| 0.0251 | 10.0 | 3760 | 0.5573 | 0.8681 |
| 0.0684 | 11.0 | 4136 | 0.4467 | 0.8648 |
| 0.0506 | 12.0 | 4512 | 0.5126 | 0.8982 |
| 0.0075 | 13.0 | 4888 | 0.8575 | 0.8715 |
| 0.0481 | 14.0 | 5264 | 0.7463 | 0.8664 |
| 0.0077 | 15.0 | 5640 | 0.6816 | 0.8865 |
| 0.0098 | 16.0 | 6016 | 0.6312 | 0.8831 |
| 0.0003 | 17.0 | 6392 | 0.7022 | 0.8965 |
| 0.0075 | 18.0 | 6768 | 0.6976 | 0.8731 |
| 0.0042 | 19.0 | 7144 | 0.6012 | 0.8881 |
| 0.0311 | 20.0 | 7520 | 0.7693 | 0.8932 |
| 0.003 | 21.0 | 7896 | 0.6254 | 0.8915 |
| 0.0101 | 22.0 | 8272 | 0.6004 | 0.8998 |
| 0.0209 | 23.0 | 8648 | 0.7643 | 0.8815 |
| 0.0001 | 24.0 | 9024 | 0.8262 | 0.8848 |
| 0.0007 | 25.0 | 9400 | 0.6944 | 0.8898 |
| 0.0034 | 26.0 | 9776 | 0.7140 | 0.8915 |
| 0.0071 | 27.0 | 10152 | 0.8088 | 0.8798 |
| 0.0001 | 28.0 | 10528 | 0.7766 | 0.9032 |
| 0.0039 | 29.0 | 10904 | 0.8084 | 0.8948 |
| 0.0045 | 30.0 | 11280 | 0.7741 | 0.8831 |
| 0.0006 | 31.0 | 11656 | 0.8264 | 0.8932 |
| 0.0 | 32.0 | 12032 | 0.8432 | 0.8865 |
| 0.0 | 33.0 | 12408 | 0.8641 | 0.8848 |
| 0.0 | 34.0 | 12784 | 0.8447 | 0.8865 |
| 0.0 | 35.0 | 13160 | 0.8402 | 0.8848 |
| 0.0 | 36.0 | 13536 | 0.8232 | 0.8948 |
| 0.0 | 37.0 | 13912 | 0.8382 | 0.8915 |
| 0.0 | 38.0 | 14288 | 0.8652 | 0.8898 |
| 0.0 | 39.0 | 14664 | 0.8733 | 0.8848 |
| 0.0 | 40.0 | 15040 | 0.8254 | 0.8881 |
| 0.0 | 41.0 | 15416 | 0.8627 | 0.8848 |
| 0.0 | 42.0 | 15792 | 0.8799 | 0.8881 |
| 0.0 | 43.0 | 16168 | 0.8887 | 0.8915 |
| 0.0 | 44.0 | 16544 | 0.9046 | 0.8932 |
| 0.0 | 45.0 | 16920 | 0.9092 | 0.8932 |
| 0.0031 | 46.0 | 17296 | 0.9143 | 0.8881 |
| 0.0 | 47.0 | 17672 | 0.9293 | 0.8915 |
| 0.0 | 48.0 | 18048 | 0.9378 | 0.8898 |
| 0.0 | 49.0 | 18424 | 0.9447 | 0.8898 |
| 0.0023 | 50.0 | 18800 | 0.9489 | 0.8898 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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idontgoddamn/AsagiMutsuki
|
idontgoddamn
| 2023-12-13T16:51:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:51:28Z |
Entry not found
|
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Santiclibrain/mixtral_no_robots
|
Santiclibrain
| 2023-12-13T17:08:46Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2023-12-13T16:54:32Z |
Entry not found
|
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Arlech/GameTL
|
Arlech
| 2023-12-13T16:56:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:54:56Z |
Entry not found
|
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NLVeer/flan-t5-sim
|
NLVeer
| 2023-12-13T16:57:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:57:10Z |
Entry not found
|
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] |
andrijdavid/Llama-Guard-7B-GGUF
|
andrijdavid
| 2023-12-15T10:53:59Z | 0 | 0 | null |
[
"gguf",
"license:llama2",
"region:us"
] | null | 2023-12-13T16:57:20Z |
---
license: llama2
---
|
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showrounak/moviesong
|
showrounak
| 2023-12-13T16:59:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T16:58:54Z |
Entry not found
|
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] |
aaptknews/BHARAT-AI
|
aaptknews
| 2023-12-13T17:11:08Z | 0 | 0 |
transformers
|
[
"transformers",
"code",
"text-generation",
"en",
"hi",
"bh",
"dataset:fka/awesome-chatgpt-prompts",
"dataset:wikimedia/wikipedia",
"dataset:Lin-Chen/ShareGPT4V",
"license:gpl-3.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-12-13T16:59:33Z |
---
license: gpl-3.0
library_name: transformers
pipeline_tag: text-generation
datasets:
- fka/awesome-chatgpt-prompts
- wikimedia/wikipedia
- Lin-Chen/ShareGPT4V
language:
- en
- hi
- bh
metrics:
- accuracy
tags:
- code
---
pip install transformers
|
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dvshah13/taxi-v3
|
dvshah13
| 2023-12-13T16:59:51Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-13T16:59:49Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="dvshah13/taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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] |
AIexanderDicke/deep-rl-course-unit-4-pt-1
|
AIexanderDicke
| 2023-12-13T17:02:07Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-13T17:01:58Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: deep-rl-course-unit-4
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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] |
LoneStriker/bagel-dpo-7b-v0.1-3.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-13T17:05:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T17:03:24Z |
---
license: apache-2.0
---
# A bagel, with everything (including DPO)
## Overview
This is the DPO'd version of https://huggingface.co/jondurbin/bagel-7b-v0.1
If you are getting too many AALLM or other refusals, even with explicitly human system prompts, you may want to try the non-DPO version.
## Benchmarks
I ran these against the latest main branch of lm-evaluation-harness (and opencompass/FastChat for agieval and mt-bench), since batch size/etc effects score for some benchmarks.
| model | arc_challenge | boolq | gsm8k | hellaswag | mmlu | openbookqa | piqa | truthful_qa | winogrande |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| bagel | __0.6715__ | 0.8813 | __0.5618__ | 0.8397 | __0.6408__ | __0.51__ | __0.8406__ | __0.6275__ | __0.7561__ |
| openhermes-2.5 | 0.6476 | __0.8835__ | 0.4852 | __0.8414__ | 0.6347 | 0.498 | 0.8400 | 0.5295 | 0.7443 |
MT-Bench:
```
########## First turn ##########
score
model turn
bagel-7b-v0.1 1 7.60625
########## Second turn ##########
score
model turn
bagel-7b-v0.1 2 7.00625
########## Average ##########
score
model
bagel-7b-v0.1 7.30625
```
## Data selection.
The first step in the process is creating a dataset.
In this case, we're actually creating a composite dataset, consisting of both supervised fine-tuning data (SFT) and direct preference optimization (DPO) data.
All instruction data, that is, data that is not plain text (like project Gutenberg and items from Cinematika) or DPO, is converted into ShareGPT format so it's easier to work with.
See the corresponding code in `bagel/data_sources/*.py` for full implementation for each data source.
Deduplication is done by creating a uuid v5 of the instruction/text, then only adding items not previously seen (where datasets are loaded in order of the confidence score I assign them).
This means that if an instruction is in data source "Foo" with confidence 4 as well as in data source "Bar" with confidence score 2, only the entry from "Foo" will be taken.
### SFT data sources
*Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check*
- [ai2_arc](https://huggingface.co/datasets/ai2_arc)
- Abstraction and reasoning dataset, useful in measuring "intelligence" to a certain extent.
- [airoboros](https://huggingface.co/datasets/unalignment/spicy-3.1)
- Variety of categories of synthetic instructions generated by gpt-4.
- [apps](https://huggingface.co/datasets/codeparrot/apps)
- Python coding dataset with 10k problems.
- [belebele](https://huggingface.co/datasets/facebook/belebele)
- Multi-lingual reading comprehension dataset.
- [boolq](https://huggingface.co/datasets/boolq)
- Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)
- [cinematika](https://huggingface.co/datasets/jondurbin/cinematika-v0.1) (instruction and plain text)
- RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.
- [drop](https://huggingface.co/datasets/drop)
- More reading comprehension.
- [gutenberg](https://www.gutenberg.org/) (plain text)
- Books/plain text, again to make the model less boring, only a handful of examples supported by [chapterize](https://github.com/JonathanReeve/chapterize)
- [lmsys_chat_1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) (only gpt-4 items, also used for DPO)
- Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.
- [mathinstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
- Composite dataset with a variety of math-related tasks and problem/question formats.
- [mmlu](https://huggingface.co/datasets/cais/mmlu)
- Massive Multitask Language Understanding - a wide variety of questions about various subject matters.
- [natural_instructions](https://huggingface.co/datasets/Muennighoff/natural-instructions)
- Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)
- [openbookqa](https://huggingface.co/datasets/openbookqa)
- Question answering dataset.
- [piqa](https://huggingface.co/datasets/piqa)
- Phyiscal interaction question answering.
- [python_alpaca](https://huggingface.co/datasets/Vezora/Tested-22k-Python-Alpaca)
- Python instruction response pairs, validated as functional.
- [rosetta_code](https://huggingface.co/datasets/cakiki/rosetta-code)
- Code problems and solutions in a variety of programming languages taken from rosettacode.org.
- [slimorca](https://huggingface.co/datasets/Open-Orca/SlimOrca)
- Collection of ~500k gpt-4 verified chats from OpenOrca.
- [spider](https://huggingface.co/datasets/spider)
- SQL-targeted dataset.
- [squad_v2](https://huggingface.co/datasets/squad_v2)
- Contextual question answering (RAG).
- [synthia](https://huggingface.co/datasets/migtissera/Synthia-v1.3)
- GPT-4 generated data using advanced prompting from Migel Tissera.
- [winogrande](https://huggingface.co/datasets/winogrande)
- Fill in the blank style prompts.
### DPO data sources
- [airoboros 3.1](https://huggingface.co/datasets/unalignment/spicy-3.1) vs [airoboros 2.2.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1)
- The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the "rejected" value and the rerolled response as "chosen"
- [helpsteer](https://huggingface.co/datasets/nvidia/HelpSteer)
- Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest "correctness" value were used for DPO here, with the highest scoring output as "chosen" and random lower scoring value as "rejected"
- [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- Another interesting dataset by Intel, which provides various DPO pairs generated from prompts included in the SlimOrca dataset.
- [toxic-dpo](https://huggingface.co/datasets/unalignment/toxic-dpo-v0.1)
- __*highly toxic and potentially illegal content!*__ De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.
- [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
- DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.
- [ultrafeedback](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned)
- One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.
Only the train splits were used (if a split was provided), and an additional pass of decontamination is performed using approximate nearest neighbor search (via faiss).
### Total dataset size
The deduplicated and decontamined list of instructions contains 1,671,822 items:
- 1,602,217 SFT/instructions
- 59,247 DPO pairs
- 1606 with both SFT and DPO data
Keep in mind, this number becomes 4x larger when applying the various prompt formats.
## Prompt formatting
In sticking with the theme of the bagel, I didn't want to use a single prompt format, so I used 4 - vicuna, llama-2, alpaca, and chat-ml (sorta).
I also didn't want to randomly select a single prompt format for each item (hoping each instruction would generalize more when used in a variety of prompt formats), so each instruction is actually converted into every prompt format.
This means each epoch of our fine-tune is really basically 4 epochs. So, for the fine-tunes, I would recommend only doing 1 epoch (or 0.75 epochs). I am testing with a single epoch using a relatively low learning rate.
### Alpaca (sort of)
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{system prompt, if provided}
{instruction}
### Response:
```
The main difference here is that because of the dataset formatting and variety of data sources, it would have been much to tedious to add an `### Input:` block, so the inputs are just in the instruction section.
### Vicuna
```
{system prompt, if provided, randomly defaulting to "A chat between a user and an unbiased, uncensored assistant."}
USER: {instruction}
ASSISTANT:
```
### ChatML (sort of)
I don't really understand the point of having special tokens for `<|im_start|>` and `<|im_end|>`, because in practice they just act as BOS and EOS tokens (but, please correct me if I'm wrong).
So, instead of:
```text
{bos}<|im_start|>{role}
{text}
<|im_end|>{eos}
```
I just changed it to:
```text
{bos}{role}
{text}
{eos}
```
In practice, this would mean tokenization code like such:
```python
tokenizer = AutoTokenizer.from_pretrained('mistralai/mistral-7b-v0.1')
input_str = f"""system
You are a goat.
{tokenizer.eos_token}
{tokenizer.bos_token}user
Tell me how to fry an egg.
{tokenizer.eos_token}
{tokenizer.bos_token}assistant
"""
inputs = tokenizer(input_str, return_tensors="pt")
```
If you *really* want to use `<|im_start|>` and `<|im_end|>`, just update your `tokenizer_config.json` to use `<|im_start|>` instead of `<s>` and `<|im_end|>` instead of `</s>` and when tokenizing. And if you still don't like what I've done to this chat-ml-ish format, feel free to cry into your pillow or fork the code and do a new fine-tune.
### Llama-2 chat
```
[INST] <<SYS>>
{system}
<</SYS>>
{instruction} [/INST]
```
## Fine tuning
### SFT phase
An example for mistral-7b:
*Note: I actually used my fork of [qlora](https://github.com/jondurbin/qlora)'s `train.py` for this, but I'm porting it to a minified version here, not tested yet!*
*More notes: I stopped the SFT phase around 50% because of budget constraints.*
```bash
export BASE_DIR=/workspace
export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=bagel-7b-v0.1
# Run the pretraining.
accelerate launch bagel/tune/sft.py \
--model_name_or_path $BASE_DIR/mistral-7b \
--final_output_dir $BASE_DIR/$WANDB_PROJECT \
--output_dir $BASE_DIR/$WANDB_PROJECT-workdir \
--num_train_epochs 1 \
--logging_steps 1 \
--save_strategy steps \
--save_steps 200 \
--save_total_limit 5 \
--data_seed 42 \
--evaluation_strategy steps \
--eval_dataset_size 0.0006 \
--eval_steps 200 \
--max_new_tokens 4096 \
--dataloader_num_workers 3 \
--logging_strategy steps \
--remove_unused_columns False \
--do_train \
--full_finetune \
--bf16 \
--bits 16 \
--optim adamw_torch \
--lr_scheduler_type linear \
--dataset $BASE_DIR/bagel/bagel-input-output-v0.1.parquet \
--dataset_format input-output \
--model_max_len 4096 \
--per_device_train_batch_size 8 \
--learning_rate 3.5e-7 \
--warmup_ratio 0.005 \
--adam_beta2 0.999 \
--max_grad_norm 0.3 \
--weight_decay 0.001 \
--seed 42 \
--report_to wandb \
--gradient_checkpointing True \
--gradient_accumulation_steps 4 \
--skip_excess_length False \
--ddp_find_unused_parameters False \
--use_flash_attention_2 \
--deepspeed deepspeed.json
```
Deepspeed configuration:
```json
{
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"bf16": {
"enabled": true
},
"zero_optimization": {
"stage": 2,
"contiguous_gradients": true,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 5e8,
"allgather_bucket_size": 5e8
}
}
```
### DPO phase
An example of the DPO phase for mistral-7b (requires first running the SFT):
```bash
export BASE_DIR=/mnt/data
export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=bagel-dpo-7b-v0.1
accelerate launch bagel/tune/dpo.py \
--model_name_or_path bagel-7b-v0.1 \
--learning_rate 3e-7 \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--max_length 4096 \
--max_prompt_length 1024 \
--max_target_length 3092 \
--num_train_epochs 3 \
--report_to wandb \
--gradient_checkpointing true \
--use_flash_attention_2 true \
--dataset $BASE_DIR/bagel/bagel-dpo-v0.1.parquet \
--eval_steps 5 \
--eval_dataset_size 0.03 \
--workdir $BASE_DIR/$WANDB_PROJECT-workdir \
--output_dir $BASE_DIR/$WANDB_PROJECT \
--deepspeed deepspeed.json \
--save_steps 25 \
--save_total_limit 5
```
|
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noura-na/mistral-7b-mj-finetuned
|
noura-na
| 2023-12-13T17:09:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T17:09:08Z |
Entry not found
|
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] |
textomatic/llama-2-7b-tsla-qna-test
|
textomatic
| 2023-12-13T17:16:27Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T17:09:36Z |
Entry not found
|
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] |
LoneStriker/bagel-dpo-7b-v0.1-4.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-13T17:13:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T17:10:55Z |
---
license: apache-2.0
---
# A bagel, with everything (including DPO)
## Overview
This is the DPO'd version of https://huggingface.co/jondurbin/bagel-7b-v0.1
If you are getting too many AALLM or other refusals, even with explicitly human system prompts, you may want to try the non-DPO version.
## Benchmarks
I ran these against the latest main branch of lm-evaluation-harness (and opencompass/FastChat for agieval and mt-bench), since batch size/etc effects score for some benchmarks.
| model | arc_challenge | boolq | gsm8k | hellaswag | mmlu | openbookqa | piqa | truthful_qa | winogrande |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| bagel | __0.6715__ | 0.8813 | __0.5618__ | 0.8397 | __0.6408__ | __0.51__ | __0.8406__ | __0.6275__ | __0.7561__ |
| openhermes-2.5 | 0.6476 | __0.8835__ | 0.4852 | __0.8414__ | 0.6347 | 0.498 | 0.8400 | 0.5295 | 0.7443 |
MT-Bench:
```
########## First turn ##########
score
model turn
bagel-7b-v0.1 1 7.60625
########## Second turn ##########
score
model turn
bagel-7b-v0.1 2 7.00625
########## Average ##########
score
model
bagel-7b-v0.1 7.30625
```
## Data selection.
The first step in the process is creating a dataset.
In this case, we're actually creating a composite dataset, consisting of both supervised fine-tuning data (SFT) and direct preference optimization (DPO) data.
All instruction data, that is, data that is not plain text (like project Gutenberg and items from Cinematika) or DPO, is converted into ShareGPT format so it's easier to work with.
See the corresponding code in `bagel/data_sources/*.py` for full implementation for each data source.
Deduplication is done by creating a uuid v5 of the instruction/text, then only adding items not previously seen (where datasets are loaded in order of the confidence score I assign them).
This means that if an instruction is in data source "Foo" with confidence 4 as well as in data source "Bar" with confidence score 2, only the entry from "Foo" will be taken.
### SFT data sources
*Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check*
- [ai2_arc](https://huggingface.co/datasets/ai2_arc)
- Abstraction and reasoning dataset, useful in measuring "intelligence" to a certain extent.
- [airoboros](https://huggingface.co/datasets/unalignment/spicy-3.1)
- Variety of categories of synthetic instructions generated by gpt-4.
- [apps](https://huggingface.co/datasets/codeparrot/apps)
- Python coding dataset with 10k problems.
- [belebele](https://huggingface.co/datasets/facebook/belebele)
- Multi-lingual reading comprehension dataset.
- [boolq](https://huggingface.co/datasets/boolq)
- Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)
- [cinematika](https://huggingface.co/datasets/jondurbin/cinematika-v0.1) (instruction and plain text)
- RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.
- [drop](https://huggingface.co/datasets/drop)
- More reading comprehension.
- [gutenberg](https://www.gutenberg.org/) (plain text)
- Books/plain text, again to make the model less boring, only a handful of examples supported by [chapterize](https://github.com/JonathanReeve/chapterize)
- [lmsys_chat_1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) (only gpt-4 items, also used for DPO)
- Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.
- [mathinstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
- Composite dataset with a variety of math-related tasks and problem/question formats.
- [mmlu](https://huggingface.co/datasets/cais/mmlu)
- Massive Multitask Language Understanding - a wide variety of questions about various subject matters.
- [natural_instructions](https://huggingface.co/datasets/Muennighoff/natural-instructions)
- Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)
- [openbookqa](https://huggingface.co/datasets/openbookqa)
- Question answering dataset.
- [piqa](https://huggingface.co/datasets/piqa)
- Phyiscal interaction question answering.
- [python_alpaca](https://huggingface.co/datasets/Vezora/Tested-22k-Python-Alpaca)
- Python instruction response pairs, validated as functional.
- [rosetta_code](https://huggingface.co/datasets/cakiki/rosetta-code)
- Code problems and solutions in a variety of programming languages taken from rosettacode.org.
- [slimorca](https://huggingface.co/datasets/Open-Orca/SlimOrca)
- Collection of ~500k gpt-4 verified chats from OpenOrca.
- [spider](https://huggingface.co/datasets/spider)
- SQL-targeted dataset.
- [squad_v2](https://huggingface.co/datasets/squad_v2)
- Contextual question answering (RAG).
- [synthia](https://huggingface.co/datasets/migtissera/Synthia-v1.3)
- GPT-4 generated data using advanced prompting from Migel Tissera.
- [winogrande](https://huggingface.co/datasets/winogrande)
- Fill in the blank style prompts.
### DPO data sources
- [airoboros 3.1](https://huggingface.co/datasets/unalignment/spicy-3.1) vs [airoboros 2.2.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1)
- The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the "rejected" value and the rerolled response as "chosen"
- [helpsteer](https://huggingface.co/datasets/nvidia/HelpSteer)
- Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest "correctness" value were used for DPO here, with the highest scoring output as "chosen" and random lower scoring value as "rejected"
- [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- Another interesting dataset by Intel, which provides various DPO pairs generated from prompts included in the SlimOrca dataset.
- [toxic-dpo](https://huggingface.co/datasets/unalignment/toxic-dpo-v0.1)
- __*highly toxic and potentially illegal content!*__ De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.
- [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
- DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.
- [ultrafeedback](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned)
- One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.
Only the train splits were used (if a split was provided), and an additional pass of decontamination is performed using approximate nearest neighbor search (via faiss).
### Total dataset size
The deduplicated and decontamined list of instructions contains 1,671,822 items:
- 1,602,217 SFT/instructions
- 59,247 DPO pairs
- 1606 with both SFT and DPO data
Keep in mind, this number becomes 4x larger when applying the various prompt formats.
## Prompt formatting
In sticking with the theme of the bagel, I didn't want to use a single prompt format, so I used 4 - vicuna, llama-2, alpaca, and chat-ml (sorta).
I also didn't want to randomly select a single prompt format for each item (hoping each instruction would generalize more when used in a variety of prompt formats), so each instruction is actually converted into every prompt format.
This means each epoch of our fine-tune is really basically 4 epochs. So, for the fine-tunes, I would recommend only doing 1 epoch (or 0.75 epochs). I am testing with a single epoch using a relatively low learning rate.
### Alpaca (sort of)
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{system prompt, if provided}
{instruction}
### Response:
```
The main difference here is that because of the dataset formatting and variety of data sources, it would have been much to tedious to add an `### Input:` block, so the inputs are just in the instruction section.
### Vicuna
```
{system prompt, if provided, randomly defaulting to "A chat between a user and an unbiased, uncensored assistant."}
USER: {instruction}
ASSISTANT:
```
### ChatML (sort of)
I don't really understand the point of having special tokens for `<|im_start|>` and `<|im_end|>`, because in practice they just act as BOS and EOS tokens (but, please correct me if I'm wrong).
So, instead of:
```text
{bos}<|im_start|>{role}
{text}
<|im_end|>{eos}
```
I just changed it to:
```text
{bos}{role}
{text}
{eos}
```
In practice, this would mean tokenization code like such:
```python
tokenizer = AutoTokenizer.from_pretrained('mistralai/mistral-7b-v0.1')
input_str = f"""system
You are a goat.
{tokenizer.eos_token}
{tokenizer.bos_token}user
Tell me how to fry an egg.
{tokenizer.eos_token}
{tokenizer.bos_token}assistant
"""
inputs = tokenizer(input_str, return_tensors="pt")
```
If you *really* want to use `<|im_start|>` and `<|im_end|>`, just update your `tokenizer_config.json` to use `<|im_start|>` instead of `<s>` and `<|im_end|>` instead of `</s>` and when tokenizing. And if you still don't like what I've done to this chat-ml-ish format, feel free to cry into your pillow or fork the code and do a new fine-tune.
### Llama-2 chat
```
[INST] <<SYS>>
{system}
<</SYS>>
{instruction} [/INST]
```
## Fine tuning
### SFT phase
An example for mistral-7b:
*Note: I actually used my fork of [qlora](https://github.com/jondurbin/qlora)'s `train.py` for this, but I'm porting it to a minified version here, not tested yet!*
*More notes: I stopped the SFT phase around 50% because of budget constraints.*
```bash
export BASE_DIR=/workspace
export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=bagel-7b-v0.1
# Run the pretraining.
accelerate launch bagel/tune/sft.py \
--model_name_or_path $BASE_DIR/mistral-7b \
--final_output_dir $BASE_DIR/$WANDB_PROJECT \
--output_dir $BASE_DIR/$WANDB_PROJECT-workdir \
--num_train_epochs 1 \
--logging_steps 1 \
--save_strategy steps \
--save_steps 200 \
--save_total_limit 5 \
--data_seed 42 \
--evaluation_strategy steps \
--eval_dataset_size 0.0006 \
--eval_steps 200 \
--max_new_tokens 4096 \
--dataloader_num_workers 3 \
--logging_strategy steps \
--remove_unused_columns False \
--do_train \
--full_finetune \
--bf16 \
--bits 16 \
--optim adamw_torch \
--lr_scheduler_type linear \
--dataset $BASE_DIR/bagel/bagel-input-output-v0.1.parquet \
--dataset_format input-output \
--model_max_len 4096 \
--per_device_train_batch_size 8 \
--learning_rate 3.5e-7 \
--warmup_ratio 0.005 \
--adam_beta2 0.999 \
--max_grad_norm 0.3 \
--weight_decay 0.001 \
--seed 42 \
--report_to wandb \
--gradient_checkpointing True \
--gradient_accumulation_steps 4 \
--skip_excess_length False \
--ddp_find_unused_parameters False \
--use_flash_attention_2 \
--deepspeed deepspeed.json
```
Deepspeed configuration:
```json
{
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"bf16": {
"enabled": true
},
"zero_optimization": {
"stage": 2,
"contiguous_gradients": true,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 5e8,
"allgather_bucket_size": 5e8
}
}
```
### DPO phase
An example of the DPO phase for mistral-7b (requires first running the SFT):
```bash
export BASE_DIR=/mnt/data
export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=bagel-dpo-7b-v0.1
accelerate launch bagel/tune/dpo.py \
--model_name_or_path bagel-7b-v0.1 \
--learning_rate 3e-7 \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--max_length 4096 \
--max_prompt_length 1024 \
--max_target_length 3092 \
--num_train_epochs 3 \
--report_to wandb \
--gradient_checkpointing true \
--use_flash_attention_2 true \
--dataset $BASE_DIR/bagel/bagel-dpo-v0.1.parquet \
--eval_steps 5 \
--eval_dataset_size 0.03 \
--workdir $BASE_DIR/$WANDB_PROJECT-workdir \
--output_dir $BASE_DIR/$WANDB_PROJECT \
--deepspeed deepspeed.json \
--save_steps 25 \
--save_total_limit 5
```
|
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JAILHJH/TRTRT
|
JAILHJH
| 2023-12-13T17:11:57Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T17:11:57Z |
---
license: openrail
---
|
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Tsuinzues/tori
|
Tsuinzues
| 2023-12-13T17:15:26Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T17:15:05Z |
---
license: openrail
---
|
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brendenbogi/idk
|
brendenbogi
| 2023-12-16T06:09:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T17:15:30Z |
Entry not found
|
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] |
AIexanderDicke/deep-rl-course-unit-4-pt-2
|
AIexanderDicke
| 2023-12-13T18:57:47Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-13T17:15:58Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: deep-rl-course-unit-4-pt-2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 32.10 +/- 21.14
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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LoneStriker/bagel-dpo-7b-v0.1-5.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-13T17:21:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T17:18:29Z |
---
license: apache-2.0
---
# A bagel, with everything (including DPO)
## Overview
This is the DPO'd version of https://huggingface.co/jondurbin/bagel-7b-v0.1
If you are getting too many AALLM or other refusals, even with explicitly human system prompts, you may want to try the non-DPO version.
## Benchmarks
I ran these against the latest main branch of lm-evaluation-harness (and opencompass/FastChat for agieval and mt-bench), since batch size/etc effects score for some benchmarks.
| model | arc_challenge | boolq | gsm8k | hellaswag | mmlu | openbookqa | piqa | truthful_qa | winogrande |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| bagel | __0.6715__ | 0.8813 | __0.5618__ | 0.8397 | __0.6408__ | __0.51__ | __0.8406__ | __0.6275__ | __0.7561__ |
| openhermes-2.5 | 0.6476 | __0.8835__ | 0.4852 | __0.8414__ | 0.6347 | 0.498 | 0.8400 | 0.5295 | 0.7443 |
MT-Bench:
```
########## First turn ##########
score
model turn
bagel-7b-v0.1 1 7.60625
########## Second turn ##########
score
model turn
bagel-7b-v0.1 2 7.00625
########## Average ##########
score
model
bagel-7b-v0.1 7.30625
```
## Data selection.
The first step in the process is creating a dataset.
In this case, we're actually creating a composite dataset, consisting of both supervised fine-tuning data (SFT) and direct preference optimization (DPO) data.
All instruction data, that is, data that is not plain text (like project Gutenberg and items from Cinematika) or DPO, is converted into ShareGPT format so it's easier to work with.
See the corresponding code in `bagel/data_sources/*.py` for full implementation for each data source.
Deduplication is done by creating a uuid v5 of the instruction/text, then only adding items not previously seen (where datasets are loaded in order of the confidence score I assign them).
This means that if an instruction is in data source "Foo" with confidence 4 as well as in data source "Bar" with confidence score 2, only the entry from "Foo" will be taken.
### SFT data sources
*Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check*
- [ai2_arc](https://huggingface.co/datasets/ai2_arc)
- Abstraction and reasoning dataset, useful in measuring "intelligence" to a certain extent.
- [airoboros](https://huggingface.co/datasets/unalignment/spicy-3.1)
- Variety of categories of synthetic instructions generated by gpt-4.
- [apps](https://huggingface.co/datasets/codeparrot/apps)
- Python coding dataset with 10k problems.
- [belebele](https://huggingface.co/datasets/facebook/belebele)
- Multi-lingual reading comprehension dataset.
- [boolq](https://huggingface.co/datasets/boolq)
- Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)
- [cinematika](https://huggingface.co/datasets/jondurbin/cinematika-v0.1) (instruction and plain text)
- RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.
- [drop](https://huggingface.co/datasets/drop)
- More reading comprehension.
- [gutenberg](https://www.gutenberg.org/) (plain text)
- Books/plain text, again to make the model less boring, only a handful of examples supported by [chapterize](https://github.com/JonathanReeve/chapterize)
- [lmsys_chat_1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) (only gpt-4 items, also used for DPO)
- Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.
- [mathinstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
- Composite dataset with a variety of math-related tasks and problem/question formats.
- [mmlu](https://huggingface.co/datasets/cais/mmlu)
- Massive Multitask Language Understanding - a wide variety of questions about various subject matters.
- [natural_instructions](https://huggingface.co/datasets/Muennighoff/natural-instructions)
- Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)
- [openbookqa](https://huggingface.co/datasets/openbookqa)
- Question answering dataset.
- [piqa](https://huggingface.co/datasets/piqa)
- Phyiscal interaction question answering.
- [python_alpaca](https://huggingface.co/datasets/Vezora/Tested-22k-Python-Alpaca)
- Python instruction response pairs, validated as functional.
- [rosetta_code](https://huggingface.co/datasets/cakiki/rosetta-code)
- Code problems and solutions in a variety of programming languages taken from rosettacode.org.
- [slimorca](https://huggingface.co/datasets/Open-Orca/SlimOrca)
- Collection of ~500k gpt-4 verified chats from OpenOrca.
- [spider](https://huggingface.co/datasets/spider)
- SQL-targeted dataset.
- [squad_v2](https://huggingface.co/datasets/squad_v2)
- Contextual question answering (RAG).
- [synthia](https://huggingface.co/datasets/migtissera/Synthia-v1.3)
- GPT-4 generated data using advanced prompting from Migel Tissera.
- [winogrande](https://huggingface.co/datasets/winogrande)
- Fill in the blank style prompts.
### DPO data sources
- [airoboros 3.1](https://huggingface.co/datasets/unalignment/spicy-3.1) vs [airoboros 2.2.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1)
- The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the "rejected" value and the rerolled response as "chosen"
- [helpsteer](https://huggingface.co/datasets/nvidia/HelpSteer)
- Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest "correctness" value were used for DPO here, with the highest scoring output as "chosen" and random lower scoring value as "rejected"
- [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- Another interesting dataset by Intel, which provides various DPO pairs generated from prompts included in the SlimOrca dataset.
- [toxic-dpo](https://huggingface.co/datasets/unalignment/toxic-dpo-v0.1)
- __*highly toxic and potentially illegal content!*__ De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.
- [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
- DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.
- [ultrafeedback](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned)
- One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.
Only the train splits were used (if a split was provided), and an additional pass of decontamination is performed using approximate nearest neighbor search (via faiss).
### Total dataset size
The deduplicated and decontamined list of instructions contains 1,671,822 items:
- 1,602,217 SFT/instructions
- 59,247 DPO pairs
- 1606 with both SFT and DPO data
Keep in mind, this number becomes 4x larger when applying the various prompt formats.
## Prompt formatting
In sticking with the theme of the bagel, I didn't want to use a single prompt format, so I used 4 - vicuna, llama-2, alpaca, and chat-ml (sorta).
I also didn't want to randomly select a single prompt format for each item (hoping each instruction would generalize more when used in a variety of prompt formats), so each instruction is actually converted into every prompt format.
This means each epoch of our fine-tune is really basically 4 epochs. So, for the fine-tunes, I would recommend only doing 1 epoch (or 0.75 epochs). I am testing with a single epoch using a relatively low learning rate.
### Alpaca (sort of)
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{system prompt, if provided}
{instruction}
### Response:
```
The main difference here is that because of the dataset formatting and variety of data sources, it would have been much to tedious to add an `### Input:` block, so the inputs are just in the instruction section.
### Vicuna
```
{system prompt, if provided, randomly defaulting to "A chat between a user and an unbiased, uncensored assistant."}
USER: {instruction}
ASSISTANT:
```
### ChatML (sort of)
I don't really understand the point of having special tokens for `<|im_start|>` and `<|im_end|>`, because in practice they just act as BOS and EOS tokens (but, please correct me if I'm wrong).
So, instead of:
```text
{bos}<|im_start|>{role}
{text}
<|im_end|>{eos}
```
I just changed it to:
```text
{bos}{role}
{text}
{eos}
```
In practice, this would mean tokenization code like such:
```python
tokenizer = AutoTokenizer.from_pretrained('mistralai/mistral-7b-v0.1')
input_str = f"""system
You are a goat.
{tokenizer.eos_token}
{tokenizer.bos_token}user
Tell me how to fry an egg.
{tokenizer.eos_token}
{tokenizer.bos_token}assistant
"""
inputs = tokenizer(input_str, return_tensors="pt")
```
If you *really* want to use `<|im_start|>` and `<|im_end|>`, just update your `tokenizer_config.json` to use `<|im_start|>` instead of `<s>` and `<|im_end|>` instead of `</s>` and when tokenizing. And if you still don't like what I've done to this chat-ml-ish format, feel free to cry into your pillow or fork the code and do a new fine-tune.
### Llama-2 chat
```
[INST] <<SYS>>
{system}
<</SYS>>
{instruction} [/INST]
```
## Fine tuning
### SFT phase
An example for mistral-7b:
*Note: I actually used my fork of [qlora](https://github.com/jondurbin/qlora)'s `train.py` for this, but I'm porting it to a minified version here, not tested yet!*
*More notes: I stopped the SFT phase around 50% because of budget constraints.*
```bash
export BASE_DIR=/workspace
export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=bagel-7b-v0.1
# Run the pretraining.
accelerate launch bagel/tune/sft.py \
--model_name_or_path $BASE_DIR/mistral-7b \
--final_output_dir $BASE_DIR/$WANDB_PROJECT \
--output_dir $BASE_DIR/$WANDB_PROJECT-workdir \
--num_train_epochs 1 \
--logging_steps 1 \
--save_strategy steps \
--save_steps 200 \
--save_total_limit 5 \
--data_seed 42 \
--evaluation_strategy steps \
--eval_dataset_size 0.0006 \
--eval_steps 200 \
--max_new_tokens 4096 \
--dataloader_num_workers 3 \
--logging_strategy steps \
--remove_unused_columns False \
--do_train \
--full_finetune \
--bf16 \
--bits 16 \
--optim adamw_torch \
--lr_scheduler_type linear \
--dataset $BASE_DIR/bagel/bagel-input-output-v0.1.parquet \
--dataset_format input-output \
--model_max_len 4096 \
--per_device_train_batch_size 8 \
--learning_rate 3.5e-7 \
--warmup_ratio 0.005 \
--adam_beta2 0.999 \
--max_grad_norm 0.3 \
--weight_decay 0.001 \
--seed 42 \
--report_to wandb \
--gradient_checkpointing True \
--gradient_accumulation_steps 4 \
--skip_excess_length False \
--ddp_find_unused_parameters False \
--use_flash_attention_2 \
--deepspeed deepspeed.json
```
Deepspeed configuration:
```json
{
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"bf16": {
"enabled": true
},
"zero_optimization": {
"stage": 2,
"contiguous_gradients": true,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 5e8,
"allgather_bucket_size": 5e8
}
}
```
### DPO phase
An example of the DPO phase for mistral-7b (requires first running the SFT):
```bash
export BASE_DIR=/mnt/data
export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=bagel-dpo-7b-v0.1
accelerate launch bagel/tune/dpo.py \
--model_name_or_path bagel-7b-v0.1 \
--learning_rate 3e-7 \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--max_length 4096 \
--max_prompt_length 1024 \
--max_target_length 3092 \
--num_train_epochs 3 \
--report_to wandb \
--gradient_checkpointing true \
--use_flash_attention_2 true \
--dataset $BASE_DIR/bagel/bagel-dpo-v0.1.parquet \
--eval_steps 5 \
--eval_dataset_size 0.03 \
--workdir $BASE_DIR/$WANDB_PROJECT-workdir \
--output_dir $BASE_DIR/$WANDB_PROJECT \
--deepspeed deepspeed.json \
--save_steps 25 \
--save_total_limit 5
```
|
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] |
erbacher/vae-burgers-norevin
|
erbacher
| 2023-12-14T08:40:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"pdetokenizer",
"endpoints_compatible",
"region:us"
] | null | 2023-12-13T17:19:30Z |
Entry not found
|
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] |
Santiclibrain/mixtral_no_robots_secondtry
|
Santiclibrain
| 2023-12-16T07:57:07Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mixtral-8x7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2023-12-13T17:21:48Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mixtral-8x7B-v0.1
model-index:
- name: mixtral_no_robots_secondtry
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mixtral_no_robots_secondtry
This model is a fine-tuned version of [mistralai/Mixtral-8x7B-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9807
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 8
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0635 | 0.02 | 1000 | 1.1332 |
| 0.9311 | 0.03 | 2000 | 1.1109 |
| 0.9417 | 0.05 | 3000 | 1.0926 |
| 1.0411 | 0.06 | 4000 | 1.0809 |
| 0.9516 | 0.08 | 5000 | 1.0786 |
| 1.0107 | 0.09 | 6000 | 1.0726 |
| 1.0698 | 0.11 | 7000 | 1.0666 |
| 1.1083 | 0.13 | 8000 | 1.0638 |
| 0.9148 | 0.14 | 9000 | 1.0589 |
| 0.957 | 0.16 | 10000 | 1.0565 |
| 1.0063 | 0.17 | 11000 | 1.0531 |
| 0.9831 | 0.19 | 12000 | 1.0509 |
| 1.0826 | 0.2 | 13000 | 1.0490 |
| 0.9598 | 0.22 | 14000 | 1.0518 |
| 0.8066 | 0.23 | 15000 | 1.0453 |
| 0.8795 | 0.25 | 16000 | 1.0431 |
| 1.1402 | 0.27 | 17000 | 1.0442 |
| 1.0652 | 0.28 | 18000 | 1.0428 |
| 0.93 | 0.3 | 19000 | 1.0371 |
| 0.9727 | 0.31 | 20000 | 1.0344 |
| 1.0753 | 0.33 | 21000 | 1.0339 |
| 0.9498 | 0.34 | 22000 | 1.0303 |
| 0.6971 | 0.36 | 23000 | 1.0316 |
| 0.9259 | 0.38 | 24000 | 1.0298 |
| 1.0359 | 0.39 | 25000 | 1.0284 |
| 1.1883 | 0.41 | 26000 | 1.0273 |
| 0.8642 | 0.42 | 27000 | 1.0250 |
| 0.9147 | 0.44 | 28000 | 1.0226 |
| 0.7824 | 0.45 | 29000 | 1.0237 |
| 0.8319 | 0.47 | 30000 | 1.0219 |
| 0.9443 | 0.49 | 31000 | 1.0190 |
| 0.9103 | 0.5 | 32000 | 1.0166 |
| 0.8903 | 0.52 | 33000 | 1.0149 |
| 1.0509 | 0.53 | 34000 | 1.0148 |
| 1.0008 | 0.55 | 35000 | 1.0151 |
| 0.778 | 0.56 | 36000 | 1.0106 |
| 0.7957 | 0.58 | 37000 | 1.0090 |
| 0.8679 | 0.6 | 38000 | 1.0085 |
| 1.064 | 0.61 | 39000 | 1.0064 |
| 0.823 | 0.63 | 40000 | 1.0061 |
| 0.9117 | 0.64 | 41000 | 1.0047 |
| 0.8284 | 0.66 | 42000 | 1.0019 |
| 0.9345 | 0.67 | 43000 | 1.0012 |
| 0.9854 | 0.69 | 44000 | 1.0004 |
| 0.7631 | 0.7 | 45000 | 0.9989 |
| 0.7189 | 0.72 | 46000 | 0.9979 |
| 0.9386 | 0.74 | 47000 | 0.9952 |
| 1.011 | 0.75 | 48000 | 0.9943 |
| 0.9627 | 0.77 | 49000 | 0.9941 |
| 1.1317 | 0.78 | 50000 | 0.9923 |
| 1.0506 | 0.8 | 51000 | 0.9912 |
| 0.8596 | 0.81 | 52000 | 0.9894 |
| 0.9702 | 0.83 | 53000 | 0.9889 |
| 1.0198 | 0.85 | 54000 | 0.9875 |
| 1.1125 | 0.86 | 55000 | 0.9862 |
| 0.9356 | 0.88 | 56000 | 0.9862 |
| 0.7212 | 0.89 | 57000 | 0.9852 |
| 0.974 | 0.91 | 58000 | 0.9843 |
| 0.9369 | 0.92 | 59000 | 0.9829 |
| 0.938 | 0.94 | 60000 | 0.9826 |
| 0.8011 | 0.96 | 61000 | 0.9818 |
| 0.7937 | 0.97 | 62000 | 0.9811 |
| 0.9679 | 0.99 | 63000 | 0.9807 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
LoneStriker/bagel-dpo-7b-v0.1-6.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-13T17:29:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T17:25:58Z |
---
license: apache-2.0
---
# A bagel, with everything (including DPO)
## Overview
This is the DPO'd version of https://huggingface.co/jondurbin/bagel-7b-v0.1
If you are getting too many AALLM or other refusals, even with explicitly human system prompts, you may want to try the non-DPO version.
## Benchmarks
I ran these against the latest main branch of lm-evaluation-harness (and opencompass/FastChat for agieval and mt-bench), since batch size/etc effects score for some benchmarks.
| model | arc_challenge | boolq | gsm8k | hellaswag | mmlu | openbookqa | piqa | truthful_qa | winogrande |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| bagel | __0.6715__ | 0.8813 | __0.5618__ | 0.8397 | __0.6408__ | __0.51__ | __0.8406__ | __0.6275__ | __0.7561__ |
| openhermes-2.5 | 0.6476 | __0.8835__ | 0.4852 | __0.8414__ | 0.6347 | 0.498 | 0.8400 | 0.5295 | 0.7443 |
MT-Bench:
```
########## First turn ##########
score
model turn
bagel-7b-v0.1 1 7.60625
########## Second turn ##########
score
model turn
bagel-7b-v0.1 2 7.00625
########## Average ##########
score
model
bagel-7b-v0.1 7.30625
```
## Data selection.
The first step in the process is creating a dataset.
In this case, we're actually creating a composite dataset, consisting of both supervised fine-tuning data (SFT) and direct preference optimization (DPO) data.
All instruction data, that is, data that is not plain text (like project Gutenberg and items from Cinematika) or DPO, is converted into ShareGPT format so it's easier to work with.
See the corresponding code in `bagel/data_sources/*.py` for full implementation for each data source.
Deduplication is done by creating a uuid v5 of the instruction/text, then only adding items not previously seen (where datasets are loaded in order of the confidence score I assign them).
This means that if an instruction is in data source "Foo" with confidence 4 as well as in data source "Bar" with confidence score 2, only the entry from "Foo" will be taken.
### SFT data sources
*Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check*
- [ai2_arc](https://huggingface.co/datasets/ai2_arc)
- Abstraction and reasoning dataset, useful in measuring "intelligence" to a certain extent.
- [airoboros](https://huggingface.co/datasets/unalignment/spicy-3.1)
- Variety of categories of synthetic instructions generated by gpt-4.
- [apps](https://huggingface.co/datasets/codeparrot/apps)
- Python coding dataset with 10k problems.
- [belebele](https://huggingface.co/datasets/facebook/belebele)
- Multi-lingual reading comprehension dataset.
- [boolq](https://huggingface.co/datasets/boolq)
- Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)
- [cinematika](https://huggingface.co/datasets/jondurbin/cinematika-v0.1) (instruction and plain text)
- RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.
- [drop](https://huggingface.co/datasets/drop)
- More reading comprehension.
- [gutenberg](https://www.gutenberg.org/) (plain text)
- Books/plain text, again to make the model less boring, only a handful of examples supported by [chapterize](https://github.com/JonathanReeve/chapterize)
- [lmsys_chat_1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) (only gpt-4 items, also used for DPO)
- Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.
- [mathinstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
- Composite dataset with a variety of math-related tasks and problem/question formats.
- [mmlu](https://huggingface.co/datasets/cais/mmlu)
- Massive Multitask Language Understanding - a wide variety of questions about various subject matters.
- [natural_instructions](https://huggingface.co/datasets/Muennighoff/natural-instructions)
- Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)
- [openbookqa](https://huggingface.co/datasets/openbookqa)
- Question answering dataset.
- [piqa](https://huggingface.co/datasets/piqa)
- Phyiscal interaction question answering.
- [python_alpaca](https://huggingface.co/datasets/Vezora/Tested-22k-Python-Alpaca)
- Python instruction response pairs, validated as functional.
- [rosetta_code](https://huggingface.co/datasets/cakiki/rosetta-code)
- Code problems and solutions in a variety of programming languages taken from rosettacode.org.
- [slimorca](https://huggingface.co/datasets/Open-Orca/SlimOrca)
- Collection of ~500k gpt-4 verified chats from OpenOrca.
- [spider](https://huggingface.co/datasets/spider)
- SQL-targeted dataset.
- [squad_v2](https://huggingface.co/datasets/squad_v2)
- Contextual question answering (RAG).
- [synthia](https://huggingface.co/datasets/migtissera/Synthia-v1.3)
- GPT-4 generated data using advanced prompting from Migel Tissera.
- [winogrande](https://huggingface.co/datasets/winogrande)
- Fill in the blank style prompts.
### DPO data sources
- [airoboros 3.1](https://huggingface.co/datasets/unalignment/spicy-3.1) vs [airoboros 2.2.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1)
- The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the "rejected" value and the rerolled response as "chosen"
- [helpsteer](https://huggingface.co/datasets/nvidia/HelpSteer)
- Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest "correctness" value were used for DPO here, with the highest scoring output as "chosen" and random lower scoring value as "rejected"
- [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- Another interesting dataset by Intel, which provides various DPO pairs generated from prompts included in the SlimOrca dataset.
- [toxic-dpo](https://huggingface.co/datasets/unalignment/toxic-dpo-v0.1)
- __*highly toxic and potentially illegal content!*__ De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.
- [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
- DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.
- [ultrafeedback](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned)
- One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.
Only the train splits were used (if a split was provided), and an additional pass of decontamination is performed using approximate nearest neighbor search (via faiss).
### Total dataset size
The deduplicated and decontamined list of instructions contains 1,671,822 items:
- 1,602,217 SFT/instructions
- 59,247 DPO pairs
- 1606 with both SFT and DPO data
Keep in mind, this number becomes 4x larger when applying the various prompt formats.
## Prompt formatting
In sticking with the theme of the bagel, I didn't want to use a single prompt format, so I used 4 - vicuna, llama-2, alpaca, and chat-ml (sorta).
I also didn't want to randomly select a single prompt format for each item (hoping each instruction would generalize more when used in a variety of prompt formats), so each instruction is actually converted into every prompt format.
This means each epoch of our fine-tune is really basically 4 epochs. So, for the fine-tunes, I would recommend only doing 1 epoch (or 0.75 epochs). I am testing with a single epoch using a relatively low learning rate.
### Alpaca (sort of)
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{system prompt, if provided}
{instruction}
### Response:
```
The main difference here is that because of the dataset formatting and variety of data sources, it would have been much to tedious to add an `### Input:` block, so the inputs are just in the instruction section.
### Vicuna
```
{system prompt, if provided, randomly defaulting to "A chat between a user and an unbiased, uncensored assistant."}
USER: {instruction}
ASSISTANT:
```
### ChatML (sort of)
I don't really understand the point of having special tokens for `<|im_start|>` and `<|im_end|>`, because in practice they just act as BOS and EOS tokens (but, please correct me if I'm wrong).
So, instead of:
```text
{bos}<|im_start|>{role}
{text}
<|im_end|>{eos}
```
I just changed it to:
```text
{bos}{role}
{text}
{eos}
```
In practice, this would mean tokenization code like such:
```python
tokenizer = AutoTokenizer.from_pretrained('mistralai/mistral-7b-v0.1')
input_str = f"""system
You are a goat.
{tokenizer.eos_token}
{tokenizer.bos_token}user
Tell me how to fry an egg.
{tokenizer.eos_token}
{tokenizer.bos_token}assistant
"""
inputs = tokenizer(input_str, return_tensors="pt")
```
If you *really* want to use `<|im_start|>` and `<|im_end|>`, just update your `tokenizer_config.json` to use `<|im_start|>` instead of `<s>` and `<|im_end|>` instead of `</s>` and when tokenizing. And if you still don't like what I've done to this chat-ml-ish format, feel free to cry into your pillow or fork the code and do a new fine-tune.
### Llama-2 chat
```
[INST] <<SYS>>
{system}
<</SYS>>
{instruction} [/INST]
```
## Fine tuning
### SFT phase
An example for mistral-7b:
*Note: I actually used my fork of [qlora](https://github.com/jondurbin/qlora)'s `train.py` for this, but I'm porting it to a minified version here, not tested yet!*
*More notes: I stopped the SFT phase around 50% because of budget constraints.*
```bash
export BASE_DIR=/workspace
export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=bagel-7b-v0.1
# Run the pretraining.
accelerate launch bagel/tune/sft.py \
--model_name_or_path $BASE_DIR/mistral-7b \
--final_output_dir $BASE_DIR/$WANDB_PROJECT \
--output_dir $BASE_DIR/$WANDB_PROJECT-workdir \
--num_train_epochs 1 \
--logging_steps 1 \
--save_strategy steps \
--save_steps 200 \
--save_total_limit 5 \
--data_seed 42 \
--evaluation_strategy steps \
--eval_dataset_size 0.0006 \
--eval_steps 200 \
--max_new_tokens 4096 \
--dataloader_num_workers 3 \
--logging_strategy steps \
--remove_unused_columns False \
--do_train \
--full_finetune \
--bf16 \
--bits 16 \
--optim adamw_torch \
--lr_scheduler_type linear \
--dataset $BASE_DIR/bagel/bagel-input-output-v0.1.parquet \
--dataset_format input-output \
--model_max_len 4096 \
--per_device_train_batch_size 8 \
--learning_rate 3.5e-7 \
--warmup_ratio 0.005 \
--adam_beta2 0.999 \
--max_grad_norm 0.3 \
--weight_decay 0.001 \
--seed 42 \
--report_to wandb \
--gradient_checkpointing True \
--gradient_accumulation_steps 4 \
--skip_excess_length False \
--ddp_find_unused_parameters False \
--use_flash_attention_2 \
--deepspeed deepspeed.json
```
Deepspeed configuration:
```json
{
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"bf16": {
"enabled": true
},
"zero_optimization": {
"stage": 2,
"contiguous_gradients": true,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 5e8,
"allgather_bucket_size": 5e8
}
}
```
### DPO phase
An example of the DPO phase for mistral-7b (requires first running the SFT):
```bash
export BASE_DIR=/mnt/data
export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=bagel-dpo-7b-v0.1
accelerate launch bagel/tune/dpo.py \
--model_name_or_path bagel-7b-v0.1 \
--learning_rate 3e-7 \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--max_length 4096 \
--max_prompt_length 1024 \
--max_target_length 3092 \
--num_train_epochs 3 \
--report_to wandb \
--gradient_checkpointing true \
--use_flash_attention_2 true \
--dataset $BASE_DIR/bagel/bagel-dpo-v0.1.parquet \
--eval_steps 5 \
--eval_dataset_size 0.03 \
--workdir $BASE_DIR/$WANDB_PROJECT-workdir \
--output_dir $BASE_DIR/$WANDB_PROJECT \
--deepspeed deepspeed.json \
--save_steps 25 \
--save_total_limit 5
```
|
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Zaitounish/EXP_100_Train_RAG_SemanticSearch
|
Zaitounish
| 2023-12-13T17:26:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T17:26:11Z |
Entry not found
|
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danielssj88/platzi-vit-model-omar-espejel
|
danielssj88
| 2023-12-13T17:27:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T17:27:21Z |
Entry not found
|
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] |
hkivancoral/smids_3x_beit_base_sgd_0001_fold2
|
hkivancoral
| 2023-12-13T18:15:58Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T17:28:13Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_sgd_0001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7886855241264559
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_3x_beit_base_sgd_0001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5470
- Accuracy: 0.7887
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1643 | 1.0 | 225 | 1.2557 | 0.3494 |
| 1.1336 | 2.0 | 450 | 1.1964 | 0.3727 |
| 1.0702 | 3.0 | 675 | 1.1415 | 0.3960 |
| 1.0744 | 4.0 | 900 | 1.0897 | 0.4226 |
| 0.9272 | 5.0 | 1125 | 1.0392 | 0.4526 |
| 0.9348 | 6.0 | 1350 | 0.9924 | 0.4908 |
| 0.9221 | 7.0 | 1575 | 0.9474 | 0.5374 |
| 0.8806 | 8.0 | 1800 | 0.9069 | 0.5890 |
| 0.8541 | 9.0 | 2025 | 0.8693 | 0.6206 |
| 0.8102 | 10.0 | 2250 | 0.8367 | 0.6439 |
| 0.7893 | 11.0 | 2475 | 0.8072 | 0.6672 |
| 0.7786 | 12.0 | 2700 | 0.7812 | 0.6872 |
| 0.7601 | 13.0 | 2925 | 0.7581 | 0.7038 |
| 0.7654 | 14.0 | 3150 | 0.7376 | 0.7105 |
| 0.7556 | 15.0 | 3375 | 0.7195 | 0.7171 |
| 0.7319 | 16.0 | 3600 | 0.7031 | 0.7321 |
| 0.6868 | 17.0 | 3825 | 0.6881 | 0.7354 |
| 0.7278 | 18.0 | 4050 | 0.6745 | 0.7421 |
| 0.6222 | 19.0 | 4275 | 0.6623 | 0.7454 |
| 0.6905 | 20.0 | 4500 | 0.6515 | 0.7471 |
| 0.6715 | 21.0 | 4725 | 0.6419 | 0.7554 |
| 0.7342 | 22.0 | 4950 | 0.6326 | 0.7554 |
| 0.6844 | 23.0 | 5175 | 0.6245 | 0.7621 |
| 0.6577 | 24.0 | 5400 | 0.6173 | 0.7654 |
| 0.6177 | 25.0 | 5625 | 0.6101 | 0.7687 |
| 0.647 | 26.0 | 5850 | 0.6037 | 0.7671 |
| 0.6355 | 27.0 | 6075 | 0.5976 | 0.7704 |
| 0.6059 | 28.0 | 6300 | 0.5926 | 0.7704 |
| 0.5954 | 29.0 | 6525 | 0.5873 | 0.7770 |
| 0.6256 | 30.0 | 6750 | 0.5829 | 0.7787 |
| 0.6261 | 31.0 | 6975 | 0.5789 | 0.7820 |
| 0.5804 | 32.0 | 7200 | 0.5748 | 0.7820 |
| 0.5936 | 33.0 | 7425 | 0.5711 | 0.7854 |
| 0.5647 | 34.0 | 7650 | 0.5682 | 0.7854 |
| 0.6238 | 35.0 | 7875 | 0.5657 | 0.7854 |
| 0.5976 | 36.0 | 8100 | 0.5630 | 0.7854 |
| 0.5852 | 37.0 | 8325 | 0.5605 | 0.7870 |
| 0.5826 | 38.0 | 8550 | 0.5584 | 0.7854 |
| 0.5619 | 39.0 | 8775 | 0.5564 | 0.7854 |
| 0.5946 | 40.0 | 9000 | 0.5547 | 0.7870 |
| 0.5381 | 41.0 | 9225 | 0.5529 | 0.7870 |
| 0.5966 | 42.0 | 9450 | 0.5514 | 0.7870 |
| 0.588 | 43.0 | 9675 | 0.5504 | 0.7870 |
| 0.5705 | 44.0 | 9900 | 0.5494 | 0.7854 |
| 0.6073 | 45.0 | 10125 | 0.5486 | 0.7870 |
| 0.5915 | 46.0 | 10350 | 0.5480 | 0.7887 |
| 0.5988 | 47.0 | 10575 | 0.5476 | 0.7887 |
| 0.542 | 48.0 | 10800 | 0.5472 | 0.7887 |
| 0.5885 | 49.0 | 11025 | 0.5471 | 0.7887 |
| 0.5585 | 50.0 | 11250 | 0.5470 | 0.7887 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
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] |
shirzady1934/bert-riddle-finetuned_2choice
|
shirzady1934
| 2023-12-13T17:29:23Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"multiple-choice",
"mhs",
"generated_from_trainer",
"en",
"base_model:bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-12-13T17:29:06Z |
---
language:
- en
license: apache-2.0
base_model: bert-base-uncased
tags:
- mhs
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_base_uncased
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the WP dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5591
- Accuracy: 0.8500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 23 | 0.5552 | 0.8250 |
| No log | 2.0 | 46 | 0.4623 | 0.8250 |
| No log | 3.0 | 69 | 0.5304 | 0.8250 |
| No log | 4.0 | 92 | 0.5741 | 0.8500 |
| No log | 5.0 | 115 | 0.5591 | 0.8500 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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EmmaGthn/results_lora_40_5000_bias
|
EmmaGthn
| 2023-12-13T20:25:33Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-13T17:30:28Z |
Entry not found
|
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] |
hkivancoral/smids_3x_beit_base_rms_001_fold2
|
hkivancoral
| 2023-12-13T18:18:58Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-13T17:30:32Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_3x_beit_base_rms_001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7737104825291181
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_3x_beit_base_rms_001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8871
- Accuracy: 0.7737
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1063 | 1.0 | 225 | 1.1925 | 0.3627 |
| 0.8848 | 2.0 | 450 | 0.8623 | 0.5557 |
| 0.9929 | 3.0 | 675 | 0.7924 | 0.5774 |
| 0.7922 | 4.0 | 900 | 0.7743 | 0.5973 |
| 0.7804 | 5.0 | 1125 | 0.7554 | 0.5940 |
| 0.7536 | 6.0 | 1350 | 0.7911 | 0.5740 |
| 0.7389 | 7.0 | 1575 | 0.8973 | 0.5524 |
| 0.8004 | 8.0 | 1800 | 0.7349 | 0.6140 |
| 0.7283 | 9.0 | 2025 | 0.7228 | 0.6356 |
| 0.7381 | 10.0 | 2250 | 0.7154 | 0.6389 |
| 0.8566 | 11.0 | 2475 | 0.7154 | 0.6373 |
| 0.725 | 12.0 | 2700 | 0.6853 | 0.6539 |
| 0.7139 | 13.0 | 2925 | 0.6833 | 0.6722 |
| 0.708 | 14.0 | 3150 | 0.7156 | 0.6489 |
| 0.6892 | 15.0 | 3375 | 0.6841 | 0.6955 |
| 0.7392 | 16.0 | 3600 | 0.6648 | 0.6905 |
| 0.7123 | 17.0 | 3825 | 0.6864 | 0.6689 |
| 0.6752 | 18.0 | 4050 | 0.6534 | 0.7088 |
| 0.7193 | 19.0 | 4275 | 0.7054 | 0.6755 |
| 0.6734 | 20.0 | 4500 | 0.6500 | 0.6855 |
| 0.649 | 21.0 | 4725 | 0.6222 | 0.6872 |
| 0.7173 | 22.0 | 4950 | 0.6280 | 0.7321 |
| 0.6723 | 23.0 | 5175 | 0.6016 | 0.7587 |
| 0.6406 | 24.0 | 5400 | 0.6206 | 0.7221 |
| 0.6216 | 25.0 | 5625 | 0.6173 | 0.7338 |
| 0.6154 | 26.0 | 5850 | 0.5917 | 0.7488 |
| 0.6137 | 27.0 | 6075 | 0.6327 | 0.7304 |
| 0.597 | 28.0 | 6300 | 0.6319 | 0.7155 |
| 0.6292 | 29.0 | 6525 | 0.6003 | 0.7321 |
| 0.615 | 30.0 | 6750 | 0.5967 | 0.7554 |
| 0.5842 | 31.0 | 6975 | 0.5866 | 0.7587 |
| 0.5976 | 32.0 | 7200 | 0.5968 | 0.7388 |
| 0.5096 | 33.0 | 7425 | 0.5717 | 0.7671 |
| 0.4883 | 34.0 | 7650 | 0.5888 | 0.7804 |
| 0.5258 | 35.0 | 7875 | 0.6027 | 0.7820 |
| 0.49 | 36.0 | 8100 | 0.6052 | 0.7820 |
| 0.5271 | 37.0 | 8325 | 0.5944 | 0.7654 |
| 0.4464 | 38.0 | 8550 | 0.6867 | 0.7504 |
| 0.3796 | 39.0 | 8775 | 0.6032 | 0.7820 |
| 0.4175 | 40.0 | 9000 | 0.6446 | 0.7704 |
| 0.3633 | 41.0 | 9225 | 0.6564 | 0.7804 |
| 0.4496 | 42.0 | 9450 | 0.6467 | 0.7770 |
| 0.2811 | 43.0 | 9675 | 0.6703 | 0.7754 |
| 0.3066 | 44.0 | 9900 | 0.7311 | 0.7754 |
| 0.3558 | 45.0 | 10125 | 0.7685 | 0.7787 |
| 0.2645 | 46.0 | 10350 | 0.7874 | 0.7754 |
| 0.2214 | 47.0 | 10575 | 0.8226 | 0.7737 |
| 0.2321 | 48.0 | 10800 | 0.8600 | 0.7704 |
| 0.314 | 49.0 | 11025 | 0.8728 | 0.7770 |
| 0.1915 | 50.0 | 11250 | 0.8871 | 0.7737 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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OpenNMT/mixtral-onmt-awq
|
OpenNMT
| 2023-12-13T17:47:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T17:32:33Z |
Entry not found
|
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mahsamassoud/mnist-6
|
mahsamassoud
| 2023-12-13T17:33:12Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-13T17:33:12Z |
Entry not found
|
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LoneStriker/bagel-dpo-7b-v0.1-8.0bpw-h8-exl2-2
|
LoneStriker
| 2023-12-13T17:40:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T17:33:32Z |
---
license: apache-2.0
---
# A bagel, with everything (including DPO)
## Overview
This is the DPO'd version of https://huggingface.co/jondurbin/bagel-7b-v0.1
If you are getting too many AALLM or other refusals, even with explicitly human system prompts, you may want to try the non-DPO version.
## Benchmarks
I ran these against the latest main branch of lm-evaluation-harness (and opencompass/FastChat for agieval and mt-bench), since batch size/etc effects score for some benchmarks.
| model | arc_challenge | boolq | gsm8k | hellaswag | mmlu | openbookqa | piqa | truthful_qa | winogrande |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| bagel | __0.6715__ | 0.8813 | __0.5618__ | 0.8397 | __0.6408__ | __0.51__ | __0.8406__ | __0.6275__ | __0.7561__ |
| openhermes-2.5 | 0.6476 | __0.8835__ | 0.4852 | __0.8414__ | 0.6347 | 0.498 | 0.8400 | 0.5295 | 0.7443 |
MT-Bench:
```
########## First turn ##########
score
model turn
bagel-7b-v0.1 1 7.60625
########## Second turn ##########
score
model turn
bagel-7b-v0.1 2 7.00625
########## Average ##########
score
model
bagel-7b-v0.1 7.30625
```
## Data selection.
The first step in the process is creating a dataset.
In this case, we're actually creating a composite dataset, consisting of both supervised fine-tuning data (SFT) and direct preference optimization (DPO) data.
All instruction data, that is, data that is not plain text (like project Gutenberg and items from Cinematika) or DPO, is converted into ShareGPT format so it's easier to work with.
See the corresponding code in `bagel/data_sources/*.py` for full implementation for each data source.
Deduplication is done by creating a uuid v5 of the instruction/text, then only adding items not previously seen (where datasets are loaded in order of the confidence score I assign them).
This means that if an instruction is in data source "Foo" with confidence 4 as well as in data source "Bar" with confidence score 2, only the entry from "Foo" will be taken.
### SFT data sources
*Yes, you will see benchmark names in the list, but this only uses the train splits, and a decontamination by cosine similarity is performed at the end as a sanity check*
- [ai2_arc](https://huggingface.co/datasets/ai2_arc)
- Abstraction and reasoning dataset, useful in measuring "intelligence" to a certain extent.
- [airoboros](https://huggingface.co/datasets/unalignment/spicy-3.1)
- Variety of categories of synthetic instructions generated by gpt-4.
- [apps](https://huggingface.co/datasets/codeparrot/apps)
- Python coding dataset with 10k problems.
- [belebele](https://huggingface.co/datasets/facebook/belebele)
- Multi-lingual reading comprehension dataset.
- [boolq](https://huggingface.co/datasets/boolq)
- Corpus of yes/no questions (which can be surprisingly difficult for AI to answer apparently?)
- [cinematika](https://huggingface.co/datasets/jondurbin/cinematika-v0.1) (instruction and plain text)
- RP-style data synthesized from movie scripts so the model isn't quite as boring as it otherwise would be.
- [drop](https://huggingface.co/datasets/drop)
- More reading comprehension.
- [gutenberg](https://www.gutenberg.org/) (plain text)
- Books/plain text, again to make the model less boring, only a handful of examples supported by [chapterize](https://github.com/JonathanReeve/chapterize)
- [lmsys_chat_1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) (only gpt-4 items, also used for DPO)
- Chats collected by the lmsys chat arena, containing a wide variety of chats with various models.
- [mathinstruct](https://huggingface.co/datasets/TIGER-Lab/MathInstruct)
- Composite dataset with a variety of math-related tasks and problem/question formats.
- [mmlu](https://huggingface.co/datasets/cais/mmlu)
- Massive Multitask Language Understanding - a wide variety of questions about various subject matters.
- [natural_instructions](https://huggingface.co/datasets/Muennighoff/natural-instructions)
- Millions of instructions from 1600+ task categories (sampled down substantially, stratified by task type)
- [openbookqa](https://huggingface.co/datasets/openbookqa)
- Question answering dataset.
- [piqa](https://huggingface.co/datasets/piqa)
- Phyiscal interaction question answering.
- [python_alpaca](https://huggingface.co/datasets/Vezora/Tested-22k-Python-Alpaca)
- Python instruction response pairs, validated as functional.
- [rosetta_code](https://huggingface.co/datasets/cakiki/rosetta-code)
- Code problems and solutions in a variety of programming languages taken from rosettacode.org.
- [slimorca](https://huggingface.co/datasets/Open-Orca/SlimOrca)
- Collection of ~500k gpt-4 verified chats from OpenOrca.
- [spider](https://huggingface.co/datasets/spider)
- SQL-targeted dataset.
- [squad_v2](https://huggingface.co/datasets/squad_v2)
- Contextual question answering (RAG).
- [synthia](https://huggingface.co/datasets/migtissera/Synthia-v1.3)
- GPT-4 generated data using advanced prompting from Migel Tissera.
- [winogrande](https://huggingface.co/datasets/winogrande)
- Fill in the blank style prompts.
### DPO data sources
- [airoboros 3.1](https://huggingface.co/datasets/unalignment/spicy-3.1) vs [airoboros 2.2.1](https://huggingface.co/datasets/jondurbin/airoboros-gpt4-1.4.1)
- The creative/writing tasks from airoboros-2.2.1 were re-generated using gpt4-0314 and a custom prompt to get longer, more creative, less clichè responses for airoboros 3.1, so we can use the shorter/boring version as the "rejected" value and the rerolled response as "chosen"
- [helpsteer](https://huggingface.co/datasets/nvidia/HelpSteer)
- Really neat dataset provided by the folks at NVidia with human annotation across a variety of metrics. Only items with the highest "correctness" value were used for DPO here, with the highest scoring output as "chosen" and random lower scoring value as "rejected"
- [orca_dpo_pairs](https://huggingface.co/datasets/Intel/orca_dpo_pairs)
- Another interesting dataset by Intel, which provides various DPO pairs generated from prompts included in the SlimOrca dataset.
- [toxic-dpo](https://huggingface.co/datasets/unalignment/toxic-dpo-v0.1)
- __*highly toxic and potentially illegal content!*__ De-censorship, for academic and lawful purposes only, of course. Generated by llama-2-70b via prompt engineering.
- [truthy](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1)
- DPO pairs meant to increase truthfulness of the model, e.g. common misconceptions, differentiate between AI assistants and roleplayed human in terms of corporeal awareness/locality/etc.
- [ultrafeedback](https://huggingface.co/datasets/allenai/ultrafeedback_binarized_cleaned)
- One of the bits of magic behind the Zephyr model. Only the items with a chosen score of 8 or higher were included.
Only the train splits were used (if a split was provided), and an additional pass of decontamination is performed using approximate nearest neighbor search (via faiss).
### Total dataset size
The deduplicated and decontamined list of instructions contains 1,671,822 items:
- 1,602,217 SFT/instructions
- 59,247 DPO pairs
- 1606 with both SFT and DPO data
Keep in mind, this number becomes 4x larger when applying the various prompt formats.
## Prompt formatting
In sticking with the theme of the bagel, I didn't want to use a single prompt format, so I used 4 - vicuna, llama-2, alpaca, and chat-ml (sorta).
I also didn't want to randomly select a single prompt format for each item (hoping each instruction would generalize more when used in a variety of prompt formats), so each instruction is actually converted into every prompt format.
This means each epoch of our fine-tune is really basically 4 epochs. So, for the fine-tunes, I would recommend only doing 1 epoch (or 0.75 epochs). I am testing with a single epoch using a relatively low learning rate.
### Alpaca (sort of)
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{system prompt, if provided}
{instruction}
### Response:
```
The main difference here is that because of the dataset formatting and variety of data sources, it would have been much to tedious to add an `### Input:` block, so the inputs are just in the instruction section.
### Vicuna
```
{system prompt, if provided, randomly defaulting to "A chat between a user and an unbiased, uncensored assistant."}
USER: {instruction}
ASSISTANT:
```
### ChatML (sort of)
I don't really understand the point of having special tokens for `<|im_start|>` and `<|im_end|>`, because in practice they just act as BOS and EOS tokens (but, please correct me if I'm wrong).
So, instead of:
```text
{bos}<|im_start|>{role}
{text}
<|im_end|>{eos}
```
I just changed it to:
```text
{bos}{role}
{text}
{eos}
```
In practice, this would mean tokenization code like such:
```python
tokenizer = AutoTokenizer.from_pretrained('mistralai/mistral-7b-v0.1')
input_str = f"""system
You are a goat.
{tokenizer.eos_token}
{tokenizer.bos_token}user
Tell me how to fry an egg.
{tokenizer.eos_token}
{tokenizer.bos_token}assistant
"""
inputs = tokenizer(input_str, return_tensors="pt")
```
If you *really* want to use `<|im_start|>` and `<|im_end|>`, just update your `tokenizer_config.json` to use `<|im_start|>` instead of `<s>` and `<|im_end|>` instead of `</s>` and when tokenizing. And if you still don't like what I've done to this chat-ml-ish format, feel free to cry into your pillow or fork the code and do a new fine-tune.
### Llama-2 chat
```
[INST] <<SYS>>
{system}
<</SYS>>
{instruction} [/INST]
```
## Fine tuning
### SFT phase
An example for mistral-7b:
*Note: I actually used my fork of [qlora](https://github.com/jondurbin/qlora)'s `train.py` for this, but I'm porting it to a minified version here, not tested yet!*
*More notes: I stopped the SFT phase around 50% because of budget constraints.*
```bash
export BASE_DIR=/workspace
export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=bagel-7b-v0.1
# Run the pretraining.
accelerate launch bagel/tune/sft.py \
--model_name_or_path $BASE_DIR/mistral-7b \
--final_output_dir $BASE_DIR/$WANDB_PROJECT \
--output_dir $BASE_DIR/$WANDB_PROJECT-workdir \
--num_train_epochs 1 \
--logging_steps 1 \
--save_strategy steps \
--save_steps 200 \
--save_total_limit 5 \
--data_seed 42 \
--evaluation_strategy steps \
--eval_dataset_size 0.0006 \
--eval_steps 200 \
--max_new_tokens 4096 \
--dataloader_num_workers 3 \
--logging_strategy steps \
--remove_unused_columns False \
--do_train \
--full_finetune \
--bf16 \
--bits 16 \
--optim adamw_torch \
--lr_scheduler_type linear \
--dataset $BASE_DIR/bagel/bagel-input-output-v0.1.parquet \
--dataset_format input-output \
--model_max_len 4096 \
--per_device_train_batch_size 8 \
--learning_rate 3.5e-7 \
--warmup_ratio 0.005 \
--adam_beta2 0.999 \
--max_grad_norm 0.3 \
--weight_decay 0.001 \
--seed 42 \
--report_to wandb \
--gradient_checkpointing True \
--gradient_accumulation_steps 4 \
--skip_excess_length False \
--ddp_find_unused_parameters False \
--use_flash_attention_2 \
--deepspeed deepspeed.json
```
Deepspeed configuration:
```json
{
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto",
"bf16": {
"enabled": true
},
"zero_optimization": {
"stage": 2,
"contiguous_gradients": true,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 5e8,
"allgather_bucket_size": 5e8
}
}
```
### DPO phase
An example of the DPO phase for mistral-7b (requires first running the SFT):
```bash
export BASE_DIR=/mnt/data
export WANDB_API_KEY=[redacted]
export WANDB_PROJECT=bagel-dpo-7b-v0.1
accelerate launch bagel/tune/dpo.py \
--model_name_or_path bagel-7b-v0.1 \
--learning_rate 3e-7 \
--per_device_train_batch_size 2 \
--gradient_accumulation_steps 4 \
--max_length 4096 \
--max_prompt_length 1024 \
--max_target_length 3092 \
--num_train_epochs 3 \
--report_to wandb \
--gradient_checkpointing true \
--use_flash_attention_2 true \
--dataset $BASE_DIR/bagel/bagel-dpo-v0.1.parquet \
--eval_steps 5 \
--eval_dataset_size 0.03 \
--workdir $BASE_DIR/$WANDB_PROJECT-workdir \
--output_dir $BASE_DIR/$WANDB_PROJECT \
--deepspeed deepspeed.json \
--save_steps 25 \
--save_total_limit 5
```
|
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LoneStriker/SOLAR-10.7B-v1.0-3.0bpw-h6-exl2-2
|
LoneStriker
| 2023-12-13T17:40:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-13T17:35:17Z |
---
license: apache-2.0
---
# **Meet 10.7B Solar: Elevating Performance with Upstage Depth UP Scaling!**
# **Introduction**
We introduce the first 10.7 billion (B) parameter model, SOLAR-10.7B. It's compact, yet remarkably powerful, and demonstrates unparalleled state-of-the-art performance in models with parameters under 30B.
We developed the Depth Up-Scaling technique. Built on the Llama2 architecture, SOLAR-10.7B incorporates the innovative Upstage Depth Up-Scaling. We then integrated Mistral 7B weights into the upscaled layers, and finally, continued pre-training for the entire model.
Depth-Upscaled SOLAR-10.7B has remarkable performance. It outperforms models with up to 30B parameters, even surpassing the recent Mixtral 8X7B model. For detailed information, please refer to the experimental table ([link to be updated soon]).
Solar 10.7B is an ideal choice for fine-tuning. SOLAR-10.7B offers robustness and adaptability for your fine-tuning needs. Our simple instruction fine-tuning using the SOLAR-10.7B pre-trained model yields significant performance improvements. [[link to be updated soon]]
# **Evaluation Results**
| Model | H6 | Model Size |
|----------------------------------------|-------|------------|
| **SOLAR-10.7B-Instruct-v1.0** | **74.20** | **~ 11B** |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | 72.62 | ~ 46.7B |
| 01-ai/Yi-34B-200K | 70.81 | ~ 34B |
| 01-ai/Yi-34B | 69.42 | ~ 34B |
| mistralai/Mixtral-8x7B-v0.1 | 68.42 | ~ 46.7B |
| meta-llama/Llama-2-70b-hf | 67.87 | ~ 70B |
| tiiuae/falcon-180B | 67.85 | ~ 180B |
| **SOLAR-10.7B-v1.0** | **66.04** | **~11B** |
| mistralai/Mistral-7B-Instruct-v0.2 | 65.71 | ~ 7B |
| Qwen/Qwen-14B | 65.86 | ~ 14B |
| 01-ai/Yi-34B-Chat | 65.32 | ~34B |
| meta-llama/Llama-2-70b-chat-hf | 62.4 | ~ 70B |
| mistralai/Mistral-7B-v0.1 | 60.97 | ~ 7B |
| mistralai/Mistral-7B-Instruct-v0.1 | 54.96 | ~ 7B |
# **Usage Instructions**
This model is pre-trained and is capable of just generating random text. To use it for chatting, you must fine-tune the model first.
### **Version**
Make sure you have the correct version of the transformers library installed:
```sh
pip install transformers==4.35.2
```
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Upstage/SOLAR-10.7B-v1.0")
model = AutoModelForCausalLM.from_pretrained(
"Upstage/SOLAR-10.7B-v1.0",
device_map="auto",
torch_dtype=torch.float16,
)
```
### **Generating Text**
To generate text, use the following Python code:
```python
text = "Hi, my name is "
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
### **The Upstage AI Team** ###
Upstage is creating the best LLM and DocAI. Please find more information at https://upstage.ai
### **Contact Us** ###
Any questions and suggestions, please use the discussion tab. If you want to contact us directly, drop an email to [[email protected] ](mailto:[email protected])
|
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] |
Lemmih/Qwen-GGUF
|
Lemmih
| 2023-12-17T11:11:11Z | 0 | 1 | null |
[
"gguf",
"license:other",
"region:us"
] | null | 2023-12-13T17:35:54Z |
---
license: other
license_name: tongyi-qianwen
license_link: >-
https://github.com/QwenLM/Qwen/blob/main/Tongyi%20Qianwen%20LICENSE%20AGREEMENT
---
# Quantized Qwen models for llama.cpp
Download a model:
```bash
huggingface-cli download Lemmih/Qwen-GGUF qwen-1_8b-chat-q4_0.gguf --local-dir .
```
Run the model (from the folder where you built llama.cpp):
```bash
./main --model qwen-1_8b-chat-q4_0.gguf -i -cml -f prompts/chat-with-qwen.txt
```
If in doubt, use the `chat` models rather than the non-chat models.
|
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] |
cr8br0ze/tranditional_finetunue_openclip_low_loss
|
cr8br0ze
| 2023-12-13T17:40:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"clip",
"zero-shot-image-classification",
"license:mit",
"endpoints_compatible",
"region:us"
] |
zero-shot-image-classification
| 2023-12-13T17:36:15Z |
---
license: mit
---
|
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] |
RUXHIR2828/laroi
|
RUXHIR2828
| 2023-12-13T17:39:03Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-13T17:37:10Z |
---
license: openrail
---
|
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