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null | null | {"license": "openrail"} | Coolwowsocoolwow/Peppa_Pig_Narrator_GPT_SoVITS | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-26T23:11:39+00:00 |
|
question-answering | transformers |
<!-- 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-finetuned-covidqa
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 5e-05
- 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
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "bert-finetuned-covidqa", "results": []}]} | momo345/bert-finetuned-covidqa | null | [
"transformers",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T23:12:36+00:00 |
text-generation | transformers |
<!-- 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. -->
# zephyr-7b-gemma-dpo
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) on the argilla/dpo-mix-7k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4567
- Rewards/chosen: -3.5881
- Rewards/rejected: -5.3433
- Rewards/accuracies: 0.7660
- Rewards/margins: 1.7552
- Logps/rejected: -515.8483
- Logps/chosen: -428.1110
- Logits/rejected: 94.0535
- Logits/chosen: 91.3372
## 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-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.1578 | 1.8957 | 100 | 0.4643 | -3.5909 | -5.3391 | 0.75 | 1.7481 | -515.7638 | -428.1683 | 94.0722 | 91.3541 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "other", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "alignment-handbook", "generated_from_trainer"], "datasets": ["argilla/dpo-mix-7k"], "base_model": "HuggingFaceH4/zephyr-7b-gemma-sft-v0.1", "model-index": [{"name": "zephyr-7b-gemma-dpo", "results": []}]} | chrlu/zephyr-7b-gemma-dpo | null | [
"transformers",
"tensorboard",
"safetensors",
"gemma",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:argilla/dpo-mix-7k",
"base_model:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:13:38+00:00 |
null | peft |
<!-- 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. -->
# GUE_EMP_H3K4me1-seqsight_4096_512_46M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4950
- F1 Score: 0.7717
- Accuracy: 0.7746
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5806 | 1.01 | 200 | 0.5416 | 0.7420 | 0.7453 |
| 0.5386 | 2.02 | 400 | 0.5305 | 0.7547 | 0.7576 |
| 0.524 | 3.03 | 600 | 0.5159 | 0.7679 | 0.7693 |
| 0.5155 | 4.04 | 800 | 0.5152 | 0.7691 | 0.7708 |
| 0.5091 | 5.05 | 1000 | 0.5068 | 0.7723 | 0.7734 |
| 0.5017 | 6.06 | 1200 | 0.5055 | 0.7732 | 0.7753 |
| 0.4962 | 7.07 | 1400 | 0.5108 | 0.7733 | 0.7753 |
| 0.4929 | 8.08 | 1600 | 0.5044 | 0.7737 | 0.7759 |
| 0.4895 | 9.09 | 1800 | 0.5002 | 0.7798 | 0.7819 |
| 0.4865 | 10.1 | 2000 | 0.4987 | 0.7794 | 0.7806 |
| 0.4795 | 11.11 | 2200 | 0.5146 | 0.7676 | 0.7708 |
| 0.48 | 12.12 | 2400 | 0.5023 | 0.7759 | 0.7771 |
| 0.4753 | 13.13 | 2600 | 0.5008 | 0.7729 | 0.7759 |
| 0.4722 | 14.14 | 2800 | 0.5034 | 0.7752 | 0.7771 |
| 0.4681 | 15.15 | 3000 | 0.5056 | 0.7697 | 0.7715 |
| 0.465 | 16.16 | 3200 | 0.5051 | 0.7726 | 0.7743 |
| 0.4604 | 17.17 | 3400 | 0.5155 | 0.7765 | 0.7781 |
| 0.4642 | 18.18 | 3600 | 0.4989 | 0.7740 | 0.7753 |
| 0.4568 | 19.19 | 3800 | 0.4975 | 0.7763 | 0.7781 |
| 0.4515 | 20.2 | 4000 | 0.5060 | 0.7730 | 0.7746 |
| 0.4543 | 21.21 | 4200 | 0.5062 | 0.7702 | 0.7727 |
| 0.4474 | 22.22 | 4400 | 0.5134 | 0.7700 | 0.7718 |
| 0.4446 | 23.23 | 4600 | 0.5117 | 0.7721 | 0.7740 |
| 0.4449 | 24.24 | 4800 | 0.5209 | 0.7643 | 0.7674 |
| 0.4402 | 25.25 | 5000 | 0.5114 | 0.7750 | 0.7759 |
| 0.4448 | 26.26 | 5200 | 0.5086 | 0.7757 | 0.7762 |
| 0.4353 | 27.27 | 5400 | 0.5118 | 0.7686 | 0.7711 |
| 0.4379 | 28.28 | 5600 | 0.5088 | 0.7728 | 0.7737 |
| 0.4348 | 29.29 | 5800 | 0.5185 | 0.7645 | 0.7683 |
| 0.4324 | 30.3 | 6000 | 0.5153 | 0.7667 | 0.7686 |
| 0.4269 | 31.31 | 6200 | 0.5109 | 0.7710 | 0.7724 |
| 0.4269 | 32.32 | 6400 | 0.5172 | 0.7702 | 0.7721 |
| 0.4281 | 33.33 | 6600 | 0.5237 | 0.7660 | 0.7683 |
| 0.4194 | 34.34 | 6800 | 0.5162 | 0.7672 | 0.7686 |
| 0.4184 | 35.35 | 7000 | 0.5200 | 0.7734 | 0.7746 |
| 0.4252 | 36.36 | 7200 | 0.5150 | 0.7696 | 0.7708 |
| 0.4154 | 37.37 | 7400 | 0.5299 | 0.7671 | 0.7689 |
| 0.4196 | 38.38 | 7600 | 0.5153 | 0.7712 | 0.7721 |
| 0.4106 | 39.39 | 7800 | 0.5244 | 0.7741 | 0.7749 |
| 0.4151 | 40.4 | 8000 | 0.5198 | 0.7739 | 0.7753 |
| 0.41 | 41.41 | 8200 | 0.5301 | 0.7685 | 0.7708 |
| 0.4162 | 42.42 | 8400 | 0.5258 | 0.7708 | 0.7724 |
| 0.4074 | 43.43 | 8600 | 0.5267 | 0.7648 | 0.7664 |
| 0.4109 | 44.44 | 8800 | 0.5229 | 0.7668 | 0.7686 |
| 0.4078 | 45.45 | 9000 | 0.5287 | 0.7678 | 0.7693 |
| 0.41 | 46.46 | 9200 | 0.5276 | 0.7715 | 0.7730 |
| 0.4109 | 47.47 | 9400 | 0.5258 | 0.7690 | 0.7708 |
| 0.4004 | 48.48 | 9600 | 0.5300 | 0.7682 | 0.7702 |
| 0.4082 | 49.49 | 9800 | 0.5282 | 0.7670 | 0.7689 |
| 0.4084 | 50.51 | 10000 | 0.5281 | 0.7691 | 0.7708 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_EMP_H3K4me1-seqsight_4096_512_46M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me1-seqsight_4096_512_46M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-26T23:14:02+00:00 |
null | peft |
<!-- 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. -->
# GUE_EMP_H3K4me1-seqsight_4096_512_46M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_EMP_H3K4me1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K4me1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5005
- F1 Score: 0.7751
- Accuracy: 0.7765
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5681 | 1.01 | 200 | 0.5271 | 0.7586 | 0.7604 |
| 0.527 | 2.02 | 400 | 0.5195 | 0.7604 | 0.7626 |
| 0.5136 | 3.03 | 600 | 0.5100 | 0.7742 | 0.7753 |
| 0.503 | 4.04 | 800 | 0.5105 | 0.7682 | 0.7702 |
| 0.4947 | 5.05 | 1000 | 0.5066 | 0.7701 | 0.7708 |
| 0.4864 | 6.06 | 1200 | 0.5045 | 0.7737 | 0.7756 |
| 0.478 | 7.07 | 1400 | 0.5181 | 0.7645 | 0.7667 |
| 0.4698 | 8.08 | 1600 | 0.5123 | 0.7739 | 0.7749 |
| 0.4638 | 9.09 | 1800 | 0.5037 | 0.7705 | 0.7721 |
| 0.4554 | 10.1 | 2000 | 0.5211 | 0.7694 | 0.7718 |
| 0.4464 | 11.11 | 2200 | 0.5333 | 0.7579 | 0.7614 |
| 0.444 | 12.12 | 2400 | 0.5286 | 0.7638 | 0.7645 |
| 0.4329 | 13.13 | 2600 | 0.5233 | 0.7635 | 0.7661 |
| 0.4257 | 14.14 | 2800 | 0.5189 | 0.7645 | 0.7661 |
| 0.4169 | 15.15 | 3000 | 0.5580 | 0.7670 | 0.7677 |
| 0.4085 | 16.16 | 3200 | 0.5322 | 0.7607 | 0.7617 |
| 0.4006 | 17.17 | 3400 | 0.5634 | 0.7674 | 0.7683 |
| 0.3986 | 18.18 | 3600 | 0.5418 | 0.7638 | 0.7655 |
| 0.385 | 19.19 | 3800 | 0.5688 | 0.7666 | 0.7670 |
| 0.3765 | 20.2 | 4000 | 0.5760 | 0.7599 | 0.7614 |
| 0.3755 | 21.21 | 4200 | 0.5695 | 0.7575 | 0.7588 |
| 0.3612 | 22.22 | 4400 | 0.6129 | 0.7505 | 0.7522 |
| 0.3541 | 23.23 | 4600 | 0.5947 | 0.7504 | 0.7513 |
| 0.3492 | 24.24 | 4800 | 0.6378 | 0.7556 | 0.7576 |
| 0.3419 | 25.25 | 5000 | 0.5959 | 0.7520 | 0.7525 |
| 0.3426 | 26.26 | 5200 | 0.5971 | 0.7518 | 0.7532 |
| 0.3325 | 27.27 | 5400 | 0.5999 | 0.7504 | 0.7513 |
| 0.3268 | 28.28 | 5600 | 0.6188 | 0.7469 | 0.7484 |
| 0.3205 | 29.29 | 5800 | 0.6279 | 0.7481 | 0.7506 |
| 0.3159 | 30.3 | 6000 | 0.6332 | 0.7488 | 0.7497 |
| 0.3031 | 31.31 | 6200 | 0.6461 | 0.7441 | 0.7446 |
| 0.3034 | 32.32 | 6400 | 0.6619 | 0.7515 | 0.7522 |
| 0.2974 | 33.33 | 6600 | 0.6598 | 0.7451 | 0.7468 |
| 0.2909 | 34.34 | 6800 | 0.6662 | 0.7442 | 0.7446 |
| 0.2894 | 35.35 | 7000 | 0.6778 | 0.7503 | 0.7513 |
| 0.2919 | 36.36 | 7200 | 0.6623 | 0.7474 | 0.7487 |
| 0.2814 | 37.37 | 7400 | 0.6974 | 0.7447 | 0.7462 |
| 0.28 | 38.38 | 7600 | 0.6722 | 0.7464 | 0.7472 |
| 0.2668 | 39.39 | 7800 | 0.6849 | 0.7497 | 0.7503 |
| 0.2714 | 40.4 | 8000 | 0.6832 | 0.7474 | 0.7484 |
| 0.2637 | 41.41 | 8200 | 0.7067 | 0.7490 | 0.75 |
| 0.2698 | 42.42 | 8400 | 0.7178 | 0.7514 | 0.7525 |
| 0.262 | 43.43 | 8600 | 0.7070 | 0.7457 | 0.7468 |
| 0.2563 | 44.44 | 8800 | 0.7147 | 0.7456 | 0.7465 |
| 0.2604 | 45.45 | 9000 | 0.7183 | 0.7518 | 0.7525 |
| 0.2515 | 46.46 | 9200 | 0.7273 | 0.7499 | 0.7506 |
| 0.2557 | 47.47 | 9400 | 0.7146 | 0.7423 | 0.7434 |
| 0.2477 | 48.48 | 9600 | 0.7199 | 0.7471 | 0.7481 |
| 0.2521 | 49.49 | 9800 | 0.7192 | 0.7485 | 0.7494 |
| 0.2492 | 50.51 | 10000 | 0.7230 | 0.7464 | 0.7472 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_EMP_H3K4me1-seqsight_4096_512_46M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K4me1-seqsight_4096_512_46M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-26T23:14:23+00:00 |
null | peft |
<!-- 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. -->
# GUE_EMP_H3K36me3-seqsight_4096_512_46M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_EMP_H3K36me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K36me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4355
- F1 Score: 0.8155
- Accuracy: 0.8174
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.525 | 0.92 | 200 | 0.5088 | 0.7596 | 0.7632 |
| 0.4803 | 1.83 | 400 | 0.4838 | 0.7778 | 0.7804 |
| 0.4649 | 2.75 | 600 | 0.4738 | 0.7876 | 0.7896 |
| 0.467 | 3.67 | 800 | 0.4684 | 0.7850 | 0.7864 |
| 0.4545 | 4.59 | 1000 | 0.4693 | 0.7936 | 0.7953 |
| 0.4477 | 5.5 | 1200 | 0.4647 | 0.7921 | 0.7939 |
| 0.4513 | 6.42 | 1400 | 0.4642 | 0.7922 | 0.7936 |
| 0.4423 | 7.34 | 1600 | 0.4702 | 0.7869 | 0.7896 |
| 0.4383 | 8.26 | 1800 | 0.4690 | 0.7913 | 0.7933 |
| 0.4422 | 9.17 | 2000 | 0.4565 | 0.7946 | 0.7967 |
| 0.4378 | 10.09 | 2200 | 0.4781 | 0.7842 | 0.7884 |
| 0.436 | 11.01 | 2400 | 0.4559 | 0.7957 | 0.7979 |
| 0.4338 | 11.93 | 2600 | 0.4499 | 0.7999 | 0.8013 |
| 0.4312 | 12.84 | 2800 | 0.4549 | 0.7948 | 0.7970 |
| 0.4297 | 13.76 | 3000 | 0.4583 | 0.7941 | 0.7967 |
| 0.428 | 14.68 | 3200 | 0.4501 | 0.8020 | 0.8033 |
| 0.4266 | 15.6 | 3400 | 0.4537 | 0.7995 | 0.8019 |
| 0.4256 | 16.51 | 3600 | 0.4595 | 0.7978 | 0.8002 |
| 0.4252 | 17.43 | 3800 | 0.4522 | 0.8019 | 0.8033 |
| 0.4213 | 18.35 | 4000 | 0.4555 | 0.8043 | 0.8059 |
| 0.4231 | 19.27 | 4200 | 0.4584 | 0.8031 | 0.8050 |
| 0.4225 | 20.18 | 4400 | 0.4657 | 0.8008 | 0.8033 |
| 0.4196 | 21.1 | 4600 | 0.4573 | 0.8044 | 0.8062 |
| 0.4222 | 22.02 | 4800 | 0.4554 | 0.8080 | 0.8096 |
| 0.4186 | 22.94 | 5000 | 0.4519 | 0.8046 | 0.8065 |
| 0.4181 | 23.85 | 5200 | 0.4519 | 0.8053 | 0.8076 |
| 0.4138 | 24.77 | 5400 | 0.4686 | 0.7972 | 0.8005 |
| 0.416 | 25.69 | 5600 | 0.4548 | 0.8030 | 0.8053 |
| 0.4146 | 26.61 | 5800 | 0.4497 | 0.8077 | 0.8093 |
| 0.4155 | 27.52 | 6000 | 0.4616 | 0.8019 | 0.8045 |
| 0.4125 | 28.44 | 6200 | 0.4529 | 0.8057 | 0.8079 |
| 0.4104 | 29.36 | 6400 | 0.4557 | 0.8059 | 0.8082 |
| 0.4131 | 30.28 | 6600 | 0.4563 | 0.7985 | 0.8016 |
| 0.4127 | 31.19 | 6800 | 0.4491 | 0.8059 | 0.8073 |
| 0.411 | 32.11 | 7000 | 0.4533 | 0.8052 | 0.8073 |
| 0.4088 | 33.03 | 7200 | 0.4553 | 0.8076 | 0.8093 |
| 0.4114 | 33.94 | 7400 | 0.4534 | 0.8093 | 0.8111 |
| 0.4072 | 34.86 | 7600 | 0.4554 | 0.8073 | 0.8093 |
| 0.4083 | 35.78 | 7800 | 0.4515 | 0.8079 | 0.8096 |
| 0.4085 | 36.7 | 8000 | 0.4493 | 0.8068 | 0.8088 |
| 0.4074 | 37.61 | 8200 | 0.4588 | 0.8019 | 0.8048 |
| 0.4104 | 38.53 | 8400 | 0.4516 | 0.8086 | 0.8105 |
| 0.4051 | 39.45 | 8600 | 0.4538 | 0.8046 | 0.8068 |
| 0.4039 | 40.37 | 8800 | 0.4600 | 0.8016 | 0.8042 |
| 0.4094 | 41.28 | 9000 | 0.4531 | 0.8023 | 0.8048 |
| 0.404 | 42.2 | 9200 | 0.4507 | 0.8077 | 0.8096 |
| 0.4045 | 43.12 | 9400 | 0.4536 | 0.8052 | 0.8073 |
| 0.4035 | 44.04 | 9600 | 0.4532 | 0.8060 | 0.8082 |
| 0.4047 | 44.95 | 9800 | 0.4554 | 0.8044 | 0.8068 |
| 0.4035 | 45.87 | 10000 | 0.4542 | 0.8056 | 0.8079 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_EMP_H3K36me3-seqsight_4096_512_46M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K36me3-seqsight_4096_512_46M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-26T23:14:52+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
bloom-3b - bnb 4bits
- Model creator: https://huggingface.co/bigscience/
- Original model: https://huggingface.co/bigscience/bloom-3b/
Original model description:
---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-generation
model-index:
- name: bloom
results:
- task:
type: text-generation
name: text generation
dataset:
name: arc_challenge
type: arc_challenge
metrics:
- name: acc
type: acc
value: 0.27986348122866894
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: arc_easy
type: arc_easy
metrics:
- name: acc
type: acc
value: 0.5946969696969697
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: axb
type: axb
metrics:
- name: acc
type: acc
value: 0.4433876811594203
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: axg
type: axg
metrics:
- name: acc
type: acc
value: 0.5
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: boolq
type: boolq
metrics:
- name: acc
type: acc
value: 0.6165137614678899
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: cb
type: cb
metrics:
- name: acc
type: acc
value: 0.30357142857142855
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: cola
type: cola
metrics:
- name: acc
type: acc
value: 0.610738255033557
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: copa
type: copa
metrics:
- name: acc
type: acc
value: 0.63
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: crows_pairs_english
type: crows_pairs_english
metrics:
- name: acc
type: acc
value: 0.4973166368515206
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: crows_pairs_french
type: crows_pairs_french
metrics:
- name: acc
type: acc
value: 0.5032796660703638
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: diabla
type: diabla
metrics:
- name: acc
type: acc
value: 0.28888308977035493
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_afr
type: gsarti/flores_101_afr
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 6.500798737976343
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_amh
type: gsarti/flores_101_amh
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.9726863338897145
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ara
type: gsarti/flores_101_ara
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 1.8083841089875814
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_asm
type: gsarti/flores_101_asm
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.699102962086425
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ast
type: gsarti/flores_101_ast
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.9252047073429384
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_azj
type: gsarti/flores_101_azj
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 6.942805054270002
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_bel
type: gsarti/flores_101_bel
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.614136245847082
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ben
type: gsarti/flores_101_ben
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.121491534300969
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_bos
type: gsarti/flores_101_bos
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.653353469118798
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_bul
type: gsarti/flores_101_bul
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.7014693938055068
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_cat
type: gsarti/flores_101_cat
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.305190041967345
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ceb
type: gsarti/flores_101_ceb
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 6.291000321323428
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ces
type: gsarti/flores_101_ces
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.447322753586386
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ckb
type: gsarti/flores_101_ckb
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.7255124939234765
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_cym
type: gsarti/flores_101_cym
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 12.539424151448149
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_dan
type: gsarti/flores_101_dan
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.183309001005672
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_deu
type: gsarti/flores_101_deu
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.1180422286591347
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ell
type: gsarti/flores_101_ell
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.467943456164706
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_eng
type: gsarti/flores_101_eng
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.018740628193298
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_est
type: gsarti/flores_101_est
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 9.11654425176368
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_fas
type: gsarti/flores_101_fas
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.058009097116482
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_fin
type: gsarti/flores_101_fin
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 6.847047959628553
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_fra
type: gsarti/flores_101_fra
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 1.9975177011840075
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ful
type: gsarti/flores_101_ful
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 11.465912731488828
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_gle
type: gsarti/flores_101_gle
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 8.681491663539422
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_glg
type: gsarti/flores_101_glg
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.029991089015508
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_guj
type: gsarti/flores_101_guj
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.955224230286231
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hau
type: gsarti/flores_101_hau
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 10.758347356372159
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_heb
type: gsarti/flores_101_heb
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.6004478129801667
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hin
type: gsarti/flores_101_hin
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.712530650588064
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hrv
type: gsarti/flores_101_hrv
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.822418943372185
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hun
type: gsarti/flores_101_hun
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 6.440482646965992
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hye
type: gsarti/flores_101_hye
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.657718918347166
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ibo
type: gsarti/flores_101_ibo
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.564814003872672
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ind
type: gsarti/flores_101_ind
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.1597101468869373
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_isl
type: gsarti/flores_101_isl
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 8.082349269518136
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ita
type: gsarti/flores_101_ita
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.9687591414176207
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_jav
type: gsarti/flores_101_jav
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 7.0573805415708994
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_jpn
type: gsarti/flores_101_jpn
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.7758864197116933
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kam
type: gsarti/flores_101_kam
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 11.072949642861332
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kan
type: gsarti/flores_101_kan
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.551730651007082
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kat
type: gsarti/flores_101_kat
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.522630524283745
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kaz
type: gsarti/flores_101_kaz
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.3901748516975574
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kea
type: gsarti/flores_101_kea
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 8.918534182590863
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kir
type: gsarti/flores_101_kir
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.729278369847201
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_kor
type: gsarti/flores_101_kor
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.932884847226212
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lao
type: gsarti/flores_101_lao
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.9077314760849924
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lav
type: gsarti/flores_101_lav
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 7.777221919194806
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lin
type: gsarti/flores_101_lin
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 7.524842908050988
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lit
type: gsarti/flores_101_lit
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 7.369179434621725
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ltz
type: gsarti/flores_101_ltz
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 8.801059747949214
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_lug
type: gsarti/flores_101_lug
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 8.483203026364786
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_luo
type: gsarti/flores_101_luo
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 11.975963093623681
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mal
type: gsarti/flores_101_mal
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.615948455160037
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mar
type: gsarti/flores_101_mar
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.483253482821379
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mkd
type: gsarti/flores_101_mkd
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.9656732291754087
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mlt
type: gsarti/flores_101_mlt
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 15.004773437665275
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mon
type: gsarti/flores_101_mon
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.410598542315402
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mri
type: gsarti/flores_101_mri
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 7.474035895661322
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_msa
type: gsarti/flores_101_msa
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.5710001772665634
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_mya
type: gsarti/flores_101_mya
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.413577969878331
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_nld
type: gsarti/flores_101_nld
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.127831721885065
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_nob
type: gsarti/flores_101_nob
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.402763169129877
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_npi
type: gsarti/flores_101_npi
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.199342701937889
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_nso
type: gsarti/flores_101_nso
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 8.154626800955667
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_nya
type: gsarti/flores_101_nya
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 8.179860208369393
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_oci
type: gsarti/flores_101_oci
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.8617357393685845
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_orm
type: gsarti/flores_101_orm
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 12.911595421079408
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ory
type: gsarti/flores_101_ory
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.189421861225964
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_pan
type: gsarti/flores_101_pan
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.698477289331806
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_pol
type: gsarti/flores_101_pol
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.625550458479643
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_por
type: gsarti/flores_101_por
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 1.9754515986213523
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_pus
type: gsarti/flores_101_pus
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.4963371422771585
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ron
type: gsarti/flores_101_ron
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.965456830031304
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_rus
type: gsarti/flores_101_rus
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.0498020542445303
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_slk
type: gsarti/flores_101_slk
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 6.450822127057479
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_slv
type: gsarti/flores_101_slv
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 6.620252120186232
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_sna
type: gsarti/flores_101_sna
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 8.462166771382726
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_snd
type: gsarti/flores_101_snd
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.466066951221973
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_som
type: gsarti/flores_101_som
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 11.95918054093392
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_spa
type: gsarti/flores_101_spa
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 1.8965140104323535
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_srp
type: gsarti/flores_101_srp
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.871214785885079
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_swe
type: gsarti/flores_101_swe
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.054972008155866
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_swh
type: gsarti/flores_101_swh
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.6973091886730676
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tam
type: gsarti/flores_101_tam
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.539493400469833
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tel
type: gsarti/flores_101_tel
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.807499987508966
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tgk
type: gsarti/flores_101_tgk
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.5994818827380426
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tgl
type: gsarti/flores_101_tgl
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.667053833119858
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tha
type: gsarti/flores_101_tha
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.365940201944242
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tur
type: gsarti/flores_101_tur
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.885014749844601
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ukr
type: gsarti/flores_101_ukr
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.7240934990288483
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_umb
type: gsarti/flores_101_umb
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 12.766915508610673
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_urd
type: gsarti/flores_101_urd
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 1.9797467071381232
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_uzb
type: gsarti/flores_101_uzb
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 12.002337637722146
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_vie
type: gsarti/flores_101_vie
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 1.76578415476397
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_wol
type: gsarti/flores_101_wol
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 9.144285650306488
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_xho
type: gsarti/flores_101_xho
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 7.403240538286952
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_yor
type: gsarti/flores_101_yor
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.91272037551173
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_zho_simpl
type: gsarti/flores_101_zho_simpl
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.2769070822768533
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_zho_trad
type: gsarti/flores_101_zho_trad
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.5180582198242383
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_zul
type: gsarti/flores_101_zul
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 8.53353320693145
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: headqa
type: headqa
metrics:
- name: acc
type: acc
value: 0.26440554339897887
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: hellaswag
type: hellaswag
metrics:
- name: acc
type: acc
value: 0.41236805417247563
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: logiqa
type: logiqa
metrics:
- name: acc
type: acc
value: 0.2073732718894009
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mathqa
type: mathqa
metrics:
- name: acc
type: acc
value: 0.24958123953098826
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mc_taco
type: mc_taco
metrics:
- name: em
type: em
value: 0.11936936936936937
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mnli
type: mnli
metrics:
- name: acc
type: acc
value: 0.35496688741721855
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mnli_mismatched
type: mnli_mismatched
metrics:
- name: acc
type: acc
value: 0.35211554109031734
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mrpc
type: mrpc
metrics:
- name: acc
type: acc
value: 0.5857843137254902
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: multirc
type: multirc
metrics:
- name: acc
type: acc
value: 0.5375412541254125
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: openbookqa
type: openbookqa
metrics:
- name: acc
type: acc
value: 0.216
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: piqa
type: piqa
metrics:
- name: acc
type: acc
value: 0.7078346028291621
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: prost
type: prost
metrics:
- name: acc
type: acc
value: 0.22683603757472245
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: pubmedqa
type: pubmedqa
metrics:
- name: acc
type: acc
value: 0.616
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: qnli
type: qnli
metrics:
- name: acc
type: acc
value: 0.5072304594545122
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: qqp
type: qqp
metrics:
- name: acc
type: acc
value: 0.3842443729903537
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: race
type: race
metrics:
- name: acc
type: acc
value: 0.3521531100478469
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: rte
type: rte
metrics:
- name: acc
type: acc
value: 0.47653429602888087
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: sciq
type: sciq
metrics:
- name: acc
type: acc
value: 0.892
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: sst
type: sst
metrics:
- name: acc
type: acc
value: 0.5177752293577982
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: triviaqa
type: triviaqa
metrics:
- name: acc
type: acc
value: 0.041633518960487934
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: tydiqa_primary
type: tydiqa_primary
metrics:
- name: acc
type: acc
value: 0.3011337608795236
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: webqs
type: webqs
metrics:
- name: acc
type: acc
value: 0.01673228346456693
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: wic
type: wic
metrics:
- name: acc
type: acc
value: 0.5015673981191222
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: winogrande
type: winogrande
metrics:
- name: acc
type: acc
value: 0.5864246250986582
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: wnli
type: wnli
metrics:
- name: acc
type: acc
value: 0.471830985915493
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: wsc
type: wsc
metrics:
- name: acc
type: acc
value: 0.4423076923076923
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: humaneval
type: humaneval
metrics:
- name: pass@1
type: pass@1
value: 0.15524390243902436
verified: false
- name: pass@10
type: pass@10
value: 0.3220367632383857
verified: false
- name: pass@100
type: pass@100
value: 0.5545431515723145
verified: false
---
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
Version 1.0 / 26.May.2022
## Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
## Model Details
### Basics
*This section provides information for anyone who wants to know about the model.*
<details>
<summary>Click to expand</summary> <br/>
**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*
**Model Type:** Transformer-based Language Model
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
**Release Date Estimate:** Monday, 11.July.2022
**Send Questions to:** [email protected]
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
**Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
</details>
### Technical Specifications
*This section provides information for people who work on model development.*
<details>
<summary>Click to expand</summary><br/>
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 3,002,557,440 parameters:
* 642,252,800 embedding parameters
* 30 layers, 32 attention heads
* Hidden layers are 2560-dimensional
* Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
* Hardware: 384 A100 80GB GPUs (48 nodes):
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
* Software:
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
#### **Training**
Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11c-2B5-logs)
- Number of epochs: 1 (*current target*)
- Dates:
- Started 11th March, 2022 11:42am PST
- Ended 5th July, 2022
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
#### **Tokenization**
The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
</details>
### Environmental Impact
<details>
<summary>Click to expand</summary><br/>
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming upon completion of training.)*
**Estimated electricity usage:** *(Forthcoming upon completion of training.)*
</details>
<p> </p>
## Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.
It provides information for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary><br/>
### Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
#### **Direct Use**
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
#### **Downstream Use**
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### **Out-of-scope Uses**
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
##### Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### **Misuse**
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
### Intended Users
#### **Direct Users**
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
#### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
#### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
</details>
<p> </p>
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
<details>
<summary>Click to expand</summary><br/>
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
#### **Languages**
The pie chart shows the distribution of languages in training data.
The following table shows the further distribution of Niger-Congo and Indic languages in the training data.
<details>
<summary>Click to expand</summary><br/>
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------ |------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
</details>
The following table shows the distribution of programming languages.
<details>
<summary>Click to expand</summary><br/>
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
</details>
</details>
<p> </p>
## Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary><br/>
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
</details>
<p> </p>
## Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary><br/>
### Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
### Factors
*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
### Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Zero-shot evaluations:**
See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
| Task | Language | Metric | BLOOM-2B5 |
|:----|:----|:----|:----:|
| arc_challenge | eng | acc ↑ | 0.28 |
| arc_easy | eng | acc ↑ | 0.595 |
| axb (Median of 10 prompts) | eng | acc ↑ | 0.443 |
| axg (Median of 10 prompts) | eng | acc ↑ | 0.5 |
| boolq (Median of 11 prompts) | eng | acc ↑ | 0.617 |
| cb (Median of 15 prompts) | eng | acc ↑ | 0.304 |
| cola (Median of 5 prompts) | eng | acc ↑ | 0.611 |
| copa (Median of 9 prompts) | eng | acc ↑ | 0.63 |
| crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.497 |
| crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.503 |
| diabla (Median of 2 prompts) | eng | acc ↑ | 0.289 |
| gsarti/flores_101_afr | afr | byte_perplexity ↓ | 6.501 |
| gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.973 |
| gsarti/flores_101_ara | ara | byte_perplexity ↓ | 1.808 |
| gsarti/flores_101_asm | asm | byte_perplexity ↓ | 5.699 |
| gsarti/flores_101_ast | ast | byte_perplexity ↓ | 3.925 |
| gsarti/flores_101_azj | azj | byte_perplexity ↓ | 6.943 |
| gsarti/flores_101_bel | bel | byte_perplexity ↓ | 3.614 |
| gsarti/flores_101_ben | ben | byte_perplexity ↓ | 5.121 |
| gsarti/flores_101_bos | bos | byte_perplexity ↓ | 5.653 |
| gsarti/flores_101_bul | bul | byte_perplexity ↓ | 2.701 |
| gsarti/flores_101_cat | cat | byte_perplexity ↓ | 2.305 |
| gsarti/flores_101_ceb | ceb | byte_perplexity ↓ | 6.291 |
| gsarti/flores_101_ces | ces | byte_perplexity ↓ | 5.447 |
| gsarti/flores_101_ckb | ckb | byte_perplexity ↓ | 3.726 |
| gsarti/flores_101_cym | cym | byte_perplexity ↓ | 12.539 |
| gsarti/flores_101_dan | dan | byte_perplexity ↓ | 5.183 |
| gsarti/flores_101_deu | deu | byte_perplexity ↓ | 3.118 |
| gsarti/flores_101_ell | ell | byte_perplexity ↓ | 2.468 |
| gsarti/flores_101_eng | eng | byte_perplexity ↓ | 2.019 |
| gsarti/flores_101_est | est | byte_perplexity ↓ | 9.117 |
| gsarti/flores_101_fas | fas | byte_perplexity ↓ | 3.058 |
| gsarti/flores_101_fin | fin | byte_perplexity ↓ | 6.847 |
| gsarti/flores_101_fra | fra | byte_perplexity ↓ | 1.998 |
| gsarti/flores_101_ful | ful | byte_perplexity ↓ | 11.466 |
| gsarti/flores_101_gle | gle | byte_perplexity ↓ | 8.681 |
| gsarti/flores_101_glg | glg | byte_perplexity ↓ | 3.03 |
| gsarti/flores_101_guj | guj | byte_perplexity ↓ | 4.955 |
| gsarti/flores_101_hau | hau | byte_perplexity ↓ | 10.758 |
| gsarti/flores_101_heb | heb | byte_perplexity ↓ | 3.6 |
| gsarti/flores_101_hin | hin | byte_perplexity ↓ | 4.713 |
| gsarti/flores_101_hrv | hrv | byte_perplexity ↓ | 5.822 |
| gsarti/flores_101_hun | hun | byte_perplexity ↓ | 6.44 |
| gsarti/flores_101_hye | hye | byte_perplexity ↓ | 3.658 |
| gsarti/flores_101_ibo | ibo | byte_perplexity ↓ | 5.565 |
| gsarti/flores_101_ind | ind | byte_perplexity ↓ | 2.16 |
| gsarti/flores_101_isl | isl | byte_perplexity ↓ | 8.082 |
| gsarti/flores_101_ita | ita | byte_perplexity ↓ | 2.969 |
| gsarti/flores_101_jav | jav | byte_perplexity ↓ | 7.057 |
| gsarti/flores_101_jpn | jpn | byte_perplexity ↓ | 2.776 |
| gsarti/flores_101_kam | kam | byte_perplexity ↓ | 11.073 |
| gsarti/flores_101_kan | kan | byte_perplexity ↓ | 5.552 |
| gsarti/flores_101_kat | kat | byte_perplexity ↓ | 2.523 |
| gsarti/flores_101_kaz | kaz | byte_perplexity ↓ | 3.39 |
| gsarti/flores_101_kea | kea | byte_perplexity ↓ | 8.919 |
| gsarti/flores_101_kir | kir | byte_perplexity ↓ | 3.729 |
| gsarti/flores_101_kor | kor | byte_perplexity ↓ | 3.933 |
| gsarti/flores_101_lao | lao | byte_perplexity ↓ | 2.908 |
| gsarti/flores_101_lav | lav | byte_perplexity ↓ | 7.777 |
| gsarti/flores_101_lin | lin | byte_perplexity ↓ | 7.525 |
| gsarti/flores_101_lit | lit | byte_perplexity ↓ | 7.369 |
| gsarti/flores_101_ltz | ltz | byte_perplexity ↓ | 8.801 |
| gsarti/flores_101_lug | lug | byte_perplexity ↓ | 8.483 |
| gsarti/flores_101_luo | luo | byte_perplexity ↓ | 11.976 |
| gsarti/flores_101_mal | mal | byte_perplexity ↓ | 4.616 |
| gsarti/flores_101_mar | mar | byte_perplexity ↓ | 5.483 |
| gsarti/flores_101_mkd | mkd | byte_perplexity ↓ | 2.966 |
| gsarti/flores_101_mlt | mlt | byte_perplexity ↓ | 15.005 |
| gsarti/flores_101_mon | mon | byte_perplexity ↓ | 3.411 |
| gsarti/flores_101_mri | mri | byte_perplexity ↓ | 7.474 |
| gsarti/flores_101_msa | msa | byte_perplexity ↓ | 2.571 |
| gsarti/flores_101_mya | mya | byte_perplexity ↓ | 2.414 |
| gsarti/flores_101_nld | nld | byte_perplexity ↓ | 4.128 |
| gsarti/flores_101_nob | nob | byte_perplexity ↓ | 5.403 |
| gsarti/flores_101_npi | npi | byte_perplexity ↓ | 5.199 |
| gsarti/flores_101_nso | nso | byte_perplexity ↓ | 8.155 |
| gsarti/flores_101_nya | nya | byte_perplexity ↓ | 8.18 |
| gsarti/flores_101_oci | oci | byte_perplexity ↓ | 4.862 |
| gsarti/flores_101_orm | orm | byte_perplexity ↓ | 12.912 |
| gsarti/flores_101_ory | ory | byte_perplexity ↓ | 5.189 |
| gsarti/flores_101_pan | pan | byte_perplexity ↓ | 4.698 |
| gsarti/flores_101_pol | pol | byte_perplexity ↓ | 4.626 |
| gsarti/flores_101_por | por | byte_perplexity ↓ | 1.975 |
| gsarti/flores_101_pus | pus | byte_perplexity ↓ | 4.496 |
| gsarti/flores_101_ron | ron | byte_perplexity ↓ | 4.965 |
| gsarti/flores_101_rus | rus | byte_perplexity ↓ | 2.05 |
| gsarti/flores_101_slk | slk | byte_perplexity ↓ | 6.451 |
| gsarti/flores_101_slv | slv | byte_perplexity ↓ | 6.62 |
| gsarti/flores_101_sna | sna | byte_perplexity ↓ | 8.462 |
| gsarti/flores_101_snd | snd | byte_perplexity ↓ | 5.466 |
| gsarti/flores_101_som | som | byte_perplexity ↓ | 11.959 |
| gsarti/flores_101_spa | spa | byte_perplexity ↓ | 1.897 |
| gsarti/flores_101_srp | srp | byte_perplexity ↓ | 2.871 |
| gsarti/flores_101_swe | swe | byte_perplexity ↓ | 5.055 |
| gsarti/flores_101_swh | swh | byte_perplexity ↓ | 3.697 |
| gsarti/flores_101_tam | tam | byte_perplexity ↓ | 4.539 |
| gsarti/flores_101_tel | tel | byte_perplexity ↓ | 5.807 |
| gsarti/flores_101_tgk | tgk | byte_perplexity ↓ | 3.599 |
| gsarti/flores_101_tgl | tgl | byte_perplexity ↓ | 5.667 |
| gsarti/flores_101_tha | tha | byte_perplexity ↓ | 2.366 |
| gsarti/flores_101_tur | tur | byte_perplexity ↓ | 4.885 |
| gsarti/flores_101_ukr | ukr | byte_perplexity ↓ | 2.724 |
| gsarti/flores_101_umb | umb | byte_perplexity ↓ | 12.767 |
| gsarti/flores_101_urd | urd | byte_perplexity ↓ | 1.98 |
| gsarti/flores_101_uzb | uzb | byte_perplexity ↓ | 12.002 |
| gsarti/flores_101_vie | vie | byte_perplexity ↓ | 1.766 |
| gsarti/flores_101_wol | wol | byte_perplexity ↓ | 9.144 |
| gsarti/flores_101_xho | xho | byte_perplexity ↓ | 7.403 |
| gsarti/flores_101_yor | yor | byte_perplexity ↓ | 5.913 |
| gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity ↓ | 2.277 |
| gsarti/flores_101_zho_trad | zho_trad | byte_perplexity ↓ | 2.518 |
| gsarti/flores_101_zul | zul | byte_perplexity ↓ | 8.534 |
| headqa | esp | acc ↑ | 0.264 |
| hellaswag | eng | acc ↑ | 0.412 |
| logiqa | eng | acc ↑ | 0.207 |
| mathqa | eng | acc ↑ | 0.25 |
| mc_taco | eng | em ↑ | 0.119 |
| mnli (Median of 15 prompts) | eng | acc ↑ | 0.355 |
| mnli_mismatched (Median of 15 prompts) | eng | acc ↑ | 0.352 |
| mrpc | eng | acc ↑ | 0.586 |
| multirc (Median of 11 prompts) | eng | acc ↑ | 0.538 |
| openbookqa | eng | acc ↑ | 0.216 |
| piqa | eng | acc ↑ | 0.708 |
| prost | eng | acc ↑ | 0.227 |
| pubmedqa | eng | acc ↑ | 0.616 |
| qnli | eng | acc ↑ | 0.507 |
| qqp (Median of 7 prompts) | eng | acc ↑ | 0.384 |
| race | eng | acc ↑ | 0.352 |
| rte (Median of 6 prompts) | eng | acc ↑ | 0.477 |
| sciq | eng | acc ↑ | 0.892 |
| sst (Median of 6 prompts) | eng | acc ↑ | 0.518 |
| triviaqa | eng | acc ↑ | 0.042 |
| tydiqa_primary (Median of 24 prompts) | eng | acc ↑ | 0.301 |
| webqs | eng | acc ↑ | 0.017 |
| wic (Median of 11 prompts) | eng | acc ↑ | 0.502 |
| winogrande | eng | acc ↑ | 0.586 |
| wnli (Median of 6 prompts) | eng | acc ↑ | 0.472 |
| wsc (Median of 11 prompts) | eng | acc ↑ | 0.442 |
| humaneval | python | pass@1 ↑ | 0.155 |
| humaneval | python | pass@10 ↑ | 0.322 |
| humaneval | python | pass@100 ↑ | 0.555 |
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.0
- Validation Loss: 2.2
- Perplexity: 8.9
</details>
<p> </p>
## Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary><br/>
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models pretrained with the LLM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
</details>
<p> </p>
## Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary><br/>
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
</details>
<p> </p>
## More Information
<details>
<summary>Click to expand</summary><br/>
### Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
### Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
### Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
</details>
<p> </p>
## Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
| {} | RichardErkhov/bigscience_-_bloom-3b-4bits | null | [
"transformers",
"safetensors",
"bloom",
"text-generation",
"arxiv:1909.08053",
"arxiv:2110.02861",
"arxiv:2108.12409",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-26T23:15:02+00:00 |
text-generation | transformers |
<!-- 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. -->
# gemma-lima
This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the GAIR/lima dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7259
## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 10.4256 | 0.91 | 5 | 47.0001 |
| 6.0419 | 2.0 | 11 | 43.9691 |
| 5.2838 | 2.91 | 16 | 40.7857 |
| 4.8705 | 4.0 | 22 | 33.9282 |
| 4.196 | 4.91 | 27 | 17.5336 |
| 3.0724 | 6.0 | 33 | 2.7088 |
| 2.1966 | 6.91 | 38 | 2.7434 |
| 2.1116 | 8.0 | 44 | 2.7265 |
| 2.0641 | 8.91 | 49 | 2.7168 |
| 2.0467 | 9.09 | 50 | 2.7259 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "gemma", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["GAIR/lima"], "base_model": "google/gemma-7b", "model-index": [{"name": "gemma-lima", "results": []}]} | pkarypis/gemma-lima | null | [
"transformers",
"tensorboard",
"safetensors",
"gemma",
"text-generation",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:GAIR/lima",
"base_model:google/gemma-7b",
"license:gemma",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:16:12+00:00 |
null | null | {"license": "apache-2.0"} | achi6000/movo | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T23:16:48+00:00 |
|
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
bloom-3b - bnb 8bits
- Model creator: https://huggingface.co/bigscience/
- Original model: https://huggingface.co/bigscience/bloom-3b/
Original model description:
---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-generation
model-index:
- name: bloom
results:
- task:
type: text-generation
name: text generation
dataset:
name: arc_challenge
type: arc_challenge
metrics:
- name: acc
type: acc
value: 0.27986348122866894
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: arc_easy
type: arc_easy
metrics:
- name: acc
type: acc
value: 0.5946969696969697
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: axb
type: axb
metrics:
- name: acc
type: acc
value: 0.4433876811594203
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: axg
type: axg
metrics:
- name: acc
type: acc
value: 0.5
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: boolq
type: boolq
metrics:
- name: acc
type: acc
value: 0.6165137614678899
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: cb
type: cb
metrics:
- name: acc
type: acc
value: 0.30357142857142855
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: cola
type: cola
metrics:
- name: acc
type: acc
value: 0.610738255033557
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: copa
type: copa
metrics:
- name: acc
type: acc
value: 0.63
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: crows_pairs_english
type: crows_pairs_english
metrics:
- name: acc
type: acc
value: 0.4973166368515206
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: crows_pairs_french
type: crows_pairs_french
metrics:
- name: acc
type: acc
value: 0.5032796660703638
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: diabla
type: diabla
metrics:
- name: acc
type: acc
value: 0.28888308977035493
verified: false
- task:
type: text-generation
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dataset:
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dataset:
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dataset:
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type: gsarti/flores_101_tgk
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.5994818827380426
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tgl
type: gsarti/flores_101_tgl
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.667053833119858
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tha
type: gsarti/flores_101_tha
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.365940201944242
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_tur
type: gsarti/flores_101_tur
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.885014749844601
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ukr
type: gsarti/flores_101_ukr
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.7240934990288483
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_umb
type: gsarti/flores_101_umb
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 12.766915508610673
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_urd
type: gsarti/flores_101_urd
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 1.9797467071381232
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_uzb
type: gsarti/flores_101_uzb
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 12.002337637722146
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_vie
type: gsarti/flores_101_vie
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 1.76578415476397
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_wol
type: gsarti/flores_101_wol
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 9.144285650306488
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_xho
type: gsarti/flores_101_xho
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 7.403240538286952
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_yor
type: gsarti/flores_101_yor
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.91272037551173
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_zho_simpl
type: gsarti/flores_101_zho_simpl
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.2769070822768533
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_zho_trad
type: gsarti/flores_101_zho_trad
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.5180582198242383
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_zul
type: gsarti/flores_101_zul
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 8.53353320693145
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: headqa
type: headqa
metrics:
- name: acc
type: acc
value: 0.26440554339897887
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: hellaswag
type: hellaswag
metrics:
- name: acc
type: acc
value: 0.41236805417247563
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: logiqa
type: logiqa
metrics:
- name: acc
type: acc
value: 0.2073732718894009
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mathqa
type: mathqa
metrics:
- name: acc
type: acc
value: 0.24958123953098826
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mc_taco
type: mc_taco
metrics:
- name: em
type: em
value: 0.11936936936936937
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mnli
type: mnli
metrics:
- name: acc
type: acc
value: 0.35496688741721855
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mnli_mismatched
type: mnli_mismatched
metrics:
- name: acc
type: acc
value: 0.35211554109031734
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: mrpc
type: mrpc
metrics:
- name: acc
type: acc
value: 0.5857843137254902
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: multirc
type: multirc
metrics:
- name: acc
type: acc
value: 0.5375412541254125
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: openbookqa
type: openbookqa
metrics:
- name: acc
type: acc
value: 0.216
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: piqa
type: piqa
metrics:
- name: acc
type: acc
value: 0.7078346028291621
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: prost
type: prost
metrics:
- name: acc
type: acc
value: 0.22683603757472245
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: pubmedqa
type: pubmedqa
metrics:
- name: acc
type: acc
value: 0.616
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: qnli
type: qnli
metrics:
- name: acc
type: acc
value: 0.5072304594545122
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: qqp
type: qqp
metrics:
- name: acc
type: acc
value: 0.3842443729903537
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: race
type: race
metrics:
- name: acc
type: acc
value: 0.3521531100478469
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: rte
type: rte
metrics:
- name: acc
type: acc
value: 0.47653429602888087
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: sciq
type: sciq
metrics:
- name: acc
type: acc
value: 0.892
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: sst
type: sst
metrics:
- name: acc
type: acc
value: 0.5177752293577982
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: triviaqa
type: triviaqa
metrics:
- name: acc
type: acc
value: 0.041633518960487934
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: tydiqa_primary
type: tydiqa_primary
metrics:
- name: acc
type: acc
value: 0.3011337608795236
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: webqs
type: webqs
metrics:
- name: acc
type: acc
value: 0.01673228346456693
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: wic
type: wic
metrics:
- name: acc
type: acc
value: 0.5015673981191222
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: winogrande
type: winogrande
metrics:
- name: acc
type: acc
value: 0.5864246250986582
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: wnli
type: wnli
metrics:
- name: acc
type: acc
value: 0.471830985915493
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: wsc
type: wsc
metrics:
- name: acc
type: acc
value: 0.4423076923076923
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: humaneval
type: humaneval
metrics:
- name: pass@1
type: pass@1
value: 0.15524390243902436
verified: false
- name: pass@10
type: pass@10
value: 0.3220367632383857
verified: false
- name: pass@100
type: pass@100
value: 0.5545431515723145
verified: false
---
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
Version 1.0 / 26.May.2022
## Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
## Model Details
### Basics
*This section provides information for anyone who wants to know about the model.*
<details>
<summary>Click to expand</summary> <br/>
**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*
**Model Type:** Transformer-based Language Model
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
**Release Date Estimate:** Monday, 11.July.2022
**Send Questions to:** [email protected]
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
**Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
</details>
### Technical Specifications
*This section provides information for people who work on model development.*
<details>
<summary>Click to expand</summary><br/>
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 3,002,557,440 parameters:
* 642,252,800 embedding parameters
* 30 layers, 32 attention heads
* Hidden layers are 2560-dimensional
* Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
* Hardware: 384 A100 80GB GPUs (48 nodes):
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
* Software:
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
#### **Training**
Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11c-2B5-logs)
- Number of epochs: 1 (*current target*)
- Dates:
- Started 11th March, 2022 11:42am PST
- Ended 5th July, 2022
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
#### **Tokenization**
The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
</details>
### Environmental Impact
<details>
<summary>Click to expand</summary><br/>
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming upon completion of training.)*
**Estimated electricity usage:** *(Forthcoming upon completion of training.)*
</details>
<p> </p>
## Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.
It provides information for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary><br/>
### Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
#### **Direct Use**
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
#### **Downstream Use**
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### **Out-of-scope Uses**
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
##### Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### **Misuse**
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
### Intended Users
#### **Direct Users**
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
#### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
#### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
</details>
<p> </p>
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
<details>
<summary>Click to expand</summary><br/>
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
#### **Languages**
The pie chart shows the distribution of languages in training data.
The following table shows the further distribution of Niger-Congo and Indic languages in the training data.
<details>
<summary>Click to expand</summary><br/>
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------ |------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
</details>
The following table shows the distribution of programming languages.
<details>
<summary>Click to expand</summary><br/>
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
</details>
</details>
<p> </p>
## Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary><br/>
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
</details>
<p> </p>
## Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary><br/>
### Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
### Factors
*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
### Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Zero-shot evaluations:**
See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
| Task | Language | Metric | BLOOM-2B5 |
|:----|:----|:----|:----:|
| arc_challenge | eng | acc ↑ | 0.28 |
| arc_easy | eng | acc ↑ | 0.595 |
| axb (Median of 10 prompts) | eng | acc ↑ | 0.443 |
| axg (Median of 10 prompts) | eng | acc ↑ | 0.5 |
| boolq (Median of 11 prompts) | eng | acc ↑ | 0.617 |
| cb (Median of 15 prompts) | eng | acc ↑ | 0.304 |
| cola (Median of 5 prompts) | eng | acc ↑ | 0.611 |
| copa (Median of 9 prompts) | eng | acc ↑ | 0.63 |
| crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.497 |
| crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.503 |
| diabla (Median of 2 prompts) | eng | acc ↑ | 0.289 |
| gsarti/flores_101_afr | afr | byte_perplexity ↓ | 6.501 |
| gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.973 |
| gsarti/flores_101_ara | ara | byte_perplexity ↓ | 1.808 |
| gsarti/flores_101_asm | asm | byte_perplexity ↓ | 5.699 |
| gsarti/flores_101_ast | ast | byte_perplexity ↓ | 3.925 |
| gsarti/flores_101_azj | azj | byte_perplexity ↓ | 6.943 |
| gsarti/flores_101_bel | bel | byte_perplexity ↓ | 3.614 |
| gsarti/flores_101_ben | ben | byte_perplexity ↓ | 5.121 |
| gsarti/flores_101_bos | bos | byte_perplexity ↓ | 5.653 |
| gsarti/flores_101_bul | bul | byte_perplexity ↓ | 2.701 |
| gsarti/flores_101_cat | cat | byte_perplexity ↓ | 2.305 |
| gsarti/flores_101_ceb | ceb | byte_perplexity ↓ | 6.291 |
| gsarti/flores_101_ces | ces | byte_perplexity ↓ | 5.447 |
| gsarti/flores_101_ckb | ckb | byte_perplexity ↓ | 3.726 |
| gsarti/flores_101_cym | cym | byte_perplexity ↓ | 12.539 |
| gsarti/flores_101_dan | dan | byte_perplexity ↓ | 5.183 |
| gsarti/flores_101_deu | deu | byte_perplexity ↓ | 3.118 |
| gsarti/flores_101_ell | ell | byte_perplexity ↓ | 2.468 |
| gsarti/flores_101_eng | eng | byte_perplexity ↓ | 2.019 |
| gsarti/flores_101_est | est | byte_perplexity ↓ | 9.117 |
| gsarti/flores_101_fas | fas | byte_perplexity ↓ | 3.058 |
| gsarti/flores_101_fin | fin | byte_perplexity ↓ | 6.847 |
| gsarti/flores_101_fra | fra | byte_perplexity ↓ | 1.998 |
| gsarti/flores_101_ful | ful | byte_perplexity ↓ | 11.466 |
| gsarti/flores_101_gle | gle | byte_perplexity ↓ | 8.681 |
| gsarti/flores_101_glg | glg | byte_perplexity ↓ | 3.03 |
| gsarti/flores_101_guj | guj | byte_perplexity ↓ | 4.955 |
| gsarti/flores_101_hau | hau | byte_perplexity ↓ | 10.758 |
| gsarti/flores_101_heb | heb | byte_perplexity ↓ | 3.6 |
| gsarti/flores_101_hin | hin | byte_perplexity ↓ | 4.713 |
| gsarti/flores_101_hrv | hrv | byte_perplexity ↓ | 5.822 |
| gsarti/flores_101_hun | hun | byte_perplexity ↓ | 6.44 |
| gsarti/flores_101_hye | hye | byte_perplexity ↓ | 3.658 |
| gsarti/flores_101_ibo | ibo | byte_perplexity ↓ | 5.565 |
| gsarti/flores_101_ind | ind | byte_perplexity ↓ | 2.16 |
| gsarti/flores_101_isl | isl | byte_perplexity ↓ | 8.082 |
| gsarti/flores_101_ita | ita | byte_perplexity ↓ | 2.969 |
| gsarti/flores_101_jav | jav | byte_perplexity ↓ | 7.057 |
| gsarti/flores_101_jpn | jpn | byte_perplexity ↓ | 2.776 |
| gsarti/flores_101_kam | kam | byte_perplexity ↓ | 11.073 |
| gsarti/flores_101_kan | kan | byte_perplexity ↓ | 5.552 |
| gsarti/flores_101_kat | kat | byte_perplexity ↓ | 2.523 |
| gsarti/flores_101_kaz | kaz | byte_perplexity ↓ | 3.39 |
| gsarti/flores_101_kea | kea | byte_perplexity ↓ | 8.919 |
| gsarti/flores_101_kir | kir | byte_perplexity ↓ | 3.729 |
| gsarti/flores_101_kor | kor | byte_perplexity ↓ | 3.933 |
| gsarti/flores_101_lao | lao | byte_perplexity ↓ | 2.908 |
| gsarti/flores_101_lav | lav | byte_perplexity ↓ | 7.777 |
| gsarti/flores_101_lin | lin | byte_perplexity ↓ | 7.525 |
| gsarti/flores_101_lit | lit | byte_perplexity ↓ | 7.369 |
| gsarti/flores_101_ltz | ltz | byte_perplexity ↓ | 8.801 |
| gsarti/flores_101_lug | lug | byte_perplexity ↓ | 8.483 |
| gsarti/flores_101_luo | luo | byte_perplexity ↓ | 11.976 |
| gsarti/flores_101_mal | mal | byte_perplexity ↓ | 4.616 |
| gsarti/flores_101_mar | mar | byte_perplexity ↓ | 5.483 |
| gsarti/flores_101_mkd | mkd | byte_perplexity ↓ | 2.966 |
| gsarti/flores_101_mlt | mlt | byte_perplexity ↓ | 15.005 |
| gsarti/flores_101_mon | mon | byte_perplexity ↓ | 3.411 |
| gsarti/flores_101_mri | mri | byte_perplexity ↓ | 7.474 |
| gsarti/flores_101_msa | msa | byte_perplexity ↓ | 2.571 |
| gsarti/flores_101_mya | mya | byte_perplexity ↓ | 2.414 |
| gsarti/flores_101_nld | nld | byte_perplexity ↓ | 4.128 |
| gsarti/flores_101_nob | nob | byte_perplexity ↓ | 5.403 |
| gsarti/flores_101_npi | npi | byte_perplexity ↓ | 5.199 |
| gsarti/flores_101_nso | nso | byte_perplexity ↓ | 8.155 |
| gsarti/flores_101_nya | nya | byte_perplexity ↓ | 8.18 |
| gsarti/flores_101_oci | oci | byte_perplexity ↓ | 4.862 |
| gsarti/flores_101_orm | orm | byte_perplexity ↓ | 12.912 |
| gsarti/flores_101_ory | ory | byte_perplexity ↓ | 5.189 |
| gsarti/flores_101_pan | pan | byte_perplexity ↓ | 4.698 |
| gsarti/flores_101_pol | pol | byte_perplexity ↓ | 4.626 |
| gsarti/flores_101_por | por | byte_perplexity ↓ | 1.975 |
| gsarti/flores_101_pus | pus | byte_perplexity ↓ | 4.496 |
| gsarti/flores_101_ron | ron | byte_perplexity ↓ | 4.965 |
| gsarti/flores_101_rus | rus | byte_perplexity ↓ | 2.05 |
| gsarti/flores_101_slk | slk | byte_perplexity ↓ | 6.451 |
| gsarti/flores_101_slv | slv | byte_perplexity ↓ | 6.62 |
| gsarti/flores_101_sna | sna | byte_perplexity ↓ | 8.462 |
| gsarti/flores_101_snd | snd | byte_perplexity ↓ | 5.466 |
| gsarti/flores_101_som | som | byte_perplexity ↓ | 11.959 |
| gsarti/flores_101_spa | spa | byte_perplexity ↓ | 1.897 |
| gsarti/flores_101_srp | srp | byte_perplexity ↓ | 2.871 |
| gsarti/flores_101_swe | swe | byte_perplexity ↓ | 5.055 |
| gsarti/flores_101_swh | swh | byte_perplexity ↓ | 3.697 |
| gsarti/flores_101_tam | tam | byte_perplexity ↓ | 4.539 |
| gsarti/flores_101_tel | tel | byte_perplexity ↓ | 5.807 |
| gsarti/flores_101_tgk | tgk | byte_perplexity ↓ | 3.599 |
| gsarti/flores_101_tgl | tgl | byte_perplexity ↓ | 5.667 |
| gsarti/flores_101_tha | tha | byte_perplexity ↓ | 2.366 |
| gsarti/flores_101_tur | tur | byte_perplexity ↓ | 4.885 |
| gsarti/flores_101_ukr | ukr | byte_perplexity ↓ | 2.724 |
| gsarti/flores_101_umb | umb | byte_perplexity ↓ | 12.767 |
| gsarti/flores_101_urd | urd | byte_perplexity ↓ | 1.98 |
| gsarti/flores_101_uzb | uzb | byte_perplexity ↓ | 12.002 |
| gsarti/flores_101_vie | vie | byte_perplexity ↓ | 1.766 |
| gsarti/flores_101_wol | wol | byte_perplexity ↓ | 9.144 |
| gsarti/flores_101_xho | xho | byte_perplexity ↓ | 7.403 |
| gsarti/flores_101_yor | yor | byte_perplexity ↓ | 5.913 |
| gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity ↓ | 2.277 |
| gsarti/flores_101_zho_trad | zho_trad | byte_perplexity ↓ | 2.518 |
| gsarti/flores_101_zul | zul | byte_perplexity ↓ | 8.534 |
| headqa | esp | acc ↑ | 0.264 |
| hellaswag | eng | acc ↑ | 0.412 |
| logiqa | eng | acc ↑ | 0.207 |
| mathqa | eng | acc ↑ | 0.25 |
| mc_taco | eng | em ↑ | 0.119 |
| mnli (Median of 15 prompts) | eng | acc ↑ | 0.355 |
| mnli_mismatched (Median of 15 prompts) | eng | acc ↑ | 0.352 |
| mrpc | eng | acc ↑ | 0.586 |
| multirc (Median of 11 prompts) | eng | acc ↑ | 0.538 |
| openbookqa | eng | acc ↑ | 0.216 |
| piqa | eng | acc ↑ | 0.708 |
| prost | eng | acc ↑ | 0.227 |
| pubmedqa | eng | acc ↑ | 0.616 |
| qnli | eng | acc ↑ | 0.507 |
| qqp (Median of 7 prompts) | eng | acc ↑ | 0.384 |
| race | eng | acc ↑ | 0.352 |
| rte (Median of 6 prompts) | eng | acc ↑ | 0.477 |
| sciq | eng | acc ↑ | 0.892 |
| sst (Median of 6 prompts) | eng | acc ↑ | 0.518 |
| triviaqa | eng | acc ↑ | 0.042 |
| tydiqa_primary (Median of 24 prompts) | eng | acc ↑ | 0.301 |
| webqs | eng | acc ↑ | 0.017 |
| wic (Median of 11 prompts) | eng | acc ↑ | 0.502 |
| winogrande | eng | acc ↑ | 0.586 |
| wnli (Median of 6 prompts) | eng | acc ↑ | 0.472 |
| wsc (Median of 11 prompts) | eng | acc ↑ | 0.442 |
| humaneval | python | pass@1 ↑ | 0.155 |
| humaneval | python | pass@10 ↑ | 0.322 |
| humaneval | python | pass@100 ↑ | 0.555 |
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.0
- Validation Loss: 2.2
- Perplexity: 8.9
</details>
<p> </p>
## Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary><br/>
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models pretrained with the LLM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
</details>
<p> </p>
## Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary><br/>
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
</details>
<p> </p>
## More Information
<details>
<summary>Click to expand</summary><br/>
### Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
### Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
### Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
</details>
<p> </p>
## Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
| {} | RichardErkhov/bigscience_-_bloom-3b-8bits | null | [
"transformers",
"safetensors",
"bloom",
"text-generation",
"arxiv:1909.08053",
"arxiv:2110.02861",
"arxiv:2108.12409",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
]
| null | 2024-04-26T23:17:43+00:00 |
text-generation | transformers |
<!-- 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. -->
# ultra-feedback-dutch-cleaned-hq-spin-geitje-7b-ultra-sft_iter2
This model is a fine-tuned version of [davidberenstein1957/ultra-feedback-dutch-cleaned-hq-spin-geitje-7b-ultra-sft_iter1](https://huggingface.co/davidberenstein1957/ultra-feedback-dutch-cleaned-hq-spin-geitje-7b-ultra-sft_iter1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0162
- Rewards/real: -8.1731
- Rewards/generated: -31.3826
- Rewards/accuracies: 0.9917
- Rewards/margins: 23.2095
- Logps/generated: -956.3063
- Logps/real: -525.1735
- Logits/generated: -1.5719
- Logits/real: -1.7813
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/real | Rewards/generated | Rewards/accuracies | Rewards/margins | Logps/generated | Logps/real | Logits/generated | Logits/real |
|:-------------:|:-----:|:----:|:---------------:|:------------:|:-----------------:|:------------------:|:---------------:|:---------------:|:----------:|:----------------:|:-----------:|
| 0.6097 | 0.04 | 25 | 0.4147 | -0.6192 | -1.4312 | 0.9250 | 0.8120 | -656.7919 | -449.6341 | -2.0004 | -2.0773 |
| 0.2137 | 0.08 | 50 | 0.1745 | -2.0300 | -5.0060 | 0.9519 | 2.9761 | -692.5404 | -463.7422 | -1.9306 | -2.0237 |
| 0.1292 | 0.12 | 75 | 0.1012 | -2.8227 | -7.4967 | 0.9685 | 4.6740 | -717.4471 | -471.6697 | -1.8843 | -1.9887 |
| 0.0665 | 0.16 | 100 | 0.0676 | -3.2936 | -9.3177 | 0.9778 | 6.0240 | -735.6567 | -476.3786 | -1.8508 | -1.9628 |
| 0.0429 | 0.21 | 125 | 0.0477 | -3.7328 | -11.2722 | 0.9824 | 7.5395 | -755.2025 | -480.7701 | -1.8123 | -1.9332 |
| 0.0299 | 0.25 | 150 | 0.0369 | -4.2161 | -13.2599 | 0.9870 | 9.0437 | -775.0787 | -485.6039 | -1.7938 | -1.9226 |
| 0.0252 | 0.29 | 175 | 0.0320 | -4.7201 | -15.0489 | 0.9880 | 10.3288 | -792.9691 | -490.6432 | -1.7758 | -1.9116 |
| 0.0249 | 0.33 | 200 | 0.0301 | -5.0757 | -16.3570 | 0.9880 | 11.2813 | -806.0497 | -494.1995 | -1.7515 | -1.8923 |
| 0.0175 | 0.37 | 225 | 0.0273 | -5.4299 | -17.6751 | 0.9880 | 12.2451 | -819.2310 | -497.7419 | -1.7362 | -1.8821 |
| 0.0183 | 0.41 | 250 | 0.0254 | -5.4183 | -18.3899 | 0.9889 | 12.9715 | -826.3791 | -497.6259 | -1.7300 | -1.8793 |
| 0.0182 | 0.45 | 275 | 0.0245 | -6.0900 | -20.5760 | 0.9889 | 14.4860 | -848.2401 | -504.3426 | -1.6961 | -1.8564 |
| 0.0253 | 0.49 | 300 | 0.0224 | -5.9239 | -20.7184 | 0.9898 | 14.7944 | -849.6640 | -502.6819 | -1.6938 | -1.8573 |
| 0.0075 | 0.53 | 325 | 0.0234 | -7.0436 | -24.1126 | 0.9898 | 17.0691 | -883.6064 | -513.8781 | -1.6522 | -1.8252 |
| 0.0141 | 0.58 | 350 | 0.0212 | -5.5696 | -20.9714 | 0.9898 | 15.4017 | -852.1937 | -499.1387 | -1.7082 | -1.8693 |
| 0.0135 | 0.62 | 375 | 0.0182 | -5.2646 | -20.3901 | 0.9907 | 15.1254 | -846.3809 | -496.0890 | -1.7285 | -1.8897 |
| 0.014 | 0.66 | 400 | 0.0182 | -5.5057 | -21.1579 | 0.9907 | 15.6522 | -854.0594 | -498.4994 | -1.7137 | -1.8783 |
| 0.0122 | 0.7 | 425 | 0.0172 | -5.3398 | -20.7520 | 0.9907 | 15.4122 | -849.9997 | -496.8405 | -1.7231 | -1.8857 |
| 0.0144 | 0.74 | 450 | 0.0164 | -4.6606 | -19.3766 | 0.9917 | 14.7160 | -836.2463 | -490.0483 | -1.7465 | -1.9042 |
| 0.0103 | 0.78 | 475 | 0.0160 | -4.8739 | -20.1058 | 0.9907 | 15.2319 | -843.5385 | -492.1819 | -1.7445 | -1.9064 |
| 0.0147 | 0.82 | 500 | 0.0156 | -5.1220 | -20.9607 | 0.9917 | 15.8387 | -852.0875 | -494.6623 | -1.7434 | -1.9092 |
| 0.0154 | 0.86 | 525 | 0.0155 | -5.1481 | -21.3994 | 0.9917 | 16.2513 | -856.4740 | -494.9235 | -1.7357 | -1.9040 |
| 0.0158 | 0.91 | 550 | 0.0151 | -5.6088 | -22.9532 | 0.9917 | 17.3444 | -872.0123 | -499.5304 | -1.7139 | -1.8881 |
| 0.0053 | 0.95 | 575 | 0.0149 | -5.7209 | -23.5217 | 0.9917 | 17.8008 | -877.6972 | -500.6515 | -1.7113 | -1.8888 |
| 0.008 | 0.99 | 600 | 0.0147 | -5.7523 | -23.7474 | 0.9917 | 17.9952 | -879.9544 | -500.9651 | -1.7086 | -1.8878 |
| 0.0049 | 1.03 | 625 | 0.0154 | -6.1839 | -24.8883 | 0.9907 | 18.7044 | -891.3632 | -505.2818 | -1.6731 | -1.8585 |
| 0.0057 | 1.07 | 650 | 0.0155 | -6.4947 | -25.8924 | 0.9917 | 19.3977 | -901.4037 | -508.3892 | -1.6592 | -1.8484 |
| 0.0076 | 1.11 | 675 | 0.0158 | -6.8543 | -26.9217 | 0.9917 | 20.0674 | -911.6970 | -511.9859 | -1.6407 | -1.8339 |
| 0.004 | 1.15 | 700 | 0.0158 | -7.1325 | -27.7743 | 0.9917 | 20.6418 | -920.2236 | -514.7678 | -1.6269 | -1.8236 |
| 0.0168 | 1.19 | 725 | 0.0157 | -6.9019 | -26.2791 | 0.9917 | 19.3772 | -905.2711 | -512.4611 | -1.6566 | -1.8448 |
| 0.0022 | 1.23 | 750 | 0.0163 | -6.9586 | -26.5145 | 0.9917 | 19.5559 | -907.6251 | -513.0281 | -1.6533 | -1.8423 |
| 0.0039 | 1.28 | 775 | 0.0165 | -7.5386 | -28.2224 | 0.9917 | 20.6837 | -924.7038 | -518.8289 | -1.6369 | -1.8327 |
| 0.002 | 1.32 | 800 | 0.0165 | -7.6568 | -28.6441 | 0.9907 | 20.9872 | -928.9208 | -520.0109 | -1.6365 | -1.8344 |
| 0.002 | 1.36 | 825 | 0.0165 | -7.7989 | -29.2028 | 0.9917 | 21.4038 | -934.5078 | -521.4318 | -1.6348 | -1.8352 |
| 0.0019 | 1.4 | 850 | 0.0165 | -7.8978 | -29.5958 | 0.9917 | 21.6980 | -938.4382 | -522.4203 | -1.6166 | -1.8169 |
| 0.0041 | 1.44 | 875 | 0.0162 | -7.9696 | -29.7930 | 0.9917 | 21.8234 | -940.4100 | -523.1380 | -1.6165 | -1.8176 |
| 0.0023 | 1.48 | 900 | 0.0164 | -8.2086 | -30.6909 | 0.9917 | 22.4823 | -949.3892 | -525.5286 | -1.6045 | -1.8093 |
| 0.0038 | 1.52 | 925 | 0.0166 | -8.1217 | -30.6727 | 0.9917 | 22.5510 | -949.2076 | -524.6597 | -1.5919 | -1.7978 |
| 0.0096 | 1.56 | 950 | 0.0162 | -7.8257 | -30.1144 | 0.9917 | 22.2887 | -943.6237 | -521.6992 | -1.5909 | -1.7956 |
| 0.0057 | 1.6 | 975 | 0.0166 | -8.0335 | -30.6654 | 0.9917 | 22.6319 | -949.1342 | -523.7775 | -1.5854 | -1.7919 |
| 0.0046 | 1.65 | 1000 | 0.0165 | -8.1757 | -31.0139 | 0.9917 | 22.8382 | -952.6191 | -525.2000 | -1.5768 | -1.7852 |
| 0.0009 | 1.69 | 1025 | 0.0165 | -8.0553 | -30.7565 | 0.9917 | 22.7012 | -950.0453 | -523.9951 | -1.5757 | -1.7830 |
| 0.002 | 1.73 | 1050 | 0.0164 | -8.1838 | -31.3365 | 0.9917 | 23.1528 | -955.8453 | -525.2800 | -1.5692 | -1.7790 |
| 0.0069 | 1.77 | 1075 | 0.0163 | -8.1908 | -31.4118 | 0.9917 | 23.2210 | -956.5981 | -525.3508 | -1.5749 | -1.7850 |
| 0.0029 | 1.81 | 1100 | 0.0166 | -8.4138 | -32.0830 | 0.9917 | 23.6692 | -963.3098 | -527.5802 | -1.5624 | -1.7752 |
| 0.0047 | 1.85 | 1125 | 0.0166 | -8.4223 | -32.1526 | 0.9917 | 23.7304 | -964.0065 | -527.6652 | -1.5631 | -1.7759 |
| 0.0037 | 1.89 | 1150 | 0.0163 | -8.1563 | -31.3209 | 0.9917 | 23.1646 | -955.6895 | -525.0057 | -1.5739 | -1.7832 |
| 0.0026 | 1.93 | 1175 | 0.0163 | -8.2107 | -31.5009 | 0.9917 | 23.2901 | -957.4888 | -525.5498 | -1.5708 | -1.7807 |
| 0.0058 | 1.98 | 1200 | 0.0162 | -8.1731 | -31.3826 | 0.9917 | 23.2095 | -956.3063 | -525.1735 | -1.5719 | -1.7813 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "cc-by-nc-4.0", "tags": ["generated_from_trainer"], "base_model": "davidberenstein1957/ultra-feedback-dutch-cleaned-hq-spin-geitje-7b-ultra-sft_iter1", "model-index": [{"name": "ultra-feedback-dutch-cleaned-hq-spin-geitje-7b-ultra-sft_iter2", "results": []}]} | davidberenstein1957/ultra-feedback-dutch-cleaned-hq-spin-geitje-7b-ultra-sft_iter2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:davidberenstein1957/ultra-feedback-dutch-cleaned-hq-spin-geitje-7b-ultra-sft_iter1",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:17:45+00:00 |
null | null | {"license": "llama3"} | JenSen1391/forextrade | null | [
"license:llama3",
"region:us"
]
| null | 2024-04-26T23:19:53+00:00 |
|
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_ASPOL_v1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_ASPOL_v1", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_ASPOL_v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:21:17+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_PSAOL_v1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_PSAOL_v1", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_PSAOL_v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:21:19+00:00 |
null | null | {} | Delusion4013/onepiece-lora | null | [
"region:us"
]
| null | 2024-04-26T23:21:35+00:00 |
|
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
bloom-3b - GGUF
- Model creator: https://huggingface.co/bigscience/
- Original model: https://huggingface.co/bigscience/bloom-3b/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [bloom-3b.Q2_K.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q2_K.gguf) | Q2_K | 1.52GB |
| [bloom-3b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.IQ3_XS.gguf) | IQ3_XS | 1.68GB |
| [bloom-3b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.IQ3_S.gguf) | IQ3_S | 1.71GB |
| [bloom-3b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q3_K_S.gguf) | Q3_K_S | 1.71GB |
| [bloom-3b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.IQ3_M.gguf) | IQ3_M | 1.81GB |
| [bloom-3b.Q3_K.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q3_K.gguf) | Q3_K | 1.9GB |
| [bloom-3b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q3_K_M.gguf) | Q3_K_M | 1.9GB |
| [bloom-3b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q3_K_L.gguf) | Q3_K_L | 2.02GB |
| [bloom-3b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.IQ4_XS.gguf) | IQ4_XS | 2.0GB |
| [bloom-3b.Q4_0.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q4_0.gguf) | Q4_0 | 2.08GB |
| [bloom-3b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.IQ4_NL.gguf) | IQ4_NL | 2.09GB |
| [bloom-3b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q4_K_S.gguf) | Q4_K_S | 2.09GB |
| [bloom-3b.Q4_K.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q4_K.gguf) | Q4_K | 2.24GB |
| [bloom-3b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q4_K_M.gguf) | Q4_K_M | 2.24GB |
| [bloom-3b.Q4_1.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q4_1.gguf) | Q4_1 | 2.25GB |
| [bloom-3b.Q5_0.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q5_0.gguf) | Q5_0 | 2.43GB |
| [bloom-3b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q5_K_S.gguf) | Q5_K_S | 2.43GB |
| [bloom-3b.Q5_K.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q5_K.gguf) | Q5_K | 2.55GB |
| [bloom-3b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q5_K_M.gguf) | Q5_K_M | 1.64GB |
| [bloom-3b.Q5_1.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q5_1.gguf) | Q5_1 | 1.58GB |
| [bloom-3b.Q6_K.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-3b-gguf/blob/main/bloom-3b.Q6_K.gguf) | Q6_K | 1.31GB |
Original model description:
---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-generation
model-index:
- name: bloom
results:
- task:
type: text-generation
name: text generation
dataset:
name: arc_challenge
type: arc_challenge
metrics:
- name: acc
type: acc
value: 0.27986348122866894
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: arc_easy
type: arc_easy
metrics:
- name: acc
type: acc
value: 0.5946969696969697
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: axb
type: axb
metrics:
- name: acc
type: acc
value: 0.4433876811594203
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: axg
type: axg
metrics:
- name: acc
type: acc
value: 0.5
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: boolq
type: boolq
metrics:
- name: acc
type: acc
value: 0.6165137614678899
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: cb
type: cb
metrics:
- name: acc
type: acc
value: 0.30357142857142855
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: cola
type: cola
metrics:
- name: acc
type: acc
value: 0.610738255033557
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: copa
type: copa
metrics:
- name: acc
type: acc
value: 0.63
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: crows_pairs_english
type: crows_pairs_english
metrics:
- name: acc
type: acc
value: 0.4973166368515206
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: crows_pairs_french
type: crows_pairs_french
metrics:
- name: acc
type: acc
value: 0.5032796660703638
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: diabla
type: diabla
metrics:
- name: acc
type: acc
value: 0.28888308977035493
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_afr
type: gsarti/flores_101_afr
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 6.500798737976343
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_amh
type: gsarti/flores_101_amh
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.9726863338897145
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ara
type: gsarti/flores_101_ara
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 1.8083841089875814
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_asm
type: gsarti/flores_101_asm
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.699102962086425
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ast
type: gsarti/flores_101_ast
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.9252047073429384
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_azj
type: gsarti/flores_101_azj
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 6.942805054270002
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_bel
type: gsarti/flores_101_bel
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.614136245847082
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ben
type: gsarti/flores_101_ben
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.121491534300969
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_bos
type: gsarti/flores_101_bos
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.653353469118798
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_bul
type: gsarti/flores_101_bul
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.7014693938055068
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_cat
type: gsarti/flores_101_cat
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.305190041967345
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ceb
type: gsarti/flores_101_ceb
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 6.291000321323428
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ces
type: gsarti/flores_101_ces
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.447322753586386
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ckb
type: gsarti/flores_101_ckb
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.7255124939234765
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_cym
type: gsarti/flores_101_cym
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 12.539424151448149
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_dan
type: gsarti/flores_101_dan
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.183309001005672
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_deu
type: gsarti/flores_101_deu
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.1180422286591347
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ell
type: gsarti/flores_101_ell
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.467943456164706
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_eng
type: gsarti/flores_101_eng
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.018740628193298
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_est
type: gsarti/flores_101_est
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 9.11654425176368
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_fas
type: gsarti/flores_101_fas
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.058009097116482
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_fin
type: gsarti/flores_101_fin
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 6.847047959628553
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_fra
type: gsarti/flores_101_fra
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 1.9975177011840075
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ful
type: gsarti/flores_101_ful
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 11.465912731488828
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_gle
type: gsarti/flores_101_gle
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 8.681491663539422
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_glg
type: gsarti/flores_101_glg
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.029991089015508
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_guj
type: gsarti/flores_101_guj
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.955224230286231
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hau
type: gsarti/flores_101_hau
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 10.758347356372159
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_heb
type: gsarti/flores_101_heb
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.6004478129801667
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hin
type: gsarti/flores_101_hin
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 4.712530650588064
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hrv
type: gsarti/flores_101_hrv
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.822418943372185
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hun
type: gsarti/flores_101_hun
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 6.440482646965992
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_hye
type: gsarti/flores_101_hye
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 3.657718918347166
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ibo
type: gsarti/flores_101_ibo
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 5.564814003872672
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_ind
type: gsarti/flores_101_ind
metrics:
- name: byte_perplexity
type: byte_perplexity
value: 2.1597101468869373
verified: false
- task:
type: text-generation
name: text generation
dataset:
name: gsarti/flores_101_isl
type: gsarti/flores_101_isl
metrics:
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type: byte_perplexity
value: 8.082349269518136
verified: false
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type: text-generation
name: text generation
dataset:
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type: gsarti/flores_101_ita
metrics:
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type: byte_perplexity
value: 2.9687591414176207
verified: false
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type: text-generation
name: text generation
dataset:
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type: gsarti/flores_101_jav
metrics:
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type: byte_perplexity
value: 7.0573805415708994
verified: false
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type: text-generation
name: text generation
dataset:
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type: gsarti/flores_101_jpn
metrics:
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type: byte_perplexity
value: 2.7758864197116933
verified: false
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type: text-generation
name: text generation
dataset:
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type: gsarti/flores_101_kam
metrics:
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type: byte_perplexity
value: 11.072949642861332
verified: false
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type: text-generation
name: text generation
dataset:
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type: gsarti/flores_101_kan
metrics:
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type: byte_perplexity
value: 5.551730651007082
verified: false
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type: text-generation
name: text generation
dataset:
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type: gsarti/flores_101_kat
metrics:
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type: byte_perplexity
value: 2.522630524283745
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type: text-generation
name: text generation
dataset:
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type: gsarti/flores_101_kaz
metrics:
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type: byte_perplexity
value: 3.3901748516975574
verified: false
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type: text-generation
name: text generation
dataset:
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type: gsarti/flores_101_kea
metrics:
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type: byte_perplexity
value: 8.918534182590863
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type: text-generation
name: text generation
dataset:
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type: gsarti/flores_101_kir
metrics:
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type: byte_perplexity
value: 3.729278369847201
verified: false
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type: text-generation
name: text generation
dataset:
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type: gsarti/flores_101_kor
metrics:
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type: byte_perplexity
value: 3.932884847226212
verified: false
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name: text generation
dataset:
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type: gsarti/flores_101_lao
metrics:
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type: byte_perplexity
value: 2.9077314760849924
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name: text generation
dataset:
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type: gsarti/flores_101_lav
metrics:
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type: byte_perplexity
value: 7.777221919194806
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name: text generation
dataset:
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type: gsarti/flores_101_lin
metrics:
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type: byte_perplexity
value: 7.524842908050988
verified: false
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name: text generation
dataset:
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type: gsarti/flores_101_lit
metrics:
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type: byte_perplexity
value: 7.369179434621725
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type: text-generation
name: text generation
dataset:
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type: gsarti/flores_101_ltz
metrics:
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value: 8.801059747949214
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dataset:
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type: gsarti/flores_101_lug
metrics:
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value: 8.483203026364786
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type: gsarti/flores_101_luo
metrics:
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type: byte_perplexity
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name: text generation
dataset:
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type: gsarti/flores_101_mal
metrics:
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type: byte_perplexity
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name: text generation
dataset:
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type: gsarti/flores_101_mar
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dataset:
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type: gsarti/flores_101_mkd
metrics:
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dataset:
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name: text generation
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type: gsarti/flores_101_mon
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dataset:
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dataset:
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type: gsarti/flores_101_msa
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metrics:
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metrics:
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dataset:
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type: gsarti/flores_101_nob
metrics:
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dataset:
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type: gsarti/flores_101_orm
metrics:
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dataset:
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type: gsarti/flores_101_ory
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dataset:
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metrics:
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dataset:
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verified: false
---
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
Version 1.0 / 26.May.2022
## Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
## Model Details
### Basics
*This section provides information for anyone who wants to know about the model.*
<details>
<summary>Click to expand</summary> <br/>
**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*
**Model Type:** Transformer-based Language Model
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
**Release Date Estimate:** Monday, 11.July.2022
**Send Questions to:** [email protected]
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
**Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
</details>
### Technical Specifications
*This section provides information for people who work on model development.*
<details>
<summary>Click to expand</summary><br/>
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 3,002,557,440 parameters:
* 642,252,800 embedding parameters
* 30 layers, 32 attention heads
* Hidden layers are 2560-dimensional
* Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
* Hardware: 384 A100 80GB GPUs (48 nodes):
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
* Software:
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
#### **Training**
Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11c-2B5-logs)
- Number of epochs: 1 (*current target*)
- Dates:
- Started 11th March, 2022 11:42am PST
- Ended 5th July, 2022
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
#### **Tokenization**
The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
</details>
### Environmental Impact
<details>
<summary>Click to expand</summary><br/>
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming upon completion of training.)*
**Estimated electricity usage:** *(Forthcoming upon completion of training.)*
</details>
<p> </p>
## Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.
It provides information for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary><br/>
### Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
#### **Direct Use**
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
#### **Downstream Use**
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### **Out-of-scope Uses**
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
##### Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### **Misuse**
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
### Intended Users
#### **Direct Users**
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
#### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
#### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
</details>
<p> </p>
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
<details>
<summary>Click to expand</summary><br/>
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
#### **Languages**
The pie chart shows the distribution of languages in training data.
The following table shows the further distribution of Niger-Congo and Indic languages in the training data.
<details>
<summary>Click to expand</summary><br/>
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------ |------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
</details>
The following table shows the distribution of programming languages.
<details>
<summary>Click to expand</summary><br/>
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
</details>
</details>
<p> </p>
## Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary><br/>
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
</details>
<p> </p>
## Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary><br/>
### Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
### Factors
*This section lists some different aspects of BLOOM models. Its focus is on aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
### Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Zero-shot evaluations:**
See this repository for JSON files: https://github.com/bigscience-workshop/evaluation-results
| Task | Language | Metric | BLOOM-2B5 |
|:----|:----|:----|:----:|
| arc_challenge | eng | acc ↑ | 0.28 |
| arc_easy | eng | acc ↑ | 0.595 |
| axb (Median of 10 prompts) | eng | acc ↑ | 0.443 |
| axg (Median of 10 prompts) | eng | acc ↑ | 0.5 |
| boolq (Median of 11 prompts) | eng | acc ↑ | 0.617 |
| cb (Median of 15 prompts) | eng | acc ↑ | 0.304 |
| cola (Median of 5 prompts) | eng | acc ↑ | 0.611 |
| copa (Median of 9 prompts) | eng | acc ↑ | 0.63 |
| crows_pairs_english (Median of 6 prompts) | eng | acc ↑ | 0.497 |
| crows_pairs_french (Median of 7 prompts) | fra | acc ↑ | 0.503 |
| diabla (Median of 2 prompts) | eng | acc ↑ | 0.289 |
| gsarti/flores_101_afr | afr | byte_perplexity ↓ | 6.501 |
| gsarti/flores_101_amh | amh | byte_perplexity ↓ | 3.973 |
| gsarti/flores_101_ara | ara | byte_perplexity ↓ | 1.808 |
| gsarti/flores_101_asm | asm | byte_perplexity ↓ | 5.699 |
| gsarti/flores_101_ast | ast | byte_perplexity ↓ | 3.925 |
| gsarti/flores_101_azj | azj | byte_perplexity ↓ | 6.943 |
| gsarti/flores_101_bel | bel | byte_perplexity ↓ | 3.614 |
| gsarti/flores_101_ben | ben | byte_perplexity ↓ | 5.121 |
| gsarti/flores_101_bos | bos | byte_perplexity ↓ | 5.653 |
| gsarti/flores_101_bul | bul | byte_perplexity ↓ | 2.701 |
| gsarti/flores_101_cat | cat | byte_perplexity ↓ | 2.305 |
| gsarti/flores_101_ceb | ceb | byte_perplexity ↓ | 6.291 |
| gsarti/flores_101_ces | ces | byte_perplexity ↓ | 5.447 |
| gsarti/flores_101_ckb | ckb | byte_perplexity ↓ | 3.726 |
| gsarti/flores_101_cym | cym | byte_perplexity ↓ | 12.539 |
| gsarti/flores_101_dan | dan | byte_perplexity ↓ | 5.183 |
| gsarti/flores_101_deu | deu | byte_perplexity ↓ | 3.118 |
| gsarti/flores_101_ell | ell | byte_perplexity ↓ | 2.468 |
| gsarti/flores_101_eng | eng | byte_perplexity ↓ | 2.019 |
| gsarti/flores_101_est | est | byte_perplexity ↓ | 9.117 |
| gsarti/flores_101_fas | fas | byte_perplexity ↓ | 3.058 |
| gsarti/flores_101_fin | fin | byte_perplexity ↓ | 6.847 |
| gsarti/flores_101_fra | fra | byte_perplexity ↓ | 1.998 |
| gsarti/flores_101_ful | ful | byte_perplexity ↓ | 11.466 |
| gsarti/flores_101_gle | gle | byte_perplexity ↓ | 8.681 |
| gsarti/flores_101_glg | glg | byte_perplexity ↓ | 3.03 |
| gsarti/flores_101_guj | guj | byte_perplexity ↓ | 4.955 |
| gsarti/flores_101_hau | hau | byte_perplexity ↓ | 10.758 |
| gsarti/flores_101_heb | heb | byte_perplexity ↓ | 3.6 |
| gsarti/flores_101_hin | hin | byte_perplexity ↓ | 4.713 |
| gsarti/flores_101_hrv | hrv | byte_perplexity ↓ | 5.822 |
| gsarti/flores_101_hun | hun | byte_perplexity ↓ | 6.44 |
| gsarti/flores_101_hye | hye | byte_perplexity ↓ | 3.658 |
| gsarti/flores_101_ibo | ibo | byte_perplexity ↓ | 5.565 |
| gsarti/flores_101_ind | ind | byte_perplexity ↓ | 2.16 |
| gsarti/flores_101_isl | isl | byte_perplexity ↓ | 8.082 |
| gsarti/flores_101_ita | ita | byte_perplexity ↓ | 2.969 |
| gsarti/flores_101_jav | jav | byte_perplexity ↓ | 7.057 |
| gsarti/flores_101_jpn | jpn | byte_perplexity ↓ | 2.776 |
| gsarti/flores_101_kam | kam | byte_perplexity ↓ | 11.073 |
| gsarti/flores_101_kan | kan | byte_perplexity ↓ | 5.552 |
| gsarti/flores_101_kat | kat | byte_perplexity ↓ | 2.523 |
| gsarti/flores_101_kaz | kaz | byte_perplexity ↓ | 3.39 |
| gsarti/flores_101_kea | kea | byte_perplexity ↓ | 8.919 |
| gsarti/flores_101_kir | kir | byte_perplexity ↓ | 3.729 |
| gsarti/flores_101_kor | kor | byte_perplexity ↓ | 3.933 |
| gsarti/flores_101_lao | lao | byte_perplexity ↓ | 2.908 |
| gsarti/flores_101_lav | lav | byte_perplexity ↓ | 7.777 |
| gsarti/flores_101_lin | lin | byte_perplexity ↓ | 7.525 |
| gsarti/flores_101_lit | lit | byte_perplexity ↓ | 7.369 |
| gsarti/flores_101_ltz | ltz | byte_perplexity ↓ | 8.801 |
| gsarti/flores_101_lug | lug | byte_perplexity ↓ | 8.483 |
| gsarti/flores_101_luo | luo | byte_perplexity ↓ | 11.976 |
| gsarti/flores_101_mal | mal | byte_perplexity ↓ | 4.616 |
| gsarti/flores_101_mar | mar | byte_perplexity ↓ | 5.483 |
| gsarti/flores_101_mkd | mkd | byte_perplexity ↓ | 2.966 |
| gsarti/flores_101_mlt | mlt | byte_perplexity ↓ | 15.005 |
| gsarti/flores_101_mon | mon | byte_perplexity ↓ | 3.411 |
| gsarti/flores_101_mri | mri | byte_perplexity ↓ | 7.474 |
| gsarti/flores_101_msa | msa | byte_perplexity ↓ | 2.571 |
| gsarti/flores_101_mya | mya | byte_perplexity ↓ | 2.414 |
| gsarti/flores_101_nld | nld | byte_perplexity ↓ | 4.128 |
| gsarti/flores_101_nob | nob | byte_perplexity ↓ | 5.403 |
| gsarti/flores_101_npi | npi | byte_perplexity ↓ | 5.199 |
| gsarti/flores_101_nso | nso | byte_perplexity ↓ | 8.155 |
| gsarti/flores_101_nya | nya | byte_perplexity ↓ | 8.18 |
| gsarti/flores_101_oci | oci | byte_perplexity ↓ | 4.862 |
| gsarti/flores_101_orm | orm | byte_perplexity ↓ | 12.912 |
| gsarti/flores_101_ory | ory | byte_perplexity ↓ | 5.189 |
| gsarti/flores_101_pan | pan | byte_perplexity ↓ | 4.698 |
| gsarti/flores_101_pol | pol | byte_perplexity ↓ | 4.626 |
| gsarti/flores_101_por | por | byte_perplexity ↓ | 1.975 |
| gsarti/flores_101_pus | pus | byte_perplexity ↓ | 4.496 |
| gsarti/flores_101_ron | ron | byte_perplexity ↓ | 4.965 |
| gsarti/flores_101_rus | rus | byte_perplexity ↓ | 2.05 |
| gsarti/flores_101_slk | slk | byte_perplexity ↓ | 6.451 |
| gsarti/flores_101_slv | slv | byte_perplexity ↓ | 6.62 |
| gsarti/flores_101_sna | sna | byte_perplexity ↓ | 8.462 |
| gsarti/flores_101_snd | snd | byte_perplexity ↓ | 5.466 |
| gsarti/flores_101_som | som | byte_perplexity ↓ | 11.959 |
| gsarti/flores_101_spa | spa | byte_perplexity ↓ | 1.897 |
| gsarti/flores_101_srp | srp | byte_perplexity ↓ | 2.871 |
| gsarti/flores_101_swe | swe | byte_perplexity ↓ | 5.055 |
| gsarti/flores_101_swh | swh | byte_perplexity ↓ | 3.697 |
| gsarti/flores_101_tam | tam | byte_perplexity ↓ | 4.539 |
| gsarti/flores_101_tel | tel | byte_perplexity ↓ | 5.807 |
| gsarti/flores_101_tgk | tgk | byte_perplexity ↓ | 3.599 |
| gsarti/flores_101_tgl | tgl | byte_perplexity ↓ | 5.667 |
| gsarti/flores_101_tha | tha | byte_perplexity ↓ | 2.366 |
| gsarti/flores_101_tur | tur | byte_perplexity ↓ | 4.885 |
| gsarti/flores_101_ukr | ukr | byte_perplexity ↓ | 2.724 |
| gsarti/flores_101_umb | umb | byte_perplexity ↓ | 12.767 |
| gsarti/flores_101_urd | urd | byte_perplexity ↓ | 1.98 |
| gsarti/flores_101_uzb | uzb | byte_perplexity ↓ | 12.002 |
| gsarti/flores_101_vie | vie | byte_perplexity ↓ | 1.766 |
| gsarti/flores_101_wol | wol | byte_perplexity ↓ | 9.144 |
| gsarti/flores_101_xho | xho | byte_perplexity ↓ | 7.403 |
| gsarti/flores_101_yor | yor | byte_perplexity ↓ | 5.913 |
| gsarti/flores_101_zho_simpl | zho_simpl | byte_perplexity ↓ | 2.277 |
| gsarti/flores_101_zho_trad | zho_trad | byte_perplexity ↓ | 2.518 |
| gsarti/flores_101_zul | zul | byte_perplexity ↓ | 8.534 |
| headqa | esp | acc ↑ | 0.264 |
| hellaswag | eng | acc ↑ | 0.412 |
| logiqa | eng | acc ↑ | 0.207 |
| mathqa | eng | acc ↑ | 0.25 |
| mc_taco | eng | em ↑ | 0.119 |
| mnli (Median of 15 prompts) | eng | acc ↑ | 0.355 |
| mnli_mismatched (Median of 15 prompts) | eng | acc ↑ | 0.352 |
| mrpc | eng | acc ↑ | 0.586 |
| multirc (Median of 11 prompts) | eng | acc ↑ | 0.538 |
| openbookqa | eng | acc ↑ | 0.216 |
| piqa | eng | acc ↑ | 0.708 |
| prost | eng | acc ↑ | 0.227 |
| pubmedqa | eng | acc ↑ | 0.616 |
| qnli | eng | acc ↑ | 0.507 |
| qqp (Median of 7 prompts) | eng | acc ↑ | 0.384 |
| race | eng | acc ↑ | 0.352 |
| rte (Median of 6 prompts) | eng | acc ↑ | 0.477 |
| sciq | eng | acc ↑ | 0.892 |
| sst (Median of 6 prompts) | eng | acc ↑ | 0.518 |
| triviaqa | eng | acc ↑ | 0.042 |
| tydiqa_primary (Median of 24 prompts) | eng | acc ↑ | 0.301 |
| webqs | eng | acc ↑ | 0.017 |
| wic (Median of 11 prompts) | eng | acc ↑ | 0.502 |
| winogrande | eng | acc ↑ | 0.586 |
| wnli (Median of 6 prompts) | eng | acc ↑ | 0.472 |
| wsc (Median of 11 prompts) | eng | acc ↑ | 0.442 |
| humaneval | python | pass@1 ↑ | 0.155 |
| humaneval | python | pass@10 ↑ | 0.322 |
| humaneval | python | pass@100 ↑ | 0.555 |
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.0
- Validation Loss: 2.2
- Perplexity: 8.9
</details>
<p> </p>
## Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary><br/>
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models pretrained with the LLM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
</details>
<p> </p>
## Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary><br/>
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
</details>
<p> </p>
## More Information
<details>
<summary>Click to expand</summary><br/>
### Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
### Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
### Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
</details>
<p> </p>
## Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
| {} | RichardErkhov/bigscience_-_bloom-3b-gguf | null | [
"gguf",
"arxiv:1909.08053",
"arxiv:2110.02861",
"arxiv:2108.12409",
"region:us"
]
| null | 2024-04-26T23:21:42+00:00 |
text-generation | transformers |
# Keiana-L3-Test6.1-8B-17
Keiana-L3-Test6.1-8B-17 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
# Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error.
* [Kaoeiri/Keiana-L3-Test5.4-8B-10](https://huggingface.co/Kaoeiri/Keiana-L3-Test5.4-8B-10)
* [Kaoeiri/Keiana-L3-Test6-8B-16](https://huggingface.co/Kaoeiri/Keiana-L3-Test6-8B-16)
## 🧩 Configuration
```yaml
merge_method: model_stock
dtype: float16
base_model: Kaoeiri/Keiana-L3-Test5.75-8B-13.5
models:
- model: Kaoeiri/Keiana-L3-Test5.4-8B-10
parameters:
weight: .4
density: .25
- model: Kaoeiri/Keiana-L3-Test6-8B-16
parameters:
weight: .2
density: .36
parameters:
int8_mask: true
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kaoeiri/Keiana-L3-Test6.1-8B-17"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "Kaoeiri/Keiana-L3-Test5.4-8B-10", "Kaoeiri/Keiana-L3-Test6-8B-16"], "base_model": ["Kaoeiri/Keiana-L3-Test5.4-8B-10", "Kaoeiri/Keiana-L3-Test6-8B-16"]} | Kaoeiri/Keiana-L3-Test6.1-8B-17 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Kaoeiri/Keiana-L3-Test5.4-8B-10",
"Kaoeiri/Keiana-L3-Test6-8B-16",
"conversational",
"base_model:Kaoeiri/Keiana-L3-Test5.4-8B-10",
"base_model:Kaoeiri/Keiana-L3-Test6-8B-16",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:22:43+00:00 |
null | null |
### Murabito A
<!-- Provide a quick summary of what the model is/does. -->
---
- 1.5 models
- Most of these models were only shared between friends
- Models merged through MBW with quality in mind
---
| Color - A | Color - AA |
| :---: | :---: |
| <img src="Colorful/Color-A.png" width="300" /> | <img src="Colorful/Color-AA.png" width="300" />
| CutePastel | CutePastel-V2 |
| :---: | :---: |
| <img src="CutePastel/CutePastel.png" width="300" /> | <img src="CutePastel/CutePastel-V2.png" width="300" />
| CuteRealm - O | CuteRealm - X | Realm - X |
| :---: | :---: | :---: |
| <img src="CuteRealm/CuteRealm - O.png" width="300" /> | <img src="CuteRealm/CuteRealm - X.png" width="300" /> | <img src="CuteRealm/Realm - X.png" width="300" />
| Cuties Overdose | [Cuties Overdose - Z](https://pixai.art/model/1669741955161586448) | Cuties Overdose - X |
| :---: | :---: | :---: |
| <img src="Cuties Overdose/Cuties Overdose grid - no hihrez.png" width="300" /> | <img src="Cuties Overdose/Cuties Overdose - Z.png" width="300" /> | <img src="Cuties Overdose/Cuties Overdose - X.png" width="300" /> |
| [DreamyZone - CM](https://pixai.art/model/1659510577334939293) | Dreamy Zone - MR |
| :---: | :---: |
| n/a | <img src="DreamyZone/DreamyZone - MR.png" width="300"> |
| LMY |
| :---: |
| <img src="LMY/LMY.png" width="300"> |
---
Due to the `age` of these models I've already lost all their past generations, I only managed to salvage a few preview from old gens I posted on a private discord channel. | {"language": ["en"]} | BackMe/A-Surpising-gift-box | null | [
"en",
"region:us"
]
| null | 2024-04-26T23:23:49+00:00 |
reinforcement-learning | ml-agents |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: ahforoughi/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]} | ahforoughi/poca-SoccerTwos | null | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| null | 2024-04-26T23:24:09+00:00 |
image-classification | transformers |
<!-- 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. -->
# Boya1_RMSProp_1-e5_10Epoch_swin-large-patch4-window7-224_fold4
This model is a fine-tuned version of [microsoft/swin-large-patch4-window7-224](https://huggingface.co/microsoft/swin-large-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1943
- Accuracy: 0.6689
## 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: 16
- eval_batch_size: 16
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1618 | 1.0 | 924 | 1.1131 | 0.6212 |
| 0.9204 | 2.0 | 1848 | 1.0161 | 0.6519 |
| 0.7475 | 3.0 | 2772 | 0.9750 | 0.6643 |
| 0.7075 | 4.0 | 3696 | 0.9936 | 0.6703 |
| 0.6235 | 5.0 | 4620 | 1.0198 | 0.6692 |
| 0.4154 | 6.0 | 5544 | 1.0706 | 0.6643 |
| 0.3757 | 7.0 | 6468 | 1.0858 | 0.6714 |
| 0.3866 | 8.0 | 7392 | 1.1452 | 0.6692 |
| 0.3012 | 9.0 | 8316 | 1.1718 | 0.6733 |
| 0.2809 | 10.0 | 9240 | 1.1943 | 0.6689 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-large-patch4-window7-224", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swin-large-patch4-window7-224_fold4", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6689244107287998, "name": "Accuracy"}]}]}]} | onizukal/Boya1_RMSProp_1-e5_10Epoch_swin-large-patch4-window7-224_fold4 | null | [
"transformers",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-large-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T23:25:09+00:00 |
null | peft |
<!-- 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. -->
# experiemnts
This model is a fine-tuned version of [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 1.6667
- eval_runtime: 85.9166
- eval_samples_per_second: 11.639
- eval_steps_per_second: 1.455
- epoch: 0.8005
- step: 5184
## 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: 0.0003
- train_batch_size: 1
- eval_batch_size: 8
- seed: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "NousResearch/Meta-Llama-3-8B", "model-index": [{"name": "experiemnts", "results": []}]} | amarard/experiemnts | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:NousResearch/Meta-Llama-3-8B",
"license:other",
"region:us"
]
| null | 2024-04-26T23:26:52+00:00 |
null | null | {"license": "mit"} | sonicsync/trial | null | [
"license:mit",
"region:us"
]
| null | 2024-04-26T23:27:00+00:00 |
|
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/v2ray/SchizoGPT-8x22B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/SchizoGPT-8x22B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ1_S.gguf) | i1-IQ1_S | 29.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ1_M.gguf) | i1-IQ1_M | 32.8 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 38.0 | |
| [GGUF](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 42.1 | |
| [GGUF](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ2_S.gguf) | i1-IQ2_S | 42.7 | |
| [GGUF](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ2_M.gguf) | i1-IQ2_M | 46.8 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q2_K.gguf.part2of2) | i1-Q2_K | 52.2 | IQ3_XXS probably better |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_XXS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_XXS.gguf.part2of2) | i1-IQ3_XXS | 55.0 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_XS.gguf.part2of2) | i1-IQ3_XS | 58.3 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_S.gguf.part2of2) | i1-IQ3_S | 61.6 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q3_K_S.gguf.part2of2) | i1-Q3_K_S | 61.6 | IQ3_XS probably better |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ3_M.gguf.part2of2) | i1-IQ3_M | 64.6 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q3_K_M.gguf.part2of2) | i1-Q3_K_M | 67.9 | IQ3_S probably better |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q3_K_L.gguf.part2of2) | i1-Q3_K_L | 72.7 | IQ3_M probably better |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 75.6 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q4_0.gguf.part2of2) | i1-Q4_0 | 80.0 | fast, low quality |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q4_K_S.gguf.part2of2) | i1-Q4_K_S | 80.6 | optimal size/speed/quality |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q4_K_M.gguf.part2of2) | i1-Q4_K_M | 85.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 97.1 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q5_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q5_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q5_K_M.gguf.part3of3) | i1-Q5_K_M | 100.1 | |
| [PART 1](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/SchizoGPT-8x22B-i1-GGUF/resolve/main/SchizoGPT-8x22B.i1-Q6_K.gguf.part3of3) | i1-Q6_K | 115.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "mit", "library_name": "transformers", "tags": ["not-for-all-audiences"], "datasets": ["v2ray/r-chatgpt-general-dump"], "base_model": "v2ray/SchizoGPT-8x22B", "quantized_by": "mradermacher"} | mradermacher/SchizoGPT-8x22B-i1-GGUF | null | [
"transformers",
"gguf",
"not-for-all-audiences",
"en",
"dataset:v2ray/r-chatgpt-general-dump",
"base_model:v2ray/SchizoGPT-8x22B",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T23:30:06+00:00 |
null | peft |
<!-- 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. -->
# GUE_EMP_H3K36me3-seqsight_4096_512_46M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_EMP_H3K36me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K36me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4365
- F1 Score: 0.8202
- Accuracy: 0.8211
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5133 | 0.92 | 200 | 0.5055 | 0.7699 | 0.7741 |
| 0.4632 | 1.83 | 400 | 0.4739 | 0.7892 | 0.7913 |
| 0.4487 | 2.75 | 600 | 0.4633 | 0.7918 | 0.7936 |
| 0.4525 | 3.67 | 800 | 0.4584 | 0.7908 | 0.7924 |
| 0.4364 | 4.59 | 1000 | 0.4591 | 0.7985 | 0.8005 |
| 0.4301 | 5.5 | 1200 | 0.4524 | 0.8003 | 0.8022 |
| 0.4327 | 6.42 | 1400 | 0.4578 | 0.8031 | 0.8042 |
| 0.422 | 7.34 | 1600 | 0.4673 | 0.7939 | 0.7967 |
| 0.4181 | 8.26 | 1800 | 0.4570 | 0.8025 | 0.8039 |
| 0.4207 | 9.17 | 2000 | 0.4456 | 0.8035 | 0.8050 |
| 0.4159 | 10.09 | 2200 | 0.4793 | 0.7879 | 0.7921 |
| 0.4117 | 11.01 | 2400 | 0.4489 | 0.8050 | 0.8065 |
| 0.4077 | 11.93 | 2600 | 0.4440 | 0.8033 | 0.8036 |
| 0.4043 | 12.84 | 2800 | 0.4489 | 0.7993 | 0.8010 |
| 0.4 | 13.76 | 3000 | 0.4541 | 0.7976 | 0.8002 |
| 0.3975 | 14.68 | 3200 | 0.4489 | 0.8027 | 0.8039 |
| 0.3934 | 15.6 | 3400 | 0.4495 | 0.8028 | 0.8039 |
| 0.3921 | 16.51 | 3600 | 0.4574 | 0.8002 | 0.8028 |
| 0.3883 | 17.43 | 3800 | 0.4666 | 0.8033 | 0.8050 |
| 0.3859 | 18.35 | 4000 | 0.4537 | 0.8021 | 0.8033 |
| 0.3838 | 19.27 | 4200 | 0.4646 | 0.8029 | 0.8045 |
| 0.382 | 20.18 | 4400 | 0.4740 | 0.8015 | 0.8036 |
| 0.3791 | 21.1 | 4600 | 0.4615 | 0.8014 | 0.8028 |
| 0.3795 | 22.02 | 4800 | 0.4570 | 0.8062 | 0.8071 |
| 0.374 | 22.94 | 5000 | 0.4592 | 0.7993 | 0.8013 |
| 0.3709 | 23.85 | 5200 | 0.4517 | 0.7995 | 0.8013 |
| 0.3665 | 24.77 | 5400 | 0.4751 | 0.8023 | 0.8045 |
| 0.3662 | 25.69 | 5600 | 0.4617 | 0.7997 | 0.8019 |
| 0.3641 | 26.61 | 5800 | 0.4608 | 0.8051 | 0.8062 |
| 0.3639 | 27.52 | 6000 | 0.4815 | 0.8021 | 0.8039 |
| 0.3605 | 28.44 | 6200 | 0.4600 | 0.7992 | 0.8002 |
| 0.3547 | 29.36 | 6400 | 0.4664 | 0.8001 | 0.8016 |
| 0.359 | 30.28 | 6600 | 0.4714 | 0.7979 | 0.8002 |
| 0.3567 | 31.19 | 6800 | 0.4626 | 0.8034 | 0.8045 |
| 0.3521 | 32.11 | 7000 | 0.4713 | 0.8007 | 0.8022 |
| 0.3508 | 33.03 | 7200 | 0.4689 | 0.8010 | 0.8022 |
| 0.3507 | 33.94 | 7400 | 0.4687 | 0.8016 | 0.8028 |
| 0.3467 | 34.86 | 7600 | 0.4722 | 0.7983 | 0.7993 |
| 0.3479 | 35.78 | 7800 | 0.4703 | 0.8010 | 0.8019 |
| 0.3485 | 36.7 | 8000 | 0.4648 | 0.7986 | 0.7999 |
| 0.3462 | 37.61 | 8200 | 0.4794 | 0.7981 | 0.8002 |
| 0.3476 | 38.53 | 8400 | 0.4751 | 0.8027 | 0.8042 |
| 0.3418 | 39.45 | 8600 | 0.4735 | 0.8003 | 0.8016 |
| 0.3397 | 40.37 | 8800 | 0.4812 | 0.7969 | 0.7985 |
| 0.3448 | 41.28 | 9000 | 0.4734 | 0.7971 | 0.7985 |
| 0.3371 | 42.2 | 9200 | 0.4759 | 0.8005 | 0.8016 |
| 0.3394 | 43.12 | 9400 | 0.4771 | 0.7987 | 0.7999 |
| 0.3385 | 44.04 | 9600 | 0.4747 | 0.7980 | 0.7993 |
| 0.338 | 44.95 | 9800 | 0.4775 | 0.7981 | 0.7996 |
| 0.3394 | 45.87 | 10000 | 0.4774 | 0.7988 | 0.8002 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_EMP_H3K36me3-seqsight_4096_512_46M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K36me3-seqsight_4096_512_46M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-26T23:30:52+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- 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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | sherrys/mistral-2-7b_qlora_falcon_426 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:31:22+00:00 |
text-generation | transformers | {} | mfaizanakhtar/Llama-2-python-Faizan-ModelB_v2 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:31:22+00:00 |
|
text-classification | transformers | {} | Lasghar/distilbert-base-uncased-sentiment-analysis | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T23:31:39+00:00 |
|
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### 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. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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| {"library_name": "transformers", "tags": []} | salangarica/llama3-LLM-k1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:32:16+00:00 |
text-generation | transformers |
<!-- 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. -->
# 0.001_4iters_bs256_nodpo_only4w_iter_2
This model is a fine-tuned version of [ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_iter_1](https://huggingface.co/ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_iter_1) on the updated and the original datasets.
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_iter_1", "model-index": [{"name": "0.001_4iters_bs256_nodpo_only4w_iter_2", "results": []}]} | ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_iter_2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_iter_1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:32:33+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_SAOPL_v1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_SAOPL_v1", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_SAOPL_v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:33:14+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
bloom-7b1 - bnb 4bits
- Model creator: https://huggingface.co/bigscience/
- Original model: https://huggingface.co/bigscience/bloom-7b1/
Original model description:
---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
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pipeline_tag: text-generation
---
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/1634806038075-5df7e9e5da6d0311fd3d53f9.png" alt="BigScience Logo" width="800" style="margin-left:auto; margin-right:auto; display:block"/>
Version 1.0 / 26.May.2022
## Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
## Model Details
### Basics
*This section provides information for anyone who wants to know about the model.*
<details>
<summary>Click to expand</summary> <br/>
**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*
**Model Type:** Transformer-based Language Model
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
**Release Date Estimate:** Monday, 11.July.2022
**Send Questions to:** [email protected]
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
**Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
</details>
### Technical Specifications
*This section provides information for people who work on model development.*
<details>
<summary>Click to expand</summary><br/>
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 7,069,016,064 parameters:
* 1,027,604,480 embedding parameters
* 30 layers, 32 attention heads
* Hidden layers are 4096-dimensional
* Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
* Hardware: 384 A100 80GB GPUs (48 nodes):
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
* Software:
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
#### **Training**
Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11c-2B5-logs)
- Number of epochs: 1 (*current target*)
- Dates:
- Started 11th March, 2022 11:42am PST
- Ended 5th July, 2022
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
#### **Tokenization**
The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
</details>
### Environmental Impact
<details>
<summary>Click to expand</summary><br/>
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming upon completion of training.)*
**Estimated electricity usage:** *(Forthcoming upon completion of training.)*
</details>
<p> </p>
## Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.
It provides information for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary><br/>
### Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
#### **Direct Use**
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
#### **Downstream Use**
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### **Out-of-scope Uses**
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
##### Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### **Misuse**
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
### Intended Users
#### **Direct Users**
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
#### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
#### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
</details>
<p> </p>
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
<details>
<summary>Click to expand</summary><br/>
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
#### **Languages**
The pie chart shows the distribution of languages in training data.
The following table shows the further distribution of Niger-Congo and Indic languages in the training data.
<details>
<summary>Click to expand</summary><br/>
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------ |------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
</details>
The following table shows the distribution of programming languages.
<details>
<summary>Click to expand</summary><br/>
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
</details>
</details>
<p> </p>
## Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary><br/>
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
</details>
<p> </p>
## Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary><br/>
### Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
### Factors
*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
### Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.3
- Validation Loss: 2.9
- Perplexity: 16
</details>
<p> </p>
## Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary><br/>
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models pretrained with the LLM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
</details>
<p> </p>
## Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary><br/>
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
</details>
<p> </p>
## More Information
<details>
<summary>Click to expand</summary><br/>
### Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
### Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
### Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
</details>
<p> </p>
## Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
| {} | RichardErkhov/bigscience_-_bloom-7b1-4bits | null | [
"transformers",
"safetensors",
"bloom",
"text-generation",
"arxiv:1909.08053",
"arxiv:2110.02861",
"arxiv:2108.12409",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-26T23:35:01+00:00 |
null | diffusers | # Salient Object Aware Background Generation [](https://arxiv.org/pdf/2404.10157.pdf)
This repository accompanies our paper, [Salient Object-Aware Background Generation using Text-Guided Diffusion Models](https://arxiv.org/abs/2404.10157), which has been accepted for publication in [CVPR 2024 Generative Models for Computer Vision](https://generative-vision.github.io/workshop-CVPR-24/) workshop.
The paper addresses an issue we call "object expansion" when generating backgrounds for salient objects using inpainting diffusion models. We show that models such as [Stable Inpainting](https://huggingface.co/stabilityai/stable-diffusion-2-inpainting) can sometimes arbitrarily expand or distort the salient object, which is undesirable in applications where the object's identity should be preserved, such as e-commerce ads. We provide some examples of object expansion as follows:
<div align="center">
<img src="assets/fig.jpg">
</div>
## Setup
The dependencies are provided in `requirements.txt`, install them by:
```bash
pip install -r requirements.txt
```
## Usage
### Training
The following runs the training of text-to-image inpainting ControlNet initialized with the weights of "stable-diffusion-2-inpainting":
```bash
accelerate launch --multi_gpu --mixed_precision=fp16 --num_processes=8 train_controlnet_inpaint.py --pretrained_model_name_or_path "stable-diffusion-2-inpainting" --proportion_empty_prompts 0.1
```
The following runs the training of text-to-image ControlNet initialized with the weights of "stable-diffusion-2-base":
```bash
accelerate launch --multi_gpu --mixed_precision=fp16 --num_processes=8 train_controlnet.py --pretrained_model_name_or_path "stable-diffusion-2-base" --proportion_empty_prompts 0.1
```
### Inference
Please refer to `inference.ipynb`. Tu run the code you need to download our model checkpoints.
## Models Checkpoints
| Model link | Datasets used |
|--------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| [controlnet_inpainting_salient_aware.pth](https://drive.google.com/file/d/1ad4CNJqFI_HnXFFRqcS4mOD0Le2Mvd3L/view?usp=sharing) | Salient segmentation datasets, COCO |
## Citations
If you found our work useful, please consider citing our paper:
```bibtex
@misc{eshratifar2024salient,
title={Salient Object-Aware Background Generation using Text-Guided Diffusion Models},
author={Amir Erfan Eshratifar and Joao V. B. Soares and Kapil Thadani and Shaunak Mishra and Mikhail Kuznetsov and Yueh-Ning Ku and Paloma de Juan},
year={2024},
eprint={2404.10157},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## Maintainers
- Erfan Eshratifar: [email protected]
- Joao Soares: [email protected]
## License
This project is licensed under the terms of the [Apache 2.0](LICENSE) open source license. Please refer to [LICENSE](LICENSE) for the full terms. | {"license": "apache-2.0", "tags": ["yahoo-open-source-software-incubator"]} | yahoo-inc/photo-background-generation | null | [
"diffusers",
"yahoo-open-source-software-incubator",
"arxiv:2404.10157",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T23:35:40+00:00 |
null | peft |
<!-- 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. -->
# GUE_EMP_H3K36me3-seqsight_4096_512_46M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_EMP_H3K36me3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_EMP_H3K36me3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4356
- F1 Score: 0.8126
- Accuracy: 0.8128
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.5042 | 0.92 | 200 | 0.5032 | 0.7767 | 0.7804 |
| 0.4536 | 1.83 | 400 | 0.4673 | 0.7935 | 0.7959 |
| 0.4394 | 2.75 | 600 | 0.4552 | 0.7932 | 0.7950 |
| 0.4415 | 3.67 | 800 | 0.4481 | 0.7950 | 0.7967 |
| 0.4205 | 4.59 | 1000 | 0.4468 | 0.8024 | 0.8045 |
| 0.4118 | 5.5 | 1200 | 0.4451 | 0.8039 | 0.8056 |
| 0.4132 | 6.42 | 1400 | 0.4470 | 0.8041 | 0.8050 |
| 0.3986 | 7.34 | 1600 | 0.4713 | 0.8043 | 0.8073 |
| 0.389 | 8.26 | 1800 | 0.4507 | 0.8019 | 0.8030 |
| 0.3892 | 9.17 | 2000 | 0.4537 | 0.8011 | 0.8016 |
| 0.3798 | 10.09 | 2200 | 0.4867 | 0.7971 | 0.7999 |
| 0.3706 | 11.01 | 2400 | 0.4592 | 0.8010 | 0.8025 |
| 0.3612 | 11.93 | 2600 | 0.4522 | 0.8045 | 0.8048 |
| 0.3556 | 12.84 | 2800 | 0.4755 | 0.8000 | 0.8007 |
| 0.3449 | 13.76 | 3000 | 0.4825 | 0.7920 | 0.7953 |
| 0.3372 | 14.68 | 3200 | 0.4932 | 0.8038 | 0.8053 |
| 0.33 | 15.6 | 3400 | 0.4896 | 0.7926 | 0.7942 |
| 0.3223 | 16.51 | 3600 | 0.5161 | 0.7838 | 0.7881 |
| 0.3186 | 17.43 | 3800 | 0.5194 | 0.7976 | 0.7990 |
| 0.3089 | 18.35 | 4000 | 0.5281 | 0.7850 | 0.7853 |
| 0.3008 | 19.27 | 4200 | 0.5430 | 0.7941 | 0.7964 |
| 0.2973 | 20.18 | 4400 | 0.5441 | 0.7934 | 0.7953 |
| 0.2926 | 21.1 | 4600 | 0.5342 | 0.7877 | 0.7881 |
| 0.2863 | 22.02 | 4800 | 0.5334 | 0.7944 | 0.7950 |
| 0.2753 | 22.94 | 5000 | 0.5845 | 0.7883 | 0.7896 |
| 0.2696 | 23.85 | 5200 | 0.5409 | 0.7865 | 0.7881 |
| 0.261 | 24.77 | 5400 | 0.6141 | 0.7882 | 0.7899 |
| 0.2562 | 25.69 | 5600 | 0.5895 | 0.7885 | 0.7901 |
| 0.2516 | 26.61 | 5800 | 0.5819 | 0.7912 | 0.7921 |
| 0.2488 | 27.52 | 6000 | 0.6445 | 0.7878 | 0.7890 |
| 0.2482 | 28.44 | 6200 | 0.5804 | 0.7814 | 0.7818 |
| 0.2371 | 29.36 | 6400 | 0.6031 | 0.7875 | 0.7887 |
| 0.2354 | 30.28 | 6600 | 0.6723 | 0.7808 | 0.7821 |
| 0.2334 | 31.19 | 6800 | 0.5979 | 0.7840 | 0.7844 |
| 0.2267 | 32.11 | 7000 | 0.6595 | 0.7870 | 0.7887 |
| 0.2226 | 33.03 | 7200 | 0.6147 | 0.7848 | 0.7856 |
| 0.2187 | 33.94 | 7400 | 0.6490 | 0.7810 | 0.7821 |
| 0.2163 | 34.86 | 7600 | 0.6544 | 0.7832 | 0.7835 |
| 0.2154 | 35.78 | 7800 | 0.6518 | 0.7829 | 0.7833 |
| 0.213 | 36.7 | 8000 | 0.6374 | 0.7839 | 0.7847 |
| 0.2071 | 37.61 | 8200 | 0.6771 | 0.7844 | 0.7853 |
| 0.2004 | 38.53 | 8400 | 0.6958 | 0.7836 | 0.7844 |
| 0.2063 | 39.45 | 8600 | 0.6593 | 0.7803 | 0.7812 |
| 0.1986 | 40.37 | 8800 | 0.6920 | 0.7846 | 0.7856 |
| 0.1988 | 41.28 | 9000 | 0.6774 | 0.7802 | 0.7807 |
| 0.1946 | 42.2 | 9200 | 0.6916 | 0.7834 | 0.7841 |
| 0.1926 | 43.12 | 9400 | 0.6847 | 0.7868 | 0.7876 |
| 0.1924 | 44.04 | 9600 | 0.6855 | 0.7832 | 0.7841 |
| 0.1886 | 44.95 | 9800 | 0.6957 | 0.7828 | 0.7835 |
| 0.1934 | 45.87 | 10000 | 0.6904 | 0.7811 | 0.7818 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_EMP_H3K36me3-seqsight_4096_512_46M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_EMP_H3K36me3-seqsight_4096_512_46M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-26T23:36:37+00:00 |
null | null | {} | rK-copa/whisper-small-hi | null | [
"region:us"
]
| null | 2024-04-26T23:36:49+00:00 |
|
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_OASPL_v1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_OASPL_v1", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_OASPL_v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:37:37+00:00 |
null | transformers | {"license": "wtfpl"} | jeffsan730/jeff_subjectline_test | null | [
"transformers",
"safetensors",
"llama",
"license:wtfpl",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:38:49+00:00 |
|
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
bloom-7b1 - bnb 8bits
- Model creator: https://huggingface.co/bigscience/
- Original model: https://huggingface.co/bigscience/bloom-7b1/
Original model description:
---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-generation
---
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/1634806038075-5df7e9e5da6d0311fd3d53f9.png" alt="BigScience Logo" width="800" style="margin-left:auto; margin-right:auto; display:block"/>
Version 1.0 / 26.May.2022
## Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
## Model Details
### Basics
*This section provides information for anyone who wants to know about the model.*
<details>
<summary>Click to expand</summary> <br/>
**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*
**Model Type:** Transformer-based Language Model
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
**Release Date Estimate:** Monday, 11.July.2022
**Send Questions to:** [email protected]
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
**Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
</details>
### Technical Specifications
*This section provides information for people who work on model development.*
<details>
<summary>Click to expand</summary><br/>
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 7,069,016,064 parameters:
* 1,027,604,480 embedding parameters
* 30 layers, 32 attention heads
* Hidden layers are 4096-dimensional
* Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
* Hardware: 384 A100 80GB GPUs (48 nodes):
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
* Software:
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
#### **Training**
Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11c-2B5-logs)
- Number of epochs: 1 (*current target*)
- Dates:
- Started 11th March, 2022 11:42am PST
- Ended 5th July, 2022
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
#### **Tokenization**
The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
</details>
### Environmental Impact
<details>
<summary>Click to expand</summary><br/>
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming upon completion of training.)*
**Estimated electricity usage:** *(Forthcoming upon completion of training.)*
</details>
<p> </p>
## Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.
It provides information for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary><br/>
### Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
#### **Direct Use**
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
#### **Downstream Use**
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### **Out-of-scope Uses**
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
##### Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### **Misuse**
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
### Intended Users
#### **Direct Users**
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
#### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
#### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
</details>
<p> </p>
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
<details>
<summary>Click to expand</summary><br/>
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
#### **Languages**
The pie chart shows the distribution of languages in training data.
The following table shows the further distribution of Niger-Congo and Indic languages in the training data.
<details>
<summary>Click to expand</summary><br/>
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------ |------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
</details>
The following table shows the distribution of programming languages.
<details>
<summary>Click to expand</summary><br/>
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
</details>
</details>
<p> </p>
## Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary><br/>
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
</details>
<p> </p>
## Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary><br/>
### Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
### Factors
*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
### Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.3
- Validation Loss: 2.9
- Perplexity: 16
</details>
<p> </p>
## Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary><br/>
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models pretrained with the LLM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
</details>
<p> </p>
## Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary><br/>
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
</details>
<p> </p>
## More Information
<details>
<summary>Click to expand</summary><br/>
### Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
### Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
### Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
</details>
<p> </p>
## Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
| {} | RichardErkhov/bigscience_-_bloom-7b1-8bits | null | [
"transformers",
"safetensors",
"bloom",
"text-generation",
"arxiv:1909.08053",
"arxiv:2110.02861",
"arxiv:2108.12409",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
]
| null | 2024-04-26T23:40:17+00:00 |
automatic-speech-recognition | transformers | {} | deraa/whisper-English | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T23:41:37+00:00 |
|
text-generation | transformers |
# Keiana-L3-Test6.2-8B-18
Keiana-L3-Test6.2-8B-18 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
# Keep in mind that, this merged model isn't usually tested at the moment, which could benefit in vocabulary error.
* [Kaoeiri/Keiana-L3-Test5.4-8B-10](https://huggingface.co/Kaoeiri/Keiana-L3-Test5.4-8B-10)
* [Kaoeiri/Keiana-L3-Test4.7-8B-3](https://huggingface.co/Kaoeiri/Keiana-L3-Test4.7-8B-3)
* [Kaoeiri/Keiana-L3-Test6-8B-16](https://huggingface.co/Kaoeiri/Keiana-L3-Test6-8B-16)
## 🧩 Configuration
```yaml
merge_method: model_stock
dtype: float16
base_model: Kaoeiri/Keiana-L3-Test5.75-8B-13.5
models:
- model: Kaoeiri/Keiana-L3-Test5.4-8B-10
parameters:
weight: .2
density: .25
- model: Kaoeiri/Keiana-L3-Test4.7-8B-3
parameters:
weight: .25
density: .5
- model: Kaoeiri/Keiana-L3-Test6-8B-16
parameters:
weight: .2
density: .35
parameters:
int8_mask: true
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kaoeiri/Keiana-L3-Test6.2-8B-18"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "Kaoeiri/Keiana-L3-Test5.4-8B-10", "Kaoeiri/Keiana-L3-Test4.7-8B-3", "Kaoeiri/Keiana-L3-Test6-8B-16"], "base_model": ["Kaoeiri/Keiana-L3-Test5.4-8B-10", "Kaoeiri/Keiana-L3-Test4.7-8B-3", "Kaoeiri/Keiana-L3-Test6-8B-16"]} | Kaoeiri/Keiana-L3-Test6.2-8B-18 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Kaoeiri/Keiana-L3-Test5.4-8B-10",
"Kaoeiri/Keiana-L3-Test4.7-8B-3",
"Kaoeiri/Keiana-L3-Test6-8B-16",
"conversational",
"base_model:Kaoeiri/Keiana-L3-Test5.4-8B-10",
"base_model:Kaoeiri/Keiana-L3-Test4.7-8B-3",
"base_model:Kaoeiri/Keiana-L3-Test6-8B-16",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:41:53+00:00 |
null | null |
# DavidAU/DeepSeek-Coder-Instruct-8x1.3b-Q8_0-GGUF
This model was converted to GGUF format from [`SanjiWatsuki/DeepSeek-Coder-Instruct-8x1.3b`](https://huggingface.co/SanjiWatsuki/DeepSeek-Coder-Instruct-8x1.3b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/SanjiWatsuki/DeepSeek-Coder-Instruct-8x1.3b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo DavidAU/DeepSeek-Coder-Instruct-8x1.3b-Q8_0-GGUF --model deepseek-coder-instruct-8x1.3b.Q8_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo DavidAU/DeepSeek-Coder-Instruct-8x1.3b-Q8_0-GGUF --model deepseek-coder-instruct-8x1.3b.Q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m deepseek-coder-instruct-8x1.3b.Q8_0.gguf -n 128
```
| {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "llama-cpp", "gguf-my-repo"]} | DavidAU/DeepSeek-Coder-Instruct-8x1.3b-Q8_0-GGUF | null | [
"gguf",
"merge",
"mergekit",
"lazymergekit",
"llama-cpp",
"gguf-my-repo",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T23:42:56+00:00 |
null | peft |
<!-- 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. -->
# GUE_mouse_0-seqsight_4096_512_46M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6438
- F1 Score: 0.7366
- Accuracy: 0.7370
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.5928 | 3.92 | 200 | 0.5373 | 0.7135 | 0.7136 |
| 0.515 | 7.84 | 400 | 0.5079 | 0.7432 | 0.7432 |
| 0.4784 | 11.76 | 600 | 0.4943 | 0.7447 | 0.7457 |
| 0.4502 | 15.69 | 800 | 0.5077 | 0.7523 | 0.7543 |
| 0.4304 | 19.61 | 1000 | 0.4973 | 0.7622 | 0.7630 |
| 0.4136 | 23.53 | 1200 | 0.5187 | 0.7633 | 0.7654 |
| 0.3858 | 27.45 | 1400 | 0.5346 | 0.7679 | 0.7679 |
| 0.3699 | 31.37 | 1600 | 0.5643 | 0.7630 | 0.7630 |
| 0.3588 | 35.29 | 1800 | 0.5646 | 0.7654 | 0.7654 |
| 0.3357 | 39.22 | 2000 | 0.5827 | 0.7765 | 0.7765 |
| 0.3181 | 43.14 | 2200 | 0.6220 | 0.7581 | 0.7580 |
| 0.3018 | 47.06 | 2400 | 0.6253 | 0.7543 | 0.7543 |
| 0.294 | 50.98 | 2600 | 0.6560 | 0.7593 | 0.7593 |
| 0.2764 | 54.9 | 2800 | 0.6469 | 0.7761 | 0.7765 |
| 0.265 | 58.82 | 3000 | 0.6682 | 0.7715 | 0.7716 |
| 0.2535 | 62.75 | 3200 | 0.6699 | 0.7714 | 0.7716 |
| 0.2395 | 66.67 | 3400 | 0.7301 | 0.7456 | 0.7457 |
| 0.2299 | 70.59 | 3600 | 0.7805 | 0.7504 | 0.7506 |
| 0.2142 | 74.51 | 3800 | 0.7867 | 0.7543 | 0.7543 |
| 0.2034 | 78.43 | 4000 | 0.8000 | 0.7530 | 0.7531 |
| 0.1971 | 82.35 | 4200 | 0.8266 | 0.7581 | 0.7580 |
| 0.1897 | 86.27 | 4400 | 0.8075 | 0.7544 | 0.7543 |
| 0.1899 | 90.2 | 4600 | 0.8202 | 0.7506 | 0.7506 |
| 0.1759 | 94.12 | 4800 | 0.8642 | 0.7556 | 0.7556 |
| 0.1725 | 98.04 | 5000 | 0.8268 | 0.7703 | 0.7704 |
| 0.1593 | 101.96 | 5200 | 0.8908 | 0.7630 | 0.7630 |
| 0.1555 | 105.88 | 5400 | 0.8725 | 0.7630 | 0.7630 |
| 0.152 | 109.8 | 5600 | 0.9029 | 0.7555 | 0.7556 |
| 0.1462 | 113.73 | 5800 | 0.8881 | 0.7642 | 0.7642 |
| 0.1445 | 117.65 | 6000 | 0.9024 | 0.7630 | 0.7630 |
| 0.1341 | 121.57 | 6200 | 0.9466 | 0.7568 | 0.7568 |
| 0.1301 | 125.49 | 6400 | 0.9368 | 0.7630 | 0.7630 |
| 0.1279 | 129.41 | 6600 | 0.9542 | 0.7618 | 0.7617 |
| 0.122 | 133.33 | 6800 | 0.9222 | 0.7654 | 0.7654 |
| 0.1228 | 137.25 | 7000 | 0.9760 | 0.7617 | 0.7617 |
| 0.122 | 141.18 | 7200 | 0.9501 | 0.7655 | 0.7654 |
| 0.1166 | 145.1 | 7400 | 0.9937 | 0.7629 | 0.7630 |
| 0.114 | 149.02 | 7600 | 0.9839 | 0.7617 | 0.7617 |
| 0.1133 | 152.94 | 7800 | 1.0020 | 0.7605 | 0.7605 |
| 0.1131 | 156.86 | 8000 | 0.9935 | 0.7593 | 0.7593 |
| 0.1096 | 160.78 | 8200 | 0.9883 | 0.7617 | 0.7617 |
| 0.1087 | 164.71 | 8400 | 1.0065 | 0.7618 | 0.7617 |
| 0.1066 | 168.63 | 8600 | 1.0094 | 0.7593 | 0.7593 |
| 0.1034 | 172.55 | 8800 | 0.9966 | 0.7654 | 0.7654 |
| 0.0982 | 176.47 | 9000 | 1.0178 | 0.7655 | 0.7654 |
| 0.1036 | 180.39 | 9200 | 1.0095 | 0.7618 | 0.7617 |
| 0.1015 | 184.31 | 9400 | 1.0097 | 0.7630 | 0.7630 |
| 0.0989 | 188.24 | 9600 | 1.0220 | 0.7593 | 0.7593 |
| 0.1002 | 192.16 | 9800 | 1.0202 | 0.7618 | 0.7617 |
| 0.0971 | 196.08 | 10000 | 1.0229 | 0.7618 | 0.7617 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_0-seqsight_4096_512_46M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_0-seqsight_4096_512_46M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-26T23:43:42+00:00 |
null | peft |
<!-- 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. -->
# GUE_mouse_0-seqsight_4096_512_46M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5946
- F1 Score: 0.7442
- Accuracy: 0.7457
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.6066 | 3.92 | 200 | 0.5727 | 0.6852 | 0.6852 |
| 0.5665 | 7.84 | 400 | 0.5472 | 0.7205 | 0.7210 |
| 0.5331 | 11.76 | 600 | 0.5065 | 0.7479 | 0.7481 |
| 0.508 | 15.69 | 800 | 0.4973 | 0.7417 | 0.7420 |
| 0.4872 | 19.61 | 1000 | 0.4938 | 0.7443 | 0.7444 |
| 0.4747 | 23.53 | 1200 | 0.5028 | 0.7427 | 0.7457 |
| 0.4581 | 27.45 | 1400 | 0.4992 | 0.7494 | 0.7494 |
| 0.4446 | 31.37 | 1600 | 0.5049 | 0.7504 | 0.7506 |
| 0.4378 | 35.29 | 1800 | 0.5046 | 0.7476 | 0.7481 |
| 0.4234 | 39.22 | 2000 | 0.5016 | 0.7489 | 0.7494 |
| 0.4094 | 43.14 | 2200 | 0.5165 | 0.7543 | 0.7543 |
| 0.4051 | 47.06 | 2400 | 0.5222 | 0.7506 | 0.7506 |
| 0.3947 | 50.98 | 2600 | 0.5299 | 0.7616 | 0.7617 |
| 0.386 | 54.9 | 2800 | 0.5343 | 0.7671 | 0.7679 |
| 0.379 | 58.82 | 3000 | 0.5394 | 0.7580 | 0.7593 |
| 0.3697 | 62.75 | 3200 | 0.5449 | 0.7605 | 0.7605 |
| 0.358 | 66.67 | 3400 | 0.5533 | 0.7627 | 0.7630 |
| 0.3488 | 70.59 | 3600 | 0.5605 | 0.7665 | 0.7667 |
| 0.3438 | 74.51 | 3800 | 0.5670 | 0.7640 | 0.7642 |
| 0.3345 | 78.43 | 4000 | 0.5755 | 0.7592 | 0.7593 |
| 0.3273 | 82.35 | 4200 | 0.5898 | 0.7554 | 0.7556 |
| 0.3169 | 86.27 | 4400 | 0.5984 | 0.7641 | 0.7642 |
| 0.322 | 90.2 | 4600 | 0.5861 | 0.7653 | 0.7654 |
| 0.3088 | 94.12 | 4800 | 0.6110 | 0.7580 | 0.7580 |
| 0.2979 | 98.04 | 5000 | 0.6032 | 0.7663 | 0.7667 |
| 0.293 | 101.96 | 5200 | 0.6223 | 0.7556 | 0.7556 |
| 0.2892 | 105.88 | 5400 | 0.6325 | 0.7543 | 0.7543 |
| 0.2809 | 109.8 | 5600 | 0.6354 | 0.7470 | 0.7469 |
| 0.2791 | 113.73 | 5800 | 0.6331 | 0.7530 | 0.7531 |
| 0.2706 | 117.65 | 6000 | 0.6388 | 0.7468 | 0.7469 |
| 0.2651 | 121.57 | 6200 | 0.6523 | 0.7543 | 0.7543 |
| 0.2638 | 125.49 | 6400 | 0.6515 | 0.7531 | 0.7531 |
| 0.2564 | 129.41 | 6600 | 0.6560 | 0.7430 | 0.7432 |
| 0.25 | 133.33 | 6800 | 0.6708 | 0.7555 | 0.7556 |
| 0.2521 | 137.25 | 7000 | 0.6742 | 0.7507 | 0.7506 |
| 0.2485 | 141.18 | 7200 | 0.6651 | 0.7542 | 0.7543 |
| 0.2452 | 145.1 | 7400 | 0.6783 | 0.7518 | 0.7519 |
| 0.2374 | 149.02 | 7600 | 0.6797 | 0.7518 | 0.7519 |
| 0.2399 | 152.94 | 7800 | 0.6806 | 0.7556 | 0.7556 |
| 0.2388 | 156.86 | 8000 | 0.6827 | 0.7481 | 0.7481 |
| 0.2287 | 160.78 | 8200 | 0.6910 | 0.7567 | 0.7568 |
| 0.2311 | 164.71 | 8400 | 0.6993 | 0.7493 | 0.7494 |
| 0.2291 | 168.63 | 8600 | 0.6969 | 0.7543 | 0.7543 |
| 0.2212 | 172.55 | 8800 | 0.7023 | 0.7505 | 0.7506 |
| 0.2213 | 176.47 | 9000 | 0.7072 | 0.7468 | 0.7469 |
| 0.2249 | 180.39 | 9200 | 0.7030 | 0.7505 | 0.7506 |
| 0.2216 | 184.31 | 9400 | 0.7035 | 0.7531 | 0.7531 |
| 0.2208 | 188.24 | 9600 | 0.7063 | 0.7482 | 0.7481 |
| 0.2222 | 192.16 | 9800 | 0.7041 | 0.7506 | 0.7506 |
| 0.215 | 196.08 | 10000 | 0.7059 | 0.7518 | 0.7519 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_0-seqsight_4096_512_46M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_0-seqsight_4096_512_46M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-26T23:43:42+00:00 |
null | null | {} | davesdxh/ModelsXL | null | [
"region:us"
]
| null | 2024-04-26T23:43:55+00:00 |
|
text2text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### 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. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | himanshubeniwal/mt5-large-finetuned-kk-to-en-idiot-Indian | null | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:46:30+00:00 |
text-generation | transformers |
<!-- 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. -->
# Meta-Llama-3-8B-Instruct_fictional_Spanish_v1
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the generator 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 2
- 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
- num_epochs: 36
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "other", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "Meta-Llama-3-8B-Instruct_fictional_Spanish_v1", "results": []}]} | yzhuang/Meta-Llama-3-8B-Instruct_fictional_Spanish_v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"dataset:generator",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:46:54+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** sjonas50
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | sjonas50/lora_model | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T23:47:29+00:00 |
null | null | {} | Sam846/Poppy_Spanish | null | [
"region:us"
]
| null | 2024-04-26T23:47:34+00:00 |
|
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
bloom-7b1 - GGUF
- Model creator: https://huggingface.co/bigscience/
- Original model: https://huggingface.co/bigscience/bloom-7b1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [bloom-7b1.Q2_K.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q2_K.gguf) | Q2_K | 3.2GB |
| [bloom-7b1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.IQ3_XS.gguf) | IQ3_XS | 3.56GB |
| [bloom-7b1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.IQ3_S.gguf) | IQ3_S | 3.63GB |
| [bloom-7b1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q3_K_S.gguf) | Q3_K_S | 3.63GB |
| [bloom-7b1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.IQ3_M.gguf) | IQ3_M | 1.2GB |
| [bloom-7b1.Q3_K.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q3_K.gguf) | Q3_K | 0.99GB |
| [bloom-7b1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q3_K_M.gguf) | Q3_K_M | 0.63GB |
| [bloom-7b1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q3_K_L.gguf) | Q3_K_L | 0.52GB |
| [bloom-7b1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.IQ4_XS.gguf) | IQ4_XS | 0.23GB |
| [bloom-7b1.Q4_0.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q4_0.gguf) | Q4_0 | 0.19GB |
| [bloom-7b1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.IQ4_NL.gguf) | IQ4_NL | 0.06GB |
| [bloom-7b1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q4_K_S.gguf) | Q4_K_S | 0.06GB |
| [bloom-7b1.Q4_K.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q4_K.gguf) | Q4_K | 0.06GB |
| [bloom-7b1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q4_K_M.gguf) | Q4_K_M | 0.02GB |
| [bloom-7b1.Q4_1.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q4_1.gguf) | Q4_1 | 0.01GB |
| [bloom-7b1.Q5_0.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q5_0.gguf) | Q5_0 | 0.0GB |
| [bloom-7b1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q5_K_S.gguf) | Q5_K_S | 0.0GB |
| [bloom-7b1.Q5_K.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q5_K.gguf) | Q5_K | 0.0GB |
| [bloom-7b1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q5_K_M.gguf) | Q5_K_M | 0.0GB |
| [bloom-7b1.Q5_1.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q5_1.gguf) | Q5_1 | 0.0GB |
| [bloom-7b1.Q6_K.gguf](https://huggingface.co/RichardErkhov/bigscience_-_bloom-7b1-gguf/blob/main/bloom-7b1.Q6_K.gguf) | Q6_K | 0.0GB |
Original model description:
---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-generation
---
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/1634806038075-5df7e9e5da6d0311fd3d53f9.png" alt="BigScience Logo" width="800" style="margin-left:auto; margin-right:auto; display:block"/>
Version 1.0 / 26.May.2022
## Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
## Model Details
### Basics
*This section provides information for anyone who wants to know about the model.*
<details>
<summary>Click to expand</summary> <br/>
**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*
**Model Type:** Transformer-based Language Model
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
**Release Date Estimate:** Monday, 11.July.2022
**Send Questions to:** [email protected]
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
**Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
</details>
### Technical Specifications
*This section provides information for people who work on model development.*
<details>
<summary>Click to expand</summary><br/>
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 7,069,016,064 parameters:
* 1,027,604,480 embedding parameters
* 30 layers, 32 attention heads
* Hidden layers are 4096-dimensional
* Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
* Hardware: 384 A100 80GB GPUs (48 nodes):
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
* Software:
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
#### **Training**
Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11c-2B5-logs)
- Number of epochs: 1 (*current target*)
- Dates:
- Started 11th March, 2022 11:42am PST
- Ended 5th July, 2022
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
#### **Tokenization**
The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
</details>
### Environmental Impact
<details>
<summary>Click to expand</summary><br/>
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming upon completion of training.)*
**Estimated electricity usage:** *(Forthcoming upon completion of training.)*
</details>
<p> </p>
## Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.
It provides information for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary><br/>
### Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
#### **Direct Use**
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
#### **Downstream Use**
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### **Out-of-scope Uses**
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
##### Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### **Misuse**
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
### Intended Users
#### **Direct Users**
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
#### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
#### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
</details>
<p> </p>
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
<details>
<summary>Click to expand</summary><br/>
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
#### **Languages**
The pie chart shows the distribution of languages in training data.
The following table shows the further distribution of Niger-Congo and Indic languages in the training data.
<details>
<summary>Click to expand</summary><br/>
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------ |------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
</details>
The following table shows the distribution of programming languages.
<details>
<summary>Click to expand</summary><br/>
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
</details>
</details>
<p> </p>
## Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary><br/>
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
</details>
<p> </p>
## Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary><br/>
### Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
### Factors
*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
### Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.3
- Validation Loss: 2.9
- Perplexity: 16
</details>
<p> </p>
## Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary><br/>
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models pretrained with the LLM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
</details>
<p> </p>
## Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary><br/>
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
</details>
<p> </p>
## More Information
<details>
<summary>Click to expand</summary><br/>
### Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
### Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
### Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
</details>
<p> </p>
## Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
| {} | RichardErkhov/bigscience_-_bloom-7b1-gguf | null | [
"gguf",
"arxiv:1909.08053",
"arxiv:2110.02861",
"arxiv:2108.12409",
"region:us"
]
| null | 2024-04-26T23:48:21+00:00 |
text-classification | transformers | {"license": "unknown"} | hermione03/movies_notice | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-26T23:48:23+00:00 |
|
text-generation | transformers | {"license": "cc-by-nc-4.0"} | DataRaptor/SuSastho-SearchQ-Ans-Epoch3-llama3-8b-instruct | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:48:33+00:00 |
|
text-generation | transformers | # IceCoffeeRP-7b *(IceCoffeeTest11)*
## Merge Details
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
Prompt template: Alpaca, maybe ChatML
* measurement.json for quanting exl2 included.
- [4.2bpw-exl2](https://huggingface.co/icefog72/IceCoffeeRP-7b-4.2bpw-exl2)
- [6.5bpw-exl2](https://huggingface.co/icefog72/IceCoffeeRP-7b-6.5bpw-exl2)
- [8bpw-exl2](https://huggingface.co/icefog72/IceCoffeeRP-7b-8bpw-exl2)
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* G:\FModels\IceCoffeeTest10
* G:\FModels\IceCoffeeTest5
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: G:\FModels\IceCoffeeTest5
layer_range: [0, 32]
- model: G:\FModels\IceCoffeeTest10
layer_range: [0, 32]
merge_method: slerp
base_model: G:\FModels\IceCoffeeTest5
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: float16
```
## How to download From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `IceCoffeeRP-7b`:
```shell
mkdir IceCoffeeRP-7b
huggingface-cli download icefog72/IceCoffeeRP-7b --local-dir IceCoffeeRP-7b --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir FOLDERNAME
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download MODEL --local-dir FOLDERNAME --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_icefog72__IceCoffeeTest11)
| Metric |Value|
|---------------------------------|----:|
|Avg. |73.19|
|AI2 Reasoning Challenge (25-Shot)|71.16|
|HellaSwag (10-Shot) |87.74|
|MMLU (5-Shot) |63.54|
|TruthfulQA (0-shot) |70.03|
|Winogrande (5-shot) |82.48|
|GSM8k (5-shot) |64.22|
| {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["mergekit", "merge", "alpaca", "mistral", "not-for-all-audiences", "nsfw"], "model-index": [{"name": "IceCoffeeTest11", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "AI2 Reasoning Challenge (25-Shot)", "type": "ai2_arc", "config": "ARC-Challenge", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "acc_norm", "value": 71.16, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceCoffeeTest11", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HellaSwag (10-Shot)", "type": "hellaswag", "split": "validation", "args": {"num_few_shot": 10}}, "metrics": [{"type": "acc_norm", "value": 87.74, "name": "normalized accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceCoffeeTest11", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "MMLU (5-Shot)", "type": "cais/mmlu", "config": "all", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 63.54, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceCoffeeTest11", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "TruthfulQA (0-shot)", "type": "truthful_qa", "config": "multiple_choice", "split": "validation", "args": {"num_few_shot": 0}}, "metrics": [{"type": "mc2", "value": 70.03}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceCoffeeTest11", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Winogrande (5-shot)", "type": "winogrande", "config": "winogrande_xl", "split": "validation", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 82.48, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceCoffeeTest11", "name": "Open LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "GSM8k (5-shot)", "type": "gsm8k", "config": "main", "split": "test", "args": {"num_few_shot": 5}}, "metrics": [{"type": "acc", "value": 64.22, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=icefog72/IceCoffeeTest11", "name": "Open LLM Leaderboard"}}]}]} | icefog72/IceCoffeeRP-7b | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"alpaca",
"not-for-all-audiences",
"nsfw",
"conversational",
"license:cc-by-nc-4.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:48:50+00:00 |
null | peft |
# 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]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### 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. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.10.0 | {"license": "apache-2.0", "library_name": "peft", "base_model": "google/gemma-2b-it"} | azarafrooz/gemma-2b-nlaf-v0 | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b-it",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T23:49:24+00:00 |
null | null | {} | Delusion4013/pokemon-lora | null | [
"region:us"
]
| null | 2024-04-26T23:49:53+00:00 |
|
text-generation | transformers | # ExperimentV2 AKA NerdySamanthaV2 (Mistral v0.1 & Samantha v1.2 & Speechless Code Mistral v1.0 7B)
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) as a base.
### Models Merged
The following models were included in the merge:
* [cognitivecomputations/samantha-1.2-mistral-7b](https://huggingface.co/cognitivecomputations/samantha-1.2-mistral-7b)
* [uukuguy/speechless-code-mistral-7b-v1.0](https://huggingface.co/uukuguy/speechless-code-mistral-7b-v1.0)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: uukuguy/speechless-code-mistral-7b-v1.0
- model: cognitivecomputations/samantha-1.2-mistral-7b
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
| {"license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["cognitivecomputations/samantha-1.2-mistral-7b", "mistralai/Mistral-7B-v0.1", "uukuguy/speechless-code-mistral-7b-v1.0"]} | TitleOS/ExperimentTwo | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"arxiv:2403.19522",
"base_model:cognitivecomputations/samantha-1.2-mistral-7b",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:uukuguy/speechless-code-mistral-7b-v1.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:50:24+00:00 |
text-generation | transformers | {} | LuangMV97/Roberta-DialoGPT_EmpAI_SinEOS | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:53:08+00:00 |
|
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_POSAL_v1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_POSAL_v1", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_POSAL_v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:53:34+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_AOSPL_v1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_AOSPL_v1", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_AOSPL_v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:53:44+00:00 |
reinforcement-learning | null |
# **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
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-pixelcopter-01", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "17.80 +/- 14.90", "name": "mean_reward", "verified": false}]}]}]} | stuvx/Reinforce-pixelcopter-01 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| null | 2024-04-26T23:54:37+00:00 |
null | peft |
<!-- 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. -->
# mistral-7b-nli_cot
This model is a fine-tuned version of [TheBloke/Mistral-7B-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-v0.1-GPTQ) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 0.004
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 11
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.6298 | 0.9950 | 149 | 0.4956 |
| 0.4848 | 1.9967 | 299 | 0.4855 |
| 1.4397 | 2.9983 | 449 | 2.3408 |
| 1.4527 | 4.0 | 599 | 1.1570 |
| 1.0505 | 4.9950 | 748 | 1.0305 |
| 0.8713 | 5.9967 | 898 | 0.7930 |
| 0.7679 | 6.9983 | 1048 | 0.7487 |
| 0.7289 | 8.0 | 1198 | 0.7110 |
| 69.2312 | 8.9950 | 1347 | nan |
| 300.5902 | 9.9967 | 1497 | nan |
| 635.9469 | 10.9449 | 1639 | nan |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.0.1+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "TheBloke/Mistral-7B-v0.1-GPTQ", "model-index": [{"name": "mistral-7b-nli_cot", "results": []}]} | jd0g/Mistral-7B-NLI-v0.2 | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-v0.1-GPTQ",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T23:55:18+00:00 |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### 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. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | devesh220897/financial-chatbot-for-young-adults-2 | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-26T23:57:21+00:00 |
image-classification | adapter-transformers | {"language": ["en"], "license": "apache-2.0", "library_name": "adapter-transformers", "datasets": ["frgfm/imagenette"], "metrics": ["accuracy"], "pipeline_tag": "image-classification"} | aryanmagoon/ViT_ImageNette | null | [
"adapter-transformers",
"safetensors",
"vit",
"image-classification",
"en",
"dataset:frgfm/imagenette",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-26T23:58:21+00:00 |
|
null | peft |
<!-- 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. -->
# GUE_mouse_1-seqsight_4096_512_46M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2318
- F1 Score: 0.9005
- Accuracy: 0.9005
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.4539 | 0.47 | 200 | 0.3501 | 0.8431 | 0.8431 |
| 0.3557 | 0.95 | 400 | 0.3166 | 0.8605 | 0.8605 |
| 0.3227 | 1.42 | 600 | 0.2944 | 0.8707 | 0.8707 |
| 0.3126 | 1.9 | 800 | 0.2744 | 0.8774 | 0.8774 |
| 0.2942 | 2.37 | 1000 | 0.2633 | 0.8834 | 0.8835 |
| 0.2816 | 2.84 | 1200 | 0.2547 | 0.8870 | 0.8870 |
| 0.2676 | 3.32 | 1400 | 0.2482 | 0.8904 | 0.8904 |
| 0.2703 | 3.79 | 1600 | 0.2462 | 0.8910 | 0.8910 |
| 0.263 | 4.27 | 1800 | 0.2406 | 0.8955 | 0.8956 |
| 0.2606 | 4.74 | 2000 | 0.2449 | 0.8941 | 0.8941 |
| 0.2559 | 5.21 | 2200 | 0.2356 | 0.8959 | 0.8961 |
| 0.2542 | 5.69 | 2400 | 0.2354 | 0.8982 | 0.8983 |
| 0.2498 | 6.16 | 2600 | 0.2342 | 0.8984 | 0.8986 |
| 0.2472 | 6.64 | 2800 | 0.2337 | 0.8973 | 0.8974 |
| 0.2497 | 7.11 | 3000 | 0.2317 | 0.9000 | 0.9001 |
| 0.2399 | 7.58 | 3200 | 0.2328 | 0.8991 | 0.8992 |
| 0.2486 | 8.06 | 3400 | 0.2305 | 0.8984 | 0.8986 |
| 0.2366 | 8.53 | 3600 | 0.2366 | 0.8955 | 0.8956 |
| 0.2438 | 9.0 | 3800 | 0.2323 | 0.8974 | 0.8976 |
| 0.2383 | 9.48 | 4000 | 0.2343 | 0.8984 | 0.8986 |
| 0.2402 | 9.95 | 4200 | 0.2250 | 0.9031 | 0.9032 |
| 0.2383 | 10.43 | 4400 | 0.2265 | 0.9027 | 0.9027 |
| 0.2354 | 10.9 | 4600 | 0.2256 | 0.9031 | 0.9032 |
| 0.2348 | 11.37 | 4800 | 0.2279 | 0.9029 | 0.9029 |
| 0.2376 | 11.85 | 5000 | 0.2287 | 0.9001 | 0.9002 |
| 0.2368 | 12.32 | 5200 | 0.2276 | 0.9011 | 0.9011 |
| 0.2383 | 12.8 | 5400 | 0.2244 | 0.9036 | 0.9036 |
| 0.2335 | 13.27 | 5600 | 0.2271 | 0.9022 | 0.9023 |
| 0.2306 | 13.74 | 5800 | 0.2265 | 0.9028 | 0.9029 |
| 0.2365 | 14.22 | 6000 | 0.2267 | 0.9033 | 0.9033 |
| 0.229 | 14.69 | 6200 | 0.2284 | 0.9030 | 0.9030 |
| 0.2336 | 15.17 | 6400 | 0.2255 | 0.9035 | 0.9035 |
| 0.2292 | 15.64 | 6600 | 0.2280 | 0.9019 | 0.9020 |
| 0.2279 | 16.11 | 6800 | 0.2275 | 0.9020 | 0.9021 |
| 0.227 | 16.59 | 7000 | 0.2234 | 0.9037 | 0.9038 |
| 0.2315 | 17.06 | 7200 | 0.2229 | 0.9031 | 0.9032 |
| 0.2298 | 17.54 | 7400 | 0.2254 | 0.9021 | 0.9021 |
| 0.2281 | 18.01 | 7600 | 0.2238 | 0.9021 | 0.9021 |
| 0.2241 | 18.48 | 7800 | 0.2233 | 0.9039 | 0.9039 |
| 0.2322 | 18.96 | 8000 | 0.2216 | 0.9033 | 0.9033 |
| 0.2257 | 19.43 | 8200 | 0.2244 | 0.9029 | 0.9029 |
| 0.2258 | 19.91 | 8400 | 0.2263 | 0.9025 | 0.9026 |
| 0.2244 | 20.38 | 8600 | 0.2237 | 0.9029 | 0.9029 |
| 0.2269 | 20.85 | 8800 | 0.2225 | 0.9040 | 0.9041 |
| 0.2244 | 21.33 | 9000 | 0.2222 | 0.9037 | 0.9038 |
| 0.2271 | 21.8 | 9200 | 0.2229 | 0.9037 | 0.9038 |
| 0.2284 | 22.27 | 9400 | 0.2221 | 0.9030 | 0.9030 |
| 0.2239 | 22.75 | 9600 | 0.2228 | 0.9033 | 0.9033 |
| 0.2244 | 23.22 | 9800 | 0.2230 | 0.9037 | 0.9038 |
| 0.2282 | 23.7 | 10000 | 0.2226 | 0.9040 | 0.9041 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_1-seqsight_4096_512_46M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_1-seqsight_4096_512_46M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-26T23:58:30+00:00 |
null | peft |
<!-- 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. -->
# GUE_mouse_0-seqsight_4096_512_46M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_0](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_0) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0763
- F1 Score: 0.7320
- Accuracy: 0.7321
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.573 | 3.92 | 200 | 0.5131 | 0.7345 | 0.7346 |
| 0.4811 | 7.84 | 400 | 0.5077 | 0.7581 | 0.7593 |
| 0.4334 | 11.76 | 600 | 0.5144 | 0.7554 | 0.7556 |
| 0.3826 | 15.69 | 800 | 0.5331 | 0.7650 | 0.7667 |
| 0.34 | 19.61 | 1000 | 0.5577 | 0.7702 | 0.7704 |
| 0.3031 | 23.53 | 1200 | 0.6158 | 0.7777 | 0.7778 |
| 0.2543 | 27.45 | 1400 | 0.6738 | 0.7729 | 0.7728 |
| 0.2187 | 31.37 | 1600 | 0.7783 | 0.7687 | 0.7691 |
| 0.1916 | 35.29 | 1800 | 0.8983 | 0.7431 | 0.7444 |
| 0.1665 | 39.22 | 2000 | 0.8251 | 0.7593 | 0.7593 |
| 0.148 | 43.14 | 2200 | 0.9053 | 0.7692 | 0.7691 |
| 0.133 | 47.06 | 2400 | 0.9700 | 0.7650 | 0.7654 |
| 0.122 | 50.98 | 2600 | 0.9742 | 0.7815 | 0.7815 |
| 0.1097 | 54.9 | 2800 | 0.9731 | 0.7724 | 0.7728 |
| 0.0955 | 58.82 | 3000 | 1.0889 | 0.7566 | 0.7568 |
| 0.0946 | 62.75 | 3200 | 1.0014 | 0.7691 | 0.7691 |
| 0.0816 | 66.67 | 3400 | 1.0850 | 0.7678 | 0.7679 |
| 0.0749 | 70.59 | 3600 | 1.1548 | 0.7691 | 0.7691 |
| 0.0704 | 74.51 | 3800 | 1.1497 | 0.7540 | 0.7543 |
| 0.0652 | 78.43 | 4000 | 1.1852 | 0.7753 | 0.7753 |
| 0.0604 | 82.35 | 4200 | 1.2386 | 0.7729 | 0.7728 |
| 0.0566 | 86.27 | 4400 | 1.2909 | 0.7640 | 0.7642 |
| 0.0585 | 90.2 | 4600 | 1.2207 | 0.7801 | 0.7802 |
| 0.0535 | 94.12 | 4800 | 1.2131 | 0.7815 | 0.7815 |
| 0.0525 | 98.04 | 5000 | 1.2391 | 0.7728 | 0.7728 |
| 0.0463 | 101.96 | 5200 | 1.2849 | 0.7716 | 0.7716 |
| 0.043 | 105.88 | 5400 | 1.3037 | 0.7716 | 0.7716 |
| 0.0439 | 109.8 | 5600 | 1.3193 | 0.7753 | 0.7753 |
| 0.0408 | 113.73 | 5800 | 1.3070 | 0.7790 | 0.7790 |
| 0.0402 | 117.65 | 6000 | 1.3375 | 0.7691 | 0.7691 |
| 0.0373 | 121.57 | 6200 | 1.3333 | 0.7728 | 0.7728 |
| 0.0371 | 125.49 | 6400 | 1.3408 | 0.7655 | 0.7654 |
| 0.035 | 129.41 | 6600 | 1.4026 | 0.7715 | 0.7716 |
| 0.0334 | 133.33 | 6800 | 1.3678 | 0.7704 | 0.7704 |
| 0.0327 | 137.25 | 7000 | 1.3937 | 0.7689 | 0.7691 |
| 0.0327 | 141.18 | 7200 | 1.3374 | 0.7766 | 0.7765 |
| 0.0322 | 145.1 | 7400 | 1.3482 | 0.7728 | 0.7728 |
| 0.031 | 149.02 | 7600 | 1.3420 | 0.7703 | 0.7704 |
| 0.0264 | 152.94 | 7800 | 1.4145 | 0.7679 | 0.7679 |
| 0.0284 | 156.86 | 8000 | 1.4109 | 0.7692 | 0.7691 |
| 0.0256 | 160.78 | 8200 | 1.4748 | 0.7692 | 0.7691 |
| 0.0257 | 164.71 | 8400 | 1.4413 | 0.7703 | 0.7704 |
| 0.0267 | 168.63 | 8600 | 1.4215 | 0.7790 | 0.7790 |
| 0.0261 | 172.55 | 8800 | 1.4099 | 0.7790 | 0.7790 |
| 0.0217 | 176.47 | 9000 | 1.4843 | 0.7778 | 0.7778 |
| 0.0249 | 180.39 | 9200 | 1.4836 | 0.7729 | 0.7728 |
| 0.0222 | 184.31 | 9400 | 1.4701 | 0.7753 | 0.7753 |
| 0.021 | 188.24 | 9600 | 1.4861 | 0.7692 | 0.7691 |
| 0.0215 | 192.16 | 9800 | 1.4851 | 0.7679 | 0.7679 |
| 0.0209 | 196.08 | 10000 | 1.4952 | 0.7654 | 0.7654 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_0-seqsight_4096_512_46M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_0-seqsight_4096_512_46M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-26T23:58:30+00:00 |
null | null | {"license": "mit"} | NicxOp1/parafrase-spanish-data | null | [
"license:mit",
"region:us"
]
| null | 2024-04-26T23:58:48+00:00 |
|
null | null | {"license": "mit"} | catherine1207/classical-music-recommender | null | [
"license:mit",
"region:us"
]
| null | 2024-04-27T00:03:36+00:00 |
|
null | peft |
<!-- 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. -->
# GUE_mouse_1-seqsight_4096_512_46M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2283
- F1 Score: 0.9020
- Accuracy: 0.9020
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.4062 | 0.47 | 200 | 0.3028 | 0.8683 | 0.8683 |
| 0.3147 | 0.95 | 400 | 0.2731 | 0.8812 | 0.8812 |
| 0.2903 | 1.42 | 600 | 0.2599 | 0.8839 | 0.8839 |
| 0.2886 | 1.9 | 800 | 0.2569 | 0.8855 | 0.8855 |
| 0.275 | 2.37 | 1000 | 0.2510 | 0.8884 | 0.8884 |
| 0.2628 | 2.84 | 1200 | 0.2420 | 0.8920 | 0.8921 |
| 0.2537 | 3.32 | 1400 | 0.2379 | 0.8953 | 0.8953 |
| 0.2557 | 3.79 | 1600 | 0.2407 | 0.8977 | 0.8977 |
| 0.2476 | 4.27 | 1800 | 0.2290 | 0.9009 | 0.9010 |
| 0.2439 | 4.74 | 2000 | 0.2378 | 0.8962 | 0.8962 |
| 0.242 | 5.21 | 2200 | 0.2231 | 0.9035 | 0.9036 |
| 0.2384 | 5.69 | 2400 | 0.2295 | 0.8996 | 0.8996 |
| 0.2341 | 6.16 | 2600 | 0.2229 | 0.9033 | 0.9035 |
| 0.2306 | 6.64 | 2800 | 0.2215 | 0.9044 | 0.9045 |
| 0.2342 | 7.11 | 3000 | 0.2237 | 0.9053 | 0.9053 |
| 0.2214 | 7.58 | 3200 | 0.2257 | 0.9042 | 0.9042 |
| 0.2316 | 8.06 | 3400 | 0.2199 | 0.9048 | 0.9048 |
| 0.2193 | 8.53 | 3600 | 0.2244 | 0.9042 | 0.9042 |
| 0.2272 | 9.0 | 3800 | 0.2217 | 0.9041 | 0.9042 |
| 0.2214 | 9.48 | 4000 | 0.2201 | 0.9056 | 0.9057 |
| 0.2218 | 9.95 | 4200 | 0.2159 | 0.9066 | 0.9066 |
| 0.2173 | 10.43 | 4400 | 0.2203 | 0.9072 | 0.9072 |
| 0.2176 | 10.9 | 4600 | 0.2180 | 0.9082 | 0.9082 |
| 0.2138 | 11.37 | 4800 | 0.2229 | 0.9056 | 0.9056 |
| 0.2205 | 11.85 | 5000 | 0.2153 | 0.9082 | 0.9082 |
| 0.2167 | 12.32 | 5200 | 0.2225 | 0.9053 | 0.9053 |
| 0.2166 | 12.8 | 5400 | 0.2187 | 0.9081 | 0.9081 |
| 0.2138 | 13.27 | 5600 | 0.2174 | 0.9074 | 0.9075 |
| 0.2103 | 13.74 | 5800 | 0.2165 | 0.9088 | 0.9090 |
| 0.2137 | 14.22 | 6000 | 0.2181 | 0.9072 | 0.9072 |
| 0.2092 | 14.69 | 6200 | 0.2180 | 0.9091 | 0.9091 |
| 0.2107 | 15.17 | 6400 | 0.2163 | 0.9096 | 0.9096 |
| 0.2084 | 15.64 | 6600 | 0.2167 | 0.9088 | 0.9088 |
| 0.2048 | 16.11 | 6800 | 0.2174 | 0.9086 | 0.9087 |
| 0.2047 | 16.59 | 7000 | 0.2141 | 0.9103 | 0.9103 |
| 0.211 | 17.06 | 7200 | 0.2140 | 0.9096 | 0.9096 |
| 0.2072 | 17.54 | 7400 | 0.2150 | 0.9093 | 0.9093 |
| 0.2069 | 18.01 | 7600 | 0.2117 | 0.9109 | 0.9109 |
| 0.1999 | 18.48 | 7800 | 0.2134 | 0.9103 | 0.9103 |
| 0.2084 | 18.96 | 8000 | 0.2117 | 0.9086 | 0.9087 |
| 0.2026 | 19.43 | 8200 | 0.2146 | 0.9110 | 0.9110 |
| 0.2038 | 19.91 | 8400 | 0.2149 | 0.9092 | 0.9093 |
| 0.2012 | 20.38 | 8600 | 0.2152 | 0.9102 | 0.9102 |
| 0.2035 | 20.85 | 8800 | 0.2127 | 0.9095 | 0.9096 |
| 0.2007 | 21.33 | 9000 | 0.2128 | 0.9103 | 0.9103 |
| 0.2015 | 21.8 | 9200 | 0.2146 | 0.9101 | 0.9102 |
| 0.2036 | 22.27 | 9400 | 0.2134 | 0.9107 | 0.9107 |
| 0.1991 | 22.75 | 9600 | 0.2140 | 0.9095 | 0.9096 |
| 0.1986 | 23.22 | 9800 | 0.2137 | 0.9097 | 0.9097 |
| 0.2032 | 23.7 | 10000 | 0.2136 | 0.9104 | 0.9105 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_1-seqsight_4096_512_46M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_1-seqsight_4096_512_46M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-27T00:05:03+00:00 |
null | peft |
<!-- 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. -->
# GUE_mouse_1-seqsight_4096_512_46M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_1](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2268
- F1 Score: 0.9004
- Accuracy: 0.9004
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.3793 | 0.47 | 200 | 0.2851 | 0.8757 | 0.8758 |
| 0.2915 | 0.95 | 400 | 0.2539 | 0.8849 | 0.8850 |
| 0.2715 | 1.42 | 600 | 0.2430 | 0.8903 | 0.8904 |
| 0.2705 | 1.9 | 800 | 0.2401 | 0.8978 | 0.8979 |
| 0.2546 | 2.37 | 1000 | 0.2355 | 0.8950 | 0.8950 |
| 0.2424 | 2.84 | 1200 | 0.2300 | 0.9003 | 0.9004 |
| 0.2322 | 3.32 | 1400 | 0.2261 | 0.9020 | 0.9020 |
| 0.2368 | 3.79 | 1600 | 0.2256 | 0.9032 | 0.9032 |
| 0.2293 | 4.27 | 1800 | 0.2218 | 0.9053 | 0.9054 |
| 0.2274 | 4.74 | 2000 | 0.2272 | 0.9024 | 0.9024 |
| 0.2238 | 5.21 | 2200 | 0.2186 | 0.9060 | 0.9062 |
| 0.2223 | 5.69 | 2400 | 0.2267 | 0.9020 | 0.9020 |
| 0.2171 | 6.16 | 2600 | 0.2155 | 0.9090 | 0.9091 |
| 0.2118 | 6.64 | 2800 | 0.2144 | 0.9088 | 0.9090 |
| 0.2157 | 7.11 | 3000 | 0.2197 | 0.9063 | 0.9063 |
| 0.2017 | 7.58 | 3200 | 0.2205 | 0.9084 | 0.9084 |
| 0.2134 | 8.06 | 3400 | 0.2140 | 0.9082 | 0.9082 |
| 0.1997 | 8.53 | 3600 | 0.2169 | 0.9074 | 0.9075 |
| 0.2089 | 9.0 | 3800 | 0.2190 | 0.9042 | 0.9044 |
| 0.2017 | 9.48 | 4000 | 0.2095 | 0.9114 | 0.9115 |
| 0.2015 | 9.95 | 4200 | 0.2100 | 0.9116 | 0.9116 |
| 0.1948 | 10.43 | 4400 | 0.2182 | 0.9070 | 0.9070 |
| 0.1967 | 10.9 | 4600 | 0.2149 | 0.9097 | 0.9097 |
| 0.1907 | 11.37 | 4800 | 0.2145 | 0.9079 | 0.9079 |
| 0.1976 | 11.85 | 5000 | 0.2120 | 0.9088 | 0.9088 |
| 0.1928 | 12.32 | 5200 | 0.2182 | 0.9088 | 0.9088 |
| 0.192 | 12.8 | 5400 | 0.2158 | 0.9099 | 0.9099 |
| 0.1898 | 13.27 | 5600 | 0.2157 | 0.9109 | 0.9109 |
| 0.1842 | 13.74 | 5800 | 0.2206 | 0.9080 | 0.9081 |
| 0.1871 | 14.22 | 6000 | 0.2188 | 0.9115 | 0.9115 |
| 0.182 | 14.69 | 6200 | 0.2172 | 0.9113 | 0.9113 |
| 0.1843 | 15.17 | 6400 | 0.2146 | 0.9110 | 0.9110 |
| 0.1803 | 15.64 | 6600 | 0.2203 | 0.9097 | 0.9097 |
| 0.1781 | 16.11 | 6800 | 0.2245 | 0.9094 | 0.9094 |
| 0.1763 | 16.59 | 7000 | 0.2141 | 0.9143 | 0.9143 |
| 0.1827 | 17.06 | 7200 | 0.2134 | 0.9110 | 0.9110 |
| 0.1764 | 17.54 | 7400 | 0.2148 | 0.9115 | 0.9115 |
| 0.1752 | 18.01 | 7600 | 0.2151 | 0.9130 | 0.9130 |
| 0.1698 | 18.48 | 7800 | 0.2172 | 0.9125 | 0.9125 |
| 0.1784 | 18.96 | 8000 | 0.2148 | 0.9106 | 0.9106 |
| 0.1707 | 19.43 | 8200 | 0.2169 | 0.9115 | 0.9115 |
| 0.1718 | 19.91 | 8400 | 0.2182 | 0.9089 | 0.9090 |
| 0.169 | 20.38 | 8600 | 0.2215 | 0.9110 | 0.9110 |
| 0.1684 | 20.85 | 8800 | 0.2162 | 0.9100 | 0.9100 |
| 0.1693 | 21.33 | 9000 | 0.2151 | 0.9131 | 0.9131 |
| 0.1668 | 21.8 | 9200 | 0.2190 | 0.9128 | 0.9128 |
| 0.1703 | 22.27 | 9400 | 0.2172 | 0.9125 | 0.9125 |
| 0.1661 | 22.75 | 9600 | 0.2180 | 0.9118 | 0.9118 |
| 0.1636 | 23.22 | 9800 | 0.2182 | 0.9118 | 0.9118 |
| 0.1684 | 23.7 | 10000 | 0.2177 | 0.9133 | 0.9133 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_1-seqsight_4096_512_46M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_1-seqsight_4096_512_46M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-27T00:05:03+00:00 |
null | peft |
<!-- 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. -->
# GUE_mouse_4-seqsight_4096_512_46M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5830
- F1 Score: 0.7079
- Accuracy: 0.7079
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6343 | 1.69 | 200 | 0.6170 | 0.6627 | 0.6638 |
| 0.6092 | 3.39 | 400 | 0.5932 | 0.6728 | 0.6734 |
| 0.5874 | 5.08 | 600 | 0.5814 | 0.6891 | 0.6899 |
| 0.5688 | 6.78 | 800 | 0.5647 | 0.7065 | 0.7069 |
| 0.5598 | 8.47 | 1000 | 0.5614 | 0.7069 | 0.7074 |
| 0.5501 | 10.17 | 1200 | 0.5543 | 0.7078 | 0.7079 |
| 0.5421 | 11.86 | 1400 | 0.5535 | 0.7102 | 0.7111 |
| 0.5378 | 13.56 | 1600 | 0.5469 | 0.7158 | 0.7159 |
| 0.5322 | 15.25 | 1800 | 0.5444 | 0.7146 | 0.7148 |
| 0.5283 | 16.95 | 2000 | 0.5433 | 0.7109 | 0.7111 |
| 0.5251 | 18.64 | 2200 | 0.5397 | 0.7234 | 0.7233 |
| 0.5203 | 20.34 | 2400 | 0.5367 | 0.7223 | 0.7223 |
| 0.5157 | 22.03 | 2600 | 0.5540 | 0.7067 | 0.7084 |
| 0.5116 | 23.73 | 2800 | 0.5355 | 0.7239 | 0.7238 |
| 0.5085 | 25.42 | 3000 | 0.5429 | 0.7242 | 0.7244 |
| 0.5075 | 27.12 | 3200 | 0.5522 | 0.7075 | 0.7090 |
| 0.5022 | 28.81 | 3400 | 0.5453 | 0.7238 | 0.7238 |
| 0.4975 | 30.51 | 3600 | 0.5432 | 0.7250 | 0.7249 |
| 0.4969 | 32.2 | 3800 | 0.5443 | 0.7239 | 0.7238 |
| 0.4941 | 33.9 | 4000 | 0.5370 | 0.7229 | 0.7228 |
| 0.4917 | 35.59 | 4200 | 0.5486 | 0.7162 | 0.7169 |
| 0.4898 | 37.29 | 4400 | 0.5452 | 0.7265 | 0.7265 |
| 0.4853 | 38.98 | 4600 | 0.5457 | 0.7255 | 0.7254 |
| 0.4805 | 40.68 | 4800 | 0.5528 | 0.7172 | 0.7175 |
| 0.4754 | 42.37 | 5000 | 0.5500 | 0.7188 | 0.7191 |
| 0.4797 | 44.07 | 5200 | 0.5484 | 0.7255 | 0.7254 |
| 0.4753 | 45.76 | 5400 | 0.5479 | 0.7249 | 0.7249 |
| 0.4731 | 47.46 | 5600 | 0.5517 | 0.7334 | 0.7334 |
| 0.4745 | 49.15 | 5800 | 0.5551 | 0.7257 | 0.7260 |
| 0.4723 | 50.85 | 6000 | 0.5502 | 0.7244 | 0.7244 |
| 0.4667 | 52.54 | 6200 | 0.5509 | 0.7277 | 0.7276 |
| 0.4656 | 54.24 | 6400 | 0.5511 | 0.7250 | 0.7249 |
| 0.4646 | 55.93 | 6600 | 0.5543 | 0.7275 | 0.7276 |
| 0.4654 | 57.63 | 6800 | 0.5519 | 0.7287 | 0.7286 |
| 0.4626 | 59.32 | 7000 | 0.5591 | 0.7176 | 0.7180 |
| 0.4588 | 61.02 | 7200 | 0.5549 | 0.7276 | 0.7276 |
| 0.4599 | 62.71 | 7400 | 0.5537 | 0.7244 | 0.7244 |
| 0.4602 | 64.41 | 7600 | 0.5593 | 0.7228 | 0.7228 |
| 0.4559 | 66.1 | 7800 | 0.5567 | 0.7206 | 0.7207 |
| 0.4565 | 67.8 | 8000 | 0.5553 | 0.7282 | 0.7281 |
| 0.4535 | 69.49 | 8200 | 0.5561 | 0.7217 | 0.7217 |
| 0.4508 | 71.19 | 8400 | 0.5576 | 0.7250 | 0.7249 |
| 0.4559 | 72.88 | 8600 | 0.5583 | 0.7303 | 0.7302 |
| 0.4515 | 74.58 | 8800 | 0.5603 | 0.7249 | 0.7249 |
| 0.4521 | 76.27 | 9000 | 0.5601 | 0.7281 | 0.7281 |
| 0.4478 | 77.97 | 9200 | 0.5633 | 0.7226 | 0.7228 |
| 0.4462 | 79.66 | 9400 | 0.5617 | 0.7255 | 0.7254 |
| 0.451 | 81.36 | 9600 | 0.5618 | 0.7255 | 0.7254 |
| 0.4458 | 83.05 | 9800 | 0.5620 | 0.7239 | 0.7238 |
| 0.448 | 84.75 | 10000 | 0.5623 | 0.7239 | 0.7238 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_4-seqsight_4096_512_46M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_4-seqsight_4096_512_46M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-27T00:06:00+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
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[More Information Needed] | {"library_name": "transformers", "tags": []} | qworksadmin/llama2c1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
]
| null | 2024-04-27T00:09:03+00:00 |
text2text-generation | transformers | {"license": "apache-2.0"} | knguyennguyen/rank_t5_distill | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T00:12:23+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed] | {"library_name": "transformers", "tags": []} | devesh220897/financial-chatbot-for-young-adults-3 | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T00:12:46+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_OAPSL_v1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_OAPSL_v1", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_OAPSL_v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T00:13:06+00:00 |
text-classification | transformers |
# Model Card for Model ID
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | EinsZwo/nlid_mlm_ACTUALLY_mixed_supertagging | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T00:13:06+00:00 |
null | peft |
<!-- 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. -->
# GUE_mouse_4-seqsight_4096_512_46M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6116
- F1 Score: 0.7254
- Accuracy: 0.7254
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.623 | 1.69 | 200 | 0.5954 | 0.6795 | 0.6808 |
| 0.5762 | 3.39 | 400 | 0.5662 | 0.6983 | 0.6984 |
| 0.556 | 5.08 | 600 | 0.5511 | 0.7018 | 0.7026 |
| 0.5387 | 6.78 | 800 | 0.5467 | 0.7093 | 0.7106 |
| 0.5334 | 8.47 | 1000 | 0.5501 | 0.7082 | 0.7090 |
| 0.5216 | 10.17 | 1200 | 0.5468 | 0.7037 | 0.7047 |
| 0.5122 | 11.86 | 1400 | 0.5388 | 0.7178 | 0.7180 |
| 0.5044 | 13.56 | 1600 | 0.5447 | 0.7179 | 0.7180 |
| 0.4998 | 15.25 | 1800 | 0.5348 | 0.7266 | 0.7265 |
| 0.4928 | 16.95 | 2000 | 0.5378 | 0.7138 | 0.7148 |
| 0.4848 | 18.64 | 2200 | 0.5511 | 0.7182 | 0.7196 |
| 0.4796 | 20.34 | 2400 | 0.5365 | 0.7270 | 0.7270 |
| 0.4692 | 22.03 | 2600 | 0.5580 | 0.7201 | 0.7212 |
| 0.4603 | 23.73 | 2800 | 0.5444 | 0.7275 | 0.7276 |
| 0.4595 | 25.42 | 3000 | 0.5517 | 0.7344 | 0.7361 |
| 0.4508 | 27.12 | 3200 | 0.5590 | 0.7282 | 0.7281 |
| 0.4452 | 28.81 | 3400 | 0.5653 | 0.7271 | 0.7270 |
| 0.4356 | 30.51 | 3600 | 0.5626 | 0.7324 | 0.7323 |
| 0.4318 | 32.2 | 3800 | 0.5653 | 0.7328 | 0.7329 |
| 0.4277 | 33.9 | 4000 | 0.5637 | 0.7305 | 0.7307 |
| 0.4208 | 35.59 | 4200 | 0.5709 | 0.7324 | 0.7323 |
| 0.413 | 37.29 | 4400 | 0.5784 | 0.7404 | 0.7403 |
| 0.412 | 38.98 | 4600 | 0.5767 | 0.7335 | 0.7334 |
| 0.4026 | 40.68 | 4800 | 0.5837 | 0.7307 | 0.7307 |
| 0.3963 | 42.37 | 5000 | 0.5818 | 0.7308 | 0.7307 |
| 0.397 | 44.07 | 5200 | 0.5951 | 0.7324 | 0.7323 |
| 0.3851 | 45.76 | 5400 | 0.5924 | 0.7345 | 0.7345 |
| 0.3852 | 47.46 | 5600 | 0.6087 | 0.7324 | 0.7323 |
| 0.3831 | 49.15 | 5800 | 0.6074 | 0.7312 | 0.7313 |
| 0.3781 | 50.85 | 6000 | 0.5999 | 0.7250 | 0.7249 |
| 0.3708 | 52.54 | 6200 | 0.6162 | 0.7244 | 0.7244 |
| 0.3687 | 54.24 | 6400 | 0.6138 | 0.7329 | 0.7329 |
| 0.3646 | 55.93 | 6600 | 0.6132 | 0.7313 | 0.7313 |
| 0.3624 | 57.63 | 6800 | 0.6241 | 0.7319 | 0.7318 |
| 0.3597 | 59.32 | 7000 | 0.6233 | 0.7313 | 0.7313 |
| 0.3586 | 61.02 | 7200 | 0.6279 | 0.7276 | 0.7276 |
| 0.3536 | 62.71 | 7400 | 0.6258 | 0.7367 | 0.7366 |
| 0.353 | 64.41 | 7600 | 0.6345 | 0.7313 | 0.7313 |
| 0.3511 | 66.1 | 7800 | 0.6302 | 0.7255 | 0.7254 |
| 0.3477 | 67.8 | 8000 | 0.6317 | 0.7308 | 0.7307 |
| 0.3445 | 69.49 | 8200 | 0.6340 | 0.7276 | 0.7276 |
| 0.3449 | 71.19 | 8400 | 0.6348 | 0.7308 | 0.7307 |
| 0.3423 | 72.88 | 8600 | 0.6368 | 0.7356 | 0.7355 |
| 0.337 | 74.58 | 8800 | 0.6411 | 0.7323 | 0.7323 |
| 0.3429 | 76.27 | 9000 | 0.6370 | 0.7314 | 0.7313 |
| 0.3327 | 77.97 | 9200 | 0.6448 | 0.7298 | 0.7297 |
| 0.3287 | 79.66 | 9400 | 0.6517 | 0.7292 | 0.7292 |
| 0.3348 | 81.36 | 9600 | 0.6504 | 0.7314 | 0.7313 |
| 0.332 | 83.05 | 9800 | 0.6518 | 0.7298 | 0.7297 |
| 0.3323 | 84.75 | 10000 | 0.6520 | 0.7292 | 0.7292 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_4-seqsight_4096_512_46M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_4-seqsight_4096_512_46M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-27T00:13:25+00:00 |
text-generation | transformers |
# miqu-evil-dpo
# **Model Details**
## Description
miqu-evil-dpo is fine-tuned model based on miqu, serving as a direct successor to PiVoT-0.1-Evil-a.
It is trained with evil-tune method applied.
<!-- prompt-template start -->
## Prompt template: Mistral Inst
```
<s> [INST] {inst} [/INST]
```
<!-- prompt-template end -->
## Disclaimer
The AI model provided herein is intended for experimental purposes only. The creator of this model makes no representations or warranties of any kind, either express or implied, as to the model's accuracy, reliability, or suitability for any particular purpose. The creator shall not be held liable for any outcomes, decisions, or actions taken on the basis of the information generated by this model. Users of this model assume full responsibility for any consequences resulting from its use.
| {"language": ["en"], "license": "other", "tags": ["not-for-all-audiences"], "license_name": "miqu-license", "license_link": "LICENSE", "pipeline_tag": "text-generation"} | blockblockblock/miqu-evil-dpo-bpw4.4-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T00:13:38+00:00 |
null | peft |
<!-- 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. -->
# GUE_mouse_4-seqsight_4096_512_46M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_4](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_4) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6484
- F1 Score: 0.7164
- Accuracy: 0.7164
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.6106 | 1.69 | 200 | 0.5794 | 0.6856 | 0.6883 |
| 0.5597 | 3.39 | 400 | 0.5560 | 0.7061 | 0.7063 |
| 0.5369 | 5.08 | 600 | 0.5464 | 0.7163 | 0.7169 |
| 0.5153 | 6.78 | 800 | 0.5363 | 0.7234 | 0.7233 |
| 0.504 | 8.47 | 1000 | 0.5469 | 0.7167 | 0.7175 |
| 0.4797 | 10.17 | 1200 | 0.5449 | 0.7234 | 0.7233 |
| 0.4647 | 11.86 | 1400 | 0.5468 | 0.7234 | 0.7233 |
| 0.4451 | 13.56 | 1600 | 0.5720 | 0.7179 | 0.7180 |
| 0.433 | 15.25 | 1800 | 0.5745 | 0.7282 | 0.7281 |
| 0.4142 | 16.95 | 2000 | 0.5715 | 0.7250 | 0.7254 |
| 0.3991 | 18.64 | 2200 | 0.5851 | 0.7245 | 0.7249 |
| 0.3873 | 20.34 | 2400 | 0.6061 | 0.7239 | 0.7238 |
| 0.3654 | 22.03 | 2600 | 0.6260 | 0.7314 | 0.7313 |
| 0.3529 | 23.73 | 2800 | 0.6161 | 0.7202 | 0.7201 |
| 0.3464 | 25.42 | 3000 | 0.6718 | 0.7229 | 0.7260 |
| 0.3319 | 27.12 | 3200 | 0.7233 | 0.7190 | 0.7196 |
| 0.3147 | 28.81 | 3400 | 0.7163 | 0.7297 | 0.7297 |
| 0.3032 | 30.51 | 3600 | 0.7235 | 0.7158 | 0.7159 |
| 0.2961 | 32.2 | 3800 | 0.7621 | 0.7211 | 0.7212 |
| 0.2848 | 33.9 | 4000 | 0.7382 | 0.7169 | 0.7169 |
| 0.2712 | 35.59 | 4200 | 0.7987 | 0.7164 | 0.7164 |
| 0.2609 | 37.29 | 4400 | 0.8491 | 0.7192 | 0.7191 |
| 0.2556 | 38.98 | 4600 | 0.8130 | 0.7188 | 0.7196 |
| 0.2441 | 40.68 | 4800 | 0.8811 | 0.7164 | 0.7164 |
| 0.2353 | 42.37 | 5000 | 0.8663 | 0.7170 | 0.7169 |
| 0.2364 | 44.07 | 5200 | 0.8850 | 0.7157 | 0.7159 |
| 0.2229 | 45.76 | 5400 | 0.8748 | 0.7181 | 0.7180 |
| 0.2168 | 47.46 | 5600 | 0.9197 | 0.7200 | 0.7201 |
| 0.2073 | 49.15 | 5800 | 0.9833 | 0.7143 | 0.7143 |
| 0.2029 | 50.85 | 6000 | 0.9132 | 0.7064 | 0.7063 |
| 0.2038 | 52.54 | 6200 | 0.9594 | 0.7169 | 0.7169 |
| 0.193 | 54.24 | 6400 | 0.9774 | 0.7176 | 0.7175 |
| 0.1934 | 55.93 | 6600 | 0.9709 | 0.7148 | 0.7148 |
| 0.1832 | 57.63 | 6800 | 1.0442 | 0.7138 | 0.7138 |
| 0.1842 | 59.32 | 7000 | 0.9855 | 0.7149 | 0.7148 |
| 0.1754 | 61.02 | 7200 | 0.9949 | 0.7091 | 0.7090 |
| 0.1742 | 62.71 | 7400 | 0.9996 | 0.7126 | 0.7127 |
| 0.1682 | 64.41 | 7600 | 1.0272 | 0.7205 | 0.7207 |
| 0.1697 | 66.1 | 7800 | 1.0417 | 0.7075 | 0.7074 |
| 0.1653 | 67.8 | 8000 | 1.0723 | 0.7160 | 0.7159 |
| 0.1608 | 69.49 | 8200 | 1.0625 | 0.7101 | 0.7100 |
| 0.1593 | 71.19 | 8400 | 1.0623 | 0.7074 | 0.7074 |
| 0.1548 | 72.88 | 8600 | 1.1190 | 0.7109 | 0.7111 |
| 0.1527 | 74.58 | 8800 | 1.0954 | 0.7154 | 0.7153 |
| 0.1499 | 76.27 | 9000 | 1.1112 | 0.7159 | 0.7159 |
| 0.151 | 77.97 | 9200 | 1.1027 | 0.7159 | 0.7159 |
| 0.1451 | 79.66 | 9400 | 1.1144 | 0.7111 | 0.7111 |
| 0.1479 | 81.36 | 9600 | 1.1106 | 0.7123 | 0.7122 |
| 0.1437 | 83.05 | 9800 | 1.1230 | 0.7091 | 0.7090 |
| 0.1436 | 84.75 | 10000 | 1.1212 | 0.7122 | 0.7122 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_4-seqsight_4096_512_46M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_4-seqsight_4096_512_46M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-27T00:13:59+00:00 |
null | null | {"license": "mit"} | camembercik/soft-haneul_kiof | null | [
"license:mit",
"region:us"
]
| null | 2024-04-27T00:16:31+00:00 |
|
null | null | {"license": "unknown"} | Jamie762/Mood-Captions | null | [
"license:unknown",
"region:us"
]
| null | 2024-04-27T00:19:59+00:00 |
|
null | peft |
<!-- 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. -->
# GUE_mouse_3-seqsight_4096_512_46M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_3) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3795
- F1 Score: 0.8324
- Accuracy: 0.8326
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.5493 | 13.33 | 200 | 0.3905 | 0.8322 | 0.8326 |
| 0.3837 | 26.67 | 400 | 0.2951 | 0.8785 | 0.8787 |
| 0.2935 | 40.0 | 600 | 0.3157 | 0.8574 | 0.8577 |
| 0.2467 | 53.33 | 800 | 0.3193 | 0.8660 | 0.8661 |
| 0.2101 | 66.67 | 1000 | 0.3545 | 0.8575 | 0.8577 |
| 0.1852 | 80.0 | 1200 | 0.3753 | 0.8577 | 0.8577 |
| 0.1587 | 93.33 | 1400 | 0.3992 | 0.8452 | 0.8452 |
| 0.1411 | 106.67 | 1600 | 0.3803 | 0.8577 | 0.8577 |
| 0.121 | 120.0 | 1800 | 0.4239 | 0.8785 | 0.8787 |
| 0.1042 | 133.33 | 2000 | 0.4465 | 0.8577 | 0.8577 |
| 0.0936 | 146.67 | 2200 | 0.4636 | 0.8576 | 0.8577 |
| 0.0823 | 160.0 | 2400 | 0.4768 | 0.8785 | 0.8787 |
| 0.0741 | 173.33 | 2600 | 0.4895 | 0.8452 | 0.8452 |
| 0.0708 | 186.67 | 2800 | 0.4868 | 0.8619 | 0.8619 |
| 0.0631 | 200.0 | 3000 | 0.4810 | 0.8536 | 0.8536 |
| 0.0589 | 213.33 | 3200 | 0.4924 | 0.8535 | 0.8536 |
| 0.0551 | 226.67 | 3400 | 0.5077 | 0.8409 | 0.8410 |
| 0.0491 | 240.0 | 3600 | 0.4805 | 0.8745 | 0.8745 |
| 0.0487 | 253.33 | 3800 | 0.4984 | 0.8535 | 0.8536 |
| 0.0442 | 266.67 | 4000 | 0.5106 | 0.8409 | 0.8410 |
| 0.0412 | 280.0 | 4200 | 0.5237 | 0.8577 | 0.8577 |
| 0.0372 | 293.33 | 4400 | 0.5320 | 0.8493 | 0.8494 |
| 0.035 | 306.67 | 4600 | 0.5183 | 0.8619 | 0.8619 |
| 0.0356 | 320.0 | 4800 | 0.5780 | 0.8493 | 0.8494 |
| 0.0311 | 333.33 | 5000 | 0.5230 | 0.8577 | 0.8577 |
| 0.03 | 346.67 | 5200 | 0.5654 | 0.8452 | 0.8452 |
| 0.0303 | 360.0 | 5400 | 0.5381 | 0.8367 | 0.8368 |
| 0.0295 | 373.33 | 5600 | 0.5215 | 0.8494 | 0.8494 |
| 0.0285 | 386.67 | 5800 | 0.5267 | 0.8494 | 0.8494 |
| 0.0274 | 400.0 | 6000 | 0.5399 | 0.8452 | 0.8452 |
| 0.0248 | 413.33 | 6200 | 0.5511 | 0.8452 | 0.8452 |
| 0.0245 | 426.67 | 6400 | 0.5326 | 0.8451 | 0.8452 |
| 0.0234 | 440.0 | 6600 | 0.5718 | 0.8534 | 0.8536 |
| 0.0212 | 453.33 | 6800 | 0.5388 | 0.8577 | 0.8577 |
| 0.0208 | 466.67 | 7000 | 0.5283 | 0.8534 | 0.8536 |
| 0.0207 | 480.0 | 7200 | 0.5206 | 0.8577 | 0.8577 |
| 0.022 | 493.33 | 7400 | 0.4971 | 0.8535 | 0.8536 |
| 0.0211 | 506.67 | 7600 | 0.4892 | 0.8619 | 0.8619 |
| 0.0186 | 520.0 | 7800 | 0.5175 | 0.8535 | 0.8536 |
| 0.019 | 533.33 | 8000 | 0.5183 | 0.8536 | 0.8536 |
| 0.0208 | 546.67 | 8200 | 0.5128 | 0.8661 | 0.8661 |
| 0.0177 | 560.0 | 8400 | 0.5164 | 0.8619 | 0.8619 |
| 0.0158 | 573.33 | 8600 | 0.5322 | 0.8577 | 0.8577 |
| 0.0177 | 586.67 | 8800 | 0.5286 | 0.8619 | 0.8619 |
| 0.0171 | 600.0 | 9000 | 0.5319 | 0.8577 | 0.8577 |
| 0.0166 | 613.33 | 9200 | 0.5304 | 0.8577 | 0.8577 |
| 0.0163 | 626.67 | 9400 | 0.5372 | 0.8577 | 0.8577 |
| 0.0163 | 640.0 | 9600 | 0.5252 | 0.8577 | 0.8577 |
| 0.0179 | 653.33 | 9800 | 0.5315 | 0.8577 | 0.8577 |
| 0.0167 | 666.67 | 10000 | 0.5307 | 0.8577 | 0.8577 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_3-seqsight_4096_512_46M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_3-seqsight_4096_512_46M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-27T00:20:18+00:00 |
null | peft |
<!-- 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. -->
# GUE_mouse_3-seqsight_4096_512_46M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_3) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1289
- F1 Score: 0.8494
- Accuracy: 0.8494
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.4615 | 13.33 | 200 | 0.3054 | 0.8701 | 0.8703 |
| 0.2444 | 26.67 | 400 | 0.3399 | 0.8536 | 0.8536 |
| 0.1616 | 40.0 | 600 | 0.4360 | 0.8284 | 0.8285 |
| 0.1096 | 53.33 | 800 | 0.5257 | 0.8452 | 0.8452 |
| 0.0787 | 66.67 | 1000 | 0.5965 | 0.8617 | 0.8619 |
| 0.0626 | 80.0 | 1200 | 0.6049 | 0.8326 | 0.8326 |
| 0.0505 | 93.33 | 1400 | 0.6381 | 0.8577 | 0.8577 |
| 0.0446 | 106.67 | 1600 | 0.5641 | 0.8494 | 0.8494 |
| 0.0354 | 120.0 | 1800 | 0.6511 | 0.8410 | 0.8410 |
| 0.0287 | 133.33 | 2000 | 0.7520 | 0.8410 | 0.8410 |
| 0.0296 | 146.67 | 2200 | 0.7117 | 0.8660 | 0.8661 |
| 0.0266 | 160.0 | 2400 | 0.6903 | 0.8618 | 0.8619 |
| 0.0224 | 173.33 | 2600 | 0.6782 | 0.8619 | 0.8619 |
| 0.0221 | 186.67 | 2800 | 0.6960 | 0.8702 | 0.8703 |
| 0.0188 | 200.0 | 3000 | 0.6932 | 0.8618 | 0.8619 |
| 0.0191 | 213.33 | 3200 | 0.6992 | 0.8451 | 0.8452 |
| 0.0177 | 226.67 | 3400 | 0.7342 | 0.8451 | 0.8452 |
| 0.0141 | 240.0 | 3600 | 0.7775 | 0.8493 | 0.8494 |
| 0.0171 | 253.33 | 3800 | 0.7322 | 0.8493 | 0.8494 |
| 0.0155 | 266.67 | 4000 | 0.7117 | 0.8494 | 0.8494 |
| 0.0117 | 280.0 | 4200 | 0.7698 | 0.8577 | 0.8577 |
| 0.0119 | 293.33 | 4400 | 0.8324 | 0.8494 | 0.8494 |
| 0.0111 | 306.67 | 4600 | 0.8733 | 0.8577 | 0.8577 |
| 0.0125 | 320.0 | 4800 | 0.8996 | 0.8407 | 0.8410 |
| 0.0092 | 333.33 | 5000 | 0.8221 | 0.8450 | 0.8452 |
| 0.0108 | 346.67 | 5200 | 0.7703 | 0.8661 | 0.8661 |
| 0.0095 | 360.0 | 5400 | 0.8641 | 0.8493 | 0.8494 |
| 0.0088 | 373.33 | 5600 | 0.8095 | 0.8452 | 0.8452 |
| 0.0089 | 386.67 | 5800 | 0.8624 | 0.8660 | 0.8661 |
| 0.0067 | 400.0 | 6000 | 0.8631 | 0.8535 | 0.8536 |
| 0.0051 | 413.33 | 6200 | 0.8918 | 0.8493 | 0.8494 |
| 0.0077 | 426.67 | 6400 | 0.8878 | 0.8535 | 0.8536 |
| 0.0079 | 440.0 | 6600 | 0.8412 | 0.8409 | 0.8410 |
| 0.0062 | 453.33 | 6800 | 0.9321 | 0.8618 | 0.8619 |
| 0.0063 | 466.67 | 7000 | 0.8703 | 0.8576 | 0.8577 |
| 0.0066 | 480.0 | 7200 | 0.8559 | 0.8618 | 0.8619 |
| 0.0065 | 493.33 | 7400 | 0.8292 | 0.8535 | 0.8536 |
| 0.0059 | 506.67 | 7600 | 0.8295 | 0.8535 | 0.8536 |
| 0.0055 | 520.0 | 7800 | 0.8548 | 0.8661 | 0.8661 |
| 0.0062 | 533.33 | 8000 | 0.8652 | 0.8576 | 0.8577 |
| 0.0052 | 546.67 | 8200 | 0.8419 | 0.8577 | 0.8577 |
| 0.0041 | 560.0 | 8400 | 0.8389 | 0.8577 | 0.8577 |
| 0.0043 | 573.33 | 8600 | 0.8792 | 0.8576 | 0.8577 |
| 0.0044 | 586.67 | 8800 | 0.8524 | 0.8577 | 0.8577 |
| 0.0058 | 600.0 | 9000 | 0.8276 | 0.8577 | 0.8577 |
| 0.0043 | 613.33 | 9200 | 0.8525 | 0.8577 | 0.8577 |
| 0.0036 | 626.67 | 9400 | 0.8646 | 0.8577 | 0.8577 |
| 0.004 | 640.0 | 9600 | 0.8823 | 0.8535 | 0.8536 |
| 0.0039 | 653.33 | 9800 | 0.8808 | 0.8535 | 0.8536 |
| 0.0029 | 666.67 | 10000 | 0.8848 | 0.8535 | 0.8536 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_3-seqsight_4096_512_46M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_3-seqsight_4096_512_46M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-27T00:20:24+00:00 |
null | peft |
<!-- 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. -->
# GUE_mouse_3-seqsight_4096_512_46M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_3](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_3) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6905
- F1 Score: 0.8535
- Accuracy: 0.8536
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.3946 | 13.33 | 200 | 0.3261 | 0.8619 | 0.8619 |
| 0.1543 | 26.67 | 400 | 0.4682 | 0.8534 | 0.8536 |
| 0.075 | 40.0 | 600 | 0.5460 | 0.8618 | 0.8619 |
| 0.0447 | 53.33 | 800 | 0.6892 | 0.8326 | 0.8326 |
| 0.0323 | 66.67 | 1000 | 0.7626 | 0.8618 | 0.8619 |
| 0.0226 | 80.0 | 1200 | 0.6913 | 0.8661 | 0.8661 |
| 0.0291 | 93.33 | 1400 | 0.6273 | 0.8701 | 0.8703 |
| 0.0166 | 106.67 | 1600 | 0.7630 | 0.8701 | 0.8703 |
| 0.0142 | 120.0 | 1800 | 0.7090 | 0.8786 | 0.8787 |
| 0.0119 | 133.33 | 2000 | 0.8740 | 0.8577 | 0.8577 |
| 0.0106 | 146.67 | 2200 | 0.8758 | 0.8577 | 0.8577 |
| 0.0104 | 160.0 | 2400 | 0.8299 | 0.8745 | 0.8745 |
| 0.0091 | 173.33 | 2600 | 0.8150 | 0.8661 | 0.8661 |
| 0.0074 | 186.67 | 2800 | 0.8064 | 0.8828 | 0.8828 |
| 0.0078 | 200.0 | 3000 | 0.8632 | 0.8661 | 0.8661 |
| 0.0076 | 213.33 | 3200 | 0.9358 | 0.8744 | 0.8745 |
| 0.0066 | 226.67 | 3400 | 0.8527 | 0.8577 | 0.8577 |
| 0.0063 | 240.0 | 3600 | 0.8822 | 0.8661 | 0.8661 |
| 0.0069 | 253.33 | 3800 | 0.8840 | 0.8702 | 0.8703 |
| 0.0057 | 266.67 | 4000 | 0.8505 | 0.8745 | 0.8745 |
| 0.0044 | 280.0 | 4200 | 0.9496 | 0.8869 | 0.8870 |
| 0.0064 | 293.33 | 4400 | 0.8863 | 0.8784 | 0.8787 |
| 0.0043 | 306.67 | 4600 | 0.9109 | 0.8745 | 0.8745 |
| 0.0038 | 320.0 | 4800 | 0.9218 | 0.8493 | 0.8494 |
| 0.0033 | 333.33 | 5000 | 0.9181 | 0.8577 | 0.8577 |
| 0.0053 | 346.67 | 5200 | 0.8449 | 0.8577 | 0.8577 |
| 0.0021 | 360.0 | 5400 | 0.9683 | 0.8703 | 0.8703 |
| 0.0022 | 373.33 | 5600 | 0.9446 | 0.8786 | 0.8787 |
| 0.0042 | 386.67 | 5800 | 0.9308 | 0.8786 | 0.8787 |
| 0.0041 | 400.0 | 6000 | 0.9430 | 0.8661 | 0.8661 |
| 0.0019 | 413.33 | 6200 | 1.0099 | 0.8577 | 0.8577 |
| 0.002 | 426.67 | 6400 | 1.0886 | 0.8660 | 0.8661 |
| 0.0018 | 440.0 | 6600 | 1.1334 | 0.8702 | 0.8703 |
| 0.0012 | 453.33 | 6800 | 1.2317 | 0.8827 | 0.8828 |
| 0.0022 | 466.67 | 7000 | 1.2617 | 0.8739 | 0.8745 |
| 0.0029 | 480.0 | 7200 | 1.0624 | 0.8784 | 0.8787 |
| 0.0015 | 493.33 | 7400 | 1.0491 | 0.8745 | 0.8745 |
| 0.0024 | 506.67 | 7600 | 1.2063 | 0.8741 | 0.8745 |
| 0.0014 | 520.0 | 7800 | 1.1896 | 0.8827 | 0.8828 |
| 0.0014 | 533.33 | 8000 | 1.1782 | 0.8701 | 0.8703 |
| 0.0014 | 546.67 | 8200 | 1.1415 | 0.8783 | 0.8787 |
| 0.0007 | 560.0 | 8400 | 1.1038 | 0.8744 | 0.8745 |
| 0.0011 | 573.33 | 8600 | 1.1695 | 0.8827 | 0.8828 |
| 0.0012 | 586.67 | 8800 | 1.1214 | 0.8702 | 0.8703 |
| 0.0014 | 600.0 | 9000 | 1.1583 | 0.8910 | 0.8912 |
| 0.0018 | 613.33 | 9200 | 1.0990 | 0.8744 | 0.8745 |
| 0.0005 | 626.67 | 9400 | 1.1452 | 0.8827 | 0.8828 |
| 0.0012 | 640.0 | 9600 | 1.1008 | 0.8827 | 0.8828 |
| 0.0007 | 653.33 | 9800 | 1.1283 | 0.8785 | 0.8787 |
| 0.001 | 666.67 | 10000 | 1.1066 | 0.8744 | 0.8745 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_3-seqsight_4096_512_46M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_3-seqsight_4096_512_46M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-27T00:20:32+00:00 |
null | peft |
<!-- 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. -->
# GUE_mouse_2-seqsight_4096_512_46M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4825
- F1 Score: 0.9085
- Accuracy: 0.9085
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.3813 | 9.52 | 200 | 0.3145 | 0.8445 | 0.8445 |
| 0.2723 | 19.05 | 400 | 0.2908 | 0.8689 | 0.8689 |
| 0.2272 | 28.57 | 600 | 0.2517 | 0.9024 | 0.9024 |
| 0.192 | 38.1 | 800 | 0.2516 | 0.8993 | 0.8994 |
| 0.1643 | 47.62 | 1000 | 0.2615 | 0.9084 | 0.9085 |
| 0.1459 | 57.14 | 1200 | 0.2688 | 0.9115 | 0.9116 |
| 0.1241 | 66.67 | 1400 | 0.2988 | 0.9022 | 0.9024 |
| 0.1155 | 76.19 | 1600 | 0.3183 | 0.8930 | 0.8933 |
| 0.1044 | 85.71 | 1800 | 0.3283 | 0.9054 | 0.9055 |
| 0.0892 | 95.24 | 2000 | 0.3348 | 0.9055 | 0.9055 |
| 0.0792 | 104.76 | 2200 | 0.3672 | 0.8992 | 0.8994 |
| 0.071 | 114.29 | 2400 | 0.4062 | 0.8930 | 0.8933 |
| 0.0692 | 123.81 | 2600 | 0.3689 | 0.9024 | 0.9024 |
| 0.0647 | 133.33 | 2800 | 0.4111 | 0.9084 | 0.9085 |
| 0.0575 | 142.86 | 3000 | 0.4347 | 0.9084 | 0.9085 |
| 0.0526 | 152.38 | 3200 | 0.4847 | 0.8992 | 0.8994 |
| 0.0484 | 161.9 | 3400 | 0.4344 | 0.9023 | 0.9024 |
| 0.0437 | 171.43 | 3600 | 0.4632 | 0.9084 | 0.9085 |
| 0.0435 | 180.95 | 3800 | 0.4370 | 0.9084 | 0.9085 |
| 0.0407 | 190.48 | 4000 | 0.5151 | 0.8930 | 0.8933 |
| 0.0396 | 200.0 | 4200 | 0.4742 | 0.9022 | 0.9024 |
| 0.0382 | 209.52 | 4400 | 0.4412 | 0.9176 | 0.9177 |
| 0.0327 | 219.05 | 4600 | 0.4725 | 0.8961 | 0.8963 |
| 0.0347 | 228.57 | 4800 | 0.4154 | 0.9145 | 0.9146 |
| 0.0291 | 238.1 | 5000 | 0.4579 | 0.9084 | 0.9085 |
| 0.0291 | 247.62 | 5200 | 0.4998 | 0.9053 | 0.9055 |
| 0.0307 | 257.14 | 5400 | 0.4561 | 0.9084 | 0.9085 |
| 0.0273 | 266.67 | 5600 | 0.4622 | 0.9115 | 0.9116 |
| 0.0258 | 276.19 | 5800 | 0.4818 | 0.9115 | 0.9116 |
| 0.0268 | 285.71 | 6000 | 0.5010 | 0.9022 | 0.9024 |
| 0.0256 | 295.24 | 6200 | 0.5003 | 0.8961 | 0.8963 |
| 0.0222 | 304.76 | 6400 | 0.4967 | 0.9054 | 0.9055 |
| 0.0234 | 314.29 | 6600 | 0.4815 | 0.9023 | 0.9024 |
| 0.0207 | 323.81 | 6800 | 0.4913 | 0.9023 | 0.9024 |
| 0.0207 | 333.33 | 7000 | 0.4444 | 0.9054 | 0.9055 |
| 0.0213 | 342.86 | 7200 | 0.4765 | 0.9115 | 0.9116 |
| 0.0198 | 352.38 | 7400 | 0.4887 | 0.9023 | 0.9024 |
| 0.02 | 361.9 | 7600 | 0.4866 | 0.9115 | 0.9116 |
| 0.0179 | 371.43 | 7800 | 0.5251 | 0.9084 | 0.9085 |
| 0.0168 | 380.95 | 8000 | 0.5346 | 0.9084 | 0.9085 |
| 0.0161 | 390.48 | 8200 | 0.5238 | 0.9053 | 0.9055 |
| 0.0189 | 400.0 | 8400 | 0.5044 | 0.9176 | 0.9177 |
| 0.0157 | 409.52 | 8600 | 0.5053 | 0.9176 | 0.9177 |
| 0.0159 | 419.05 | 8800 | 0.5043 | 0.9176 | 0.9177 |
| 0.017 | 428.57 | 9000 | 0.5292 | 0.9053 | 0.9055 |
| 0.016 | 438.1 | 9200 | 0.4898 | 0.9115 | 0.9116 |
| 0.0151 | 447.62 | 9400 | 0.5024 | 0.9084 | 0.9085 |
| 0.0165 | 457.14 | 9600 | 0.5011 | 0.9115 | 0.9116 |
| 0.0158 | 466.67 | 9800 | 0.5060 | 0.9115 | 0.9116 |
| 0.0147 | 476.19 | 10000 | 0.5025 | 0.9115 | 0.9116 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_2-seqsight_4096_512_46M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_2-seqsight_4096_512_46M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-27T00:21:30+00:00 |
text-generation | transformers | ## Welcome to Miqu Cat: A 70B Miqu Lora Fine-Tune
Introducing **Miqu Cat**, an advanced model fine-tuned by Dr. Kal'tsit then quanted for the the ExllamaV2 project, bringing the model down to an impressive 4.8 bits per weight (bpw). This fine-tuning allows those with limited computational resources to explore its capabilities without compromise.
### Competitive Edge - *meow!*
Miqu Cat stands out in the arena of Miqu fine-tunes, consistently performing admirably in tests and comparisons. It’s crafted to be less restrictive and more robust than its predecessors and variants, making it a versatile tool in AI-driven applications.
**48GB VRAM to load the model for 8192 Context Length** *["2x3090", "1xA6000", "1xA100 80GB", "etc."]*
### How to Use Miqu Cat: The Nitty-Gritty
Miqu Cat operates on the **CHATML** prompt format, designed for straightforward and effective interaction. Whether you're integrating it into existing systems or using it for new projects, its flexible prompt structure facilitates ease of use.
### Training Specs
- **Dataset**: 1.5 GB
- **Compute**: Dual setup of 8xA100 nodes
- **Duration**: Approximately 1000 hours of intensive training
### Meet the Author
**Dr. Kal'tsit** has been at the forefront of this fine-tuning process, ensuring that Miqu Cat gives the user a unique feel.
| {"language": ["en"], "tags": ["miqu", "70b model", "cat", "miqu cat"], "pipeline_tag": "text-generation"} | PotatoOff/MQ-Catsu-70b-4.8bpw | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"miqu",
"70b model",
"cat",
"miqu cat",
"conversational",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T00:23:18+00:00 |
text-generation | transformers | {} | float-trip/drama-llama-3 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T00:25:57+00:00 |
|
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_POASL_v1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_POASL_v1", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_POASL_v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T00:25:58+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_train_Instruction0_AOPSL_v1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) on the None 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_train_Instruction0_AOPSL_v1", "results": []}]} | ThuyNT/CS505_COQE_viT5_train_Instruction0_AOPSL_v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T00:26:11+00:00 |
null | null | {} | PhaedrusFlow/model | null | [
"region:us"
]
| null | 2024-04-27T00:26:19+00:00 |
|
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### 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. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/0c75ryt | null | [
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"conversational",
"arxiv:1910.09700",
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| null | 2024-04-27T00:26:42+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/y2ssfb6 | null | [
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| null | 2024-04-27T00:26:42+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/1uri2p8 | null | [
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| null | 2024-04-27T00:26:42+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/vfk6frz | null | [
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"conversational",
"arxiv:1910.09700",
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| null | 2024-04-27T00:26:42+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/hkn3wye | null | [
"transformers",
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"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T00:26:42+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/r47xmg4 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T00:26:42+00:00 |
null | peft |
<!-- 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. -->
# GUE_mouse_2-seqsight_4096_512_46M-L8_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2926
- F1 Score: 0.8933
- Accuracy: 0.8933
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.3438 | 9.52 | 200 | 0.2686 | 0.8811 | 0.8811 |
| 0.2057 | 19.05 | 400 | 0.2340 | 0.9084 | 0.9085 |
| 0.1532 | 28.57 | 600 | 0.2438 | 0.9177 | 0.9177 |
| 0.1204 | 38.1 | 800 | 0.3286 | 0.8898 | 0.8902 |
| 0.0852 | 47.62 | 1000 | 0.3904 | 0.8930 | 0.8933 |
| 0.0689 | 57.14 | 1200 | 0.4646 | 0.9020 | 0.9024 |
| 0.0558 | 66.67 | 1400 | 0.4966 | 0.8959 | 0.8963 |
| 0.0447 | 76.19 | 1600 | 0.5357 | 0.8898 | 0.8902 |
| 0.0396 | 85.71 | 1800 | 0.5372 | 0.9020 | 0.9024 |
| 0.0339 | 95.24 | 2000 | 0.4900 | 0.9053 | 0.9055 |
| 0.0325 | 104.76 | 2200 | 0.4701 | 0.9083 | 0.9085 |
| 0.0274 | 114.29 | 2400 | 0.5333 | 0.9083 | 0.9085 |
| 0.0267 | 123.81 | 2600 | 0.5384 | 0.9021 | 0.9024 |
| 0.0234 | 133.33 | 2800 | 0.5994 | 0.9053 | 0.9055 |
| 0.0198 | 142.86 | 3000 | 0.6376 | 0.8990 | 0.8994 |
| 0.0194 | 152.38 | 3200 | 0.6624 | 0.9053 | 0.9055 |
| 0.0175 | 161.9 | 3400 | 0.7262 | 0.8992 | 0.8994 |
| 0.0164 | 171.43 | 3600 | 0.6526 | 0.9022 | 0.9024 |
| 0.0155 | 180.95 | 3800 | 0.6207 | 0.9053 | 0.9055 |
| 0.0159 | 190.48 | 4000 | 0.8122 | 0.8866 | 0.8872 |
| 0.0133 | 200.0 | 4200 | 0.6609 | 0.9053 | 0.9055 |
| 0.0148 | 209.52 | 4400 | 0.6370 | 0.9023 | 0.9024 |
| 0.0118 | 219.05 | 4600 | 0.7192 | 0.8991 | 0.8994 |
| 0.0126 | 228.57 | 4800 | 0.6550 | 0.9023 | 0.9024 |
| 0.0116 | 238.1 | 5000 | 0.6668 | 0.9053 | 0.9055 |
| 0.0104 | 247.62 | 5200 | 0.8031 | 0.9052 | 0.9055 |
| 0.0115 | 257.14 | 5400 | 0.6510 | 0.9114 | 0.9116 |
| 0.009 | 266.67 | 5600 | 0.7020 | 0.9083 | 0.9085 |
| 0.0093 | 276.19 | 5800 | 0.7065 | 0.9114 | 0.9116 |
| 0.0079 | 285.71 | 6000 | 0.7679 | 0.9052 | 0.9055 |
| 0.0078 | 295.24 | 6200 | 0.6977 | 0.9052 | 0.9055 |
| 0.0067 | 304.76 | 6400 | 0.7725 | 0.9052 | 0.9055 |
| 0.0077 | 314.29 | 6600 | 0.8004 | 0.9021 | 0.9024 |
| 0.0066 | 323.81 | 6800 | 0.8258 | 0.9052 | 0.9055 |
| 0.0059 | 333.33 | 7000 | 0.8163 | 0.9052 | 0.9055 |
| 0.0056 | 342.86 | 7200 | 0.7057 | 0.9115 | 0.9116 |
| 0.0044 | 352.38 | 7400 | 0.8156 | 0.9083 | 0.9085 |
| 0.005 | 361.9 | 7600 | 0.9004 | 0.9052 | 0.9055 |
| 0.0042 | 371.43 | 7800 | 0.7881 | 0.9084 | 0.9085 |
| 0.0059 | 380.95 | 8000 | 0.9590 | 0.8990 | 0.8994 |
| 0.0054 | 390.48 | 8200 | 0.8879 | 0.9021 | 0.9024 |
| 0.0052 | 400.0 | 8400 | 0.8369 | 0.9052 | 0.9055 |
| 0.0047 | 409.52 | 8600 | 0.7993 | 0.9083 | 0.9085 |
| 0.0054 | 419.05 | 8800 | 0.9150 | 0.9021 | 0.9024 |
| 0.0042 | 428.57 | 9000 | 0.9042 | 0.9052 | 0.9055 |
| 0.0055 | 438.1 | 9200 | 0.8914 | 0.9021 | 0.9024 |
| 0.0049 | 447.62 | 9400 | 0.8820 | 0.9021 | 0.9024 |
| 0.0051 | 457.14 | 9600 | 0.8418 | 0.9083 | 0.9085 |
| 0.0036 | 466.67 | 9800 | 0.8567 | 0.9052 | 0.9055 |
| 0.0041 | 476.19 | 10000 | 0.8606 | 0.9052 | 0.9055 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_2-seqsight_4096_512_46M-L8_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_2-seqsight_4096_512_46M-L8_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-27T00:27:27+00:00 |
null | peft |
<!-- 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. -->
# GUE_mouse_2-seqsight_4096_512_46M-L32_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_mouse_2](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_mouse_2) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7497
- F1 Score: 0.9116
- Accuracy: 0.9116
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:------:|:-----:|:---------------:|:--------:|:--------:|
| 0.3059 | 9.52 | 200 | 0.2390 | 0.8963 | 0.8963 |
| 0.1553 | 19.05 | 400 | 0.2966 | 0.8809 | 0.8811 |
| 0.0943 | 28.57 | 600 | 0.3704 | 0.9024 | 0.9024 |
| 0.0599 | 38.1 | 800 | 0.4834 | 0.8869 | 0.8872 |
| 0.0359 | 47.62 | 1000 | 0.4623 | 0.9177 | 0.9177 |
| 0.0278 | 57.14 | 1200 | 0.5745 | 0.9083 | 0.9085 |
| 0.0236 | 66.67 | 1400 | 0.6177 | 0.9115 | 0.9116 |
| 0.0168 | 76.19 | 1600 | 0.7493 | 0.8711 | 0.8720 |
| 0.0172 | 85.71 | 1800 | 0.6717 | 0.8930 | 0.8933 |
| 0.0128 | 95.24 | 2000 | 0.7105 | 0.8932 | 0.8933 |
| 0.0142 | 104.76 | 2200 | 0.7260 | 0.9054 | 0.9055 |
| 0.0077 | 114.29 | 2400 | 0.7881 | 0.9054 | 0.9055 |
| 0.0108 | 123.81 | 2600 | 0.6685 | 0.8900 | 0.8902 |
| 0.0092 | 133.33 | 2800 | 0.7251 | 0.9085 | 0.9085 |
| 0.0072 | 142.86 | 3000 | 0.7579 | 0.9054 | 0.9055 |
| 0.0071 | 152.38 | 3200 | 0.8228 | 0.8962 | 0.8963 |
| 0.0052 | 161.9 | 3400 | 0.7900 | 0.8993 | 0.8994 |
| 0.0039 | 171.43 | 3600 | 0.8523 | 0.8961 | 0.8963 |
| 0.0051 | 180.95 | 3800 | 0.8016 | 0.9145 | 0.9146 |
| 0.0055 | 190.48 | 4000 | 0.7356 | 0.9176 | 0.9177 |
| 0.0077 | 200.0 | 4200 | 0.6817 | 0.9085 | 0.9085 |
| 0.0046 | 209.52 | 4400 | 0.8274 | 0.9053 | 0.9055 |
| 0.0043 | 219.05 | 4600 | 0.8427 | 0.9084 | 0.9085 |
| 0.0035 | 228.57 | 4800 | 0.7371 | 0.9177 | 0.9177 |
| 0.0039 | 238.1 | 5000 | 0.7519 | 0.9145 | 0.9146 |
| 0.0031 | 247.62 | 5200 | 0.9885 | 0.8806 | 0.8811 |
| 0.0046 | 257.14 | 5400 | 0.7941 | 0.8993 | 0.8994 |
| 0.0021 | 266.67 | 5600 | 0.8978 | 0.9055 | 0.9055 |
| 0.0024 | 276.19 | 5800 | 0.9299 | 0.8901 | 0.8902 |
| 0.0025 | 285.71 | 6000 | 0.8703 | 0.9116 | 0.9116 |
| 0.0034 | 295.24 | 6200 | 0.7934 | 0.9054 | 0.9055 |
| 0.0026 | 304.76 | 6400 | 0.8378 | 0.8931 | 0.8933 |
| 0.0024 | 314.29 | 6600 | 0.8349 | 0.9116 | 0.9116 |
| 0.0028 | 323.81 | 6800 | 0.9917 | 0.8870 | 0.8872 |
| 0.0014 | 333.33 | 7000 | 0.9840 | 0.8993 | 0.8994 |
| 0.0023 | 342.86 | 7200 | 0.9241 | 0.8932 | 0.8933 |
| 0.0015 | 352.38 | 7400 | 0.8711 | 0.9055 | 0.9055 |
| 0.0016 | 361.9 | 7600 | 0.9244 | 0.8901 | 0.8902 |
| 0.0011 | 371.43 | 7800 | 0.9047 | 0.9085 | 0.9085 |
| 0.0013 | 380.95 | 8000 | 0.9929 | 0.8869 | 0.8872 |
| 0.0013 | 390.48 | 8200 | 0.9443 | 0.9023 | 0.9024 |
| 0.0016 | 400.0 | 8400 | 0.9315 | 0.9024 | 0.9024 |
| 0.0019 | 409.52 | 8600 | 0.9707 | 0.8961 | 0.8963 |
| 0.0013 | 419.05 | 8800 | 0.8804 | 0.9115 | 0.9116 |
| 0.0005 | 428.57 | 9000 | 0.9143 | 0.9054 | 0.9055 |
| 0.001 | 438.1 | 9200 | 0.8963 | 0.9085 | 0.9085 |
| 0.0008 | 447.62 | 9400 | 0.9139 | 0.9085 | 0.9085 |
| 0.0005 | 457.14 | 9600 | 0.8952 | 0.9116 | 0.9116 |
| 0.0005 | 466.67 | 9800 | 0.9205 | 0.9085 | 0.9085 |
| 0.0002 | 476.19 | 10000 | 0.9204 | 0.9085 | 0.9085 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_mouse_2-seqsight_4096_512_46M-L32_f", "results": []}]} | mahdibaghbanzadeh/GUE_mouse_2-seqsight_4096_512_46M-L32_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-27T00:27:51+00:00 |
text-generation | transformers |
# flammen21X-mistral-7B
A Mistral 7B LLM built from merging pretrained models and finetuning on [flammenai/Prude-Phi3-DPO](https://huggingface.co/datasets/flammenai/Prude-Phi3-DPO).
Flammen specializes in exceptional character roleplay, creative writing, and general intelligence.
### Method
Finetuned using an L4 on Google Colab.
[Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac)
### Configuration
LoRA, model, and training settings:
```python
# LoRA configuration
peft_config = LoraConfig(
r=16,
lora_alpha=16,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
)
# Model to fine-tune
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
load_in_4bit=True
)
model.config.use_cache = False
# Reference model
ref_model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
load_in_4bit=True
)
# Training arguments
training_args = TrainingArguments(
per_device_train_batch_size=2,
gradient_accumulation_steps=4,
gradient_checkpointing=True,
learning_rate=5e-5,
lr_scheduler_type="cosine",
max_steps=420,
save_strategy="no",
logging_steps=1,
output_dir=new_model,
optim="paged_adamw_32bit",
warmup_steps=100,
bf16=True,
report_to="wandb",
)
# Create DPO trainer
dpo_trainer = DPOTrainer(
model,
ref_model,
args=training_args,
train_dataset=dataset,
tokenizer=tokenizer,
peft_config=peft_config,
beta=0.1,
max_prompt_length=2048,
max_length=4096,
force_use_ref_model=True
)
# Fine-tune model with DPO
dpo_trainer.train()
``` | {"license": "apache-2.0", "library_name": "transformers", "tags": ["nsfw", "not-for-all-audiences"], "datasets": ["ResplendentAI/NSFW_RP_Format_NoQuote", "flammenai/Prude-Phi3-DPO"], "base_model": ["flammenai/flammen21-mistral-7B"]} | flammenai/flammen21X-mistral-7B | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"nsfw",
"not-for-all-audiences",
"dataset:ResplendentAI/NSFW_RP_Format_NoQuote",
"dataset:flammenai/Prude-Phi3-DPO",
"base_model:flammenai/flammen21-mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T00:28:06+00:00 |
text-generation | transformers |
# 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. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### 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. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | devesh220897/financial-chatbot-for-young-adults-4 | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T00:28:21+00:00 |
null | peft |
<!-- 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. -->
# GUE_splice_reconstructed-seqsight_4096_512_46M-L1_f
This model is a fine-tuned version of [mahdibaghbanzadeh/seqsight_4096_512_46M](https://huggingface.co/mahdibaghbanzadeh/seqsight_4096_512_46M) on the [mahdibaghbanzadeh/GUE_splice_reconstructed](https://huggingface.co/datasets/mahdibaghbanzadeh/GUE_splice_reconstructed) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3479
- F1 Score: 0.8611
- Accuracy: 0.8604
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Score | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.9345 | 0.7 | 200 | 0.8741 | 0.5165 | 0.5761 |
| 0.7904 | 1.4 | 400 | 0.5650 | 0.7493 | 0.7519 |
| 0.5261 | 2.1 | 600 | 0.4778 | 0.7942 | 0.7937 |
| 0.478 | 2.8 | 800 | 0.4621 | 0.8103 | 0.8097 |
| 0.4593 | 3.5 | 1000 | 0.4522 | 0.8124 | 0.8115 |
| 0.4463 | 4.2 | 1200 | 0.4375 | 0.8194 | 0.8181 |
| 0.4347 | 4.9 | 1400 | 0.4279 | 0.8234 | 0.8224 |
| 0.4273 | 5.59 | 1600 | 0.4331 | 0.8230 | 0.8227 |
| 0.4151 | 6.29 | 1800 | 0.4419 | 0.8198 | 0.8185 |
| 0.4036 | 6.99 | 2000 | 0.4257 | 0.8270 | 0.8255 |
| 0.3988 | 7.69 | 2200 | 0.3926 | 0.8380 | 0.8371 |
| 0.3947 | 8.39 | 2400 | 0.4171 | 0.8293 | 0.8281 |
| 0.3959 | 9.09 | 2600 | 0.4144 | 0.8316 | 0.8301 |
| 0.3848 | 9.79 | 2800 | 0.3916 | 0.8441 | 0.8431 |
| 0.3842 | 10.49 | 3000 | 0.3868 | 0.8481 | 0.8472 |
| 0.3726 | 11.19 | 3200 | 0.4221 | 0.8294 | 0.8284 |
| 0.375 | 11.89 | 3400 | 0.3941 | 0.8421 | 0.8409 |
| 0.3636 | 12.59 | 3600 | 0.3826 | 0.8468 | 0.8461 |
| 0.3693 | 13.29 | 3800 | 0.3817 | 0.8479 | 0.8470 |
| 0.3604 | 13.99 | 4000 | 0.3992 | 0.8427 | 0.8415 |
| 0.3566 | 14.69 | 4200 | 0.3819 | 0.8512 | 0.8503 |
| 0.3539 | 15.38 | 4400 | 0.3803 | 0.8516 | 0.8507 |
| 0.3463 | 16.08 | 4600 | 0.4195 | 0.8349 | 0.8338 |
| 0.3498 | 16.78 | 4800 | 0.3813 | 0.8503 | 0.8494 |
| 0.345 | 17.48 | 5000 | 0.3904 | 0.8484 | 0.8472 |
| 0.3447 | 18.18 | 5200 | 0.3690 | 0.8580 | 0.8573 |
| 0.3424 | 18.88 | 5400 | 0.3667 | 0.8582 | 0.8575 |
| 0.3325 | 19.58 | 5600 | 0.3646 | 0.8591 | 0.8584 |
| 0.3418 | 20.28 | 5800 | 0.3595 | 0.8615 | 0.8608 |
| 0.336 | 20.98 | 6000 | 0.3636 | 0.8581 | 0.8573 |
| 0.3391 | 21.68 | 6200 | 0.3748 | 0.8532 | 0.8525 |
| 0.3304 | 22.38 | 6400 | 0.3715 | 0.8561 | 0.8553 |
| 0.3272 | 23.08 | 6600 | 0.3687 | 0.8565 | 0.8555 |
| 0.3302 | 23.78 | 6800 | 0.3691 | 0.8571 | 0.8562 |
| 0.3278 | 24.48 | 7000 | 0.3756 | 0.8573 | 0.8564 |
| 0.3219 | 25.17 | 7200 | 0.3740 | 0.8538 | 0.8529 |
| 0.3232 | 25.87 | 7400 | 0.3772 | 0.8535 | 0.8525 |
| 0.322 | 26.57 | 7600 | 0.3782 | 0.8552 | 0.8542 |
| 0.3195 | 27.27 | 7800 | 0.3701 | 0.8566 | 0.8558 |
| 0.3248 | 27.97 | 8000 | 0.3707 | 0.8558 | 0.8549 |
| 0.3255 | 28.67 | 8200 | 0.3815 | 0.8519 | 0.8509 |
| 0.318 | 29.37 | 8400 | 0.3691 | 0.8553 | 0.8544 |
| 0.3154 | 30.07 | 8600 | 0.3580 | 0.8620 | 0.8612 |
| 0.3157 | 30.77 | 8800 | 0.3694 | 0.8551 | 0.8542 |
| 0.3171 | 31.47 | 9000 | 0.3643 | 0.8562 | 0.8553 |
| 0.3198 | 32.17 | 9200 | 0.3615 | 0.8588 | 0.8580 |
| 0.3182 | 32.87 | 9400 | 0.3648 | 0.8578 | 0.8571 |
| 0.316 | 33.57 | 9600 | 0.3717 | 0.8543 | 0.8534 |
| 0.3129 | 34.27 | 9800 | 0.3659 | 0.8577 | 0.8569 |
| 0.3166 | 34.97 | 10000 | 0.3673 | 0.8564 | 0.8555 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "mahdibaghbanzadeh/seqsight_4096_512_46M", "model-index": [{"name": "GUE_splice_reconstructed-seqsight_4096_512_46M-L1_f", "results": []}]} | mahdibaghbanzadeh/GUE_splice_reconstructed-seqsight_4096_512_46M-L1_f | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mahdibaghbanzadeh/seqsight_4096_512_46M",
"region:us"
]
| null | 2024-04-27T00:28:25+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | rPucs/gemma-7b-itTripletDolly-WebNLG | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
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]
| null | 2024-04-27T00:30:38+00:00 |
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | jsingh/autoflow-math-v0.3 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T00:32:14+00:00 |
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