id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
harsharora/gpt2_finetuned | harsharora | 2023-11-29T09:43:48Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-classification",
"generated_from_trainer",
"dataset:wiki_qa",
"base_model:gpt2",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T09:43:48Z | 2023-11-29T09:43:18.000Z | null | null | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
datasets:
- wiki_qa
model-index:
- name: gpt2_finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2_finetuned
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the wiki_qa 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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imdatta0/llama_2_13b_gateproj_alpaca_r8_lr0.0001_bg8 | imdatta0 | 2023-11-29T09:44:37Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:meta-llama/Llama-2-13b-hf",
"region:us"
] | 2023-11-29T09:44:37Z | 2023-11-29T09:44:32.000Z | null | null | ---
base_model: meta-llama/Llama-2-13b-hf
tags:
- generated_from_trainer
model-index:
- name: llama_2_13b_gateproj_alpaca_r8_lr0.0001_bg8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama_2_13b_gateproj_alpaca_r8_lr0.0001_bg8
This model is a fine-tuned version of [meta-llama/Llama-2-13b-hf](https://huggingface.co/meta-llama/Llama-2-13b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7399
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 0.02
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4009 | 0.01 | 33 | 0.9230 |
| 0.8887 | 0.02 | 66 | 0.8379 |
| 0.8519 | 0.03 | 99 | 0.7947 |
| 0.8 | 0.04 | 132 | 0.7753 |
| 0.8076 | 0.05 | 165 | 0.7653 |
| 0.7828 | 0.06 | 198 | 0.7612 |
| 0.8029 | 0.07 | 231 | 0.7593 |
| 0.7891 | 0.08 | 264 | 0.7570 |
| 0.7955 | 0.09 | 297 | 0.7546 |
| 0.8011 | 0.1 | 330 | 0.7543 |
| 0.7852 | 0.11 | 363 | 0.7531 |
| 0.7878 | 0.12 | 396 | 0.7514 |
| 0.7848 | 0.13 | 429 | 0.7507 |
| 0.7754 | 0.14 | 462 | 0.7508 |
| 0.7853 | 0.15 | 495 | 0.7488 |
| 0.7824 | 0.16 | 528 | 0.7481 |
| 0.7657 | 0.17 | 561 | 0.7483 |
| 0.7743 | 0.18 | 594 | 0.7479 |
| 0.7949 | 0.19 | 627 | 0.7486 |
| 0.7931 | 0.21 | 660 | 0.7481 |
| 0.795 | 0.22 | 693 | 0.7474 |
| 0.7807 | 0.23 | 726 | 0.7478 |
| 0.7611 | 0.24 | 759 | 0.7472 |
| 0.7998 | 0.25 | 792 | 0.7469 |
| 0.7733 | 0.26 | 825 | 0.7467 |
| 0.7735 | 0.27 | 858 | 0.7453 |
| 0.7635 | 0.28 | 891 | 0.7457 |
| 0.7631 | 0.29 | 924 | 0.7457 |
| 0.7771 | 0.3 | 957 | 0.7453 |
| 0.7965 | 0.31 | 990 | 0.7462 |
| 0.7752 | 0.32 | 1023 | 0.7454 |
| 0.7709 | 0.33 | 1056 | 0.7453 |
| 0.7637 | 0.34 | 1089 | 0.7448 |
| 0.7949 | 0.35 | 1122 | 0.7444 |
| 0.757 | 0.36 | 1155 | 0.7433 |
| 0.7641 | 0.37 | 1188 | 0.7440 |
| 0.7769 | 0.38 | 1221 | 0.7441 |
| 0.773 | 0.39 | 1254 | 0.7444 |
| 0.7705 | 0.4 | 1287 | 0.7440 |
| 0.7884 | 0.41 | 1320 | 0.7439 |
| 0.7682 | 0.42 | 1353 | 0.7440 |
| 0.7655 | 0.43 | 1386 | 0.7444 |
| 0.7681 | 0.44 | 1419 | 0.7426 |
| 0.7626 | 0.45 | 1452 | 0.7432 |
| 0.7765 | 0.46 | 1485 | 0.7430 |
| 0.7767 | 0.47 | 1518 | 0.7430 |
| 0.7805 | 0.48 | 1551 | 0.7429 |
| 0.7667 | 0.49 | 1584 | 0.7432 |
| 0.7689 | 0.5 | 1617 | 0.7423 |
| 0.7894 | 0.51 | 1650 | 0.7426 |
| 0.757 | 0.52 | 1683 | 0.7431 |
| 0.7839 | 0.53 | 1716 | 0.7433 |
| 0.7561 | 0.54 | 1749 | 0.7435 |
| 0.7738 | 0.55 | 1782 | 0.7424 |
| 0.7719 | 0.56 | 1815 | 0.7424 |
| 0.7701 | 0.57 | 1848 | 0.7428 |
| 0.7869 | 0.58 | 1881 | 0.7418 |
| 0.7956 | 0.59 | 1914 | 0.7423 |
| 0.7896 | 0.6 | 1947 | 0.7415 |
| 0.7901 | 0.62 | 1980 | 0.7420 |
| 0.7856 | 0.63 | 2013 | 0.7412 |
| 0.7665 | 0.64 | 2046 | 0.7414 |
| 0.7809 | 0.65 | 2079 | 0.7418 |
| 0.7538 | 0.66 | 2112 | 0.7410 |
| 0.7475 | 0.67 | 2145 | 0.7412 |
| 0.7845 | 0.68 | 2178 | 0.7409 |
| 0.7633 | 0.69 | 2211 | 0.7410 |
| 0.776 | 0.7 | 2244 | 0.7406 |
| 0.7691 | 0.71 | 2277 | 0.7407 |
| 0.769 | 0.72 | 2310 | 0.7415 |
| 0.7868 | 0.73 | 2343 | 0.7414 |
| 0.7693 | 0.74 | 2376 | 0.7408 |
| 0.7651 | 0.75 | 2409 | 0.7406 |
| 0.7653 | 0.76 | 2442 | 0.7405 |
| 0.7501 | 0.77 | 2475 | 0.7403 |
| 0.7706 | 0.78 | 2508 | 0.7403 |
| 0.7794 | 0.79 | 2541 | 0.7401 |
| 0.7758 | 0.8 | 2574 | 0.7404 |
| 0.7614 | 0.81 | 2607 | 0.7400 |
| 0.7814 | 0.82 | 2640 | 0.7402 |
| 0.7635 | 0.83 | 2673 | 0.7400 |
| 0.7772 | 0.84 | 2706 | 0.7401 |
| 0.7597 | 0.85 | 2739 | 0.7401 |
| 0.762 | 0.86 | 2772 | 0.7400 |
| 0.7757 | 0.87 | 2805 | 0.7400 |
| 0.7629 | 0.88 | 2838 | 0.7398 |
| 0.761 | 0.89 | 2871 | 0.7399 |
| 0.7729 | 0.9 | 2904 | 0.7399 |
| 0.7456 | 0.91 | 2937 | 0.7399 |
| 0.7477 | 0.92 | 2970 | 0.7400 |
| 0.781 | 0.93 | 3003 | 0.7399 |
| 0.7532 | 0.94 | 3036 | 0.7398 |
| 0.7844 | 0.95 | 3069 | 0.7397 |
| 0.7521 | 0.96 | 3102 | 0.7399 |
| 0.7572 | 0.97 | 3135 | 0.7400 |
| 0.7872 | 0.98 | 3168 | 0.7399 |
| 0.76 | 0.99 | 3201 | 0.7399 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.5.2
- Tokenizers 0.14.1
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alyazharr/mdeberta_base_v3_1 | alyazharr | 2023-11-29T09:45:41Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T09:45:41Z | 2023-11-29T09:45:41.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | alyazharr/mdeberta_base_v3_1 | [
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diegogarcescolab/news-classification-18-llama-2-7b | diegogarcescolab | 2023-11-29T09:45:43Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T09:45:43Z | 2023-11-29T09:45:43.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | diegogarcescolab/news-classification-18-llama-2-7b | [
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tanvirsrbd1/testvalue-t5-model1 | tanvirsrbd1 | 2023-11-29T09:50:24Z | 0 | 0 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T09:50:24Z | 2023-11-29T09:46:43.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | tanvirsrbd1/testvalue-t5-model1 | [
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Mouktafi/super-cool-model | Mouktafi | 2023-11-29T11:34:32Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T11:34:32Z | 2023-11-29T09:49:20.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Mouktafi/super-cool-model | [
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yuliu1234/firstLLM_demo | yuliu1234 | 2023-11-29T09:51:00Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T09:51:00Z | 2023-11-29T09:51:00.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | yuliu1234/firstLLM_demo | [
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damerajee/copilot-codellama-q5-7b.gguf | damerajee | 2023-11-29T10:27:48Z | 0 | 0 | null | [
"gguf",
"region:us"
] | 2023-11-29T10:27:48Z | 2023-11-29T09:51:21.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | damerajee/copilot-codellama-q5-7b.gguf | [
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hid-ishizuka/wasm-japanese-large-lm-3.6b-instruction-sft-q4f16_1 | hid-ishizuka | 2023-11-29T17:54:36Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T17:54:36Z | 2023-11-29T09:52:03.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | hid-ishizuka/wasm-japanese-large-lm-3.6b-instruction-sft-q4f16_1 | [
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makhataei/qa-persian-xlmr-large | makhataei | 2023-11-29T11:54:30Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"dataset:pquad",
"base_model:m3hrdadfi/xlmr-large-qa-fa",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:54:30Z | 2023-11-29T09:53:19.000Z | null | null | ---
base_model: m3hrdadfi/xlmr-large-qa-fa
tags:
- generated_from_trainer
datasets:
- pquad
model-index:
- name: qa-persian-xlmr-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qa-persian-xlmr-large
This model is a fine-tuned version of [m3hrdadfi/xlmr-large-qa-fa](https://huggingface.co/m3hrdadfi/xlmr-large-qa-fa) on the pquad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4953
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.01
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6966 | 0.01 | 640 | 1.4953 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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hkivancoral/hushem_5x_beit_base_rms_00001_fold3 | hkivancoral | 2023-11-29T10:30:14Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:30:14Z | 2023-11-29T09:57:57.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_rms_00001_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9069767441860465
---
<!-- 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. -->
# hushem_5x_beit_base_rms_00001_fold3
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5241
- Accuracy: 0.9070
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7661 | 1.0 | 28 | 0.5413 | 0.7907 |
| 0.1086 | 2.0 | 56 | 0.1845 | 0.9535 |
| 0.0112 | 3.0 | 84 | 0.3881 | 0.9070 |
| 0.0076 | 4.0 | 112 | 0.3276 | 0.9070 |
| 0.0043 | 5.0 | 140 | 0.4248 | 0.9070 |
| 0.0016 | 6.0 | 168 | 0.2522 | 0.9302 |
| 0.0099 | 7.0 | 196 | 0.2768 | 0.9070 |
| 0.0014 | 8.0 | 224 | 0.2639 | 0.9302 |
| 0.0013 | 9.0 | 252 | 0.3818 | 0.9070 |
| 0.0009 | 10.0 | 280 | 0.1248 | 0.9535 |
| 0.0006 | 11.0 | 308 | 0.2509 | 0.9070 |
| 0.0003 | 12.0 | 336 | 0.2923 | 0.9070 |
| 0.001 | 13.0 | 364 | 0.5107 | 0.8837 |
| 0.0019 | 14.0 | 392 | 0.3339 | 0.9535 |
| 0.0002 | 15.0 | 420 | 0.3891 | 0.9070 |
| 0.0003 | 16.0 | 448 | 0.4248 | 0.9070 |
| 0.0005 | 17.0 | 476 | 0.2832 | 0.9535 |
| 0.0003 | 18.0 | 504 | 0.3491 | 0.9070 |
| 0.0002 | 19.0 | 532 | 0.4104 | 0.9070 |
| 0.0001 | 20.0 | 560 | 0.4255 | 0.9070 |
| 0.0009 | 21.0 | 588 | 0.4651 | 0.9070 |
| 0.0015 | 22.0 | 616 | 0.4792 | 0.9070 |
| 0.0001 | 23.0 | 644 | 0.4509 | 0.9070 |
| 0.0006 | 24.0 | 672 | 0.5680 | 0.9302 |
| 0.0001 | 25.0 | 700 | 0.3224 | 0.9070 |
| 0.0001 | 26.0 | 728 | 0.3096 | 0.9302 |
| 0.0001 | 27.0 | 756 | 0.6066 | 0.9070 |
| 0.0001 | 28.0 | 784 | 0.3940 | 0.9070 |
| 0.0001 | 29.0 | 812 | 0.3550 | 0.9070 |
| 0.0 | 30.0 | 840 | 0.4157 | 0.9070 |
| 0.0001 | 31.0 | 868 | 0.4340 | 0.9070 |
| 0.0166 | 32.0 | 896 | 0.6996 | 0.9070 |
| 0.0001 | 33.0 | 924 | 0.5595 | 0.9070 |
| 0.0 | 34.0 | 952 | 0.3606 | 0.9070 |
| 0.0001 | 35.0 | 980 | 0.4821 | 0.9070 |
| 0.0013 | 36.0 | 1008 | 0.4503 | 0.9070 |
| 0.0001 | 37.0 | 1036 | 0.4301 | 0.9070 |
| 0.0001 | 38.0 | 1064 | 0.4884 | 0.9070 |
| 0.0 | 39.0 | 1092 | 0.4958 | 0.9070 |
| 0.0009 | 40.0 | 1120 | 0.5821 | 0.9070 |
| 0.0001 | 41.0 | 1148 | 0.4696 | 0.9070 |
| 0.0 | 42.0 | 1176 | 0.4577 | 0.9070 |
| 0.0 | 43.0 | 1204 | 0.4998 | 0.9070 |
| 0.0 | 44.0 | 1232 | 0.5154 | 0.9070 |
| 0.0001 | 45.0 | 1260 | 0.5227 | 0.9070 |
| 0.0003 | 46.0 | 1288 | 0.5170 | 0.9070 |
| 0.0001 | 47.0 | 1316 | 0.5187 | 0.9070 |
| 0.0001 | 48.0 | 1344 | 0.5241 | 0.9070 |
| 0.0 | 49.0 | 1372 | 0.5241 | 0.9070 |
| 0.0 | 50.0 | 1400 | 0.5241 | 0.9070 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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JOKH/KoAlpaca | JOKH | 2023-11-29T10:04:26Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:04:26Z | 2023-11-29T09:59:12.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | JOKH/KoAlpaca | [
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yrch18/finetuned_santa_test | yrch18 | 2023-11-29T11:59:44Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"custom_code",
"text-generation-inference",
"region:us"
] | 2023-11-29T11:59:44Z | 2023-11-29T10:00:02.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | yrch18/finetuned_santa_test | [
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Realgon/roberta_sst2_padding60model | Realgon | 2023-11-29T10:58:37Z | 0 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:58:37Z | 2023-11-29T10:00:16.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst2_padding60model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_sst2_padding60model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5143
- Accuracy: 0.9445
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2571 | 0.9083 |
| 0.3356 | 2.0 | 866 | 0.1977 | 0.9407 |
| 0.1872 | 3.0 | 1299 | 0.2530 | 0.9396 |
| 0.1195 | 4.0 | 1732 | 0.2908 | 0.9434 |
| 0.0754 | 5.0 | 2165 | 0.3693 | 0.9412 |
| 0.043 | 6.0 | 2598 | 0.3650 | 0.9379 |
| 0.0317 | 7.0 | 3031 | 0.4696 | 0.9390 |
| 0.0317 | 8.0 | 3464 | 0.4233 | 0.9434 |
| 0.0277 | 9.0 | 3897 | 0.4518 | 0.9385 |
| 0.0248 | 10.0 | 4330 | 0.4169 | 0.9418 |
| 0.0156 | 11.0 | 4763 | 0.5964 | 0.9325 |
| 0.0116 | 12.0 | 5196 | 0.4787 | 0.9412 |
| 0.0093 | 13.0 | 5629 | 0.4558 | 0.9456 |
| 0.0129 | 14.0 | 6062 | 0.5166 | 0.9330 |
| 0.0129 | 15.0 | 6495 | 0.5489 | 0.9407 |
| 0.0068 | 16.0 | 6928 | 0.5002 | 0.9440 |
| 0.0059 | 17.0 | 7361 | 0.5227 | 0.9445 |
| 0.002 | 18.0 | 7794 | 0.5155 | 0.9462 |
| 0.0032 | 19.0 | 8227 | 0.5027 | 0.9473 |
| 0.0008 | 20.0 | 8660 | 0.5143 | 0.9445 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
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mcfatbeard/flant5-base | mcfatbeard | 2023-11-29T13:43:34Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T13:43:34Z | 2023-11-29T10:00:33.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | mcfatbeard/flant5-base | [
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0x2a34/whisper_without_preprocessing | 0x2a34 | 2023-11-29T10:01:23Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:01:23Z | 2023-11-29T10:01:23.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | 0x2a34/whisper_without_preprocessing | [
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Felladrin/onnx-mpt-125m-c4 | Felladrin | 2023-11-29T10:04:40Z | 0 | 0 | null | [
"transformers.js",
"onnx",
"mpt",
"text-generation",
"custom_code",
"base_model:wtang06/mpt-125m-c4",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | 2023-11-29T10:04:40Z | 2023-11-29T10:01:37.000Z | null | null | ---
license: apache-2.0
library_name: "transformers.js"
base_model: wtang06/mpt-125m-c4
---
INT8 ONNX version of [wtang06/mpt-125m-c4](https://huggingface.co/wtang06/mpt-125m-c4) to use with [Transformers.js](https://huggingface.co/docs/transformers.js).
| null | transformers.js | text-generation | null | null | null | null | null | null | null | null | null | Felladrin/onnx-mpt-125m-c4 | [
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Felladrin/onnx-gpt2-alpaca | Felladrin | 2023-11-29T10:07:55Z | 0 | 0 | null | [
"transformers.js",
"onnx",
"gpt2",
"text-generation",
"base_model:vicgalle/gpt2-alpaca",
"license:mit",
"text-generation-inference",
"region:us"
] | 2023-11-29T10:07:55Z | 2023-11-29T10:05:54.000Z | null | null | ---
license: mit
library_name: "transformers.js"
base_model: vicgalle/gpt2-alpaca
---
INT8 ONNX version of [vicgalle/gpt2-alpaca](https://huggingface.co/vicgalle/gpt2-alpaca) to use with [Transformers.js](https://huggingface.co/docs/transformers.js).
| null | transformers.js | text-generation | null | null | null | null | null | null | null | null | null | Felladrin/onnx-gpt2-alpaca | [
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shihotan/BBR_BeautifulBlueRealistic | shihotan | 2023-11-29T17:01:52Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T17:01:52Z | 2023-11-29T10:06:34.000Z | null | null | ぎゃるをつくれます
PPにRealisticとかはあってもなくてもいいです
NPにworstquality系もあってもなくてもいいです
Hires.fix時にExponential系のサンプラーを使うとフォトっぽいノイズが入るのでおすすめです
VAE無くても生成できます。好きなのを入れてください。 vqgan_cfw_00011_vae_only.ckpt が好きです
ネガティブTIは bad_arien と epiCPhotoGasm-softPhoto-neg が宇宙最強のTIなので入れてればなんとかなります
japaneseやasianをPPに入れると顔が野暮ったくなるので非推奨です。どうしても入れたい場合はNPにもjapaneseかasianを入れてください
よく使う生成設定
HDR photo of a gyaru,{prompt} . High dynamic range, vivid, rich details, clear shadows and highlights, realistic, intense, enhanced contrast, highly detailed
NP
flat, low contrast, oversaturated, underexposed, overexposed, blurred, noisy, (epiCPhotoGasm-softPhoto-neg:1.2),(worst quality:1.4),( low quality:1.4),(bad quality:1.4),(ugly quality:1.4),flat color,
BREAK
bad_arien,
Steps: 30, Sampler: DPM++ 2M SDE Karras, CFG scale: 15
らいせんす
このモデルつかってへんなことしちゃダメですよ
✅ 本モデルで生成した画像を商用利用する行為
✅ 本モデルを使用したマージモデルを使用または再配布する行為
✅ 本モデルのクレジット表記をせずに使用する行為
❌ 本モデルを商用の画像生成サービスで利用する行為
❌ 本モデルや本モデルをマージしたモデルを販売する行為
❌ 本モデルを使用し意図的に違法な出力をする行為
❌ 本モデルをマージしたモデルに異なる権限を与える行為
❌ 本モデルをマージしたモデルを配布または本モデルを再配布した際に同じ使用制限を含め、CreativeML OpenRAIL-M のコピーをすべてのユーザーと共有しない行為 | null | null | null | null | null | null | null | null | null | null | null | null | shihotan/BBR_BeautifulBlueRealistic | [
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kyle0518/HW3_r64 | kyle0518 | 2023-11-29T17:43:16Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:yentinglin/Taiwan-LLM-7B-v2.0-chat",
"license:apache-2.0",
"region:us"
] | 2023-11-29T17:43:16Z | 2023-11-29T10:06:37.000Z | null | null | ---
license: apache-2.0
base_model: yentinglin/Taiwan-LLM-7B-v2.0-chat
tags:
- generated_from_trainer
model-index:
- name: HW3_r64
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# HW3_r64
This model is a fine-tuned version of [yentinglin/Taiwan-LLM-7B-v2.0-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.0-chat) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4526
## 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: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.0
- Datasets 2.15.0
- Tokenizers 0.15.0
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Lalith16/Zephyr_Beta_FT_2epochs | Lalith16 | 2023-11-29T10:08:51Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"region:us"
] | 2023-11-29T10:08:51Z | 2023-11-29T10:08:44.000Z | null | null | ---
license: mit
base_model: HuggingFaceH4/zephyr-7b-beta
tags:
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | Lalith16/Zephyr_Beta_FT_2epochs | [
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popeyeGOEL/remi-llama-2-7b-miniguanaco-gguf | popeyeGOEL | 2023-11-29T10:30:06Z | 0 | 0 | null | [
"gguf",
"region:us"
] | 2023-11-29T10:30:06Z | 2023-11-29T10:09:27.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | popeyeGOEL/remi-llama-2-7b-miniguanaco-gguf | [
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Aous/Aymen | Aous | 2023-11-29T10:10:03Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:10:03Z | 2023-11-29T10:10:02.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Aous/Aymen | [
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AhmedTaha012/hadith-finetuned-ner15 | AhmedTaha012 | 2023-11-29T10:10:09Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:10:09Z | 2023-11-29T10:10:09.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | AhmedTaha012/hadith-finetuned-ner15 | [
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julienroulle/wav2vec2-base-finetuned-soundx | julienroulle | 2023-11-29T12:41:30Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T12:41:30Z | 2023-11-29T10:11:17.000Z | null | null | Entry not found | null | transformers | audio-classification | null | null | null | null | null | null | null | null | null | julienroulle/wav2vec2-base-finetuned-soundx | [
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arbitropy/KUETLLM_Zephyr7b_gguf | arbitropy | 2023-11-29T10:23:46Z | 0 | 0 | null | [
"gguf",
"region:us"
] | 2023-11-29T10:23:46Z | 2023-11-29T10:11:45.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | arbitropy/KUETLLM_Zephyr7b_gguf | [
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Aditya455/chmmNNww11 | Aditya455 | 2023-11-29T10:14:55Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:14:55Z | 2023-11-29T10:13:00.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Aditya455/chmmNNww11 | [
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alyazharr/mdeberta_base_v3_2 | alyazharr | 2023-11-29T11:20:01Z | 0 | 0 | null | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:20:01Z | 2023-11-29T10:14:02.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | alyazharr/mdeberta_base_v3_2 | [
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tom192180/my_awesome_model | tom192180 | 2023-11-29T10:16:12Z | 0 | 0 | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:16:12Z | 2023-11-29T10:15:33.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tom192180/my_awesome_model | [
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mcfatbeard/flant5-base_v2 | mcfatbeard | 2023-11-29T10:19:49Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | 2023-11-29T10:19:49Z | 2023-11-29T10:16:32.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mcfatbeard/flant5-base_v2 | [
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kaikaih/nva-riko_ | kaikaih | 2023-11-29T10:17:16Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:17:16Z | 2023-11-29T10:17:12.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | kaikaih/nva-riko_ | [
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csukuangfj/vits-piper-uk_UA-lada-x_low | csukuangfj | 2023-11-29T10:23:13Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T10:23:13Z | 2023-11-29T10:18:27.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-uk_UA-lada-x_low | [
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acalatrava/mlc-chat-MiniChat-3B-q4f16_1 | acalatrava | 2023-11-29T10:42:52Z | 0 | 0 | null | [
"transformers",
"en",
"zh",
"arxiv:2311.07052",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:42:52Z | 2023-11-29T10:18:47.000Z | null | null | ---
license: apache-2.0
language:
- en
- zh
library_name: transformers
widget:
- text: "<s> [|User|] Hi 👋 </s>[|Assistant|]"
---
*NOTE: This is the MLC-LLM version of the following model*
## MiniChat-3B
📑 [arXiv](https://arxiv.org/abs/2311.07052) | 👻 [GitHub](https://github.com/GeneZC/MiniMA) | 🤗 [HuggingFace-MiniMA](https://huggingface.co/GeneZC/MiniMA-3B) | 🤗 [HuggingFace-MiniChat](https://huggingface.co/GeneZC/MiniChat-3B) | 🤗 [HuggingFace-MiniChat-1.5](https://huggingface.co/GeneZC/MiniChat-1.5-3B) | 🤖 [ModelScope-MiniMA](https://modelscope.cn/models/GeneZC/MiniMA-3B) | 🤖 [ModelScope-MiniChat](https://modelscope.cn/models/GeneZC/MiniChat-3B)
🆕 **Updates: MiniChat-1.5-3B**
❗ Must comply with LICENSE of LLaMA2 since it is derived from LLaMA2.
A language model distilled and finetuned from an adapted version of LLaMA2-7B following "Towards the Law of Capacity Gap in Distilling Language Models".
Outperforming a wide range of 3B competitors in GPT4 evaluation and even competing with several 7B chat models.
<img src="./teaser_b.jpg" alt="teaser_b" width="687" />
The following is an example code snippet to use MiniChat-3B:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from conversation import get_default_conv_template
# MiniChat
tokenizer = AutoTokenizer.from_pretrained("GeneZC/MiniChat-3B", use_fast=False)
# GPU.
model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="auto", torch_dtype=torch.float16).eval()
# CPU.
# model = AutoModelForCausalLM.from_pretrained("GeneZC/MiniChat-3B", use_cache=True, device_map="cpu", torch_dtype=torch.float32).eval()
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
conv = get_default_conv_template("minichat")
question = "Implement a program to find the common elements in two arrays without using any extra data structures."
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt = conv.get_prompt()
input_ids = tokenizer([prompt]).input_ids
output_ids = model.generate(
torch.as_tensor(input_ids).to(device),
do_sample=True,
temperature=0.7,
max_new_tokens=1024,
)
output_ids = output_ids[0][len(input_ids[0]):]
output = tokenizer.decode(output_ids, skip_special_tokens=True).strip()
# output: "def common_elements(arr1, arr2):\n if len(arr1) == 0:\n return []\n if len(arr2) == 0:\n return arr1\n\n common_elements = []\n for element in arr1:\n if element in arr2:\n common_elements.append(element)\n\n return common_elements"
# Multiturn conversation could be realized by continuously appending questions to `conv`.
```
## Bibtex
```bibtex
@article{zhang2023law,
title={Towards the Law of Capacity Gap in Distilling Language Models},
author={Zhang, Chen and Song, Dawei and Ye, Zheyu and Gao, Yan},
year={2023},
url={https://arxiv.org/abs/2311.07052}
}
```
# [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_GeneZC__MiniChat-3B)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 42.94 |
| ARC (25-shot) | 44.03 |
| HellaSwag (10-shot) | 67.19 |
| MMLU (5-shot) | 39.17 |
| TruthfulQA (0-shot) | 45.67 |
| Winogrande (5-shot) | 65.27 |
| GSM8K (5-shot) | 10.54 |
| DROP (3-shot) | 28.73 |
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ache0s/Hsj | ache0s | 2023-11-29T10:23:29Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T10:23:29Z | 2023-11-29T10:23:29.000Z | null | null | ---
license: openrail
---
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mcfatbeard/flant5-base_v3 | mcfatbeard | 2023-11-29T13:42:56Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | 2023-11-29T13:42:56Z | 2023-11-29T10:23:48.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mcfatbeard/flant5-base_v3 | [
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tom192180/distilbert-base-uncased_tvl_zephyr_0shot | tom192180 | 2023-11-29T13:46:39Z | 0 | 0 | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:46:39Z | 2023-11-29T10:24:22.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | tom192180/distilbert-base-uncased_tvl_zephyr_0shot | [
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csukuangfj/vits-piper-uk_UA-ukrainian_tts-medium | csukuangfj | 2023-11-29T10:25:57Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T10:25:57Z | 2023-11-29T10:24:31.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-uk_UA-ukrainian_tts-medium | [
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Deni016/jorge | Deni016 | 2023-11-29T10:25:48Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T10:25:48Z | 2023-11-29T10:25:09.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Deni016/jorge | [
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leibd/ladies | leibd | 2023-11-29T10:26:34Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:26:34Z | 2023-11-29T10:26:34.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | leibd/ladies | [
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honeyybadger/code-search-net-tokenizer | honeyybadger | 2023-11-29T10:27:22Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:27:22Z | 2023-11-29T10:27:21.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | honeyybadger/code-search-net-tokenizer | [
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sheduele/mbert_ver_3_task_1A | sheduele | 2023-11-29T12:32:15Z | 0 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T12:32:15Z | 2023-11-29T10:29:08.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | sheduele/mbert_ver_3_task_1A | [
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jadasdn/wav2vec2-2 | jadasdn | 2023-11-29T18:07:49Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:jadasdn/wav2vec2-1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T18:07:49Z | 2023-11-29T10:29:59.000Z | null | null | ---
license: apache-2.0
base_model: jadasdn/wav2vec2-1
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-2
This model is a fine-tuned version of [jadasdn/wav2vec2-1](https://huggingface.co/jadasdn/wav2vec2-1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8353
- Wer: 0.3593
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4516 | 0.5 | 500 | 0.4973 | 0.3748 |
| 0.4624 | 1.0 | 1000 | 0.4486 | 0.3958 |
| 0.5211 | 1.5 | 1500 | 0.5173 | 0.3916 |
| 0.5317 | 2.0 | 2000 | 0.4713 | 0.3992 |
| 0.4277 | 2.5 | 2500 | 0.4859 | 0.3888 |
| 0.4495 | 3.0 | 3000 | 0.4962 | 0.3862 |
| 0.3712 | 3.5 | 3500 | 0.5237 | 0.3899 |
| 0.3855 | 4.0 | 4000 | 0.4975 | 0.3850 |
| 0.3254 | 4.5 | 4500 | 0.5405 | 0.3897 |
| 0.331 | 5.0 | 5000 | 0.5255 | 0.3950 |
| 0.2907 | 5.5 | 5500 | 0.5646 | 0.3852 |
| 0.2949 | 6.0 | 6000 | 0.5782 | 0.3965 |
| 0.2521 | 6.5 | 6500 | 0.5563 | 0.3879 |
| 0.2663 | 7.0 | 7000 | 0.5627 | 0.3829 |
| 0.2342 | 7.5 | 7500 | 0.6145 | 0.3872 |
| 0.2374 | 8.0 | 8000 | 0.5860 | 0.3883 |
| 0.2099 | 8.5 | 8500 | 0.6920 | 0.3810 |
| 0.2133 | 9.0 | 9000 | 0.6354 | 0.3895 |
| 0.1887 | 9.5 | 9500 | 0.6618 | 0.3813 |
| 0.1924 | 10.0 | 10000 | 0.6522 | 0.3850 |
| 0.1728 | 10.5 | 10500 | 0.6324 | 0.3813 |
| 0.1797 | 11.0 | 11000 | 0.6637 | 0.3882 |
| 0.163 | 11.5 | 11500 | 0.6806 | 0.3799 |
| 0.1623 | 12.0 | 12000 | 0.6801 | 0.3811 |
| 0.149 | 12.5 | 12500 | 0.6723 | 0.3832 |
| 0.1493 | 13.0 | 13000 | 0.7032 | 0.3888 |
| 0.1389 | 13.5 | 13500 | 0.7294 | 0.3793 |
| 0.1383 | 14.0 | 14000 | 0.7311 | 0.3800 |
| 0.127 | 14.5 | 14500 | 0.7088 | 0.3773 |
| 0.127 | 15.0 | 15000 | 0.7352 | 0.3775 |
| 0.1159 | 15.5 | 15500 | 0.7886 | 0.3792 |
| 0.114 | 16.0 | 16000 | 0.7582 | 0.3802 |
| 0.1103 | 16.5 | 16500 | 0.7662 | 0.3717 |
| 0.1088 | 17.0 | 17000 | 0.7855 | 0.3704 |
| 0.1021 | 17.5 | 17500 | 0.7326 | 0.3717 |
| 0.104 | 18.0 | 18000 | 0.7518 | 0.3723 |
| 0.096 | 18.5 | 18500 | 0.7468 | 0.3743 |
| 0.0914 | 19.0 | 19000 | 0.7906 | 0.3741 |
| 0.0881 | 19.5 | 19500 | 0.7879 | 0.3740 |
| 0.0908 | 20.0 | 20000 | 0.8111 | 0.3676 |
| 0.0832 | 20.5 | 20500 | 0.8114 | 0.3681 |
| 0.0848 | 21.0 | 21000 | 0.8178 | 0.3651 |
| 0.0762 | 21.5 | 21500 | 0.8212 | 0.3686 |
| 0.0728 | 22.0 | 22000 | 0.8142 | 0.3673 |
| 0.074 | 22.5 | 22500 | 0.8177 | 0.3666 |
| 0.0691 | 23.0 | 23000 | 0.8323 | 0.3662 |
| 0.0689 | 23.5 | 23500 | 0.8020 | 0.3678 |
| 0.0643 | 24.0 | 24000 | 0.8145 | 0.3653 |
| 0.0647 | 24.5 | 24500 | 0.8376 | 0.3594 |
| 0.0654 | 25.0 | 25000 | 0.8307 | 0.3608 |
| 0.061 | 25.5 | 25500 | 0.8432 | 0.3600 |
| 0.0573 | 26.0 | 26000 | 0.8361 | 0.3629 |
| 0.0583 | 26.5 | 26500 | 0.8363 | 0.3625 |
| 0.054 | 27.0 | 27000 | 0.8277 | 0.3625 |
| 0.058 | 27.5 | 27500 | 0.8354 | 0.3614 |
| 0.0531 | 28.0 | 28000 | 0.8363 | 0.3595 |
| 0.0522 | 28.5 | 28500 | 0.8429 | 0.3588 |
| 0.0503 | 29.0 | 29000 | 0.8267 | 0.3595 |
| 0.0504 | 29.5 | 29500 | 0.8401 | 0.3597 |
| 0.0511 | 30.0 | 30000 | 0.8353 | 0.3593 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | jadasdn/wav2vec2-2 | [
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hkivancoral/hushem_5x_beit_base_rms_00001_fold4 | hkivancoral | 2023-11-29T11:03:58Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:03:58Z | 2023-11-29T10:31:03.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_rms_00001_fold4
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9047619047619048
---
<!-- 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. -->
# hushem_5x_beit_base_rms_00001_fold4
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4242
- Accuracy: 0.9048
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7963 | 1.0 | 28 | 0.5873 | 0.8095 |
| 0.1378 | 2.0 | 56 | 0.2600 | 0.9048 |
| 0.0372 | 3.0 | 84 | 0.1249 | 0.9286 |
| 0.0142 | 4.0 | 112 | 0.1881 | 0.9048 |
| 0.0031 | 5.0 | 140 | 0.2720 | 0.9524 |
| 0.0011 | 6.0 | 168 | 0.2309 | 0.9286 |
| 0.0018 | 7.0 | 196 | 0.3809 | 0.9048 |
| 0.0008 | 8.0 | 224 | 0.3332 | 0.9048 |
| 0.0014 | 9.0 | 252 | 0.3365 | 0.8810 |
| 0.0123 | 10.0 | 280 | 0.2089 | 0.9286 |
| 0.0005 | 11.0 | 308 | 0.1962 | 0.9286 |
| 0.0038 | 12.0 | 336 | 0.2845 | 0.9048 |
| 0.0078 | 13.0 | 364 | 0.2498 | 0.9048 |
| 0.001 | 14.0 | 392 | 0.0353 | 1.0 |
| 0.0002 | 15.0 | 420 | 0.1604 | 0.9286 |
| 0.0003 | 16.0 | 448 | 0.6770 | 0.8810 |
| 0.0002 | 17.0 | 476 | 0.3566 | 0.9048 |
| 0.0001 | 18.0 | 504 | 0.1974 | 0.8810 |
| 0.0004 | 19.0 | 532 | 0.0247 | 1.0 |
| 0.0001 | 20.0 | 560 | 0.0905 | 0.9286 |
| 0.0001 | 21.0 | 588 | 0.1806 | 0.9286 |
| 0.0011 | 22.0 | 616 | 0.2156 | 0.9524 |
| 0.0007 | 23.0 | 644 | 0.4203 | 0.9286 |
| 0.0002 | 24.0 | 672 | 0.2731 | 0.9286 |
| 0.0054 | 25.0 | 700 | 0.2589 | 0.8810 |
| 0.0001 | 26.0 | 728 | 0.2893 | 0.9048 |
| 0.0 | 27.0 | 756 | 0.3737 | 0.8810 |
| 0.0002 | 28.0 | 784 | 0.3310 | 0.9048 |
| 0.0001 | 29.0 | 812 | 0.2394 | 0.9048 |
| 0.0 | 30.0 | 840 | 0.2320 | 0.9048 |
| 0.0001 | 31.0 | 868 | 0.2751 | 0.9048 |
| 0.0012 | 32.0 | 896 | 0.2756 | 0.9048 |
| 0.0 | 33.0 | 924 | 0.1983 | 0.9048 |
| 0.0001 | 34.0 | 952 | 0.1565 | 0.9048 |
| 0.0 | 35.0 | 980 | 0.1912 | 0.9048 |
| 0.0001 | 36.0 | 1008 | 0.2103 | 0.9048 |
| 0.0 | 37.0 | 1036 | 0.1693 | 0.9048 |
| 0.0 | 38.0 | 1064 | 0.1895 | 0.9048 |
| 0.0 | 39.0 | 1092 | 0.2300 | 0.9048 |
| 0.0018 | 40.0 | 1120 | 0.7391 | 0.9048 |
| 0.0 | 41.0 | 1148 | 0.6660 | 0.9048 |
| 0.0 | 42.0 | 1176 | 0.5981 | 0.9048 |
| 0.0001 | 43.0 | 1204 | 0.6379 | 0.9048 |
| 0.0001 | 44.0 | 1232 | 0.5736 | 0.9048 |
| 0.0002 | 45.0 | 1260 | 0.4940 | 0.9048 |
| 0.0001 | 46.0 | 1288 | 0.4348 | 0.9048 |
| 0.0001 | 47.0 | 1316 | 0.4551 | 0.9048 |
| 0.0 | 48.0 | 1344 | 0.4241 | 0.9048 |
| 0.0026 | 49.0 | 1372 | 0.4242 | 0.9048 |
| 0.0 | 50.0 | 1400 | 0.4242 | 0.9048 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | image-classification | null | null | null | null | null | null | null | null | null | hkivancoral/hushem_5x_beit_base_rms_00001_fold4 | [
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csukuangfj/vits-piper-ru_RU-denis-medium | csukuangfj | 2023-11-29T10:43:43Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T10:43:43Z | 2023-11-29T10:35:09.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-ru_RU-denis-medium | [
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nijoow/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 | nijoow | 2023-11-29T14:31:42Z | 0 | 0 | null | [
"transformers",
"safetensors",
"git",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:31:42Z | 2023-11-29T10:35:55.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | nijoow/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 | [
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marcuschuawy/roberta-base_twitter_disaster | marcuschuawy | 2023-11-29T10:37:14Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:37:14Z | 2023-11-29T10:37:14.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | marcuschuawy/roberta-base_twitter_disaster | [
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xykkndjq/instruct-pix2pix-model | xykkndjq | 2023-11-30T00:27:30Z | 0 | 0 | null | [
"diffusers",
"tensorboard",
"diffusers:StableDiffusionInstructPix2PixPipeline",
"region:us"
] | 2023-11-30T00:27:30Z | 2023-11-29T10:38:29.000Z | null | null | Entry not found | null | diffusers | null | null | null | null | null | null | null | null | null | null | xykkndjq/instruct-pix2pix-model | [
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chandra21/xlsr_hindi | chandra21 | 2023-11-29T13:36:35Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_11_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:36:35Z | 2023-11-29T10:39:17.000Z | null | null | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
model-index:
- name: xlsr_hindi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlsr_hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_11_0 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1826
- eval_wer: 0.1940
- eval_runtime: 236.1561
- eval_samples_per_second: 12.255
- eval_steps_per_second: 1.533
- epoch: 16.22
- step: 2400
## 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: 10
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 30
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | chandra21/xlsr_hindi | [
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csukuangfj/vits-piper-ru_RU-dmitri-medium | csukuangfj | 2023-11-29T10:43:44Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T10:43:44Z | 2023-11-29T10:39:34.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-ru_RU-dmitri-medium | [
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Ema10/cragger | Ema10 | 2023-11-29T10:41:58Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T10:41:58Z | 2023-11-29T10:40:07.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Ema10/cragger | [
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csukuangfj/vits-piper-ru_RU-irina-medium | csukuangfj | 2023-11-29T10:43:49Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T10:43:49Z | 2023-11-29T10:40:13.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-ru_RU-irina-medium | [
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csukuangfj/vits-piper-ru_RU-ruslan-medium | csukuangfj | 2023-11-29T10:43:45Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T10:43:45Z | 2023-11-29T10:40:51.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-ru_RU-ruslan-medium | [
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christinacdl/flan_T5_hate_multilingual | christinacdl | 2023-11-29T14:46:59Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T14:46:59Z | 2023-11-29T10:41:08.000Z | null | null | ---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
model-index:
- name: flan_T5_hate_multilingual
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# flan_T5_hate_multilingual
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) 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: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu121
- Datasets 2.13.1
- Tokenizers 0.14.1
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Lin0He/text-summary-gpt2-short | Lin0He | 2023-11-29T20:36:14Z | 0 | 0 | null | [
"summarization",
"license:mit",
"region:us"
] | 2023-11-29T20:36:14Z | 2023-11-29T10:41:50.000Z | null | null | ---
license: mit
pipeline_tag: summarization
--- | null | null | summarization | null | null | null | null | null | null | null | null | null | Lin0He/text-summary-gpt2-short | [
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MGDragon/SensitiveDataQLora1 | MGDragon | 2023-11-29T11:09:03Z | 0 | 0 | null | [
"tensorboard",
"generated_from_trainer",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"license:apache-2.0",
"region:us"
] | 2023-11-29T11:09:03Z | 2023-11-29T10:44:23.000Z | null | null | ---
license: apache-2.0
base_model: vilsonrodrigues/falcon-7b-instruct-sharded
tags:
- generated_from_trainer
model-index:
- name: SensitiveDataQLora1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SensitiveDataQLora1
This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Mlxa/tuned-flat-flat_shuffle | Mlxa | 2023-11-29T10:44:28Z | 0 | 0 | null | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:44:28Z | 2023-11-29T10:44:26.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Mlxa/tuned-flat-flat_shuffle | [
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t800se/Dreambooth_trained_models | t800se | 2023-11-29T14:32:09Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:32:09Z | 2023-11-29T10:44:46.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | t800se/Dreambooth_trained_models | [
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Areeb123/mt5-small-finetuned_samsum_summarization_model | Areeb123 | 2023-11-29T10:44:59Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:44:59Z | 2023-11-29T10:44:59.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Areeb123/mt5-small-finetuned_samsum_summarization_model | [
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rmv1971/Mani | rmv1971 | 2023-11-29T10:47:13Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:47:13Z | 2023-11-29T10:47:13.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | rmv1971/Mani | [
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jadasdn/open-ai-small-2 | jadasdn | 2023-11-29T18:19:00Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:jadasdn/open-ai-small-1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T18:19:00Z | 2023-11-29T10:48:37.000Z | null | null | ---
license: apache-2.0
base_model: jadasdn/open-ai-small-1
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: open-ai-small-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# open-ai-small-2
This model is a fine-tuned version of [jadasdn/open-ai-small-1](https://huggingface.co/jadasdn/open-ai-small-1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3953
- Wer: 21.4497
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1941 | 2.0 | 1000 | 0.2854 | 21.5383 |
| 0.0212 | 4.0 | 2000 | 0.3384 | 18.9827 |
| 0.0032 | 6.0 | 3000 | 0.3801 | 20.4698 |
| 0.0015 | 8.0 | 4000 | 0.3953 | 21.4497 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | jadasdn/open-ai-small-2 | [
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bumblebee-testing/tiny-random-LlamaModel | bumblebee-testing | 2023-11-29T10:54:10Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"feature-extraction",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T10:54:10Z | 2023-11-29T10:49:43.000Z | null | null | Entry not found | null | transformers | feature-extraction | null | null | null | null | null | null | null | null | null | bumblebee-testing/tiny-random-LlamaModel | [
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bumblebee-testing/tiny-random-LlamaForSequenceClassification | bumblebee-testing | 2023-11-29T10:54:16Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-classification",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T10:54:16Z | 2023-11-29T10:49:49.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | bumblebee-testing/tiny-random-LlamaForSequenceClassification | [
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Ema10/laval | Ema10 | 2023-11-29T10:51:00Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T10:51:00Z | 2023-11-29T10:49:56.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Ema10/laval | [
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robocan/my_awesome_eli5_clm-model | robocan | 2023-11-29T10:50:36Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:50:36Z | 2023-11-29T10:50:36.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | robocan/my_awesome_eli5_clm-model | [
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Q-bert/outputs | Q-bert | 2023-11-29T10:51:42Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:51:42Z | 2023-11-29T10:51:42.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Q-bert/outputs | [
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Nishant2609/chatbot | Nishant2609 | 2023-11-29T10:52:03Z | 0 | 0 | null | [
"region:us"
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oliverjthomas2000/panther-alpha-tau | oliverjthomas2000 | 2023-11-29T11:04:49Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T11:04:49Z | 2023-11-29T10:53:24.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | oliverjthomas2000/panther-alpha-tau | [
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zjunlp/knowlm-7b-chat | zjunlp | 2023-11-29T11:08:11Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T11:08:11Z | 2023-11-29T10:53:43.000Z | null | null | ---
license: apache-2.0
---
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | zjunlp/knowlm-7b-chat | [
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Tachi67/CodeFileEditFlowModule | Tachi67 | 2023-11-29T21:57:46Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T21:57:46Z | 2023-11-29T10:55:40.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | Tachi67/CodeFileEditFlowModule | [
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alyazharr/mdeberta_base_v3_3 | alyazharr | 2023-11-29T10:57:23Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T10:57:23Z | 2023-11-29T10:57:23.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | alyazharr/mdeberta_base_v3_3 | [
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Realgon/roberta_sst2_padding70model | Realgon | 2023-11-29T12:04:35Z | 0 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T12:04:35Z | 2023-11-29T10:58:41.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst2_padding70model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_sst2_padding70model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5675
- Accuracy: 0.9412
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.1869 | 0.9308 |
| 0.3423 | 2.0 | 866 | 0.4539 | 0.9039 |
| 0.2033 | 3.0 | 1299 | 0.2940 | 0.9379 |
| 0.1087 | 4.0 | 1732 | 0.3001 | 0.9396 |
| 0.0742 | 5.0 | 2165 | 0.3625 | 0.9379 |
| 0.0511 | 6.0 | 2598 | 0.4227 | 0.9407 |
| 0.028 | 7.0 | 3031 | 0.4785 | 0.9352 |
| 0.028 | 8.0 | 3464 | 0.3861 | 0.9412 |
| 0.023 | 9.0 | 3897 | 0.4512 | 0.9407 |
| 0.0201 | 10.0 | 4330 | 0.5132 | 0.9368 |
| 0.0123 | 11.0 | 4763 | 0.4452 | 0.9423 |
| 0.0115 | 12.0 | 5196 | 0.4293 | 0.9500 |
| 0.0071 | 13.0 | 5629 | 0.7280 | 0.9182 |
| 0.0186 | 14.0 | 6062 | 0.5646 | 0.9368 |
| 0.0186 | 15.0 | 6495 | 0.5034 | 0.9434 |
| 0.0049 | 16.0 | 6928 | 0.5029 | 0.9418 |
| 0.0078 | 17.0 | 7361 | 0.4935 | 0.9456 |
| 0.0039 | 18.0 | 7794 | 0.5453 | 0.9418 |
| 0.0027 | 19.0 | 8227 | 0.5793 | 0.9385 |
| 0.0006 | 20.0 | 8660 | 0.5675 | 0.9412 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/roberta_sst2_padding70model | [
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xia0t1an/sms-spam-model-tf | xia0t1an | 2023-11-30T01:26:25Z | 0 | 0 | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:26:25Z | 2023-11-29T11:00:25.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: xia0t1an/sms-spam-model-tf
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# xia0t1an/sms-spam-model-tf
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1311
- Validation Loss: 0.1352
- Train Accuracy: 0.9605
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 8130, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.1385 | 0.1352 | 0.9605 | 0 |
| 0.1311 | 0.1352 | 0.9605 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.10.0
- Datasets 2.15.0
- Tokenizers 0.15.0
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meetplace1/classificationlora | meetplace1 | 2023-11-29T11:02:26Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T11:02:26Z | 2023-11-29T11:00:32.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | meetplace1/classificationlora | [
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onizukal/SMIDS_10x_beit_base_adamax_lr00001_fold5 | onizukal | 2023-11-29T15:04:30Z | 0 | 0 | null | [
"transformers",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T15:04:30Z | 2023-11-29T11:01:18.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: SMIDS_10x_beit_base_adamax_lr00001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.915
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SMIDS_10x_beit_base_adamax_lr00001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8027
- Accuracy: 0.915
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2656 | 1.0 | 750 | 0.2436 | 0.91 |
| 0.1527 | 2.0 | 1500 | 0.2225 | 0.91 |
| 0.1164 | 3.0 | 2250 | 0.2325 | 0.9133 |
| 0.0862 | 4.0 | 3000 | 0.3140 | 0.9067 |
| 0.0944 | 5.0 | 3750 | 0.3351 | 0.9167 |
| 0.0582 | 6.0 | 4500 | 0.3614 | 0.915 |
| 0.0113 | 7.0 | 5250 | 0.4397 | 0.91 |
| 0.0179 | 8.0 | 6000 | 0.4880 | 0.9033 |
| 0.0353 | 9.0 | 6750 | 0.4984 | 0.9117 |
| 0.0412 | 10.0 | 7500 | 0.6099 | 0.9117 |
| 0.0143 | 11.0 | 8250 | 0.7086 | 0.8967 |
| 0.0135 | 12.0 | 9000 | 0.6359 | 0.91 |
| 0.0006 | 13.0 | 9750 | 0.6999 | 0.905 |
| 0.0014 | 14.0 | 10500 | 0.6181 | 0.9117 |
| 0.0008 | 15.0 | 11250 | 0.6822 | 0.9067 |
| 0.0174 | 16.0 | 12000 | 0.7225 | 0.9133 |
| 0.0097 | 17.0 | 12750 | 0.7261 | 0.9133 |
| 0.0036 | 18.0 | 13500 | 0.6940 | 0.9133 |
| 0.0001 | 19.0 | 14250 | 0.6758 | 0.915 |
| 0.0017 | 20.0 | 15000 | 0.6780 | 0.915 |
| 0.0002 | 21.0 | 15750 | 0.6936 | 0.9117 |
| 0.0101 | 22.0 | 16500 | 0.7415 | 0.9067 |
| 0.0297 | 23.0 | 17250 | 0.7382 | 0.9133 |
| 0.0159 | 24.0 | 18000 | 0.7558 | 0.9133 |
| 0.0094 | 25.0 | 18750 | 0.6971 | 0.915 |
| 0.0001 | 26.0 | 19500 | 0.7330 | 0.915 |
| 0.0408 | 27.0 | 20250 | 0.8217 | 0.9067 |
| 0.0046 | 28.0 | 21000 | 0.7818 | 0.9117 |
| 0.0 | 29.0 | 21750 | 0.7247 | 0.9133 |
| 0.0119 | 30.0 | 22500 | 0.7522 | 0.915 |
| 0.003 | 31.0 | 23250 | 0.8172 | 0.91 |
| 0.0001 | 32.0 | 24000 | 0.7301 | 0.9183 |
| 0.0013 | 33.0 | 24750 | 0.7150 | 0.915 |
| 0.0007 | 34.0 | 25500 | 0.7996 | 0.915 |
| 0.0 | 35.0 | 26250 | 0.8170 | 0.91 |
| 0.0011 | 36.0 | 27000 | 0.8219 | 0.9067 |
| 0.0002 | 37.0 | 27750 | 0.7587 | 0.9167 |
| 0.0011 | 38.0 | 28500 | 0.8565 | 0.905 |
| 0.0362 | 39.0 | 29250 | 0.7660 | 0.9133 |
| 0.0044 | 40.0 | 30000 | 0.7815 | 0.9133 |
| 0.0171 | 41.0 | 30750 | 0.7927 | 0.9067 |
| 0.0 | 42.0 | 31500 | 0.7914 | 0.915 |
| 0.0217 | 43.0 | 32250 | 0.7637 | 0.9133 |
| 0.0001 | 44.0 | 33000 | 0.7931 | 0.91 |
| 0.0 | 45.0 | 33750 | 0.8007 | 0.9117 |
| 0.005 | 46.0 | 34500 | 0.8261 | 0.9083 |
| 0.0 | 47.0 | 35250 | 0.8218 | 0.91 |
| 0.0 | 48.0 | 36000 | 0.7908 | 0.915 |
| 0.0351 | 49.0 | 36750 | 0.8032 | 0.915 |
| 0.0 | 50.0 | 37500 | 0.8027 | 0.915 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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Felladrin/onnx-LaMini-Neo-125M-Evol-Instruct | Felladrin | 2023-11-29T11:05:06Z | 0 | 1 | null | [
"transformers.js",
"onnx",
"gpt_neo",
"text-generation",
"base_model:Felladrin/LaMini-Neo-125M-Evol-Instruct",
"license:cc-by-nc-4.0",
"region:us"
] | 2023-11-29T11:05:06Z | 2023-11-29T11:02:33.000Z | null | null | ---
license: cc-by-nc-4.0
library_name: "transformers.js"
base_model: Felladrin/LaMini-Neo-125M-Evol-Instruct
---
INT8 ONNX version of [Felladrin/LaMini-Neo-125M-Evol-Instruct](https://huggingface.co/Felladrin/LaMini-Neo-125M-Evol-Instruct) to use with [Transformers.js](https://huggingface.co/docs/transformers.js).
| null | transformers.js | text-generation | null | null | null | null | null | null | null | null | null | Felladrin/onnx-LaMini-Neo-125M-Evol-Instruct | [
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Sneka/mistral-test-finetune | Sneka | 2023-11-29T11:02:59Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | 2023-11-29T11:02:59Z | 2023-11-29T11:02:45.000Z | null | null | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.1
tags:
- generated_from_trainer
model-index:
- name: mistral-test-finetune
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-test-finetune
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8121
## 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: 2.5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 18
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 5
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6808 | 0.57 | 50 | 1.2276 |
| 1.0885 | 1.14 | 100 | 1.0376 |
| 0.8412 | 1.71 | 150 | 0.9040 |
| 0.7611 | 2.29 | 200 | 0.8607 |
| 0.6751 | 2.86 | 250 | 0.8183 |
| 0.6346 | 3.43 | 300 | 0.7849 |
| 0.5533 | 4.0 | 350 | 0.7519 |
| 0.48 | 4.57 | 400 | 0.7388 |
| 0.4358 | 5.14 | 450 | 0.7579 |
| 0.3733 | 5.71 | 500 | 0.7200 |
| 0.3463 | 6.29 | 550 | 0.7373 |
| 0.3031 | 6.86 | 600 | 0.7091 |
| 0.2735 | 7.43 | 650 | 0.7422 |
| 0.2602 | 8.0 | 700 | 0.7170 |
| 0.212 | 8.57 | 750 | 0.7628 |
| 0.2157 | 9.14 | 800 | 0.7739 |
| 0.1905 | 9.71 | 850 | 0.7628 |
| 0.1847 | 10.29 | 900 | 0.7887 |
| 0.1816 | 10.86 | 950 | 0.7831 |
| 0.1605 | 11.43 | 1000 | 0.8121 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | Sneka/mistral-test-finetune | [
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gelbanna/wav2vec2_base-inputwaveform-ls_960-e196-lr_fixed5e-05-gradacc3-gradclip0-rs42-maskprobs0.65 | gelbanna | 2023-11-29T11:03:32Z | 0 | 0 | null | [
"transformers",
"pytorch",
"wav2vec2",
"pretraining",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:03:32Z | 2023-11-29T11:03:15.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | gelbanna/wav2vec2_base-inputwaveform-ls_960-e196-lr_fixed5e-05-gradacc3-gradclip0-rs42-maskprobs0.65 | [
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hkivancoral/hushem_5x_beit_base_rms_00001_fold5 | hkivancoral | 2023-11-29T11:37:49Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:37:49Z | 2023-11-29T11:04:46.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: hushem_5x_beit_base_rms_00001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8048780487804879
---
<!-- 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. -->
# hushem_5x_beit_base_rms_00001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1845
- Accuracy: 0.8049
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9367 | 1.0 | 28 | 0.6533 | 0.7073 |
| 0.1926 | 2.0 | 56 | 0.5512 | 0.7805 |
| 0.047 | 3.0 | 84 | 0.6007 | 0.8049 |
| 0.0193 | 4.0 | 112 | 0.2590 | 0.9024 |
| 0.0089 | 5.0 | 140 | 0.4654 | 0.8293 |
| 0.0038 | 6.0 | 168 | 0.5932 | 0.8293 |
| 0.0017 | 7.0 | 196 | 0.6877 | 0.8293 |
| 0.0014 | 8.0 | 224 | 0.7982 | 0.8049 |
| 0.0007 | 9.0 | 252 | 0.6044 | 0.8293 |
| 0.0007 | 10.0 | 280 | 0.6788 | 0.8537 |
| 0.0003 | 11.0 | 308 | 0.6662 | 0.8537 |
| 0.0003 | 12.0 | 336 | 0.6588 | 0.8537 |
| 0.0002 | 13.0 | 364 | 0.6343 | 0.8293 |
| 0.0046 | 14.0 | 392 | 1.0649 | 0.7805 |
| 0.0012 | 15.0 | 420 | 0.7359 | 0.8293 |
| 0.0005 | 16.0 | 448 | 0.7345 | 0.8293 |
| 0.0066 | 17.0 | 476 | 0.7816 | 0.8537 |
| 0.0014 | 18.0 | 504 | 0.6553 | 0.8780 |
| 0.0003 | 19.0 | 532 | 0.5879 | 0.8780 |
| 0.0001 | 20.0 | 560 | 0.6539 | 0.8537 |
| 0.0001 | 21.0 | 588 | 0.5762 | 0.8293 |
| 0.0006 | 22.0 | 616 | 0.3307 | 0.8293 |
| 0.0001 | 23.0 | 644 | 0.6447 | 0.8293 |
| 0.0002 | 24.0 | 672 | 0.7471 | 0.8537 |
| 0.0002 | 25.0 | 700 | 0.6200 | 0.8537 |
| 0.0001 | 26.0 | 728 | 0.9057 | 0.8537 |
| 0.0001 | 27.0 | 756 | 0.8578 | 0.8537 |
| 0.0004 | 28.0 | 784 | 0.7354 | 0.8537 |
| 0.0001 | 29.0 | 812 | 0.8285 | 0.8537 |
| 0.0004 | 30.0 | 840 | 0.7442 | 0.8780 |
| 0.0001 | 31.0 | 868 | 0.9315 | 0.8049 |
| 0.0002 | 32.0 | 896 | 1.0255 | 0.8049 |
| 0.0 | 33.0 | 924 | 1.0401 | 0.7805 |
| 0.0001 | 34.0 | 952 | 1.0520 | 0.8293 |
| 0.0004 | 35.0 | 980 | 0.9869 | 0.8537 |
| 0.0 | 36.0 | 1008 | 0.9764 | 0.8537 |
| 0.0001 | 37.0 | 1036 | 0.9356 | 0.8537 |
| 0.0001 | 38.0 | 1064 | 1.1522 | 0.8049 |
| 0.0 | 39.0 | 1092 | 1.0978 | 0.8049 |
| 0.0005 | 40.0 | 1120 | 1.0647 | 0.8293 |
| 0.0003 | 41.0 | 1148 | 1.2331 | 0.8049 |
| 0.0 | 42.0 | 1176 | 1.3110 | 0.8049 |
| 0.0 | 43.0 | 1204 | 1.2050 | 0.8049 |
| 0.0 | 44.0 | 1232 | 1.1647 | 0.8049 |
| 0.0002 | 45.0 | 1260 | 1.2154 | 0.8049 |
| 0.0001 | 46.0 | 1288 | 1.2000 | 0.8049 |
| 0.0001 | 47.0 | 1316 | 1.1915 | 0.8049 |
| 0.0 | 48.0 | 1344 | 1.1844 | 0.8049 |
| 0.0001 | 49.0 | 1372 | 1.1845 | 0.8049 |
| 0.0 | 50.0 | 1400 | 1.1845 | 0.8049 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | image-classification | null | null | null | null | null | null | null | null | null | hkivancoral/hushem_5x_beit_base_rms_00001_fold5 | [
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csukuangfj/vits-piper-ar_JO-kareem-low | csukuangfj | 2023-11-29T11:09:46Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T11:09:46Z | 2023-11-29T11:06:08.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-ar_JO-kareem-low | [
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csukuangfj/vits-piper-ar_JO-kareem-medium | csukuangfj | 2023-11-29T11:09:46Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T11:09:46Z | 2023-11-29T11:06:34.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-ar_JO-kareem-medium | [
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Zengwei/icefall-tts-ljspeech-vits-2023-11-29 | Zengwei | 2023-11-29T11:42:59Z | 0 | 0 | null | [
"tensorboard",
"onnx",
"region:us"
] | 2023-11-29T11:42:59Z | 2023-11-29T11:08:02.000Z | null | null | See https://github.com/k2-fsa/icefall/pull/1372
| null | null | null | null | null | null | null | null | null | null | null | null | Zengwei/icefall-tts-ljspeech-vits-2023-11-29 | [
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danu9327/individual_end_of_semester_project_data_finetuning | danu9327 | 2023-11-29T11:08:32Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T11:08:32Z | 2023-11-29T11:08:32.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | danu9327/individual_end_of_semester_project_data_finetuning | [
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nhanv/kandi22-decoder-banner-model | nhanv | 2023-11-29T11:09:17Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T11:09:17Z | 2023-11-29T11:09:17.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | nhanv/kandi22-decoder-banner-model | [
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ybWang/distilgpt2-finetuned-wikitext2 | ybWang | 2023-11-29T15:06:52Z | 0 | 0 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:06:52Z | 2023-11-29T11:14:14.000Z | null | null | ---
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1293
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1387 | 1.0 | 2334 | 3.1259 |
| 2.9604 | 2.0 | 4668 | 3.1257 |
| 2.8686 | 3.0 | 7002 | 3.1293 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.14.7
- Tokenizers 0.13.3
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | ybWang/distilgpt2-finetuned-wikitext2 | [
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decem/test-model | decem | 2023-11-29T11:44:25Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | 2023-11-29T11:44:25Z | 2023-11-29T11:14:49.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | decem/test-model | [
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alyazharr/mdeberta_base_v3_4 | alyazharr | 2023-11-29T11:21:27Z | 0 | 0 | null | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:21:27Z | 2023-11-29T11:20:09.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | alyazharr/mdeberta_base_v3_4 | [
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DaisyStar004/ChatGLM2-ELEC | DaisyStar004 | 2023-11-29T11:31:22Z | 0 | 0 | null | [
"transformers",
"pytorch",
"chatglm",
"feature-extraction",
"custom_code",
"region:us"
] | 2023-11-29T11:31:22Z | 2023-11-29T11:20:59.000Z | null | null | Entry not found | null | transformers | feature-extraction | null | null | null | null | null | null | null | null | null | DaisyStar004/ChatGLM2-ELEC | [
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alyazharr/mdeberta_base_v3_5 | alyazharr | 2023-11-29T11:22:37Z | 0 | 0 | null | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:22:37Z | 2023-11-29T11:21:18.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | alyazharr/mdeberta_base_v3_5 | [
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Parikshith/ha-en-incremental-0.8-finetune-og-0.9-improve-1 | Parikshith | 2023-11-29T11:22:42Z | 0 | 0 | null | [
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:22:42Z | 2023-11-29T11:21:38.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | Parikshith/ha-en-incremental-0.8-finetune-og-0.9-improve-1 | [
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csukuangfj/vits-piper-ca_ES-upc_ona-medium | csukuangfj | 2023-11-29T11:25:44Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T11:25:44Z | 2023-11-29T11:21:42.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-ca_ES-upc_ona-medium | [
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csukuangfj/vits-piper-ca_ES-upc_ona-x_low | csukuangfj | 2023-11-29T11:25:39Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T11:25:39Z | 2023-11-29T11:22:02.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-ca_ES-upc_ona-x_low | [
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csukuangfj/vits-piper-ca_ES-upc_pau-x_low | csukuangfj | 2023-11-29T11:25:41Z | 0 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T11:25:41Z | 2023-11-29T11:22:50.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-ca_ES-upc_pau-x_low | [
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pnikoulis/q-FrozenLake-v1-4x4-noSlippery | pnikoulis | 2023-11-29T11:23:46Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T11:23:46Z | 2023-11-29T11:23:44.000Z | null | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="pnikoulis/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | pnikoulis/q-FrozenLake-v1-4x4-noSlippery | [
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abhi1051/falcon-7b-peft-lora | abhi1051 | 2023-11-29T11:24:05Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T11:24:05Z | 2023-11-29T11:24:05.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | abhi1051/falcon-7b-peft-lora | [
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Jalulu/Ava | Jalulu | 2023-11-29T11:26:02Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T11:26:02Z | 2023-11-29T11:26:02.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Jalulu/Ava | [
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pnikoulis/q-taxi-v3 | pnikoulis | 2023-11-29T11:42:09Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T11:42:09Z | 2023-11-29T11:26:34.000Z | null | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="pnikoulis/q-taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | pnikoulis/q-taxi-v3 | [
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maddes8cht/smallcloudai-Refact-1_6B-fim-gguf | maddes8cht | 2023-11-29T11:54:57Z | 0 | 0 | null | [
"transformers",
"gguf",
"code",
"text-generation",
"en",
"dataset:bigcode/the-stack-dedup",
"dataset:rombodawg/2XUNCENSORED_MegaCodeTraining188k",
"dataset:bigcode/commitpackft",
"arxiv:2108.12409",
"arxiv:1607.06450",
"arxiv:1910.07467",
"arxiv:1911.02150",
"license:bigscience-openrail-m",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:54:57Z | 2023-11-29T11:28:05.000Z | null | null | ---
pipeline_tag: text-generation
inference: true
widget:
- text: 'def print_hello_world():'
example_title: Hello world
group: Python
license: bigscience-openrail-m
pretrain-datasets:
- books
- arxiv
- c4
- falcon-refinedweb
- wiki
- github-issues
- stack_markdown
- self-made dataset of permissive github code
datasets:
- bigcode/the-stack-dedup
- rombodawg/2XUNCENSORED_MegaCodeTraining188k
- bigcode/commitpackft
metrics:
- code_eval
library_name: transformers
tags:
- code
model-index:
- name: Refact-1.6B
results:
- task:
type: text-generation
dataset:
type: openai_humaneval
name: HumanEval
metrics:
- name: pass@1 (T=0.01)
type: pass@1
value: 32.0
verified: false
- name: pass@1 (T=0.2)
type: pass@1
value: 31.5
verified: false
- name: pass@10 (T=0.8)
type: pass@10
value: 53.0
verified: false
- name: pass@100 (T=0.8)
type: pass@100
value: 76.9
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize Python
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 35.8
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize JavaScript
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 31.6
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize Java
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 29.1
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize Go
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: -1
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize C++
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 26.3
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize Rust
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: -1
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesize Average
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: -1
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixTests Python
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 18.38
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixTests JavaScript
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 12.28
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixTests Java
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 15.12
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixTests Go
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: -1
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixTests C++
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 13.17
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixTests Rust
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 2.8
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixTests Average
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: -1
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixDocs Python
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 26.92
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixDocs JavaScript
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 26.85
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixDocs Java
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 30.76
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixDocs Go
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: -1
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixDocs C++
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 25.94
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixDocs Rust
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 8.44
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalFixDocs Average
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: -1
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain Python
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 26.46
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain JavaScript
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 17.86
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain Java
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 20.94
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain Go
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: -1
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain C++
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 18.78
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain Rust
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: -1
verified: false
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type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalExplain Average
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: -1
verified: false
- task:
type: text-generation
dataset:
type: mbpp
name: MBPP
metrics:
- name: pass@1 (T=0.01)
type: pass@1
value: 31.15
verified: false
- task:
type: text-generation
dataset:
type: ds1000
name: DS-1000 (Overall Completion)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 10.1
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (C++)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 21.61
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (C#)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 13.91
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (D)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 9.5
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Go)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 53.57
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Java)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 21.58
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Julia)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 13.75
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (JavaScript)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 26.88
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Lua)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 15.26
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (PHP)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 23.04
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Perl)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 12.1
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Python)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 29.6
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (R)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 13.77
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Ruby)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 12.68
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Racket)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 4.29
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Rust)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 19.54
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Scala)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 18.33
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Bash)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 5.7
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (Swift)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 17.68
verified: false
- task:
type: text-generation
dataset:
type: nuprl/MultiPL-E
name: MultiPL-HumanEval (TypeScript)
metrics:
- name: pass@1 (T=0.2)
type: pass@1
value: 25
verified: false
language:
- en
---
[]()
I'm constantly enhancing these model descriptions to provide you with the most relevant and comprehensive information
# Refact-1_6B-fim - GGUF
- Model creator: [smallcloudai](https://huggingface.co/smallcloudai)
- Original model: [Refact-1_6B-fim](https://huggingface.co/smallcloudai/Refact-1_6B-fim)
Refact seems to be an original model so far without any descendants.
It was [anounced](https://refact.ai/blog/2023/applying-recent-innovations-to-train-model/) on the refact.ai website and published on Huggingface.
# About GGUF format
`gguf` is the current file format used by the [`ggml`](https://github.com/ggerganov/ggml) library.
A growing list of Software is using it and can therefore use this model.
The core project making use of the ggml library is the [llama.cpp](https://github.com/ggerganov/llama.cpp) project by Georgi Gerganov
# Quantization variants
There is a bunch of quantized files available to cater to your specific needs. Here's how to choose the best option for you:
# Legacy quants
Q4_0, Q4_1, Q5_0, Q5_1 and Q8 are `legacy` quantization types.
Nevertheless, they are fully supported, as there are several circumstances that cause certain model not to be compatible with the modern K-quants.
## Note:
Now there's a new option to use K-quants even for previously 'incompatible' models, although this involves some fallback solution that makes them not *real* K-quants. More details can be found in affected model descriptions.
(This mainly refers to Falcon 7b and Starcoder models)
# K-quants
K-quants are designed with the idea that different levels of quantization in specific parts of the model can optimize performance, file size, and memory load.
So, if possible, use K-quants.
With a Q6_K, you'll likely find it challenging to discern a quality difference from the original model - ask your model two times the same question and you may encounter bigger quality differences.
---
# Original Model Card:
# Refact-1.6B
Finally, the model we started training with our [blog post](https://refact.ai/blog/2023/applying-recent-innovations-to-train-model/) is ready 🎉
After fine-tuning on generated data, it beats Replit 3b, Stability Code 3b and many other models. It almost beats
StarCoder ten times the size!
Model | Size | HumanEval pass@1 | HumanEval pass@10 |
----------------------|---------------|--------------------|--------------------|
DeciCoder-1b | 1b | 19.1% | |
<b>Refact-1.6-fim</b> | <b>1.6b</b> | <b>32.0%</b> | <b>53.0%</b> |
StableCode | 3b | 20.2% | 33.8% |
ReplitCode v1 | 3b | 21.9% | |
CodeGen2.5-multi | 7b | 28.4% | 47.5% |
CodeLlama | 7b | 33.5% | 59.6% |
StarCoder | 15b | 33.6% | |
Likely, it's the best model for practical use in your IDE for code completion because it's smart and fast!
You can start using it right now by downloading the
[Refact plugin](https://refact.ai/). You can host the model yourself, too, using the
[open source docker container](https://github.com/smallcloudai/refact).
And it's multi-language (see MultiPL-HumanEval and other metrics below) and it works as a chat (see the section below).
# It Works As a Chat
The primary application of this model is code completion (infill) in multiple programming languages.
But it works as a chat quite well.
HumanEval results using instruction following (chat) format, against models specialized for chat only:
Model | Size | pass@1 | pass@10 |
-----------------------|--------|----------|----------|
<b>Refact-1.6-fim</b> | 1.6b | 38.4% | 55.6% |
StableCode-instruct | 3b | 26.9% | 36.2% |
OctoGeeX | 6b | 44.7% | |
CodeLlama-instruct | 7b | 34.8% | 64.3% |
CodeGen2.5-instruct | 7b | 36.2% | 60.87 |
CodeLlama-instruct | 13b | 42.7% | 71.6% |
StarChat-β | 15b | 33.5% | |
OctoCoder | 15b | 46.2% | |
# Example
Fill-in-the-middle uses special tokens to identify the prefix/middle/suffix part of the input and output:
```python
# pip install -q transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "smallcloudai/Refact-1_6B-fim"
device = "cuda" # for GPU usage or "cpu" for CPU usage
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint, trust_remote_code=True).to(device)
prompt = '<fim_prefix>def print_hello_world():\n """<fim_suffix>\n print("Hello world!")<fim_middle>'
inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
outputs = model.generate(inputs, max_length=100, temperature=0.2)
print("-"*80)
print(tokenizer.decode(outputs[0]))
```
# Chat Format
The same model works as chat (experimental).
```python
prompt_template = "<empty_output>SYSTEM {system}\n" \
"<empty_output>USER {query}\n" \
"<empty_output>ASSISTANT"
prompt = prompt_template.format(system="You are a programming assistant",
query="How do I sort a list in Python?")
```
# Architecture
As described in more detail in the blog post, we used:
- [ALiBi](https://arxiv.org/abs/2108.12409) based attention
- [LayerNorm](https://arxiv.org/abs/1607.06450v1) instead of [RMSNorm](https://arxiv.org/pdf/1910.07467.pdf)
- [Multi Query Attention](https://arxiv.org/abs/1911.02150)
We also used LiON, flash attention, early dropout. It's not that innovative that you can't run it, in fact you can -- see an example below.
# Pretraining
For the base model, we used our own dataset that contains code with permissive licenses only, and open text datasets.
Filtering is the key to success of this model:
- We only used text in English
- Only topics related to computer science
- Applied heavy deduplication
The text to code proportion was 50:50, model trained for 1.2T tokens.
We don't release the base model, because its Fill-in-the-Middle (FIM) capability likes to repeat itself too much, so
its practical use is limited. But if you still want it, write us a message on Discord.
# Finetuning
We tested our hypothesis that chat data should boost base model performance in FIM and
regular left-to-right code completion. We found that just 15% of open
[code](https://huggingface.co/datasets/bigcode/commitpackft)
[instruction-following](https://huggingface.co/datasets/rombodawg/2XUNCENSORED_MegaCodeTraining188k) datasets,
that we filtered for quality, improves almost all metrics.
Additionally, to improve FIM, we observed common failure modes, and prepared a synthetic dataset based on
[The Stack dedup v1.1](https://huggingface.co/datasets/bigcode/the-stack-dedup) to address them.
There is a distribution shift between typical code on the internet, and the code you write in your IDE.
The former is likely finished, so the model tries to come up with a suggestion that makes the code complete.
You are likely to have half-written code as you work on it, there is no single addition that can repair it
fully.
In practice, model needs to have a tendency to stop after a couple of lines are added, and sometimes don't write
anything at all. We found that just giving it empty completions, single line completions, multiline
completions that end with a smaller text indent or at least a newline -- makes it much more usable. This data
was used as the rest 85% of the finetune dataset.
The final model is the result of several attempts to make it work as good as possible for code completion,
and to perform well on a wide range of metrics. The best attempt took 40B tokens.
# Limitations and Bias
The Refact-1.6B model was trained on text in English. But it has seen a lot more languages in
code comments. Its performance on non-English languages is lower, for sure.
# Model Stats
- **Architecture:** LLAMA-like model with multi-query attention
- **Objectives** Fill-in-the-Middle, Chat
- **Tokens context:** 4096
- **Pretraining tokens:** 1.2T
- **Finetuning tokens:** 40B
- **Precision:** bfloat16
- **GPUs** 64 NVidia A5000
- **Training time** 28 days
# License
The model is licensed under the BigScience OpenRAIL-M v1 license agreement
# Citation
If you are using this model, please give a link to this page.
***End of original Model File***
---
## Please consider to support my work
**Coming Soon:** I'm in the process of launching a sponsorship/crowdfunding campaign for my work. I'm evaluating Kickstarter, Patreon, or the new GitHub Sponsors platform, and I am hoping for some support and contribution to the continued availability of these kind of models. Your support will enable me to provide even more valuable resources and maintain the models you rely on. Your patience and ongoing support are greatly appreciated as I work to make this page an even more valuable resource for the community.
<center>
[](https://maddes8cht.github.io)
[](https://stackexchange.com/users/26485911)
[](https://github.com/maddes8cht)
[](https://huggingface.co/maddes8cht)
[](https://twitter.com/maddes1966)
</center> | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | maddes8cht/smallcloudai-Refact-1_6B-fim-gguf | [
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mlwithrakesh/CartPole-v1 | mlwithrakesh | 2023-11-29T11:28:34Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | 2023-11-29T11:28:34Z | 2023-11-29T11:28:22.000Z | null | null | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 1000.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | mlwithrakesh/CartPole-v1 | [
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1.0780994892120361,
0.3779202103614807,
-0.7576474547386169,
-0.580311119556427,
0.4184612035751343,
0.08254088461399078,
0.023395786061882973,
0.6342411637306213,
0.22476913034915924,
0.29169586300849915,
0.13776983320713043,
0.3554343581199646,
-0.2336660772562027,
-0.6586130857467651,
-0.6479476690292358,
0.21091772615909576,
0.5205789804458618,
-0.49985188245773315
] |
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