modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 00:38:39
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 525
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-29 00:38:28
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
alfredolozano/CODEX_LoRA
|
alfredolozano
| 2023-11-29T15:50:29Z | 4 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"if",
"if-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-24T21:12:18Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: <class 'str'>
tags:
- if
- if-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - alfredolozano/CODEX_LoRA
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on <class 'str'> using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
hoangphatmonter/q-Taxi-v3
|
hoangphatmonter
| 2023-11-29T15:48:00Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T15:47:56Z |
---
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="hoangphatmonter/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"])
```
|
ghassenhannachi/speecht5_finetuned_voxpopuli_lt
|
ghassenhannachi
| 2023-11-29T15:39:33Z | 87 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"text-to-speech",
"dataset:facebook/voxpopuli",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2023-11-29T14:45:27Z |
---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
- text-to-speech
datasets:
- facebook/voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_lt
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. -->
# speecht5_finetuned_voxpopuli_lt
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4991
## 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: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6111 | 3.85 | 100 | 0.5507 |
| 0.6052 | 7.69 | 200 | 0.5203 |
| 0.5447 | 11.54 | 300 | 0.5029 |
| 0.5584 | 15.38 | 400 | 0.5015 |
| 0.5522 | 19.23 | 500 | 0.4991 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
machinelearningzuu/youtube-content-summarization-bart
|
machinelearningzuu
| 2023-11-29T15:39:17Z | 105 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"base_model:finetune:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-29T15:37:24Z |
---
license: mit
base_model: facebook/bart-large-cnn
tags:
- generated_from_trainer
model-index:
- name: youtube-content-summarization-bart
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. -->
# youtube-content-summarization-bart
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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_steps: 500
- num_epochs: 15
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
tizayi/taxi-v3
|
tizayi
| 2023-11-29T15:31:20Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T15:31:19Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.77
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="tizayi/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"])
```
|
tizayi/q-FrozenLake-v1-4x4-noSlippery
|
tizayi
| 2023-11-29T15:20:28Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T15:20:26Z |
---
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="tizayi/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"])
```
|
ybWang/distilgpt2-finetuned-wikitext2
|
ybWang
| 2023-11-29T15:06:52Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T11:14:14Z |
---
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
|
Realgon/roberta_sst2_padding90model
|
Realgon
| 2023-11-29T14:39:43Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-29T13:18:43Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst2_padding90model
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_padding90model
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: 0.5344
- 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 | 0.2083 | 0.9171 |
| 0.3231 | 2.0 | 866 | 0.2668 | 0.9308 |
| 0.1721 | 3.0 | 1299 | 0.3085 | 0.9303 |
| 0.1148 | 4.0 | 1732 | 0.2910 | 0.9363 |
| 0.0679 | 5.0 | 2165 | 0.4164 | 0.9325 |
| 0.0424 | 6.0 | 2598 | 0.3419 | 0.9489 |
| 0.0292 | 7.0 | 3031 | 0.4437 | 0.9418 |
| 0.0292 | 8.0 | 3464 | 0.6145 | 0.9171 |
| 0.023 | 9.0 | 3897 | 0.4383 | 0.9456 |
| 0.0181 | 10.0 | 4330 | 0.4449 | 0.9462 |
| 0.0102 | 11.0 | 4763 | 0.6482 | 0.9270 |
| 0.0131 | 12.0 | 5196 | 0.4929 | 0.9418 |
| 0.0053 | 13.0 | 5629 | 0.4902 | 0.9473 |
| 0.0065 | 14.0 | 6062 | 0.4971 | 0.9434 |
| 0.0065 | 15.0 | 6495 | 0.5722 | 0.9385 |
| 0.0071 | 16.0 | 6928 | 0.5055 | 0.9445 |
| 0.0057 | 17.0 | 7361 | 0.5643 | 0.9418 |
| 0.0033 | 18.0 | 7794 | 0.5291 | 0.9412 |
| 0.0053 | 19.0 | 8227 | 0.5127 | 0.9473 |
| 0.0006 | 20.0 | 8660 | 0.5344 | 0.9445 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
personal1802/33
|
personal1802
| 2023-11-29T14:36:08Z | 4 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:latent-consistency/lcm-lora-sdv1-5",
"base_model:adapter:latent-consistency/lcm-lora-sdv1-5",
"region:us"
] |
text-to-image
| 2023-11-29T14:27:47Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/WHITE.png
base_model: latent-consistency/lcm-lora-sdv1-5
instance_prompt: null
---
# ChosenChineseStyleNsfw_v20
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/personal1802/33/tree/main) them in the Files & versions tab.
|
personal1802/32
|
personal1802
| 2023-11-29T14:33:38Z | 3 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:latent-consistency/lcm-lora-sdv1-5",
"base_model:adapter:latent-consistency/lcm-lora-sdv1-5",
"region:us"
] |
text-to-image
| 2023-11-29T14:27:14Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/WHITE.png
base_model: latent-consistency/lcm-lora-sdv1-5
instance_prompt: null
---
# zhmixDramatic_v30
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/personal1802/32/tree/main) them in the Files & versions tab.
|
Englios/Taxi-v3
|
Englios
| 2023-11-29T14:30:18Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T14:30:16Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.73
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Englios/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"])
```
|
chahatraj/contact-bpn-alldata-13b-en
|
chahatraj
| 2023-11-29T14:27:39Z | 1 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-11-29T08:07:56Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
ErnestBeckham/mistral-finetuned-samsum
|
ErnestBeckham
| 2023-11-29T14:20:17Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"base_model:finetune:TheBloke/Mistral-7B-Instruct-v0.1-GPTQ",
"license:apache-2.0",
"region:us"
] | null | 2023-11-29T13:29:14Z |
---
license: apache-2.0
base_model: TheBloke/Mistral-7B-Instruct-v0.1-GPTQ
tags:
- generated_from_trainer
model-index:
- name: mistral-finetuned-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-finetuned-samsum
This model is a fine-tuned version of [TheBloke/Mistral-7B-Instruct-v0.1-GPTQ](https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GPTQ) 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
1aurent/vit_base_patch16_224.deblurmim_us280k_deblurring_mae
|
1aurent
| 2023-11-29T14:17:23Z | 15 | 0 |
timm
|
[
"timm",
"safetensors",
"image-classification",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2023-11-29T14:17:09Z |
---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
---
# Model card for vit_base_patch16_224.deblurmim_us280k_deblurring_mae
|
MaxT/poem_sentiment
|
MaxT
| 2023-11-29T13:59:29Z | 163 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:poem_sentiment",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-29T13:47:43Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
datasets:
- poem_sentiment
metrics:
- accuracy
model-index:
- name: poem_sentiment
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: poem_sentiment
type: poem_sentiment
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8857142857142857
---
<!-- 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. -->
# poem_sentiment
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the poem_sentiment dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4747
- 0: {'precision': 0.8571428571428571, 'recall': 0.9473684210526315, 'f1-score': 0.9, 'support': 19}
- 1: {'precision': 0.7222222222222222, 'recall': 0.7647058823529411, 'f1-score': 0.7428571428571428, 'support': 17}
- 2: {'precision': 0.9393939393939394, 'recall': 0.8985507246376812, 'f1-score': 0.9185185185185185, 'support': 69}
- Accuracy: 0.8857
- Macro avg: {'precision': 0.8395863395863395, 'recall': 0.8702083426810846, 'f1-score': 0.8537918871252205, 'support': 105}
- Weighted avg: {'precision': 0.8893492750635609, 'recall': 0.8857142857142857, 'f1-score': 0.8867271352985638, 'support': 105}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | 0 | 1 | 2 | Accuracy | Macro avg | Weighted avg |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------------:|:--------:|:-----------------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------------:|
| 1.0922 | 1.0 | 112 | 0.8825 | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 19} | {'precision': 0.0, 'recall': 0.0, 'f1-score': 0.0, 'support': 17} | {'precision': 0.6571428571428571, 'recall': 1.0, 'f1-score': 0.7931034482758621, 'support': 69} | 0.6571 | {'precision': 0.21904761904761905, 'recall': 0.3333333333333333, 'f1-score': 0.26436781609195403, 'support': 105} | {'precision': 0.43183673469387757, 'recall': 0.6571428571428571, 'f1-score': 0.5211822660098522, 'support': 105} |
| 0.6877 | 2.0 | 224 | 0.4747 | {'precision': 0.8571428571428571, 'recall': 0.9473684210526315, 'f1-score': 0.9, 'support': 19} | {'precision': 0.7222222222222222, 'recall': 0.7647058823529411, 'f1-score': 0.7428571428571428, 'support': 17} | {'precision': 0.9393939393939394, 'recall': 0.8985507246376812, 'f1-score': 0.9185185185185185, 'support': 69} | 0.8857 | {'precision': 0.8395863395863395, 'recall': 0.8702083426810846, 'f1-score': 0.8537918871252205, 'support': 105} | {'precision': 0.8893492750635609, 'recall': 0.8857142857142857, 'f1-score': 0.8867271352985638, 'support': 105} |
| 0.5299 | 3.0 | 336 | 0.6595 | {'precision': 0.8, 'recall': 0.8421052631578947, 'f1-score': 0.8205128205128205, 'support': 19} | {'precision': 1.0, 'recall': 0.4117647058823529, 'f1-score': 0.5833333333333334, 'support': 17} | {'precision': 0.8461538461538461, 'recall': 0.9565217391304348, 'f1-score': 0.8979591836734695, 'support': 69} | 0.8476 | {'precision': 0.882051282051282, 'recall': 0.7367972360568942, 'f1-score': 0.7672684458398744, 'support': 105} | {'precision': 0.8627106227106227, 'recall': 0.8476190476190476, 'f1-score': 0.8330056564750442, 'support': 105} |
| 0.9027 | 4.0 | 448 | 0.5981 | {'precision': 1.0, 'recall': 0.7368421052631579, 'f1-score': 0.8484848484848484, 'support': 19} | {'precision': 0.7333333333333333, 'recall': 0.6470588235294118, 'f1-score': 0.6875, 'support': 17} | {'precision': 0.868421052631579, 'recall': 0.9565217391304348, 'f1-score': 0.9103448275862069, 'support': 69} | 0.8667 | {'precision': 0.867251461988304, 'recall': 0.7801408893076681, 'f1-score': 0.8154432253570185, 'support': 105} | {'precision': 0.870359231411863, 'recall': 0.8666666666666667, 'f1-score': 0.863071478330099, 'support': 105} |
| 0.4588 | 5.0 | 560 | 0.7815 | {'precision': 0.7727272727272727, 'recall': 0.8947368421052632, 'f1-score': 0.8292682926829269, 'support': 19} | {'precision': 0.6470588235294118, 'recall': 0.6470588235294118, 'f1-score': 0.6470588235294118, 'support': 17} | {'precision': 0.8939393939393939, 'recall': 0.855072463768116, 'f1-score': 0.8740740740740741, 'support': 69} | 0.8286 | {'precision': 0.7712418300653595, 'recall': 0.7989560431342637, 'f1-score': 0.7834670634288043, 'support': 105} | {'precision': 0.832034632034632, 'recall': 0.8285714285714286, 'f1-score': 0.8292115111627308, 'support': 105} |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Realgon/distilbert_agnews_padding0model
|
Realgon
| 2023-11-29T13:56:42Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:40:08Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: distilbert_agnews_padding0model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9464473684210526
---
<!-- 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. -->
# distilbert_agnews_padding0model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6408
- Accuracy: 0.9464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1774 | 1.0 | 7500 | 0.1995 | 0.9392 |
| 0.1403 | 2.0 | 15000 | 0.1939 | 0.9447 |
| 0.1114 | 3.0 | 22500 | 0.2186 | 0.9459 |
| 0.0741 | 4.0 | 30000 | 0.2832 | 0.9446 |
| 0.0499 | 5.0 | 37500 | 0.3070 | 0.9408 |
| 0.0376 | 6.0 | 45000 | 0.3704 | 0.9434 |
| 0.0341 | 7.0 | 52500 | 0.3999 | 0.9426 |
| 0.0319 | 8.0 | 60000 | 0.4505 | 0.9425 |
| 0.0191 | 9.0 | 67500 | 0.4649 | 0.9399 |
| 0.013 | 10.0 | 75000 | 0.5064 | 0.9403 |
| 0.0184 | 11.0 | 82500 | 0.4858 | 0.9405 |
| 0.0081 | 12.0 | 90000 | 0.5358 | 0.9432 |
| 0.0065 | 13.0 | 97500 | 0.5440 | 0.9436 |
| 0.0053 | 14.0 | 105000 | 0.5755 | 0.9436 |
| 0.0017 | 15.0 | 112500 | 0.5907 | 0.9457 |
| 0.0042 | 16.0 | 120000 | 0.5916 | 0.9455 |
| 0.0031 | 17.0 | 127500 | 0.5976 | 0.9468 |
| 0.0017 | 18.0 | 135000 | 0.6063 | 0.9474 |
| 0.0003 | 19.0 | 142500 | 0.6248 | 0.9467 |
| 0.0003 | 20.0 | 150000 | 0.6408 | 0.9464 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
TheBloke/SauerkrautLM-7B-HerO-GPTQ
|
TheBloke
| 2023-11-29T13:56:18Z | 20 | 1 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetune",
"chatml",
"augmentation",
"german",
"conversational",
"en",
"de",
"base_model:VAGOsolutions/SauerkrautLM-7b-HerO",
"base_model:quantized:VAGOsolutions/SauerkrautLM-7b-HerO",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2023-11-29T13:20:01Z |
---
base_model: VAGOsolutions/SauerkrautLM-7b-HerO
inference: false
language:
- en
- de
library_name: transformers
license: apache-2.0
model_creator: VAGO solutions
model_name: SauerkrautLM 7B HerO
model_type: mistral
pipeline_tag: text-generation
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
tags:
- mistral
- finetune
- chatml
- augmentation
- german
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# SauerkrautLM 7B HerO - GPTQ
- Model creator: [VAGO solutions](https://huggingface.co/VAGOsolutions)
- Original model: [SauerkrautLM 7B HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO)
<!-- description start -->
# Description
This repo contains GPTQ model files for [VAGO solutions's SauerkrautLM 7B HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF)
* [VAGO solutions's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 4096 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 4096 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 4096 | 7.68 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 4096 | 4.30 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/SauerkrautLM-7B-HerO-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/SauerkrautLM-7B-HerO-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `SauerkrautLM-7B-HerO-GPTQ`:
```shell
mkdir SauerkrautLM-7B-HerO-GPTQ
huggingface-cli download TheBloke/SauerkrautLM-7B-HerO-GPTQ --local-dir SauerkrautLM-7B-HerO-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir SauerkrautLM-7B-HerO-GPTQ
huggingface-cli download TheBloke/SauerkrautLM-7B-HerO-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir SauerkrautLM-7B-HerO-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir SauerkrautLM-7B-HerO-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/SauerkrautLM-7B-HerO-GPTQ --local-dir SauerkrautLM-7B-HerO-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/SauerkrautLM-7B-HerO-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/SauerkrautLM-7B-HerO-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `SauerkrautLM-7B-HerO-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/SauerkrautLM-7B-HerO-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/SauerkrautLM-7B-HerO-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: VAGO solutions's SauerkrautLM 7B HerO
## VAGO solutions SauerkrautLM-7b-HerO
Introducing **SauerkrautLM-7b-HerO** – the pinnacle of German language model technology!
Crafted through the **merging** of **[Teknium's OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)** and **[Open-Orca's Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)** and **uniquely fine-tuned with the Sauerkraut dataset.**
SauerkrautLM-7b-HerO represents a breakthrough in language modeling, achieving an optimal balance between extensive German data and essential international sources.
This ensures the model not only excels in understanding the nuances of the German language but also retains its global capabilities.
Harnessing the innovative power of the **gradient SLERP method from MergeKit**, we've achieved a groundbreaking fusion of two of the most best performing 7B models based on the Mistral framework.
This merge has allowed us to combine the best features of both models, creating an unparalleled synergy.
Coupled with the German Sauerkraut dataset, which consists of a mix of augmented and translated data, we have successfully taught the English-speaking merged model the intricacies of the German language.
This was achieved *without the typical loss of core competencies often associated with fine-tuning in another language of models previously trained mainly in English.*
Our approach ensures that the model retains its original strengths while acquiring a profound understanding of German, **setting a new benchmark in bilingual language model proficiency.**
# Table of Contents
1. [Overview of all Her0 models](#all-hero-models)
2. [Model Details](#model-details)
- [Prompt template](#prompt-template)
- [Training Dataset](#training-dataset)
- [Merge Procedure](#merge-procedure)
3. [Evaluation](#evaluation)
- [GPT4ALL](#gpt4all)
- [Language Model evaluation Harness](#language-model-evaluation-harness)
- [BigBench](#big-bench)
- [MMLU](#mmlu)
- [TruthfulQA](#truthfulqa)
- [MT-Bench (German)](#mt-bench-german)
- [MT-Bench (English)](#mt-bench-english)
- [Additional German Benchmark results](#additional-german-benchmark-results)
5. [Disclaimer](#disclaimer)
6. [Contact](#contact)
7. [Collaborations](#collaborations)
8. [Acknowledgement](#acknowledgement)
## All HerO Models
| Model | HF | GPTQ | GGUF | AWQ |
|-------|-------|-------|-------|-------|
| SauerkrautLM-7b-HerO | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO) | coming soon | coming soon | coming soon |
## Model Details
**SauerkrautLM-7b-HerO**
- **Model Type:** SauerkrautLM-7b-HerO is an auto-regressive language model based on the transformer architecture
- **Language(s):** English, German
- **License:** APACHE 2.0
- **Contact:** [Website](https://vago-solutions.de/#Kontakt) [David Golchinfar](mailto:golchinfar@vago-solutions.de)
### Training Dataset:
SauerkrautLM-7b-HerO was trained with mix of German data augmentation and translated data.
We found, that only a simple translation of training data can lead to unnatural German phrasings.
Data augmentation techniques were used to grant grammatical, syntactical correctness and a more natural German wording in our training data.
### Merge Procedure:
SauerkrautLM-7b-HerO was merged on 1 A100 with [mergekit](https://github.com/cg123/mergekit).
The merged model contains [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca).
We applied the gradient SLERP method.
### Prompt Template:
```
<|im_start|>system
Du bist Sauerkraut-HerO, ein großes Sprachmodell, das höflich und kompetent antwortet. Schreibe deine Gedanken Schritt für Schritt auf, um Probleme sinnvoll zu lösen.<|im_end|>
<|im_start|>user
Wie geht es dir?<|im_end|>
<|im_start|>assistant
Mir geht es gut!<|im_end|>
<|im_start|>user
Bitte erkläre mir, wie die Zusammenführung von Modellen durch bestehende Spitzenmodelle profitieren kann.<|im_end|>
<|im_start|>assistant
```
## Evaluation
### GPT4ALL:
*Compared to relevant German Closed and Open Source models*
### Language Model evaluation Harness:
*Compared to Aleph Alpha Luminous Models*
**performed with newest Language Model Evaluation Harness*
### Big Bench:
**performed with newest Language Model Evaluation Harness*
### MMLU:
*Compared to Big Boy LLMs (Grok0,Grok1,GPT3.5,GPT4)*
### TruthfulQA:
*Compared to OpenAI Models (GPT3.5,GPT4)*
### MT-Bench (German):
```
########## First turn ##########
score
model turn
SauerkrautLM-70b-v1 1 7.25000
SauerkrautLM-7b-HerO <--- 1 6.96875
SauerkrautLM-7b-v1-mistral 1 6.30625
leo-hessianai-13b-chat 1 6.18750
SauerkrautLM-13b-v1 1 6.16250
leo-mistral-hessianai-7b-chat 1 6.15625
Llama-2-70b-chat-hf 1 6.03750
vicuna-13b-v1.5 1 5.80000
SauerkrautLM-7b-v1 1 5.65000
leo-hessianai-7b-chat 1 5.52500
vicuna-7b-v1.5 1 5.42500
Mistral-7B-v0.1 1 5.37500
SauerkrautLM-3b-v1 1 3.17500
Llama-2-7b 1 1.28750
open_llama_3b_v2 1 1.68750
########## Second turn ##########
score
model turn
SauerkrautLM-70b-v1 2 6.83125
SauerkrautLM-7b-HerO <--- 2 6.30625
vicuna-13b-v1.5 2 5.63125
SauerkrautLM-13b-v1 2 5.34375
SauerkrautLM-7b-v1-mistral 2 5.26250
leo-mistral-hessianai-7b-chat 2 4.99375
SauerkrautLM-7b-v1 2 4.73750
leo-hessianai-13b-chat 2 4.71250
vicuna-7b-v1.5 2 4.67500
Llama-2-70b-chat-hf 2 4.66250
Mistral-7B-v0.1 2 4.53750
leo-hessianai-7b-chat 2 2.65000
SauerkrautLM-3b-v1 2 1.98750
open_llama_3b_v2 2 1.22500
Llama-2-7b 2 1.07500
########## Average ##########
score
model
SauerkrautLM-70b-v1 7.040625
SauerkrautLM-7b-HerO <--- 6.637500
SauerkrautLM-7b-v1-mistral 5.784375
SauerkrautLM-13b-v1 5.753125
vicuna-13b-v1.5 5.715625
leo-mistral-hessianai-7b-chat 5.575000
leo-hessianai-13b-chat 5.450000
Llama-2-70b-chat-hf 5.350000
SauerkrautLM-v1-7b 5.193750
vicuna-7b-v1.5 5.050000
Mistral-7B-v0.1 4.956250
leo-hessianai-7b-chat 4.087500
SauerkrautLM-3b-v1 2.581250
open_llama_3b_v2 1.456250
Llama-2-7b 1.181250
```
**performed with the newest FastChat Version*
### MT-Bench (English):
```
########## First turn ##########
score
model turn
OpenHermes-2.5-Mistral-7B 1 8.21875
SauerkrautLM-7b-HerO <--- 1 8.03125
Mistral-7B-OpenOrca 1 7.65625
neural-chat-7b-v3-1 1 7.22500
########## Second turn ##########
score
model turn
OpenHermes-2.5-Mistral-7B 2 7.1000
SauerkrautLM-7b-HerO <--- 2 6.7875
neural-chat-7b-v3-1 2 6.4000
Mistral-7B-OpenOrca 2 6.1750
########## Average ##########
score
model
OpenHermes-2.5-Mistral-7B 7.659375
SauerkrautLM-7b-HerO <--- 7.409375
Mistral-7B-OpenOrca 6.915625
neural-chat-7b-v3-1 6.812500
```
**performed with the newest FastChat Version*
### Additional German Benchmark results:
*performed with newest Language Model Evaluation Harness
## Disclaimer
We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out.
However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided.
Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models. These models may be employed for commercial purposes, and the Apache 2.0 remains applicable and is included with the model files.
## Contact
If you are interested in customized LLMs for business applications, please get in contact with us via our website or contact us at [Dr. Daryoush Vaziri](mailto:vaziri@vago-solutions.de). We are also grateful for your feedback and suggestions.
## Collaborations
We are also keenly seeking support and investment for our startup, VAGO solutions, where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us.
## Acknowledgement
Many thanks to [OpenOrca](https://huggingface.co/Open-Orca) and [teknium](https://huggingface.co/teknium) for providing such valuable models to the Open-Source community.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
|
Yova/baseline
|
Yova
| 2023-11-29T13:50:28Z | 173 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-11-21T13:16:54Z |
---
tags:
- generated_from_trainer
model-index:
- name: baseline
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. -->
# baseline
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9254
- Exact Match: 0.702
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 400
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_steps: 4000
- training_steps: 20000
- label_smoothing_factor: 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Exact Match |
|:-------------:|:-----:|:-----:|:---------------:|:-----------:|
| 2.8524 | 16.0 | 400 | 1.7375 | 0.059 |
| 1.422 | 32.0 | 800 | 1.6708 | 0.11 |
| 1.0862 | 48.0 | 1200 | 1.7149 | 0.094 |
| 0.9374 | 64.0 | 1600 | 1.6508 | 0.159 |
| 0.8704 | 80.0 | 2000 | 1.6920 | 0.112 |
| 0.8356 | 96.0 | 2400 | 1.5605 | 0.16 |
| 0.8157 | 112.0 | 2800 | 1.5249 | 0.188 |
| 0.8029 | 128.0 | 3200 | 1.3993 | 0.25 |
| 0.7917 | 144.0 | 3600 | 1.2768 | 0.312 |
| 0.7821 | 160.0 | 4000 | 1.2213 | 0.397 |
| 0.7719 | 176.0 | 4400 | 1.1216 | 0.432 |
| 0.7635 | 192.0 | 4800 | 1.1076 | 0.458 |
| 0.7584 | 208.0 | 5200 | 1.0275 | 0.567 |
| 0.7556 | 224.0 | 5600 | 1.0464 | 0.552 |
| 0.7525 | 240.0 | 6000 | 1.0442 | 0.56 |
| 0.7496 | 256.0 | 6400 | 1.0108 | 0.581 |
| 0.7487 | 272.0 | 6800 | 0.9721 | 0.61 |
| 0.7467 | 288.0 | 7200 | 1.0326 | 0.567 |
| 0.7466 | 304.0 | 7600 | 0.9900 | 0.572 |
| 0.7449 | 320.0 | 8000 | 1.0150 | 0.604 |
| 0.7445 | 336.0 | 8400 | 0.9755 | 0.603 |
| 0.7433 | 352.0 | 8800 | 0.9705 | 0.645 |
| 0.7432 | 368.0 | 9200 | 0.9567 | 0.663 |
| 0.7432 | 384.0 | 9600 | 0.9733 | 0.68 |
| 0.7425 | 400.0 | 10000 | 0.9262 | 0.67 |
| 0.7417 | 416.0 | 10400 | 0.9216 | 0.673 |
| 0.7409 | 432.0 | 10800 | 0.9411 | 0.681 |
| 0.7404 | 448.0 | 11200 | 0.9312 | 0.674 |
| 0.7405 | 464.0 | 11600 | 0.9777 | 0.585 |
| 0.7406 | 480.0 | 12000 | 0.9191 | 0.683 |
| 0.7395 | 496.0 | 12400 | 0.9216 | 0.643 |
| 0.7396 | 512.0 | 12800 | 0.9764 | 0.645 |
| 0.7394 | 528.0 | 13200 | 0.9361 | 0.644 |
| 0.7392 | 544.0 | 13600 | 0.9210 | 0.67 |
| 0.739 | 560.0 | 14000 | 0.9387 | 0.688 |
| 0.7389 | 576.0 | 14400 | 0.9385 | 0.67 |
| 0.7383 | 592.0 | 14800 | 0.9500 | 0.655 |
| 0.7386 | 608.0 | 15200 | 0.9405 | 0.67 |
| 0.7383 | 624.0 | 15600 | 0.9335 | 0.691 |
| 0.738 | 640.0 | 16000 | 0.9079 | 0.708 |
| 0.7379 | 656.0 | 16400 | 0.9027 | 0.714 |
| 0.7376 | 672.0 | 16800 | 0.8969 | 0.703 |
| 0.7372 | 688.0 | 17200 | 0.9169 | 0.685 |
| 0.7375 | 704.0 | 17600 | 0.8895 | 0.738 |
| 0.7376 | 720.0 | 18000 | 0.8951 | 0.734 |
| 0.7371 | 736.0 | 18400 | 0.9408 | 0.673 |
| 0.737 | 752.0 | 18800 | 0.9270 | 0.693 |
| 0.7371 | 768.0 | 19200 | 0.9063 | 0.71 |
| 0.7369 | 784.0 | 19600 | 0.9253 | 0.678 |
| 0.7367 | 800.0 | 20000 | 0.9254 | 0.702 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
xverse/XVERSE-65B
|
xverse
| 2023-11-29T13:48:34Z | 476 | 38 |
transformers
|
[
"transformers",
"pytorch",
"xverse",
"text-generation",
"custom_code",
"arxiv:2005.14165",
"arxiv:2302.13971",
"arxiv:2211.05100",
"arxiv:2204.02311",
"arxiv:2203.15556",
"arxiv:2112.11446",
"arxiv:2201.11990",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2023-11-03T08:41:36Z |
---
license: apache-2.0
inference: false
---
# XVERSE-65B
## 更新信息
**[2023/11/29]** 更新模型架构及更多底座数据的相关信息。
**[2023/11/24]** 更新预训练数据的相关信息。
**[2023/11/06]** 发布 65B 尺寸的 XVERSE-65B 底座模型。
## Update Information
**[2023/11/29]** Update model architecture and additional pre-training data information.
**[2023/11/24]** Update the related information of the pre-training data.
**[2023/11/06]** Released the XVERSE-65B base model.
## 模型介绍
**XVERSE-65B** 是由深圳元象科技自主研发的支持多语言的大语言模型（Large Language Model），参数规模为 650 亿，本次开源的模型为底座模型 **XVERSE-65B**，主要特点如下：
- **模型结构**：XVERSE-65B 使用主流 Decoder-only 的标准 Transformer 网络结构，支持 16K 的上下文长度（Context Length），能满足更长的多轮对话、知识问答与摘要等需求，模型应用场景更广泛。
- **训练数据**：构建了 2.6 万亿 token 的高质量、多样化的数据对模型进行充分训练，包含中、英、俄、西等 40 多种语言，通过精细化设置不同类型数据的采样比例，使得中英两种语言表现优异，也能兼顾其他语言效果。
- **分词**：基于 BPE（Byte-Pair Encoding）算法，使用上百 GB 语料训练了一个词表大小为 100,534 的分词器，能够同时支持多语言，而无需额外扩展词表。
- **训练框架**：训练中采用 FlashAttention2 加速计算，3D 并行基础上采用虚拟流水线（virtual pipeline）技术，降低较长流水线和 16k 上下文窗口产生的过高气泡率，在千卡集群的峰值算力利用率达到业界前列。同时通过集群基础设施运营、资源调度、训练框架和调度平台协同等持续优化，打造出高稳定、低中断、强容错的训练系统，将每周有效训练率提升至 98.6%。
**XVERSE-65B**的模型大小、架构和学习率如下：
| params | d_model | n_heads | n_layers | d_ff | learning rate |
|:------:|:-------:|:-------:|:--------:|:-----:|:-------------:|
| 65B | 8192 | 64 | 80 | 22016 | 1.5e−4 |
## 底座数据介绍
在预训练阶段，**XVERSE-65B** 主要使用了 7 类不同的数据类型。以下表格展示了 XVERSE-65B 与其他一些知名模型在预训练数据集方面的比较：
| 数据类别 | [GPT3](https://arxiv.org/abs/2005.14165) | [Llama](https://arxiv.org/abs/2302.13971) | [BLOOM](https://arxiv.org/abs/2211.05100) | [PaLM](https://arxiv.org/abs/2204.02311) | [Chinchilla](https://arxiv.org/abs/2203.15556) | [Gopher](https://arxiv.org/abs/2112.11446) | [MT-NLG](https://arxiv.org/abs/2201.11990) | XVERSE-65B |
|:-------:|:--------:|:---------:|:---------:|:--------:|:--------------:|:----------:|:----------:|:----------:|
| 网页类 | Y | Y | Y | Y | Y | Y | Y | Y |
| 代码类 | | Y | Y | Y | Y | Y | Y | Y |
| 百科类 | Y | Y | | Y | Y | Y | Y | Y |
| 书籍类 | Y | Y | | Y | Y | Y | Y | Y |
| 论文类 | | Y | | | | | Y | Y |
| 问答类 | Y | Y | | Y | | | Y | Y |
> 注：'Y' 表示使用了该类数据。
在预训练阶段，不同类别数据的采样比例如下所示：
| | 网页类 | 代码类 | 百科类 | 书籍类 | 论文类 | 问答类 | 其他类 |
|:-------:|:------:|:------:|:------:|:------:|:------:|:------:|:------:|
| 比例(%) | 72.91 | 7.09 | 4.81 | 5.62 | 6.55 | 1.15 | 1.87 |
在预训练阶段，**XVERSE-65B** 主要使用了 41 种自然语言，以下表格展示了不同语种在底座数据中的占比：
| 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) |
|:----:|:-------:|:----:|:-------:|:----:|:-------:|:----:|:-------:|:----:|:-------:|:----:|:-------:|
| en | 54.91 | pl | 0.48 | hu | 0.19 | ar | 0.12 | fa | 0.07 | sl | 0.05 |
| zh | 31.09 | it | 0.36 | ko | 0.18 | ro | 0.11 | hi | 0.07 | et | 0.04 |
| ja | 3.22 | pt | 0.34 | sv | 0.15 | bg | 0.10 | no | 0.07 | lv | 0.03 |
| ru | 3.15 | cs | 0.27 | el | 0.14 | th | 0.10 | ca | 0.06 | sr | 0.03 |
| de | 1.52 | uk | 0.24 | fi | 0.14 | da | 0.09 | iw | 0.06 | ta | 0.03 |
| es | 0.91 | tr | 0.23 | id | 0.13 | mr | 0.08 | lt | 0.05 | kk | 0.02 |
| fr | 0.73 | nl | 0.20 | vi | 0.13 | sk | 0.08 | ms | 0.05 | | |
> 注：各种语言简称的对照可参考：[ISO_639-1](https://zh.wikipedia.org/wiki/ISO_639-1)
对于代码类数据，以下表格展示了不同编程语言的占比：
| 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) | 语言 | 比例(%) |
|:----------:|:-------:|:------:|:-------:|:------------:|:-------:|:----------:|:-------:|:-------------:|:-------:|:-------:|:-------:|
| PHP | 17.06 | Go | 3.38 | Shell | 0.74 | PowerShell | 0.23 | Arduino | 0.13 | R | 0.04 |
| JavaScript | 15.65 | Rust | 2.33 | Haskell | 0.46 | Groovy | 0.21 | Assembly | 0.13 | ABAP | 0.01 |
| Java | 15.18 | Ruby | 1.61 | Common Lisp | 0.43 | Pascal | 0.20 | Clojure | 0.12 | COBOL | 0.0022 |
| Python | 14.64 | Swift | 1.40 | Perl | 0.34 | FORTRAN | 0.19 | Cuda | 0.12 | Verilog | 0.0001 |
| TypeScript | 6.55 | Kotlin | 1.40 | CSS | 0.32 | Elixir | 0.17 | VHDL | 0.09 | | |
| C | 4.84 | Scala | 1.08 | Julia | 0.32 | Solidity | 0.16 | Emacs Lisp | 0.08 | | |
| C++ | 4.68 | Dart | 0.95 | Visual Basic | 0.25 | F# | 0.14 | Objective-C++ | 0.08 | | |
| C# | 3.44 | SQL | 0.76 | OCaml | 0.24 | Erlang | 0.14 | Crystal | 0.06 | | |
## Model Introduction
**XVERSE-65B** is a multilingual large language model, independently developed by Shenzhen Yuanxiang Technology. The models released this time is the base model **XVERSE-65B**. Its key features are as follows:
- **Model Structure**: XVERSE-65B uses the mainstream Decoder-only Transformer network structure, supports 16k context length, which can meet the need of longer multi-round dialogues, knowledge question-answering, and summarization. This makes the model more versatile in application scenarios.
- **Training Data**: The model has been thoroughly trained on a diversified and high-quality dataset consisting of 2.6 trillion of tokens, including more than 40 languages such as Chinese, English, Russian, and Spanish. The sampling ratio of different types of data is finely set, which makes the performance of Chinese and English excellent, and also takes into account the effect of other languages.
- **Tokenization**: Based on the BPE (Byte-Pair Encoding) algorithm, a tokenizer with a vocabulary size of 100,534 has been trained using hundreds of gigabytes of language data. This tokenizer is capable of supporting multilingual without the need for additional vocabulary expansion.
- **Training Framework**: The training utilizes FlashAttention2 for accelerated computation, and on top of 3D parallelism, virtual pipeline technology is applied to reduce the excessive bubble rate caused by longer pipelines and 16k context windows. This achieves a peak computational efficiency within the industry-leading range in the petaflop-scale cluster. Concurrently, through continuous optimization of cluster infrastructure operations, resource scheduling, training frameworks, and the scheduling platform, a highly stable, low-interruption, and robust fault-tolerant training system has been developed, enhancing the effective weekly training rate to 98.6%.
The models sizes, architectures and learning rate of **XVERSE-65B** are showed as follows:
| params | d_model | n_heads | n_layers | d_ff | learning rate |
|:------:|:-------:|:-------:|:--------:|:-----:|:-------------:|
| 65B | 8192 | 64 | 80 | 22016 | 1.5e−4 |
## Introduction of Pre-training Data
During the pre-training phase, **XVERSE-65B** primarily utilized 7 different types of data. The following table shows a comparison of the pre-training datasets of XVERSE-65B with some other well-known models:
| Data Type | [GPT3](https://arxiv.org/abs/2005.14165) | [Llama](https://arxiv.org/abs/2302.13971) | [BLOOM](https://arxiv.org/abs/2211.05100) | [PaLM](https://arxiv.org/abs/2204.02311) | [Chinchilla](https://arxiv.org/abs/2203.15556) | [Gopher](https://arxiv.org/abs/2112.11446) | [MT-NLG](https://arxiv.org/abs/2201.11990) | XVERSE-65B |
|:---------------:|:--------:|:---------:|:---------:|:--------:|:--------------:|:----------:|:----------:|:----------:|
| Web Pages | Y | Y | Y | Y | Y | Y | Y | Y |
| Code | | Y | Y | Y | Y | Y | Y | Y |
| Encyclopedia | Y | Y | | Y | Y | Y | Y | Y |
| Books | Y | Y | | Y | Y | Y | Y | Y |
| Academic Papers | | Y | | | | | Y | Y |
| QA | Y | Y | | Y | | | Y | Y |
> Note: 'Y' indicates that the data type was used.
The sampling ratios of different data types during the pre-training phase are as follows:
| | Web Pages | Code | Encyclopedia | Books | Academic Papers | QA | Other |
|:--------------:|:---------:|:----:|:------------:|:-----:|:---------------:|:----:|:-----:|
| Proportion (%) | 72.91 | 7.09 | 4.81 | 5.62 | 6.55 | 1.15 | 1.87 |
During the pre-training phase, **XVERSE-65B** primarily used 41 kinds of natural language, and the following table shows the proportion of different languages in the pre-training data:
| Language | Proportion (%) | Language | Proportion (%) | Language | Proportion (%) | Language | Proportion (%) | Language | Proportion (%) | Language | Proportion (%) |
|:--------:|:--------------:|:--------:|:--------------:|:--------:|:--------------:|:--------:|:--------------:|:--------:|:--------------:|:--------:|:--------------:|
| en | 54.91 | pl | 0.48 | hu | 0.19 | ar | 0.12 | fa | 0.07 | sl | 0.05 |
| zh | 31.09 | it | 0.36 | ko | 0.18 | ro | 0.11 | hi | 0.07 | et | 0.04 |
| ja | 3.22 | pt | 0.34 | sv | 0.15 | bg | 0.10 | no | 0.07 | lv | 0.03 |
| ru | 3.15 | cs | 0.27 | el | 0.14 | th | 0.10 | ca | 0.06 | sr | 0.03 |
| de | 1.52 | uk | 0.24 | fi | 0.14 | da | 0.09 | iw | 0.06 | ta | 0.03 |
| es | 0.91 | tr | 0.23 | id | 0.13 | mr | 0.08 | lt | 0.05 | kk | 0.02 |
| fr | 0.73 | nl | 0.20 | vi | 0.13 | sk | 0.08 | ms | 0.05 | | |
> Note: Reference to the abbreviations of different languages: [ISO_639-1](https://zh.wikipedia.org/wiki/ISO_639-1)
For the Code data, the following table shows the proportion of different programming languages:
| Programming Language | Proportion (%) | Programming Language | Proportion (%) | Programming Language | Proportion (%) | Programming Language | Proportion (%) | Programming Language | Proportion (%) | Programming Language | Proportion (%) |
|:--------------------:|:--------------:|:--------------------:|:--------------:|:--------------------:|:--------------:|:--------------------:|:--------------:|:--------------------:|:--------------:|:--------------------:|:--------------:|
| PHP | 17.06 | Go | 3.38 | Shell | 0.74 | PowerShell | 0.23 | Arduino | 0.13 | R | 0.04 |
| JavaScript | 15.65 | Rust | 2.33 | Haskell | 0.46 | Groovy | 0.21 | Assembly | 0.13 | ABAP | 0.01 |
| Java | 15.18 | Ruby | 1.61 | Common Lisp | 0.43 | Pascal | 0.20 | Clojure | 0.12 | COBOL | 0.0022 |
| Python | 14.64 | Swift | 1.40 | Perl | 0.34 | FORTRAN | 0.19 | Cuda | 0.12 | Verilog | 0.0001 |
| TypeScript | 6.55 | Kotlin | 1.40 | CSS | 0.32 | Elixir | 0.17 | VHDL | 0.09 | | |
| C | 4.84 | Scala | 1.08 | Julia | 0.32 | Solidity | 0.16 | Emacs Lisp | 0.08 | | |
| C++ | 4.68 | Dart | 0.95 | Visual Basic | 0.25 | F# | 0.14 | Objective-C++ | 0.08 | | |
| C# | 3.44 | SQL | 0.76 | OCaml | 0.24 | Erlang | 0.14 | Crystal | 0.06 | | |
## 评测结果
为了综合评估模型的性能，我们在一系列标准数据集上进行了全面测试，包括C-Eval、CMMLU、Gaokao-Bench、MMLU、GAOKAO-English、AGIEval、RACE-M、CommonSenseQA、PIQA、GSM8K和HumanEval。这些评估覆盖了模型在多个领域的能力，具体包括中文问答、英文问答、语言理解、常识问答、逻辑推理、数学问题解答以及编程能力。评估结果如下：
| 能力维度 | 数据集 | | XVERSE-65B | Llama1-65B | Llama2-70B | Falcon-180B | GPT-3.5 | GPT-4 |
| :--------: | :------------------------: | :----: | :--------: | :--------: | :--------: | :---------: | :-----: | :---: |
| 中文问答 | C-Eval | 5-shot | 68.6 | 38.8 | 49.9 | 54.2 | 54.4 | 68.7 |
| | CMMLU | 5-shot | 72.6 | 40.6 | 53.6 | 57.2 | 53.9 | 71.0 |
| | Gaokao-Bench<sup>1</sup> | 5-shot | 73.9 | 38.9 | 51.4 | 50.5 | - | - |
| 英文问答 | MMLU | 5-shot | 70.8 | 63.4 | 68.9 | 70.5 | 70.0 | 86.4 |
| | GAOKAO-English<sup>1</sup> | 5-shot | 85.3 | 67.0 | 76.6 | 63.3 | - | - |
| 中英文问答 | AGIEval<sup>1</sup> | 5-shot | 61.8 | 42.4 | 51.4 | 51.3 | - | - |
| 语言理解 | RACE-M | 0-shot | 90.6 | 67.9 | 81.5 | 87.6 | 85.6 | 93.7 |
| 常识问答 | CommonSenseQA | 7-shot | 79.8 | 74.0 | 78.5 | 82.4 | 80.2 | 88.3 |
| 推理 | PIQA | 0-shot | 80.4 | 82.8 | 82.8 | 85.3 | 81.7 | 89.2 |
| 数学 | GSM8K | 4-shot | 60.3 | 50.9 | 56.8 | 62.6 | 57.1 | 92.0 |
| 代码 | HumanEval | 0-shot | 26.8 | 23.7 | 29.9 | - | 48.1 | 67.0 |
> <sup>1：只针对其中的单项选择题进行测试，即排除了填空题、开放性问题和多项选择题</sup>
对于上述所有比较模型，我们优先汇报其官方公布的结果。在缺少官方结果的情况下，我们采用了 [OpenCompass 榜单](https://opencompass.org.cn/leaderboard-llm)的报告结果。其他结果则来自于我们自行执行的评估流程所获得的数据。
对于 MMLU ，我们采用作者提供的[评测工具](https://github.com/hendrycks/test)，C-Eval、AGIEval、GAOKAO-Bench、GAOKAO-English 与 MMLU 的评测方式相同，其余评测数据集使用 [OpenCompass 评估框架](https://github.com/open-compass/OpenCompass/)进行评估。
## Model Evaluation
To comprehensively assess the performance of the model, we conducted extensive testing across a range of standard datasets, including C-Eval, CMMLU, Gaokao-Bench, MMLU, GAOKAO-English, AGIEval, RACE-M, CommonSenseQA, PIQA, GSM8K and HumanEval. These evaluations spanned multiple capabilities of the model, specifically including Chinese question answering, English question answering, language comprehension, common sense questioning, logical reasoning, mathematical problem-solving, and coding ability. The results of the evaluations are as follows:
| Capability Dimension | Dataset | | XVERSE-65B | Llama1-65B | Llama2-70B | Falcon-180B | GPT-3.5 | GPT-4 |
| :--------------------: | :------------------------: | :----: | :--------: | :--------: | :--------: | :---------: | :-----: | :---: |
| Chinese QA | C-Eval | 5-shot | 68.6 | 38.8 | 49.9 | 54.2 | 54.4 | 68.7 |
| | CMMLU | 5-shot | 72.6 | 40.6 | 53.6 | 57.2 | 53.9 | 71.0 |
| | Gaokao-Bench<sup>1</sup> | 5-shot | 73.9 | 38.9 | 51.4 | 50.5 | - | - |
| English QA | MMLU | 5-shot | 70.8 | 63.4 | 68.9 | 70.5 | 70.0 | 86.4 |
| | GAOKAO-English<sup>1</sup> | 5-shot | 85.3 | 67.0 | 76.6 | 63.3 | - | - |
| Chinese & English QA | AGIEval<sup>1</sup> | 5-shot | 61.8 | 42.4 | 51.4 | 51.3 | - | - |
| Language Understanding | RACE-M | 0-shot | 90.6 | 67.9 | 81.5 | 87.6 | 85.6 | 93.7 |
| Common Sense QA | CommonSenseQA | 7-shot | 79.8 | 74.0 | 78.5 | 82.4 | 80.2 | 88.3 |
| Reasoning | PIQA | 0-shot | 80.4 | 82.8 | 82.8 | 85.3 | 81.7 | 89.2 |
| Math | GSM8K | 4-shot | 60.3 | 50.9 | 56.8 | 62.6 | 57.1 | 92.0 |
| Coding | HumanEval | 0-shot | 26.8 | 23.7 | 29.9 | - | 48.1 | 67.0 |
> <sup>1: Tests are conducted only on single-answer multiple-choice questions, thus excluding fill-in-the-blanks, open-ended questions, and multiple-answer multiple-choice questions.</sup>
For all the comparison models mentioned above, we prioritize the disclosure of their officially published results. In the absence of official data, we refer to the reported outcomes from [OpenCompass Leaderboard](https://opencompass.org.cn/leaderboard-llm). Results not covered by the aforementioned sources are derived from our own evaluation pipline.
For MMLU, we adopt the [evaluation tools](https://github.com/hendrycks/test) provided by the authors, C-Eval, AGIEval, GAOKAO-Bench, GAOKAO-English are the same as MMLU. For the remaining evaluation datasets, the [OpenCompass](https://github.com/open-compass/OpenCompass/) is employed for evaluation.
## 使用方法
### 硬件需求
下表列出了在 XVERSE-65B 上进行推理和微调所需要的硬件资源：
| | 类型 | 方法 | 内存 | GPU |
| ---------- | ---- | ---------------- | ------ | ---------- |
| XVERSE-65B | 训练 | LoRA with ZeRO-3 | 1500GB | 8*A800 80G |
| XVERSE-65B | 推理 | BF16/FP16 | 500GB | 2*A800 80G |
## Usage
### Hardware requirements
The following table lists the hardware resources required for inference and fine-tuning on XVERSE-65B:
| | Type | Kind | Memory | GPU |
| ---------- | --------- | ---------------- | ------ | ---------- |
| XVERSE-65B | Training | LoRA with ZeRO-3 | 1500GB | 8*A800 80G |
| XVERSE-65B | Inference | BF16/FP16 | 500GB | 2*A800 80G |
### Loading with Transformers
可通过以下代码加载 XVERSE-65B 模型进行推理：
The XVERSE-65B model can be loaded for inference using the following code:
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("xverse/XVERSE-65B")
model = AutoModelForCausalLM.from_pretrained("xverse/XVERSE-65B", trust_remote_code=True, torch_dtype=torch.bfloat16, device_map='auto')
model = model.eval()
inputs = tokenizer('北京的景点：故宫、天坛、万里长城等。\n深圳的景点：', return_tensors='pt').input_ids
inputs = inputs.cuda()
generated_ids = model.generate(inputs, max_new_tokens=64, eos_token_id=tokenizer.eos_token_id, repetition_penalty=1.1)
print(tokenizer.batch_decode(generated_ids, skip_special_tokens=True))
```
更多有关相关细节，包括文本生成demo和环境依赖，请参考我们的[Github](https://github.com/xverse-ai/XVERSE-65B)。
For more details, including the demo of text generation and environmental dependencies, please refer to our [Github](https://github.com/xverse-ai/XVERSE-65B).
### 模型微调
XVERSE-65B 支持开发者进行微调以实现更好的性能表现。在此我们尝试使用 [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) 与 XVERSE-65B 进行兼容性微调训练，并在 8 * Nvidia A800 80 GB + DeepSpeed 的环境下进行了测试。
下面我们给出了使用`LoRA with ZeRO-3`的微调方法。
#### 环境准备
下载 LLaMA-Factory 项目并按其要求[安装依赖](https://github.com/hiyouga/LLaMA-Factory#getting-started)。
#### 启动训练
训练启动脚本：
> 其中 model_path 请替换为自己的模型路径
> XVERSE-65B 基于 bfloat16 训练的，建议选用 bfloat16 做微调训练。
```bash
deepspeed --num_gpus 8 src/train_bash.py \
--deepspeed deepspeed.json \
--stage sft \
--model_name_or_path model_path \
--do_train \
--dataset alpaca_gpt4_zh \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir output_model_path \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 1 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--plot_loss \
--bf16
```
deep_speed.json 参数配置：
```json
{
"train_micro_batch_size_per_gpu":"auto",
"gradient_accumulation_steps":"auto",
"gradient_clipping":"auto",
"zero_allow_untested_optimizer":true,
"fp16":{
"enabled":false
},
"bfloat16":{
"enabled":true
},
"zero_optimization":{
"stage":3,
"allgather_partitions":true,
"reduce_scatter":true,
"overlap_comm":false,
"contiguous_gradients":true
}
}
```
### Fine-tuning
XVERSE-65B allow developers to fine-tune for improved performance. Here, we attempted to use [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) for compatible fine-tuning training with XVERSE-65B, and tested it in an environment with 8 * Nvidia A800 80 GB + DeepSpeed.
Below, we provide the fine-tuning method using `LoRA with ZeRO-3`.
#### Environment Setup
Download the LLaMA-Factory project and [install dependencies] (https://github.com/hiyouga/LLaMA-Factory#getting-started) as required.
#### Training
Training launch script:
> Replace model_path with your own model path.
> Both XVERSE-65B and XVERSE-65B-Chat are trained based on bfloat16. It is recommended to use bfloat16 for fine-tuning training.
```bash
deepspeed --num_gpus 8 src/train_bash.py \
--deepspeed deepspeed.json \
--stage sft \
--model_name_or_path model_path \
--do_train \
--dataset alpaca_gpt4_zh \
--template default \
--finetuning_type lora \
--lora_target q_proj,v_proj \
--output_dir output_model_path \
--overwrite_cache \
--per_device_train_batch_size 4 \
--gradient_accumulation_steps 4 \
--lr_scheduler_type cosine \
--logging_steps 1 \
--save_steps 1000 \
--learning_rate 5e-5 \
--num_train_epochs 3.0 \
--plot_loss \
--bf16
```
deep_speed.json parameter settings：
```json
{
"train_micro_batch_size_per_gpu":"auto",
"gradient_accumulation_steps":"auto",
"gradient_clipping":"auto",
"zero_allow_untested_optimizer":true,
"fp16":{
"enabled":false
},
"bfloat16":{
"enabled":true
},
"zero_optimization":{
"stage":3,
"allgather_partitions":true,
"reduce_scatter":true,
"overlap_comm":false,
"contiguous_gradients":true
}
}
```
## 局限性与免责申明
XVERSE-65B 与其他所有 LLM 一样，在某些情况下可能会产生不准确、有偏见或其他令人反感的内容。因此，请谨慎使用模型生成的内容，请勿将生成的有害内容进行传播，在部署任何 XVERSE-65B 的应用之前，开发人员应根据其具体应用对模型进行安全测试和调优。
我们强烈警告不要将 XVERSE-65B 模型用于制造或传播有害信息，或进行任何可能损害公众、国家、社会安全或违反法规的活动。如果使用 XVERSE-65B 模型产生任何问题，无论是数据安全问题、公共舆论风险，还是模型被误解、滥用、传播或不合规使用所引发的任何风险和问题，我们将不承担任何责任。
## Limitations and Disclaimer
Like all other Large Language Models (LLMs), XVERSE-65B may produce inaccurate, biased, or otherwise offensive content under certain circumstances. Therefore, please use the content generated by the model with caution and refrain from disseminating harmful content. Before deploying any application of XVERSE-65B, developers should conduct safety tests and optimization of the model according to its specific application.
We strongly warn against the use of the XVERSE-65B model for producing or spreading harmful information, or conducting any activities that might harm the public, national, or social security, or violate regulations. We assume no responsibility for any problems arising from the use of the XVERSE-65B model, whether it be data security issues, public opinion risks, or any risks and issues caused by misunderstanding, misuse, dissemination, or non-compliance with the model.
## 模型开源协议
使用本仓库的源码需要遵循 [Apache-2.0](https://github.com/xverse-ai/XVERSE-65B/blob/main/LICENSE) 开源协议，使用 XVERSE-65B 的模型权重则需要遵循[模型许可协议](https://github.com/xverse-ai/XVERSE-65B/blob/main/MODEL_LICENSE.pdf)。
XVERSE-65B 模型权重对学术研究**完全开放**，并且支持**免费商用**。如需申请商业许可证，请填写【[申请表](https://chat.xverse.cn/home/business.html)】，如有其他问题或合作，请联系 <opensource@xverse.cn>。
## Open Source License
The use of the source code in this repository must follow the [Apache-2.0](https://github.com/xverse-ai/XVERSE-65B/blob/main/LICENSE) open-source license, while the use of the model weights of XVERSE-65B needs to adhere to the [Model License Agreement](https://github.com/xverse-ai/XVERSE-65B/blob/main/MODEL_LICENSE.pdf).
The XVERSE-65B model weights are **fully open** to academic research and support **free commercial use**. To apply for a commercial license, please fill in the [application form](https://chat.xverse.cn/home/business.html). For other questions or collaborations, please contact <opensource@xverse.cn>.
|
chandra21/xlsr_hindi
|
chandra21
| 2023-11-29T13:36:35Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_11_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-29T10:39:17Z |
---
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
|
Yntec/Tantrum
|
Yntec
| 2023-11-29T13:22:45Z | 115 | 2 |
diffusers
|
[
"diffusers",
"safetensors",
"Anime",
"Cartoons",
"Cute",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-11-29T12:07:57Z |
---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Anime
- Cartoons
- Cute
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# Tantrum
I made this model on a tantrum.
Comparison:
Sample and prompt:
princess,cartoon,wearing white dress,golden crown,red shoes,orange hair,kart,blue eyes,looking at viewer,smiling,happy,sitting on racing kart,outside,forest,blue sky,extremely detailed,hdr,toadstool,
|
netgvarun2005/MultiModalBertHubert
|
netgvarun2005
| 2023-11-29T13:20:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-08-23T18:47:21Z |
---
# For reference on model card metadata, see the spec: https://github.com/netgvarun2012/VirtualTherapist
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/netgvarun2012/VirtualTherapist).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
A MultiModal architecture model that was created and finetuned jointly by concatenating Hubert and BERT embeddings.
Hubert model was fine-tuned with a classification head on preprocessed audio and emotion labels in supervised manner.
BERT was trained on text transcrition embeddings.
Model can accurately recognize emotions classes- Angry,Sad,Fearful,Happy,Disgusted,Surprised,Calm with ~80% accuracy.
- **Developed by:** [https://www.linkedin.com/in/sharmavaruncs/]
- **Model type:** [MultiModal - Text and Audio based]
- **Language(s) (NLP):** [NLP, Speech processing]
- **Finetuned from model [optional]:** [https://huggingface.co/docs/transformers/model_doc/hubert]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [https://github.com/netgvarun2012/VirtualTherapist/]
- **Paper [optional]:** [https://github.com/netgvarun2012/VirtualTherapist/blob/main/documentation/Speech_and_Text_based_MultiModal_Emotion_Recognizer.pdf]
- **Demo [optional]:** [https://huggingface.co/spaces/netgvarun2005/VirtualTherapist]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
'Virtual Therapist' app - an Intelligent speech and text input based assistant that can decipher emotions and generate therapeutic messages based on the Emotional state of the user.
Emotions recognized - Angry,Sad,Fearful,Happy,Disgusted,Surprised,Calm with ~80% accuracy.
Use the code below to get started with the model:
class MultimodalModel(nn.Module):
'''
Custom PyTorch model that takes as input both the audio features and the text embeddings, and concatenates the last hidden states from the Hubert and BERT models.
'''
def __init__(self, bert_model_name, num_labels):
super().__init__()
self.hubert = HubertForSequenceClassification.from_pretrained("netgvarun2005/HubertStandaloneEmoDetector", num_labels=num_labels).hubert
self.bert = AutoModel.from_pretrained(bert_model_name)
self.classifier = nn.Linear(self.hubert.config.hidden_size + self.bert.config.hidden_size, num_labels)
def forward(self, input_values, text):
hubert_output = self.hubert(input_values).last_hidden_state
bert_output = self.bert(text).last_hidden_state
# Apply mean pooling along the sequence dimension
hubert_output = hubert_output.mean(dim=1)
bert_output = bert_output.mean(dim=1)
concat_output = torch.cat((hubert_output, bert_output), dim=-1)
logits = self.classifier(concat_output)
return logits
def load_model():
"""
Load and configure various models and tokenizers for a multi-modal application.
This function loads a multi-modal model and its weights from a specified source,
initializes tokenizers for the model and an additional language model, and returns
these components for use in a multi-modal application.
Returns:
tuple: A tuple containing the following components:
- multiModel (MultimodalModel): The multi-modal model.
- tokenizer (AutoTokenizer): Tokenizer for the multi-modal model.
- model_gpt (AutoModelForCausalLM): Language model for text generation.
- tokenizer_gpt (AutoTokenizer): Tokenizer for the language model.
"""
# Load the model
multiModel = MultimodalModel(bert_model_name, num_labels)
# Load the model weights and tokenizer directly from Hugging Face Spaces
multiModel.load_state_dict(torch.hub.load_state_dict_from_url(model_weights_path, map_location=device), strict=False)
tokenizer = AutoTokenizer.from_pretrained("netgvarun2005/MultiModalBertHubertTokenizer")
# GenAI
tokenizer_gpt = AutoTokenizer.from_pretrained("netgvarun2005/GPTTherapistDeepSpeedTokenizer", pad_token='<|pad|>',bos_token='<|startoftext|>',eos_token='<|endoftext|>')
model_gpt = AutoModelForCausalLM.from_pretrained("netgvarun2005/GPTTherapistDeepSpeedModel")
return multiModel,tokenizer,model_gpt,tokenizer_gpt
## Model Card Authors [Varun Sharma]
|
alexkoo300/burgundy-puma
|
alexkoo300
| 2023-11-29T13:20:17Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-11-29T12:36:02Z |
---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [h2oai/h2ogpt-4096-llama2-13b-chat](https://huggingface.co/h2oai/h2ogpt-4096-llama2-13b-chat)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.34.0
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCES_TOKEN>)
```
- Or directly pass your <ACCES_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="alexkoo300/burgundy-puma",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
Why is drinking water so healthy?</s>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"alexkoo300/burgundy-puma",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"alexkoo300/burgundy-puma",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
res = generate_text(
"Why is drinking water so healthy?",
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "alexkoo300/burgundy-puma" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "How are you?</s>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
min_new_tokens=2,
max_new_tokens=256,
do_sample=False,
num_beams=1,
temperature=float(0.0),
repetition_penalty=float(1.2),
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
LlamaForCausalLM(
(model): LlamaModel(
(embed_tokens): Embedding(32000, 5120, padding_idx=0)
(layers): ModuleList(
(0-39): 40 x LlamaDecoderLayer(
(self_attn): LlamaAttention(
(q_proj): Linear(in_features=5120, out_features=5120, bias=False)
(k_proj): Linear(in_features=5120, out_features=5120, bias=False)
(v_proj): Linear(in_features=5120, out_features=5120, bias=False)
(o_proj): Linear(in_features=5120, out_features=5120, bias=False)
(rotary_emb): LlamaRotaryEmbedding()
)
(mlp): LlamaMLP(
(gate_proj): Linear(in_features=5120, out_features=13824, bias=False)
(up_proj): Linear(in_features=5120, out_features=13824, bias=False)
(down_proj): Linear(in_features=13824, out_features=5120, bias=False)
(act_fn): SiLUActivation()
)
(input_layernorm): LlamaRMSNorm()
(post_attention_layernorm): LlamaRMSNorm()
)
)
(norm): LlamaRMSNorm()
)
(lm_head): Linear(in_features=5120, out_features=32000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it.
|
TheBloke/SauerkrautLM-7B-HerO-AWQ
|
TheBloke
| 2023-11-29T13:20:14Z | 82 | 4 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetune",
"chatml",
"augmentation",
"german",
"conversational",
"en",
"de",
"base_model:VAGOsolutions/SauerkrautLM-7b-HerO",
"base_model:quantized:VAGOsolutions/SauerkrautLM-7b-HerO",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2023-11-28T22:50:49Z |
---
base_model: VAGOsolutions/SauerkrautLM-7b-HerO
inference: false
language:
- en
- de
library_name: transformers
license: apache-2.0
model_creator: VAGO solutions
model_name: SauerkrautLM 7B HerO
model_type: mistral
pipeline_tag: text-generation
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
tags:
- mistral
- finetune
- chatml
- augmentation
- german
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# SauerkrautLM 7B HerO - AWQ
- Model creator: [VAGO solutions](https://huggingface.co/VAGOsolutions)
- Original model: [SauerkrautLM 7B HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO)
<!-- description start -->
## Description
This repo contains AWQ model files for [VAGO solutions's SauerkrautLM 7B HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF)
* [VAGO solutions's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-AWQ/tree/main) | 4 | 128 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 4096 | 4.15 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/SauerkrautLM-7B-HerO-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `SauerkrautLM-7B-HerO-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/SauerkrautLM-7B-HerO-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/SauerkrautLM-7B-HerO-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/SauerkrautLM-7B-HerO-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/SauerkrautLM-7B-HerO-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: VAGO solutions's SauerkrautLM 7B HerO
## VAGO solutions SauerkrautLM-7b-HerO
Introducing **SauerkrautLM-7b-HerO** – the pinnacle of German language model technology!
Crafted through the **merging** of **[Teknium's OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)** and **[Open-Orca's Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)** and **uniquely fine-tuned with the Sauerkraut dataset.**
SauerkrautLM-7b-HerO represents a breakthrough in language modeling, achieving an optimal balance between extensive German data and essential international sources.
This ensures the model not only excels in understanding the nuances of the German language but also retains its global capabilities.
Harnessing the innovative power of the **gradient SLERP method from MergeKit**, we've achieved a groundbreaking fusion of two of the most best performing 7B models based on the Mistral framework.
This merge has allowed us to combine the best features of both models, creating an unparalleled synergy.
Coupled with the German Sauerkraut dataset, which consists of a mix of augmented and translated data, we have successfully taught the English-speaking merged model the intricacies of the German language.
This was achieved *without the typical loss of core competencies often associated with fine-tuning in another language of models previously trained mainly in English.*
Our approach ensures that the model retains its original strengths while acquiring a profound understanding of German, **setting a new benchmark in bilingual language model proficiency.**
# Table of Contents
1. [Overview of all Her0 models](#all-hero-models)
2. [Model Details](#model-details)
- [Prompt template](#prompt-template)
- [Training Dataset](#training-dataset)
- [Merge Procedure](#merge-procedure)
3. [Evaluation](#evaluation)
- [GPT4ALL](#gpt4all)
- [Language Model evaluation Harness](#language-model-evaluation-harness)
- [BigBench](#big-bench)
- [MMLU](#mmlu)
- [TruthfulQA](#truthfulqa)
- [MT-Bench (German)](#mt-bench-german)
- [MT-Bench (English)](#mt-bench-english)
- [Additional German Benchmark results](#additional-german-benchmark-results)
5. [Disclaimer](#disclaimer)
6. [Contact](#contact)
7. [Collaborations](#collaborations)
8. [Acknowledgement](#acknowledgement)
## All HerO Models
| Model | HF | GPTQ | GGUF | AWQ |
|-------|-------|-------|-------|-------|
| SauerkrautLM-7b-HerO | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO) | coming soon | coming soon | coming soon |
## Model Details
**SauerkrautLM-7b-HerO**
- **Model Type:** SauerkrautLM-7b-HerO is an auto-regressive language model based on the transformer architecture
- **Language(s):** English, German
- **License:** APACHE 2.0
- **Contact:** [Website](https://vago-solutions.de/#Kontakt) [David Golchinfar](mailto:golchinfar@vago-solutions.de)
### Training Dataset:
SauerkrautLM-7b-HerO was trained with mix of German data augmentation and translated data.
We found, that only a simple translation of training data can lead to unnatural German phrasings.
Data augmentation techniques were used to grant grammatical, syntactical correctness and a more natural German wording in our training data.
### Merge Procedure:
SauerkrautLM-7b-HerO was merged on 1 A100 with [mergekit](https://github.com/cg123/mergekit).
The merged model contains [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca).
We applied the gradient SLERP method.
### Prompt Template:
```
<|im_start|>system
Du bist Sauerkraut-HerO, ein großes Sprachmodell, das höflich und kompetent antwortet. Schreibe deine Gedanken Schritt für Schritt auf, um Probleme sinnvoll zu lösen.<|im_end|>
<|im_start|>user
Wie geht es dir?<|im_end|>
<|im_start|>assistant
Mir geht es gut!<|im_end|>
<|im_start|>user
Bitte erkläre mir, wie die Zusammenführung von Modellen durch bestehende Spitzenmodelle profitieren kann.<|im_end|>
<|im_start|>assistant
```
## Evaluation
### GPT4ALL:
*Compared to relevant German Closed and Open Source models*
### Language Model evaluation Harness:
*Compared to Aleph Alpha Luminous Models*
**performed with newest Language Model Evaluation Harness*
### Big Bench:
**performed with newest Language Model Evaluation Harness*
### MMLU:
*Compared to Big Boy LLMs (Grok0,Grok1,GPT3.5,GPT4)*
### TruthfulQA:
*Compared to OpenAI Models (GPT3.5,GPT4)*
### MT-Bench (German):
```
########## First turn ##########
score
model turn
SauerkrautLM-70b-v1 1 7.25000
SauerkrautLM-7b-HerO <--- 1 6.96875
SauerkrautLM-7b-v1-mistral 1 6.30625
leo-hessianai-13b-chat 1 6.18750
SauerkrautLM-13b-v1 1 6.16250
leo-mistral-hessianai-7b-chat 1 6.15625
Llama-2-70b-chat-hf 1 6.03750
vicuna-13b-v1.5 1 5.80000
SauerkrautLM-7b-v1 1 5.65000
leo-hessianai-7b-chat 1 5.52500
vicuna-7b-v1.5 1 5.42500
Mistral-7B-v0.1 1 5.37500
SauerkrautLM-3b-v1 1 3.17500
Llama-2-7b 1 1.28750
open_llama_3b_v2 1 1.68750
########## Second turn ##########
score
model turn
SauerkrautLM-70b-v1 2 6.83125
SauerkrautLM-7b-HerO <--- 2 6.30625
vicuna-13b-v1.5 2 5.63125
SauerkrautLM-13b-v1 2 5.34375
SauerkrautLM-7b-v1-mistral 2 5.26250
leo-mistral-hessianai-7b-chat 2 4.99375
SauerkrautLM-7b-v1 2 4.73750
leo-hessianai-13b-chat 2 4.71250
vicuna-7b-v1.5 2 4.67500
Llama-2-70b-chat-hf 2 4.66250
Mistral-7B-v0.1 2 4.53750
leo-hessianai-7b-chat 2 2.65000
SauerkrautLM-3b-v1 2 1.98750
open_llama_3b_v2 2 1.22500
Llama-2-7b 2 1.07500
########## Average ##########
score
model
SauerkrautLM-70b-v1 7.040625
SauerkrautLM-7b-HerO <--- 6.637500
SauerkrautLM-7b-v1-mistral 5.784375
SauerkrautLM-13b-v1 5.753125
vicuna-13b-v1.5 5.715625
leo-mistral-hessianai-7b-chat 5.575000
leo-hessianai-13b-chat 5.450000
Llama-2-70b-chat-hf 5.350000
SauerkrautLM-v1-7b 5.193750
vicuna-7b-v1.5 5.050000
Mistral-7B-v0.1 4.956250
leo-hessianai-7b-chat 4.087500
SauerkrautLM-3b-v1 2.581250
open_llama_3b_v2 1.456250
Llama-2-7b 1.181250
```
**performed with the newest FastChat Version*
### MT-Bench (English):
```
########## First turn ##########
score
model turn
OpenHermes-2.5-Mistral-7B 1 8.21875
SauerkrautLM-7b-HerO <--- 1 8.03125
Mistral-7B-OpenOrca 1 7.65625
neural-chat-7b-v3-1 1 7.22500
########## Second turn ##########
score
model turn
OpenHermes-2.5-Mistral-7B 2 7.1000
SauerkrautLM-7b-HerO <--- 2 6.7875
neural-chat-7b-v3-1 2 6.4000
Mistral-7B-OpenOrca 2 6.1750
########## Average ##########
score
model
OpenHermes-2.5-Mistral-7B 7.659375
SauerkrautLM-7b-HerO <--- 7.409375
Mistral-7B-OpenOrca 6.915625
neural-chat-7b-v3-1 6.812500
```
**performed with the newest FastChat Version*
### Additional German Benchmark results:
*performed with newest Language Model Evaluation Harness
## Disclaimer
We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out.
However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided.
Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models. These models may be employed for commercial purposes, and the Apache 2.0 remains applicable and is included with the model files.
## Contact
If you are interested in customized LLMs for business applications, please get in contact with us via our website or contact us at [Dr. Daryoush Vaziri](mailto:vaziri@vago-solutions.de). We are also grateful for your feedback and suggestions.
## Collaborations
We are also keenly seeking support and investment for our startup, VAGO solutions, where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us.
## Acknowledgement
Many thanks to [OpenOrca](https://huggingface.co/Open-Orca) and [teknium](https://huggingface.co/teknium) for providing such valuable models to the Open-Source community.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
|
TheBloke/SauerkrautLM-7B-HerO-GGUF
|
TheBloke
| 2023-11-29T13:20:07Z | 139 | 4 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"finetune",
"chatml",
"augmentation",
"german",
"text-generation",
"en",
"de",
"base_model:VAGOsolutions/SauerkrautLM-7b-HerO",
"base_model:quantized:VAGOsolutions/SauerkrautLM-7b-HerO",
"license:apache-2.0",
"region:us",
"conversational"
] |
text-generation
| 2023-11-28T22:50:49Z |
---
base_model: VAGOsolutions/SauerkrautLM-7b-HerO
inference: false
language:
- en
- de
library_name: transformers
license: apache-2.0
model_creator: VAGO solutions
model_name: SauerkrautLM 7B HerO
model_type: mistral
pipeline_tag: text-generation
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
tags:
- mistral
- finetune
- chatml
- augmentation
- german
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# SauerkrautLM 7B HerO - GGUF
- Model creator: [VAGO solutions](https://huggingface.co/VAGOsolutions)
- Original model: [SauerkrautLM 7B HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO)
<!-- description start -->
## Description
This repo contains GGUF format model files for [VAGO solutions's SauerkrautLM 7B HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF)
* [VAGO solutions's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [sauerkrautlm-7b-hero.Q2_K.gguf](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF/blob/main/sauerkrautlm-7b-hero.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [sauerkrautlm-7b-hero.Q3_K_S.gguf](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF/blob/main/sauerkrautlm-7b-hero.Q3_K_S.gguf) | Q3_K_S | 3 | 3.16 GB| 5.66 GB | very small, high quality loss |
| [sauerkrautlm-7b-hero.Q3_K_M.gguf](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF/blob/main/sauerkrautlm-7b-hero.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [sauerkrautlm-7b-hero.Q3_K_L.gguf](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF/blob/main/sauerkrautlm-7b-hero.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [sauerkrautlm-7b-hero.Q4_0.gguf](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF/blob/main/sauerkrautlm-7b-hero.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [sauerkrautlm-7b-hero.Q4_K_S.gguf](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF/blob/main/sauerkrautlm-7b-hero.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [sauerkrautlm-7b-hero.Q4_K_M.gguf](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF/blob/main/sauerkrautlm-7b-hero.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [sauerkrautlm-7b-hero.Q5_0.gguf](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF/blob/main/sauerkrautlm-7b-hero.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [sauerkrautlm-7b-hero.Q5_K_S.gguf](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF/blob/main/sauerkrautlm-7b-hero.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [sauerkrautlm-7b-hero.Q5_K_M.gguf](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF/blob/main/sauerkrautlm-7b-hero.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [sauerkrautlm-7b-hero.Q6_K.gguf](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF/blob/main/sauerkrautlm-7b-hero.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [sauerkrautlm-7b-hero.Q8_0.gguf](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF/blob/main/sauerkrautlm-7b-hero.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/SauerkrautLM-7B-HerO-GGUF and below it, a specific filename to download, such as: sauerkrautlm-7b-hero.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/SauerkrautLM-7B-HerO-GGUF sauerkrautlm-7b-hero.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/SauerkrautLM-7B-HerO-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/SauerkrautLM-7B-HerO-GGUF sauerkrautlm-7b-hero.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m sauerkrautlm-7b-hero.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./sauerkrautlm-7b-hero.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./sauerkrautlm-7b-hero.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: VAGO solutions's SauerkrautLM 7B HerO
## VAGO solutions SauerkrautLM-7b-HerO
Introducing **SauerkrautLM-7b-HerO** – the pinnacle of German language model technology!
Crafted through the **merging** of **[Teknium's OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)** and **[Open-Orca's Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)** and **uniquely fine-tuned with the Sauerkraut dataset.**
SauerkrautLM-7b-HerO represents a breakthrough in language modeling, achieving an optimal balance between extensive German data and essential international sources.
This ensures the model not only excels in understanding the nuances of the German language but also retains its global capabilities.
Harnessing the innovative power of the **gradient SLERP method from MergeKit**, we've achieved a groundbreaking fusion of two of the most best performing 7B models based on the Mistral framework.
This merge has allowed us to combine the best features of both models, creating an unparalleled synergy.
Coupled with the German Sauerkraut dataset, which consists of a mix of augmented and translated data, we have successfully taught the English-speaking merged model the intricacies of the German language.
This was achieved *without the typical loss of core competencies often associated with fine-tuning in another language of models previously trained mainly in English.*
Our approach ensures that the model retains its original strengths while acquiring a profound understanding of German, **setting a new benchmark in bilingual language model proficiency.**
# Table of Contents
1. [Overview of all Her0 models](#all-hero-models)
2. [Model Details](#model-details)
- [Prompt template](#prompt-template)
- [Training Dataset](#training-dataset)
- [Merge Procedure](#merge-procedure)
3. [Evaluation](#evaluation)
- [GPT4ALL](#gpt4all)
- [Language Model evaluation Harness](#language-model-evaluation-harness)
- [BigBench](#big-bench)
- [MMLU](#mmlu)
- [TruthfulQA](#truthfulqa)
- [MT-Bench (German)](#mt-bench-german)
- [MT-Bench (English)](#mt-bench-english)
- [Additional German Benchmark results](#additional-german-benchmark-results)
5. [Disclaimer](#disclaimer)
6. [Contact](#contact)
7. [Collaborations](#collaborations)
8. [Acknowledgement](#acknowledgement)
## All HerO Models
| Model | HF | GPTQ | GGUF | AWQ |
|-------|-------|-------|-------|-------|
| SauerkrautLM-7b-HerO | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO) | coming soon | coming soon | coming soon |
## Model Details
**SauerkrautLM-7b-HerO**
- **Model Type:** SauerkrautLM-7b-HerO is an auto-regressive language model based on the transformer architecture
- **Language(s):** English, German
- **License:** APACHE 2.0
- **Contact:** [Website](https://vago-solutions.de/#Kontakt) [David Golchinfar](mailto:golchinfar@vago-solutions.de)
### Training Dataset:
SauerkrautLM-7b-HerO was trained with mix of German data augmentation and translated data.
We found, that only a simple translation of training data can lead to unnatural German phrasings.
Data augmentation techniques were used to grant grammatical, syntactical correctness and a more natural German wording in our training data.
### Merge Procedure:
SauerkrautLM-7b-HerO was merged on 1 A100 with [mergekit](https://github.com/cg123/mergekit).
The merged model contains [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca).
We applied the gradient SLERP method.
### Prompt Template:
```
<|im_start|>system
Du bist Sauerkraut-HerO, ein großes Sprachmodell, das höflich und kompetent antwortet. Schreibe deine Gedanken Schritt für Schritt auf, um Probleme sinnvoll zu lösen.<|im_end|>
<|im_start|>user
Wie geht es dir?<|im_end|>
<|im_start|>assistant
Mir geht es gut!<|im_end|>
<|im_start|>user
Bitte erkläre mir, wie die Zusammenführung von Modellen durch bestehende Spitzenmodelle profitieren kann.<|im_end|>
<|im_start|>assistant
```
## Evaluation
### GPT4ALL:
*Compared to relevant German Closed and Open Source models*
### Language Model evaluation Harness:
*Compared to Aleph Alpha Luminous Models*
**performed with newest Language Model Evaluation Harness*
### Big Bench:
**performed with newest Language Model Evaluation Harness*
### MMLU:
*Compared to Big Boy LLMs (Grok0,Grok1,GPT3.5,GPT4)*
### TruthfulQA:
*Compared to OpenAI Models (GPT3.5,GPT4)*
### MT-Bench (German):
```
########## First turn ##########
score
model turn
SauerkrautLM-70b-v1 1 7.25000
SauerkrautLM-7b-HerO <--- 1 6.96875
SauerkrautLM-7b-v1-mistral 1 6.30625
leo-hessianai-13b-chat 1 6.18750
SauerkrautLM-13b-v1 1 6.16250
leo-mistral-hessianai-7b-chat 1 6.15625
Llama-2-70b-chat-hf 1 6.03750
vicuna-13b-v1.5 1 5.80000
SauerkrautLM-7b-v1 1 5.65000
leo-hessianai-7b-chat 1 5.52500
vicuna-7b-v1.5 1 5.42500
Mistral-7B-v0.1 1 5.37500
SauerkrautLM-3b-v1 1 3.17500
Llama-2-7b 1 1.28750
open_llama_3b_v2 1 1.68750
########## Second turn ##########
score
model turn
SauerkrautLM-70b-v1 2 6.83125
SauerkrautLM-7b-HerO <--- 2 6.30625
vicuna-13b-v1.5 2 5.63125
SauerkrautLM-13b-v1 2 5.34375
SauerkrautLM-7b-v1-mistral 2 5.26250
leo-mistral-hessianai-7b-chat 2 4.99375
SauerkrautLM-7b-v1 2 4.73750
leo-hessianai-13b-chat 2 4.71250
vicuna-7b-v1.5 2 4.67500
Llama-2-70b-chat-hf 2 4.66250
Mistral-7B-v0.1 2 4.53750
leo-hessianai-7b-chat 2 2.65000
SauerkrautLM-3b-v1 2 1.98750
open_llama_3b_v2 2 1.22500
Llama-2-7b 2 1.07500
########## Average ##########
score
model
SauerkrautLM-70b-v1 7.040625
SauerkrautLM-7b-HerO <--- 6.637500
SauerkrautLM-7b-v1-mistral 5.784375
SauerkrautLM-13b-v1 5.753125
vicuna-13b-v1.5 5.715625
leo-mistral-hessianai-7b-chat 5.575000
leo-hessianai-13b-chat 5.450000
Llama-2-70b-chat-hf 5.350000
SauerkrautLM-v1-7b 5.193750
vicuna-7b-v1.5 5.050000
Mistral-7B-v0.1 4.956250
leo-hessianai-7b-chat 4.087500
SauerkrautLM-3b-v1 2.581250
open_llama_3b_v2 1.456250
Llama-2-7b 1.181250
```
**performed with the newest FastChat Version*
### MT-Bench (English):
```
########## First turn ##########
score
model turn
OpenHermes-2.5-Mistral-7B 1 8.21875
SauerkrautLM-7b-HerO <--- 1 8.03125
Mistral-7B-OpenOrca 1 7.65625
neural-chat-7b-v3-1 1 7.22500
########## Second turn ##########
score
model turn
OpenHermes-2.5-Mistral-7B 2 7.1000
SauerkrautLM-7b-HerO <--- 2 6.7875
neural-chat-7b-v3-1 2 6.4000
Mistral-7B-OpenOrca 2 6.1750
########## Average ##########
score
model
OpenHermes-2.5-Mistral-7B 7.659375
SauerkrautLM-7b-HerO <--- 7.409375
Mistral-7B-OpenOrca 6.915625
neural-chat-7b-v3-1 6.812500
```
**performed with the newest FastChat Version*
### Additional German Benchmark results:
*performed with newest Language Model Evaluation Harness
## Disclaimer
We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out.
However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided.
Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models. These models may be employed for commercial purposes, and the Apache 2.0 remains applicable and is included with the model files.
## Contact
If you are interested in customized LLMs for business applications, please get in contact with us via our website or contact us at [Dr. Daryoush Vaziri](mailto:vaziri@vago-solutions.de). We are also grateful for your feedback and suggestions.
## Collaborations
We are also keenly seeking support and investment for our startup, VAGO solutions, where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us.
## Acknowledgement
Many thanks to [OpenOrca](https://huggingface.co/Open-Orca) and [teknium](https://huggingface.co/teknium) for providing such valuable models to the Open-Source community.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<!-- original-model-card end -->
|
Realgon/roberta_sst2_padding80model
|
Realgon
| 2023-11-29T13:18:38Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-29T12:04:38Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst2_padding80model
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_padding80model
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: 0.5696
- Accuracy: 0.9385
## 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 | 0.1681 | 0.9368 |
| 0.332 | 2.0 | 866 | 0.3784 | 0.9099 |
| 0.1989 | 3.0 | 1299 | 0.2267 | 0.9478 |
| 0.1089 | 4.0 | 1732 | 0.3591 | 0.9341 |
| 0.0727 | 5.0 | 2165 | 0.3262 | 0.9429 |
| 0.0381 | 6.0 | 2598 | 0.3532 | 0.9385 |
| 0.0307 | 7.0 | 3031 | 0.4867 | 0.9341 |
| 0.0307 | 8.0 | 3464 | 0.4819 | 0.9352 |
| 0.0201 | 9.0 | 3897 | 0.5591 | 0.9220 |
| 0.0249 | 10.0 | 4330 | 0.5679 | 0.9319 |
| 0.0142 | 11.0 | 4763 | 0.4514 | 0.9379 |
| 0.0199 | 12.0 | 5196 | 0.4652 | 0.9374 |
| 0.0095 | 13.0 | 5629 | 0.5044 | 0.9451 |
| 0.0111 | 14.0 | 6062 | 0.4807 | 0.9407 |
| 0.0111 | 15.0 | 6495 | 0.4752 | 0.9407 |
| 0.009 | 16.0 | 6928 | 0.5267 | 0.9374 |
| 0.0046 | 17.0 | 7361 | 0.5300 | 0.9396 |
| 0.0071 | 18.0 | 7794 | 0.5414 | 0.9412 |
| 0.0006 | 19.0 | 8227 | 0.5648 | 0.9407 |
| 0.0024 | 20.0 | 8660 | 0.5696 | 0.9385 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Herocat/opt-350m-finetuned-wikitext2
|
Herocat
| 2023-11-29T13:10:27Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"base_model:facebook/opt-350m",
"base_model:finetune:facebook/opt-350m",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-02T13:23:00Z |
---
license: other
base_model: facebook/opt-350m
tags:
- generated_from_trainer
model-index:
- name: opt-350m-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. -->
# opt-350m-finetuned-wikitext2
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.0919
- eval_runtime: 51.3961
- eval_samples_per_second: 19.048
- eval_steps_per_second: 2.393
- epoch: 3.04
- step: 3602
## 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: 5
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Luckyluuuc/q-FrozenLake-v1-4x4-noSlippery
|
Luckyluuuc
| 2023-11-29T13:09:40Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T13:09:36Z |
---
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="Luckyluuuc/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"])
```
|
haurajahra/QA_using_indoBERT_LORA_qv2
|
haurajahra
| 2023-11-29T13:06:35Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:indolem/indobert-base-uncased",
"base_model:finetune:indolem/indobert-base-uncased",
"license:mit",
"region:us"
] | null | 2023-11-29T04:44:22Z |
---
license: mit
base_model: indolem/indobert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: QA_using_indoBERT_LORA_qv2
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_using_indoBERT_LORA_qv2
This model is a fine-tuned version of [indolem/indobert-base-uncased](https://huggingface.co/indolem/indobert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9435
## 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: 4
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 5.7426 | 0.02 | 500 | 6.2378 |
| 5.1601 | 0.03 | 1000 | 4.0267 |
| 3.466 | 0.05 | 1500 | 3.0399 |
| 2.9304 | 0.06 | 2000 | 2.8011 |
| 2.7403 | 0.08 | 2500 | 2.7113 |
| 2.599 | 0.09 | 3000 | 2.6337 |
| 2.4993 | 0.11 | 3500 | 2.4798 |
| 2.4454 | 0.12 | 4000 | 2.4486 |
| 2.3938 | 0.14 | 4500 | 2.3848 |
| 2.3124 | 0.15 | 5000 | 2.3729 |
| 2.2595 | 0.17 | 5500 | 2.4021 |
| 2.241 | 0.18 | 6000 | 2.3487 |
| 2.3296 | 0.2 | 6500 | 2.2819 |
| 2.21 | 0.21 | 7000 | 2.2588 |
| 2.2386 | 0.23 | 7500 | 2.3498 |
| 2.164 | 0.25 | 8000 | 2.2315 |
| 2.2535 | 0.26 | 8500 | 2.2315 |
| 2.2621 | 0.28 | 9000 | 2.3788 |
| 2.364 | 0.29 | 9500 | 2.8077 |
| 2.2345 | 0.31 | 10000 | 2.2495 |
| 2.1571 | 0.32 | 10500 | 2.2306 |
| 2.0452 | 0.34 | 11000 | 2.2417 |
| 2.1279 | 0.35 | 11500 | 2.1814 |
| 2.1482 | 0.37 | 12000 | 2.1762 |
| 2.1064 | 0.38 | 12500 | 2.1931 |
| 1.9992 | 0.4 | 13000 | 2.1902 |
| 2.1265 | 0.41 | 13500 | 2.1558 |
| 2.0659 | 0.43 | 14000 | 2.2007 |
| 2.0314 | 0.44 | 14500 | 2.1326 |
| 2.0086 | 0.46 | 15000 | 2.1282 |
| 2.0168 | 0.48 | 15500 | 2.1372 |
| 2.024 | 0.49 | 16000 | 2.1111 |
| 2.0636 | 0.51 | 16500 | 2.0926 |
| 1.9673 | 0.52 | 17000 | 2.1200 |
| 2.0207 | 0.54 | 17500 | 2.1710 |
| 2.0857 | 0.55 | 18000 | 2.1886 |
| 2.1617 | 0.57 | 18500 | 2.1123 |
| 1.9912 | 0.58 | 19000 | 2.0999 |
| 2.1166 | 0.6 | 19500 | 2.0940 |
| 2.0312 | 0.61 | 20000 | 2.1436 |
| 2.1124 | 0.63 | 20500 | 2.1743 |
| 2.0399 | 0.64 | 21000 | 2.0801 |
| 1.9246 | 0.66 | 21500 | 2.0535 |
| 1.9792 | 0.67 | 22000 | 2.0926 |
| 1.9713 | 0.69 | 22500 | 2.0666 |
| 1.9285 | 0.71 | 23000 | 2.0699 |
| 1.9454 | 0.72 | 23500 | 2.0873 |
| 1.9255 | 0.74 | 24000 | 2.0515 |
| 1.9428 | 0.75 | 24500 | 2.0771 |
| 1.9093 | 0.77 | 25000 | 2.0538 |
| 1.933 | 0.78 | 25500 | 2.0308 |
| 1.8628 | 0.8 | 26000 | 2.0554 |
| 1.906 | 0.81 | 26500 | 2.0581 |
| 1.9255 | 0.83 | 27000 | 2.0167 |
| 1.8795 | 0.84 | 27500 | 2.0423 |
| 1.8987 | 0.86 | 28000 | 2.0300 |
| 1.8464 | 0.87 | 28500 | 2.0540 |
| 1.9619 | 0.89 | 29000 | 2.0068 |
| 1.9475 | 0.9 | 29500 | 2.0079 |
| 1.9399 | 0.92 | 30000 | 1.9889 |
| 1.8473 | 0.94 | 30500 | 2.0135 |
| 1.8775 | 0.95 | 31000 | 2.0096 |
| 1.8049 | 0.97 | 31500 | 2.0289 |
| 1.8029 | 0.98 | 32000 | 2.0561 |
| 1.9167 | 1.0 | 32500 | 2.0199 |
| 1.873 | 1.01 | 33000 | 2.0081 |
| 1.7915 | 1.03 | 33500 | 2.0418 |
| 1.8741 | 1.04 | 34000 | 2.0087 |
| 1.8528 | 1.06 | 34500 | 2.0023 |
| 1.8255 | 1.07 | 35000 | 2.0275 |
| 1.8667 | 1.09 | 35500 | 2.0227 |
| 1.7821 | 1.1 | 36000 | 1.9990 |
| 1.7809 | 1.12 | 36500 | 2.0067 |
| 1.8287 | 1.13 | 37000 | 1.9984 |
| 1.8026 | 1.15 | 37500 | 2.0272 |
| 1.8299 | 1.16 | 38000 | 2.0259 |
| 1.7972 | 1.18 | 38500 | 2.0382 |
| 1.8505 | 1.2 | 39000 | 1.9803 |
| 1.8319 | 1.21 | 39500 | 1.9699 |
| 1.8171 | 1.23 | 40000 | 1.9931 |
| 1.7986 | 1.24 | 40500 | 1.9933 |
| 1.8228 | 1.26 | 41000 | 1.9807 |
| 1.8793 | 1.27 | 41500 | 1.9999 |
| 1.7724 | 1.29 | 42000 | 1.9779 |
| 1.7328 | 1.3 | 42500 | 1.9725 |
| 1.8083 | 1.32 | 43000 | 1.9603 |
| 1.7829 | 1.33 | 43500 | 1.9790 |
| 1.7823 | 1.35 | 44000 | 1.9777 |
| 1.7715 | 1.36 | 44500 | 1.9831 |
| 1.8368 | 1.38 | 45000 | 1.9531 |
| 1.7688 | 1.39 | 45500 | 1.9666 |
| 1.7946 | 1.41 | 46000 | 1.9662 |
| 1.8104 | 1.43 | 46500 | 1.9799 |
| 1.758 | 1.44 | 47000 | 1.9697 |
| 1.802 | 1.46 | 47500 | 1.9617 |
| 1.7628 | 1.47 | 48000 | 1.9645 |
| 1.8014 | 1.49 | 48500 | 1.9642 |
| 1.8153 | 1.5 | 49000 | 1.9449 |
| 1.7997 | 1.52 | 49500 | 1.9682 |
| 1.8021 | 1.53 | 50000 | 1.9567 |
| 1.766 | 1.55 | 50500 | 1.9740 |
| 1.7886 | 1.56 | 51000 | 1.9513 |
| 1.7865 | 1.58 | 51500 | 1.9411 |
| 1.8403 | 1.59 | 52000 | 1.9396 |
| 1.7257 | 1.61 | 52500 | 1.9590 |
| 1.7743 | 1.62 | 53000 | 1.9408 |
| 1.7903 | 1.64 | 53500 | 1.9469 |
| 1.8302 | 1.66 | 54000 | 1.9370 |
| 1.7979 | 1.67 | 54500 | 1.9394 |
| 1.8109 | 1.69 | 55000 | 1.9440 |
| 1.7397 | 1.7 | 55500 | 1.9579 |
| 1.7374 | 1.72 | 56000 | 1.9501 |
| 1.7373 | 1.73 | 56500 | 1.9518 |
| 1.7273 | 1.75 | 57000 | 1.9474 |
| 1.8064 | 1.76 | 57500 | 1.9368 |
| 1.7913 | 1.78 | 58000 | 1.9426 |
| 1.8166 | 1.79 | 58500 | 1.9331 |
| 1.8238 | 1.81 | 59000 | 1.9341 |
| 1.8049 | 1.82 | 59500 | 1.9464 |
| 1.8735 | 1.84 | 60000 | 1.9397 |
| 1.8169 | 1.85 | 60500 | 1.9388 |
| 1.7689 | 1.87 | 61000 | 1.9393 |
| 1.7612 | 1.89 | 61500 | 1.9433 |
| 1.7768 | 1.9 | 62000 | 1.9402 |
| 1.6952 | 1.92 | 62500 | 1.9478 |
| 1.7951 | 1.93 | 63000 | 1.9395 |
| 1.764 | 1.95 | 63500 | 1.9381 |
| 1.7895 | 1.96 | 64000 | 1.9362 |
| 1.6671 | 1.98 | 64500 | 1.9428 |
| 1.7535 | 1.99 | 65000 | 1.9435 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
anetakahle/OpenChat3.5-km-finetuned
|
anetakahle
| 2023-11-29T13:04:10Z | 0 | 0 | null |
[
"text-generation",
"dataset:anetakahle/sexchat_cs1",
"license:mit",
"region:us"
] |
text-generation
| 2023-11-29T12:58:28Z |
---
license: mit
datasets:
- anetakahle/sexchat_cs1
pipeline_tag: text-generation
---
|
scikit-learn/Fish-Weight
|
scikit-learn
| 2023-11-29T12:48:44Z | 0 | 2 |
sklearn
|
[
"sklearn",
"skops",
"tabular-regression",
"license:mit",
"region:us"
] |
tabular-regression
| 2022-10-06T20:37:41Z |
---
license: mit
library_name: sklearn
tags:
- sklearn
- skops
- tabular-regression
widget:
- structuredData:
Height:
- 11.52
- 12.48
- 12.3778
Length1:
- 23.2
- 24.0
- 23.9
Length2:
- 25.4
- 26.3
- 26.5
Length3:
- 30.0
- 31.2
- 31.1
Species:
- Bream
- Bream
- Bream
Width:
- 4.02
- 4.3056
- 4.6961
---
# Model description
This is a GradientBoostingRegressor on a fish dataset.
## Intended uses & limitations
This model is intended for educational purposes.
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|-----------------------------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| memory | |
| steps | [('columntransformer', ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)])), ('gradientboostingregressor', GradientBoostingRegressor(random_state=42))] |
| verbose | False |
| columntransformer | ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)]) |
| gradientboostingregressor | GradientBoostingRegressor(random_state=42) |
| columntransformer__n_jobs | |
| columntransformer__remainder | passthrough |
| columntransformer__sparse_threshold | 0.3 |
| columntransformer__transformer_weights | |
| columntransformer__transformers | [('onehotencoder', OneHotEncoder(handle_unknown='ignore', sparse=False), <sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)] |
| columntransformer__verbose | False |
| columntransformer__verbose_feature_names_out | True |
| columntransformer__onehotencoder | OneHotEncoder(handle_unknown='ignore', sparse=False) |
| columntransformer__onehotencoder__categories | auto |
| columntransformer__onehotencoder__drop | |
| columntransformer__onehotencoder__dtype | <class 'numpy.float64'> |
| columntransformer__onehotencoder__handle_unknown | ignore |
| columntransformer__onehotencoder__sparse | False |
| gradientboostingregressor__alpha | 0.9 |
| gradientboostingregressor__ccp_alpha | 0.0 |
| gradientboostingregressor__criterion | friedman_mse |
| gradientboostingregressor__init | |
| gradientboostingregressor__learning_rate | 0.1 |
| gradientboostingregressor__loss | squared_error |
| gradientboostingregressor__max_depth | 3 |
| gradientboostingregressor__max_features | |
| gradientboostingregressor__max_leaf_nodes | |
| gradientboostingregressor__min_impurity_decrease | 0.0 |
| gradientboostingregressor__min_samples_leaf | 1 |
| gradientboostingregressor__min_samples_split | 2 |
| gradientboostingregressor__min_weight_fraction_leaf | 0.0 |
| gradientboostingregressor__n_estimators | 100 |
| gradientboostingregressor__n_iter_no_change | |
| gradientboostingregressor__random_state | 42 |
| gradientboostingregressor__subsample | 1.0 |
| gradientboostingregressor__tol | 0.0001 |
| gradientboostingregressor__validation_fraction | 0.1 |
| gradientboostingregressor__verbose | 0 |
| gradientboostingregressor__warm_start | False |
</details>
### Model Plot
The model plot is below.
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See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794 div.sk-text-repr-fallback {display: none;}</style><div id="sk-ccf5150a-bed5-4d7b-a5a9-a1a6d13a1794" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)])),('gradientboostingregressor',GradientBoostingRegressor(random_state=42))])</pre><b>Please rerun this cell to show the HTML repr or trust the notebook.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="f6612892-c085-4dd9-8dca-9cb8081c3777" type="checkbox" ><label for="f6612892-c085-4dd9-8dca-9cb8081c3777" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('columntransformer',ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)])),('gradientboostingregressor',GradientBoostingRegressor(random_state=42))])</pre></div></div></div><div class="sk-serial"><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="3d74f98b-ae31-452d-af87-2c65b0323ba2" type="checkbox" ><label for="3d74f98b-ae31-452d-af87-2c65b0323ba2" class="sk-toggleable__label sk-toggleable__label-arrow">columntransformer: ColumnTransformer</label><div class="sk-toggleable__content"><pre>ColumnTransformer(remainder='passthrough',transformers=[('onehotencoder',OneHotEncoder(handle_unknown='ignore',sparse=False),<sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0>)])</pre></div></div></div><div class="sk-parallel"><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="4af39992-03cf-4522-a288-2db0a787a63c" type="checkbox" ><label for="4af39992-03cf-4522-a288-2db0a787a63c" class="sk-toggleable__label sk-toggleable__label-arrow">onehotencoder</label><div class="sk-toggleable__content"><pre><sklearn.compose._column_transformer.make_column_selector object at 0x000001E750BBC6A0></pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="519d5e51-5fa6-45d6-a3f7-59c11370402d" type="checkbox" ><label for="519d5e51-5fa6-45d6-a3f7-59c11370402d" class="sk-toggleable__label sk-toggleable__label-arrow">OneHotEncoder</label><div class="sk-toggleable__content"><pre>OneHotEncoder(handle_unknown='ignore', sparse=False)</pre></div></div></div></div></div></div><div class="sk-parallel-item"><div class="sk-item"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="7ede29a7-2614-4eed-a021-e85f1aaa5659" type="checkbox" ><label for="7ede29a7-2614-4eed-a021-e85f1aaa5659" class="sk-toggleable__label sk-toggleable__label-arrow">remainder</label><div class="sk-toggleable__content"><pre>['Length1', 'Length2', 'Length3', 'Height', 'Width']</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="69357535-0314-4987-a311-112335d2cb52" type="checkbox" ><label for="69357535-0314-4987-a311-112335d2cb52" class="sk-toggleable__label sk-toggleable__label-arrow">passthrough</label><div class="sk-toggleable__content"><pre>passthrough</pre></div></div></div></div></div></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="f247fbf2-2247-4e99-aaa2-f6fb89ce1b13" type="checkbox" ><label for="f247fbf2-2247-4e99-aaa2-f6fb89ce1b13" class="sk-toggleable__label sk-toggleable__label-arrow">GradientBoostingRegressor</label><div class="sk-toggleable__content"><pre>GradientBoostingRegressor(random_state=42)</pre></div></div></div></div></div></div></div>
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
from skops.hub_utils import download
from skops.io import load
download("brendenc/Fish-Weight", "path_to_folder")
# make sure model file is in skops format
# if model is a pickle file, make sure it's from a source you trust
model = load("path_to_folder/example.pkl")
```
</details>
# Model Card Authors
This model card is written by following authors:
Brenden Connors
|
behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_RandomError0percentSeed100
|
behzadnet
| 2023-11-29T12:48:31Z | 1 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"base_model:adapter:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | null | 2023-11-29T12:48:28Z |
---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
|
tizayi/ppo-Huggy
|
tizayi
| 2023-11-29T12:48:01Z | 12 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-11-29T12:47:55Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: tizayi/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
MLlabs2023/sentiment_model
|
MLlabs2023
| 2023-11-29T12:43:46Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T20:49:55Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: MLlabs2023/sentiment_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# MLlabs2023/sentiment_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2052
- Train Accuracy: 0.9270
- Validation Loss: 0.2959
- Validation Accuracy: 0.8890
- 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', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': 2e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4494 | 0.7870 | 0.3609 | 0.8590 | 0 |
| 0.2052 | 0.9270 | 0.2959 | 0.8890 | 1 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
rugvedabodke/my_awesome_qa_model
|
rugvedabodke
| 2023-11-29T12:43:10Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-08-08T09:21:36Z |
---
tags:
- generated_from_trainer
model-index:
- name: my_awesome_qa_model
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. -->
# my_awesome_qa_model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0561
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 2 | 2.6419 |
| No log | 2.0 | 4 | 2.4093 |
| No log | 3.0 | 6 | 2.3048 |
| No log | 4.0 | 8 | 2.2732 |
| No log | 5.0 | 10 | 2.3026 |
| No log | 6.0 | 12 | 2.3105 |
| No log | 7.0 | 14 | 2.2481 |
| No log | 8.0 | 16 | 2.1339 |
| No log | 9.0 | 18 | 2.0817 |
| No log | 10.0 | 20 | 2.0949 |
| No log | 11.0 | 22 | 2.1169 |
| No log | 12.0 | 24 | 2.1656 |
| No log | 13.0 | 26 | 2.1781 |
| No log | 14.0 | 28 | 2.1759 |
| No log | 15.0 | 30 | 2.1443 |
| No log | 16.0 | 32 | 2.1105 |
| No log | 17.0 | 34 | 2.0871 |
| No log | 18.0 | 36 | 2.0660 |
| No log | 19.0 | 38 | 2.0585 |
| No log | 20.0 | 40 | 2.0561 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
omriKramer/ppo-LunarLander-v2
|
omriKramer
| 2023-11-29T12:38:48Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T12:38:28Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 253.79 +/- 18.21
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
EJosnin/taxi-tuto
|
EJosnin
| 2023-11-29T12:31:30Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-28T17:18:47Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-tuto
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="EJosnin/taxi-tuto", 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"])
```
|
hoangquang27/Llama-2-13b-chat-hf-ReviewRestaurant_statePA_yelp-dataset
|
hoangquang27
| 2023-11-29T12:30:56Z | 2 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-13b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-13b-chat-hf",
"region:us"
] | null | 2023-11-29T12:08:18Z |
---
library_name: peft
base_model: meta-llama/Llama-2-13b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.2
|
spokkazo/bert-finetuned-squad
|
spokkazo
| 2023-11-29T12:13:02Z | 116 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-11-29T09:50:00Z |
---
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad 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: 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
jkhsong/distilbert-base-uncased-finetuned-bioasq
|
jkhsong
| 2023-11-29T12:11:35Z | 117 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-11-28T10:25:58Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-bioasq
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. -->
# distilbert-base-uncased-finetuned-bioasq
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:
- Loss: 4.0877
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 112 | 4.1108 |
| No log | 2.0 | 224 | 4.0518 |
| No log | 3.0 | 336 | 4.0877 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
zac/Turbo_Lora
|
zac
| 2023-11-29T12:09:13Z | 7 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/sdxl-turbo",
"base_model:adapter:stabilityai/sdxl-turbo",
"region:us"
] |
text-to-image
| 2023-11-29T12:08:36Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/ComfyUI_temp_bqrnl_00006_.png
base_model: stabilityai/sdxl-turbo
instance_prompt: null
---
# Turbo Lora
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/zac/Turbo_Lora/tree/main) them in the Files & versions tab.
|
nimrita/ppo-SnowballTarget
|
nimrita
| 2023-11-29T11:55:34Z | 8 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-11-29T11:55:29Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: nimrita/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BetterThanNothing/cicada
|
BetterThanNothing
| 2023-11-29T11:47:23Z | 0 | 1 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-11-26T16:04:03Z |
---
license: creativeml-openrail-m
---
# Cicada
great for anime style,capable for handDrawn results and screencap styles, dynamic with contrasting colours.
the name comes from cicada3301.
recommended to use high cfg >8 ,and high steps are optional, the shorter and simple the prompt
the better the image is going to be.
use high-res fix for sharper image.
# example prompts:
`A gorgeous nordic teen girl, pale porcelain skin, face with round puffy cheeks, Nordic braid, platinum hair, (nano-suit), dynamic pose,
8k, high quality, intricate, dramatic lighting, (masterpiece, DOF, vivid), black, white, (orange:0.9), deep blue, light bronze, amber`
`masterpiece, picture of a witch, oil painting, hard brush, highly detailed, 1girl, fiery witch, witch hat, flowing red magic, attractvie
, haunting, beautiful, dynamic Poster by Greg Rutkowski, portrait,
Marilyn Monroe, action pose, red hair, pale skin, highlights in eyes, detailed pupils, luminous effects, highest detailed, floating particles`
`Photorealism a portait of a beautiful dark haired french girl, attractive collarbone and shoulders, holding a mug of hot tea. In the style of ilya kuvshinov, elegant,
highly detailed, photorealistic, digital painting, high resolution, artstation, concept art, smooth, often for highly detailed representation,
, or visual illusion.`
`detailed eyes, Perfect features, (masterpiece),
(best quality), moist skin, shiny skin, glossy skin, (good quality), intricate details, earrings,
Ray Tracing, (See-through), (Bokeh), (Depth of field),`
`(masterpiece, best quality:1.4), (ultra realistic:1.4), (8k uhd, very high quality:1.4), (extremely detailed face:1.4),
(dark skin and muscular mother holding her dark skin female child:1.4),
(dark caramel skin:1.4), (absurdly long ruby red hair:1.4), (smiling and happy:1.4), (bright blue eyes:1.4), (city background:1.4)`
`masterpiece,best quality,highres,cinematic lighting,dramatic angle,1girl,brown hair,blue eyes,ahoge,hairband,looking back at viewer,turning head,
,purple dress,drinking with straw,handbag,dating,crowd,sitting,chair and desk,sunshade,depth of field,streetscape,close-up,portrait,happy,`
# a different type of prompt
`Title: "Whimsical Watercolors: The Serene Elegance of a Woman in a Pastel Dreamscape",
ArtStyle: Watercolor Painting Style Drawing,
Texture: Soft and fluid,characteristic of watercolor techniques,with washes of color and gentle gradations,
Color Palette: Delicate and dreamy pastels,with an emphasis on light blues,pinks,lavenders,and soft greens,
SceneDescription:,
Subject: A woman portrayed as an embodiment of grace and tranquility within a watercolor landscape,
Appearance: Softly defined features,with a harmonious blend of colors creating a gentle,ethereal look,
Expression: A calm,reflective smile,suggesting a deep inner peace and contentment,
Attire: A flowing dress with subtle,watercolor hues,appearing as if the colors are melting into the surrounding landscape,
Action: A relaxed,graceful pose,perhaps sitting by a watercolor-rendered river or walking through a field of softly painted flowers,
Setting: A picturesque,watercolor environment,
Elements: Dreamlike interpretations of nature,such as a blurry field of flowers,a gentle stream,or a distant,misty forest,
Details: Soft splashes of color and undefined shapes that merge into one another,typical of watercolor landscapes,
Lighting: Soft,diffused light that enhances the watercolor effect,creating a warm,gentle ambiance,
Additional Details:,
Symbolic Elements: Subtle inclusions like a sketchbook,a feather,or a softly glowing lantern,adding layers of meaning to the scene,
Atmospheric Quality: Hints of color bleeding or watercolor drips,emphasizing the artistic medium,
Mood: A serene and enchanting portrayal,capturing the quiet beauty and soothing essence of a woman in a whimsical,pastel watercolor world,`
`Portrait of a very sinister looking Batman with a scary pose, cinematic 4k epic detailed bokeh hbo dark
moodyPortrait of a very sinister looking Batman with a scary pose, cinematic 4k epic detailed bokeh hbo dark moody`
`,1girl, blond hair,long hair ,blue eyes, Beautiful blue eyes,Violet Evergarden,
smirk,
pirate,pirate woman, eye pad, eyepatch,black eyepatch,0Skull mark on hat,saber,hold up a saber,Black Gloves,
mini-skirt,
on board a ship,pirate ship,Black flag with skull,pirate flag,`
any basic prompt can work, no triggered words are required;
works good with loras
merged models:
nostalgia mix.
falt2d animerge.
forgottenmix.
exo.
aux.
pika's new generation.
toonyoujp.
# results
|
Realgon/distilbert_twitterfin_padding100model
|
Realgon
| 2023-11-29T11:47:17Z | 113 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:39:32Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding100model
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. -->
# distilbert_twitterfin_padding100model
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:
- Loss: 1.0528
- Accuracy: 0.8832
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6909 | 1.0 | 597 | 0.4072 | 0.8576 |
| 0.3923 | 2.0 | 1194 | 0.3465 | 0.8765 |
| 0.2852 | 3.0 | 1791 | 0.3862 | 0.8786 |
| 0.1967 | 4.0 | 2388 | 0.4676 | 0.8794 |
| 0.1543 | 5.0 | 2985 | 0.5885 | 0.8660 |
| 0.0729 | 6.0 | 3582 | 0.6574 | 0.8681 |
| 0.0611 | 7.0 | 4179 | 0.7198 | 0.8756 |
| 0.0503 | 8.0 | 4776 | 0.7859 | 0.8656 |
| 0.0498 | 9.0 | 5373 | 0.7831 | 0.8786 |
| 0.0352 | 10.0 | 5970 | 0.8445 | 0.8740 |
| 0.0223 | 11.0 | 6567 | 0.8618 | 0.8748 |
| 0.0179 | 12.0 | 7164 | 0.8994 | 0.8815 |
| 0.0152 | 13.0 | 7761 | 0.9486 | 0.8802 |
| 0.0124 | 14.0 | 8358 | 0.9630 | 0.8823 |
| 0.009 | 15.0 | 8955 | 0.9582 | 0.8769 |
| 0.0063 | 16.0 | 9552 | 0.9968 | 0.8823 |
| 0.0034 | 17.0 | 10149 | 1.0137 | 0.8832 |
| 0.0029 | 18.0 | 10746 | 1.0714 | 0.8802 |
| 0.005 | 19.0 | 11343 | 1.0397 | 0.8827 |
| 0.0027 | 20.0 | 11940 | 1.0528 | 0.8832 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Liberty-L/Multiple_Choice_swag
|
Liberty-L
| 2023-11-29T11:46:27Z | 116 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"multiple-choice",
"generated_from_trainer",
"base_model:Liberty-L/Multiple_Choice_swag",
"base_model:finetune:Liberty-L/Multiple_Choice_swag",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
multiple-choice
| 2023-11-28T14:33:28Z |
---
license: apache-2.0
base_model: Liberty-L/Multiple_Choice_swag
tags:
- generated_from_trainer
model-index:
- name: Multiple_Choice_swag
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. -->
# Multiple_Choice_swag
This model is a fine-tuned version of [Liberty-L/Multiple_Choice_swag](https://huggingface.co/Liberty-L/Multiple_Choice_swag) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 5
- eval_batch_size: 5
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
jjmcarrascosa/qlearning-taxi-v3
|
jjmcarrascosa
| 2023-11-29T11:44:14Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T11:44:12Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: qlearning-taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.72
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="jjmcarrascosa/qlearning-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"])
```
|
jjmcarrascosa/q-FrozenLake-v1-4x4-noSlippery
|
jjmcarrascosa
| 2023-11-29T11:42:02Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T11:42:00Z |
---
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="jjmcarrascosa/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"])
```
|
deepseek-ai/deepseek-llm-67b-chat
|
deepseek-ai
| 2023-11-29T11:40:59Z | 6,868 | 193 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T03:30:00Z |
---
license: other
license_name: deepseek
license_link: LICENSE
---
<p align="center">
<img width="500px" alt="DeepSeek Chat" src="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://chat.deepseek.com/">[🤖 Chat with DeepSeek LLM]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/deepseek-ai/DeepSeek-LLM/blob/main/images/qr.jpeg">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek LLM
Introducing DeepSeek LLM, an advanced language model comprising 67 billion parameters. It has been trained from scratch on a vast dataset of 2 trillion tokens in both English and Chinese. In order to foster research, we have made DeepSeek LLM 7B/67B Base and DeepSeek LLM 7B/67B Chat open source for the research community.
### 2. Model Summary
`deepseek-llm-67b-chat` is a 67B parameter model initialized from `deepseek-llm-67b-base` and fine-tuned on extra instruction data.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-LLM](https://github.com/deepseek-ai/deepseek-LLM)
- **Chat With DeepSeek LLM:** [DeepSeek-LLM](https://chat.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### Chat Completion
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_name = "deepseek-ai/deepseek-llm-67b-chat"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
model.generation_config = GenerationConfig.from_pretrained(model_name)
model.generation_config.pad_token_id = model.generation_config.eos_token_id
messages = [
{"role": "user", "content": "Who are you?"}
]
input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
print(result)
```
Avoiding the use of the provided function `apply_chat_template`, you can also interact with our model following the sample template. Note that `messages` should be replaced by your input.
```
User: {messages[0]['content']}
Assistant: {messages[1]['content']}<｜end▁of▁sentence｜>User: {messages[2]['content']}
Assistant:
```
**Note:** By default (`add_special_tokens=True`), our tokenizer automatically adds a `bos_token` (`<｜begin▁of▁sentence｜>`) before the input text. Additionally, since the system prompt is not compatible with this version of our models, we DO NOT RECOMMEND including the system prompt in your input.
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek LLM models is subject to the Model License. DeepSeek LLM supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-LLM/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [service@deepseek.com](mailto:service@deepseek.com).
|
hkivancoral/hushem_5x_beit_base_rms_00001_fold5
|
hkivancoral
| 2023-11-29T11:37:49Z | 9 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-29T11:04:46Z |
---
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
|
Realgon/distilbert_twitterfin_padding90model
|
Realgon
| 2023-11-29T11:32:34Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:38:57Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding90model
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. -->
# distilbert_twitterfin_padding90model
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:
- Loss: 1.1388
- Accuracy: 0.8714
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6718 | 1.0 | 597 | 0.4965 | 0.8007 |
| 0.4114 | 2.0 | 1194 | 0.3593 | 0.8760 |
| 0.3053 | 3.0 | 1791 | 0.3970 | 0.8769 |
| 0.2064 | 4.0 | 2388 | 0.5193 | 0.8714 |
| 0.1666 | 5.0 | 2985 | 0.6195 | 0.8593 |
| 0.0775 | 6.0 | 3582 | 0.6972 | 0.8756 |
| 0.0734 | 7.0 | 4179 | 0.7703 | 0.8685 |
| 0.0555 | 8.0 | 4776 | 0.8217 | 0.8635 |
| 0.0394 | 9.0 | 5373 | 0.8715 | 0.8693 |
| 0.0321 | 10.0 | 5970 | 0.9257 | 0.8702 |
| 0.0199 | 11.0 | 6567 | 0.9315 | 0.8631 |
| 0.0159 | 12.0 | 7164 | 0.9733 | 0.8685 |
| 0.0155 | 13.0 | 7761 | 1.0329 | 0.8727 |
| 0.0099 | 14.0 | 8358 | 1.0347 | 0.8748 |
| 0.0113 | 15.0 | 8955 | 1.0812 | 0.8702 |
| 0.0056 | 16.0 | 9552 | 1.0760 | 0.8752 |
| 0.0077 | 17.0 | 10149 | 1.1068 | 0.8710 |
| 0.0024 | 18.0 | 10746 | 1.1203 | 0.8731 |
| 0.0042 | 19.0 | 11343 | 1.1445 | 0.8731 |
| 0.0025 | 20.0 | 11940 | 1.1388 | 0.8714 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
yuexishuihan/sd-class-butterflies-32
|
yuexishuihan
| 2023-11-29T11:32:01Z | 46 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-11-29T11:28:01Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('yuexishuihan/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
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"
] |
reinforcement-learning
| 2023-11-29T11:28:22Z |
---
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
|
Realgon/distilbert_twitterfin_padding80model
|
Realgon
| 2023-11-29T11:18:31Z | 97 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:38:25Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding80model
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. -->
# distilbert_twitterfin_padding80model
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:
- Loss: 1.2464
- Accuracy: 0.8643
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6875 | 1.0 | 597 | 0.5594 | 0.7714 |
| 0.4358 | 2.0 | 1194 | 0.4065 | 0.8606 |
| 0.3212 | 3.0 | 1791 | 0.4008 | 0.8652 |
| 0.2291 | 4.0 | 2388 | 0.5113 | 0.8589 |
| 0.1828 | 5.0 | 2985 | 0.6224 | 0.8526 |
| 0.0895 | 6.0 | 3582 | 0.7556 | 0.8417 |
| 0.0787 | 7.0 | 4179 | 0.8308 | 0.8539 |
| 0.0562 | 8.0 | 4776 | 0.8072 | 0.8660 |
| 0.0622 | 9.0 | 5373 | 0.8961 | 0.8564 |
| 0.0319 | 10.0 | 5970 | 0.9517 | 0.8626 |
| 0.0301 | 11.0 | 6567 | 1.0274 | 0.8610 |
| 0.0196 | 12.0 | 7164 | 1.0598 | 0.8606 |
| 0.0224 | 13.0 | 7761 | 1.1347 | 0.8673 |
| 0.0164 | 14.0 | 8358 | 1.1203 | 0.8664 |
| 0.0113 | 15.0 | 8955 | 1.1585 | 0.8647 |
| 0.007 | 16.0 | 9552 | 1.2019 | 0.8589 |
| 0.0082 | 17.0 | 10149 | 1.2218 | 0.8656 |
| 0.0043 | 18.0 | 10746 | 1.2438 | 0.8635 |
| 0.0047 | 19.0 | 11343 | 1.2392 | 0.8656 |
| 0.0031 | 20.0 | 11940 | 1.2464 | 0.8643 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
aashish-249/Telugu-sentiment_analysis
|
aashish-249
| 2023-11-29T11:14:37Z | 114 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"albert",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-16T05:13:33Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Telugu-sentiment_analysis
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. -->
# Telugu-sentiment_analysis
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: 0.9966
- Accuracy: 0.5381
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0084 | 1.0 | 1758 | 1.0049 | 0.5381 |
| 1.0044 | 2.0 | 3516 | 1.0062 | 0.5381 |
| 1.003 | 3.0 | 5274 | 1.0006 | 0.5381 |
| 1.0034 | 4.0 | 7032 | 0.9985 | 0.5381 |
| 1.0014 | 5.0 | 8790 | 0.9981 | 0.5381 |
| 1.0007 | 6.0 | 10548 | 0.9975 | 0.5381 |
| 1.0009 | 7.0 | 12306 | 0.9973 | 0.5381 |
| 1.0004 | 8.0 | 14064 | 0.9970 | 0.5381 |
| 0.9992 | 9.0 | 15822 | 0.9968 | 0.5381 |
| 0.9986 | 10.0 | 17580 | 0.9966 | 0.5381 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Realgon/distilbert_twitterfin_padding70model
|
Realgon
| 2023-11-29T11:05:02Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:37:53Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_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. -->
# distilbert_twitterfin_padding70model
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:
- Loss: 1.0730
- Accuracy: 0.8765
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6887 | 1.0 | 597 | 0.4586 | 0.8262 |
| 0.4254 | 2.0 | 1194 | 0.3631 | 0.8647 |
| 0.3042 | 3.0 | 1791 | 0.3923 | 0.8769 |
| 0.2115 | 4.0 | 2388 | 0.5038 | 0.8693 |
| 0.1643 | 5.0 | 2985 | 0.5552 | 0.8798 |
| 0.0827 | 6.0 | 3582 | 0.6608 | 0.8735 |
| 0.0656 | 7.0 | 4179 | 0.7668 | 0.8660 |
| 0.0523 | 8.0 | 4776 | 0.7806 | 0.8685 |
| 0.0474 | 9.0 | 5373 | 0.8615 | 0.8668 |
| 0.031 | 10.0 | 5970 | 0.9038 | 0.8714 |
| 0.0232 | 11.0 | 6567 | 0.9269 | 0.8693 |
| 0.0195 | 12.0 | 7164 | 0.9192 | 0.8723 |
| 0.0275 | 13.0 | 7761 | 0.9875 | 0.8685 |
| 0.0171 | 14.0 | 8358 | 1.0308 | 0.8714 |
| 0.0129 | 15.0 | 8955 | 1.0227 | 0.8744 |
| 0.0052 | 16.0 | 9552 | 1.0471 | 0.8685 |
| 0.0076 | 17.0 | 10149 | 1.0448 | 0.8769 |
| 0.0064 | 18.0 | 10746 | 1.0537 | 0.8769 |
| 0.0078 | 19.0 | 11343 | 1.0615 | 0.8744 |
| 0.0034 | 20.0 | 11940 | 1.0730 | 0.8765 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
hkivancoral/hushem_5x_beit_base_rms_00001_fold4
|
hkivancoral
| 2023-11-29T11:03:58Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-29T10:31:03Z |
---
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
|
DTAI-KULeuven/robbert-2022-dutch-base
|
DTAI-KULeuven
| 2023-11-29T10:55:44Z | 238 | 9 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"nl",
"dataset:oscar",
"dataset:dbrd",
"dataset:lassy-ud",
"dataset:europarl-mono",
"dataset:conll2002",
"arxiv:2211.08192",
"arxiv:2001.06286",
"arxiv:1907.11692",
"arxiv:2001.02943",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-08-15T09:48:36Z |
---
language: "nl"
thumbnail: "https://github.com/iPieter/RobBERT/raw/master/res/robbert_2022_logo.png"
tags:
- Dutch
- Flemish
- RoBERTa
- RobBERT
license: mit
datasets:
- oscar
- dbrd
- lassy-ud
- europarl-mono
- conll2002
widget:
- text: "Hallo, ik ben RobBERT-2022, het nieuwe <mask> taalmodel van de KU Leuven."
---
<p align="center">
<img src="https://github.com/iPieter/RobBERT/raw/master/res/robbert_2022_logo_with_name.png" alt="RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use" width="75%">
</p>
# RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use.
RobBERT-2022 is the latest release of the [Dutch RobBERT model](https://pieter.ai/robbert/).
It further pretrained the original [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) model on the 2022 version of the OSCAR version.
Thanks to this more recent dataset, this [DTAI-KULeuven/robbert-2022-dutch-base](https://huggingface.co/DTAI-KULeuven/robbert-2022-dutch-base) model shows increased performance on several tasks related to recent events, e.g. COVID-19-related tasks.
We also found that for some tasks that do not contain more recent information than 2019, the original [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base) RobBERT model can still outperform this newer one.
The original RobBERT model was released in January 2020. Dutch has evolved a lot since then, for example the COVID-19 pandemic introduced a wide range of new words that were suddenly used daily. Also, many other world facts that the original model considered true have also changed. To account for this and other changes in usage, we release a new Dutch BERT model trained on data from 2022: RobBERT 2022.
More in-depth information about RobBERT-2022 can be found in our [blog post](https://pieter.ai/robbert-2022/), [our paper](http://arxiv.org/abs/2211.08192), [the original RobBERT paper](https://arxiv.org/abs/2001.06286) and [the RobBERT Github repository](https://github.com/iPieter/RobBERT).
## How to use
RobBERT-2022 and RobBERT both use the [RoBERTa](https://arxiv.org/abs/1907.11692) architecture and pre-training but with a Dutch tokenizer and training data. RoBERTa is the robustly optimized English BERT model, making it even more powerful than the original BERT model. Given this same architecture, RobBERT can easily be finetuned and inferenced using [code to finetune RoBERTa](https://huggingface.co/transformers/model_doc/roberta.html) models and most code used for BERT models, e.g. as provided by [HuggingFace Transformers](https://huggingface.co/transformers/) library.
By default, RobBERT-2022 has the masked language model head used in training. This can be used as a zero-shot way to fill masks in sentences. It can be tested out for free on [RobBERT's Hosted infererence API of Huggingface](https://huggingface.co/pdelobelle/robbert-v2-dutch-base?text=De+hoofdstad+van+Belgi%C3%AB+is+%3Cmask%3E.). You can also create a new prediction head for your own task by using any of HuggingFace's [RoBERTa-runners](https://huggingface.co/transformers/v2.7.0/examples.html#language-model-training), [their fine-tuning notebooks](https://huggingface.co/transformers/v4.1.1/notebooks.html) by changing the model name to `DTAI-KULeuven/robbert-2022-dutch-base`.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("DTAI-KULeuven/robbert-2022-dutch-base")
model = AutoModelForSequenceClassification.from_pretrained("DTAI-KULeuven/robbert-2022-dutch-base")
```
You can then use most of [HuggingFace's BERT-based notebooks](https://huggingface.co/transformers/v4.1.1/notebooks.html) for finetuning RobBERT-2022 on your type of Dutch language dataset.
## Comparison of Available Dutch BERT models
There is a wide variety of Dutch BERT-based models available for fine-tuning on your tasks.
Here's a quick summary to find the one that suits your need:
- [pdelobelle/robbert-v2-dutch-base](https://huggingface.co/pdelobelle/robbert-v2-dutch-base): The RobBERT model has for years been the best performing BERT-like model for most language tasks. It is trained on a large Dutch webcrawled dataset (OSCAR) and uses the superior [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta) architecture, which robustly optimized the original [BERT model](https://huggingface.co/docs/transformers/model_doc/bert).
- [DTAI-KULeuven/robbertje-1-gb-merged](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-mergedRobBERTje): The RobBERTje model is a distilled version of RobBERT and about half the size and four times faster to perform inference on. This can help deploy more scalable language models for your language task
- [DTAI-KULeuven/robbert-2022-dutch-base](https://huggingface.co/DTAI-KULeuven/robbert-2022-dutch-base): The RobBERT-2022 is a further pre-trained RobBERT model on the OSCAR2022 dataset. It is helpful for tasks that rely on words and/or information about more recent events.
There's also the [GroNLP/bert-base-dutch-cased](https://huggingface.co/GroNLP/bert-base-dutch-cased) "BERTje" model. This model uses the outdated basic BERT model, and is trained on a smaller corpus of clean Dutch texts.
Thanks to RobBERT's more recent architecture as well as its larger and more real-world-like training corpus, most researchers and practitioners seem to achieve higher performance on their language tasks with the RobBERT model.
## Technical Details From The Paper
### Our Performance Evaluation Results
All experiments are described in more detail in our [paper](https://arxiv.org/abs/2001.06286), with the code in [our GitHub repository](https://github.com/iPieter/RobBERT).
### Sentiment analysis
Predicting whether a review is positive or negative using the [Dutch Book Reviews Dataset](https://github.com/benjaminvdb/110kDBRD).
| Model | Accuracy [%] |
|-------------------|--------------------------|
| ULMFiT | 93.8 |
| BERTje | 93.0 |
| RobBERT v2 | 94.4 |
| RobBERT 2022 | **95.1** |
### Die/Dat (coreference resolution)
We measured how well the models are able to do coreference resolution by predicting whether "die" or "dat" should be filled into a sentence.
For this, we used the [EuroParl corpus](https://www.statmt.org/europarl/).
#### Finetuning on whole dataset
| Model | Accuracy [%] | F1 [%] |
|-------------------|--------------------------|--------------|
| [Baseline](https://arxiv.org/abs/2001.02943) (LSTM) | | 75.03 |
| mBERT | 98.285 | 98.033 |
| BERTje | 98.268 | 98.014 |
| RobBERT v2 | **99.232** | **99.121** |
| RobBERT 2022 | 97.8 | |
#### Finetuning on 10K examples
We also measured the performance using only 10K training examples.
This experiment clearly illustrates that RobBERT outperforms other models when there is little data available.
| Model | Accuracy [%] | F1 [%] |
|-------------------|--------------------------|--------------|
| mBERT | 92.157 | 90.898 |
| BERTje | 93.096 | 91.279 |
| RobBERT v2 | **97.816** | **97.514** |
#### Using zero-shot word masking task
Since BERT models are pre-trained using the word masking task, we can use this to predict whether "die" or "dat" is more likely.
This experiment shows that RobBERT has internalised more information about Dutch than other models.
| Model | Accuracy [%] |
|-------------------|--------------------------|
| ZeroR | 66.70 |
| mBERT | 90.21 |
| BERTje | 94.94 |
| RobBERT v2 | **98.75** |
### Part-of-Speech Tagging.
Using the [Lassy UD dataset](https://universaldependencies.org/treebanks/nl_lassysmall/index.html).
| Model | Accuracy [%] |
|-------------------|--------------------------|
| Frog | 91.7 |
| mBERT | **96.5** |
| BERTje | 96.3 |
| RobBERT v2 | 96.4 |
| RobBERT 2022 | 96.1 |
## Credits and citation
This project is created by [Pieter Delobelle](https://people.cs.kuleuven.be/~pieter.delobelle), [Thomas Winters](https://thomaswinters.be) and [Bettina Berendt](https://people.cs.kuleuven.be/~bettina.berendt/).
If you would like to cite our paper or model, you can use the following BibTeX:
```
@inproceedings{delobelle2022robbert2022,
doi = {10.48550/ARXIV.2211.08192},
url = {https://arxiv.org/abs/2211.08192},
author = {Delobelle, Pieter and Winters, Thomas and Berendt, Bettina},
keywords = {Computation and Language (cs.CL), Machine Learning (cs.LG), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {RobBERT-2022: Updating a Dutch Language Model to Account for Evolving Language Use},
venue = {arXiv},
year = {2022},
}
@inproceedings{delobelle2020robbert,
title = "{R}ob{BERT}: a {D}utch {R}o{BERT}a-based {L}anguage {M}odel",
author = "Delobelle, Pieter and
Winters, Thomas and
Berendt, Bettina",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
month = nov,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.findings-emnlp.292",
doi = "10.18653/v1/2020.findings-emnlp.292",
pages = "3255--3265"
}
```
|
DTAI-KULeuven/robbertje-1-gb-shuffled
|
DTAI-KULeuven
| 2023-11-29T10:55:24Z | 437 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"roberta",
"fill-mask",
"Dutch",
"Flemish",
"RoBERTa",
"RobBERT",
"RobBERTje",
"nl",
"arxiv:2101.05716",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: "nl"
thumbnail: "https://github.com/iPieter/RobBERT/raw/master/res/robbert_logo.png"
tags:
- Dutch
- Flemish
- RoBERTa
- RobBERT
- RobBERTje
license: mit
datasets:
- oscar
- oscar (NL)
- dbrd
- lassy-ud
- europarl-mono
- conll2002
widget:
- text: "Hallo, ik ben RobBERTje, een gedistilleerd <mask> taalmodel van de KU Leuven."
---
<p align="center">
<img src="https://github.com/iPieter/robbertje/raw/master/images/robbertje_logo_with_name.png" alt="RobBERTje: A collection of distilled Dutch BERT-based models" width="75%">
</p>
# About RobBERTje
RobBERTje is a collection of distilled models based on [RobBERT](http://github.com/iPieter/robbert). There are multiple models with different sizes and different training settings, which you can choose for your use-case.
We are also continuously working on releasing better-performing models, so watch [the repository](http://github.com/iPieter/robbertje) for updates.
# News
- **February 21, 2022**: Our paper about RobBERTje has been published in [volume 11 of CLIN journal](https://www.clinjournal.org/clinj/article/view/131)!
- **July 2, 2021**: Publicly released 4 RobBERTje models.
- **May 12, 2021**: RobBERTje was accepted at [CLIN31](https://www.clin31.ugent.be) for an oral presentation!
# The models
| Model | Description | Parameters | Training size | Huggingface id |
|--------------|-------------|------------------|-------------------|------------------------------------------------------------------------------------|
| Non-shuffled | Trained on the non-shuffled variant of the oscar corpus, without any operations to preserve this order during training and distillation. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-non-shuffled](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-non-shuffled) |
| Shuffled | Trained on the publicly available and shuffled OSCAR corpus. | 74 M | 1 GB | this model |
| Merged (p=0.5) | Same as the non-shuffled variant, but sequential sentences of the same document are merged with a probability of 50%. | 74 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-merged](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-merged) |
| BORT | A smaller version with 8 attention heads instead of 12 and 4 layers instead of 6 (and 12 for RobBERT). | 46 M | 1 GB | [DTAI-KULeuven/robbertje-1-gb-bort](https://huggingface.co/DTAI-KULeuven/robbertje-1-gb-bort) |
# Results
## Intrinsic results
We calculated the _pseudo perplexity_ (PPPL) from [cite](), which is a built-in metric in our distillation library. This metric gives an indication of how well the model captures the input distribution.
| Model | PPPL |
|-------------------|-----------|
| RobBERT (teacher) | 7.76 |
| Non-shuffled | 12.95 |
| Shuffled | 18.74 |
| Merged (p=0.5) | 17.10 |
| BORT | 26.44 |
## Extrinsic results
We also evaluated our models on sereral downstream tasks, just like the teacher model RobBERT. Since that evaluation, a [Dutch NLI task named SICK-NL](https://arxiv.org/abs/2101.05716) was also released and we evaluated our models with it as well.
| Model | DBRD | DIE-DAT | NER | POS |SICK-NL |
|------------------|-----------|-----------|-----------|-----------|----------|
| RobBERT (teacher)|94.4 | 99.2 |89.1 |96.4 | 84.2 |
| Non-shuffled |90.2 | 98.4 |82.9 |95.5 | 83.4 |
| Shuffled |92.5 | 98.2 |82.7 |95.6 | 83.4 |
| Merged (p=0.5) |92.9 | 96.5 |81.8 |95.2 | 82.8 |
| BORT |89.6 | 92.2 |79.7 |94.3 | 81.0 |
|
ledu1017/cartoon
|
ledu1017
| 2023-11-29T10:50:45Z | 2 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2023-11-29T07:01:23Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: A cartoon painting
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
|
Realgon/distilbert_twitterfin_padding40model
|
Realgon
| 2023-11-29T10:27:30Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:36:20Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding40model
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. -->
# distilbert_twitterfin_padding40model
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:
- Loss: 1.1281
- Accuracy: 0.8744
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6696 | 1.0 | 597 | 0.5204 | 0.8049 |
| 0.3995 | 2.0 | 1194 | 0.3527 | 0.8723 |
| 0.2854 | 3.0 | 1791 | 0.3942 | 0.8756 |
| 0.1921 | 4.0 | 2388 | 0.5487 | 0.8773 |
| 0.1519 | 5.0 | 2985 | 0.6299 | 0.8735 |
| 0.0632 | 6.0 | 3582 | 0.6852 | 0.8735 |
| 0.0498 | 7.0 | 4179 | 0.7728 | 0.8702 |
| 0.0446 | 8.0 | 4776 | 0.8638 | 0.8681 |
| 0.0321 | 9.0 | 5373 | 0.8343 | 0.8765 |
| 0.0339 | 10.0 | 5970 | 0.8881 | 0.8790 |
| 0.0194 | 11.0 | 6567 | 0.9281 | 0.8752 |
| 0.0106 | 12.0 | 7164 | 0.9799 | 0.8723 |
| 0.0145 | 13.0 | 7761 | 1.0008 | 0.8752 |
| 0.0121 | 14.0 | 8358 | 1.0437 | 0.8756 |
| 0.009 | 15.0 | 8955 | 1.0609 | 0.8748 |
| 0.0062 | 16.0 | 9552 | 1.0912 | 0.8719 |
| 0.0068 | 17.0 | 10149 | 1.1045 | 0.8731 |
| 0.0046 | 18.0 | 10746 | 1.1086 | 0.8735 |
| 0.0056 | 19.0 | 11343 | 1.1271 | 0.8723 |
| 0.0029 | 20.0 | 11940 | 1.1281 | 0.8744 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
hllj/sft-mistral-v3-all
|
hllj
| 2023-11-29T10:25:14Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:hllj/mistral-vi-math",
"base_model:finetune:hllj/mistral-vi-math",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T08:56:59Z |
---
base_model: hllj/mistral-vi-math
tags:
- generated_from_trainer
model-index:
- name: sft-mistral-v3-all
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. -->
# sft-mistral-v3-all
This model is a fine-tuned version of [hllj/mistral-vi-math](https://huggingface.co/hllj/mistral-vi-math) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4937
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Ferrxni/finetuned_gw_mistral_GPTQ
|
Ferrxni
| 2023-11-29T10:24:16Z | 1 | 0 |
peft
|
[
"peft",
"mistral",
"4-bit",
"gptq",
"region:us"
] | null | 2023-11-29T09:55:56Z |
---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: gptq
- bits: 4
- tokenizer: None
- dataset: None
- group_size: 128
- damp_percent: 0.1
- desc_act: True
- sym: True
- true_sequential: True
- use_cuda_fp16: False
- model_seqlen: None
- block_name_to_quantize: None
- module_name_preceding_first_block: None
- batch_size: 1
- pad_token_id: None
- disable_exllama: False
- max_input_length: None
### Framework versions
- PEFT 0.5.0
|
Realgon/distilbert_twitterfin_padding20model
|
Realgon
| 2023-11-29T10:05:11Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:35:22Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding20model
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. -->
# distilbert_twitterfin_padding20model
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:
- Loss: 1.1861
- Accuracy: 0.8664
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6755 | 1.0 | 597 | 0.4424 | 0.8312 |
| 0.3928 | 2.0 | 1194 | 0.3581 | 0.8756 |
| 0.2881 | 3.0 | 1791 | 0.4017 | 0.8702 |
| 0.1946 | 4.0 | 2388 | 0.5543 | 0.8706 |
| 0.1448 | 5.0 | 2985 | 0.6680 | 0.8585 |
| 0.0613 | 6.0 | 3582 | 0.7264 | 0.8585 |
| 0.0517 | 7.0 | 4179 | 0.8782 | 0.8446 |
| 0.0459 | 8.0 | 4776 | 0.9466 | 0.8539 |
| 0.0331 | 9.0 | 5373 | 0.8594 | 0.8693 |
| 0.0285 | 10.0 | 5970 | 0.9603 | 0.8606 |
| 0.02 | 11.0 | 6567 | 1.0244 | 0.8585 |
| 0.0173 | 12.0 | 7164 | 1.0103 | 0.8610 |
| 0.017 | 13.0 | 7761 | 1.0972 | 0.8652 |
| 0.0102 | 14.0 | 8358 | 1.1062 | 0.8660 |
| 0.0052 | 15.0 | 8955 | 1.1196 | 0.8664 |
| 0.0054 | 16.0 | 9552 | 1.1968 | 0.8681 |
| 0.0072 | 17.0 | 10149 | 1.1564 | 0.8664 |
| 0.001 | 18.0 | 10746 | 1.1946 | 0.8723 |
| 0.0043 | 19.0 | 11343 | 1.1976 | 0.8689 |
| 0.0021 | 20.0 | 11940 | 1.1861 | 0.8664 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
En-2863/opt-125m
|
En-2863
| 2023-11-29T10:03:24Z | 166 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T10:02:56Z |
---
tags:
- generated_from_trainer
model-index:
- name: opt-125m
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. -->
# opt-125m
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1709
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.2192 | 1.0 | 1184 | 3.1833 |
| 3.024 | 2.0 | 2368 | 3.1701 |
| 2.9101 | 3.0 | 3552 | 3.1709 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Realgon/roberta_sst2_padding50model
|
Realgon
| 2023-11-29T10:00:12Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-29T09:08:44Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst2_padding50model
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_padding50model
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: 0.5107
- Accuracy: 0.9462
## 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 | 0.1735 | 0.9319 |
| 0.327 | 2.0 | 866 | 0.2500 | 0.9336 |
| 0.1893 | 3.0 | 1299 | 0.2987 | 0.9407 |
| 0.1229 | 4.0 | 1732 | 0.3376 | 0.9418 |
| 0.0753 | 5.0 | 2165 | 0.3283 | 0.9484 |
| 0.0496 | 6.0 | 2598 | 0.5720 | 0.9116 |
| 0.0349 | 7.0 | 3031 | 0.4278 | 0.9363 |
| 0.0349 | 8.0 | 3464 | 0.4501 | 0.9379 |
| 0.0254 | 9.0 | 3897 | 0.4728 | 0.9374 |
| 0.0217 | 10.0 | 4330 | 0.4662 | 0.9368 |
| 0.0171 | 11.0 | 4763 | 0.4622 | 0.9418 |
| 0.0082 | 12.0 | 5196 | 0.4804 | 0.9429 |
| 0.0094 | 13.0 | 5629 | 0.4789 | 0.9445 |
| 0.0047 | 14.0 | 6062 | 0.5459 | 0.9423 |
| 0.0047 | 15.0 | 6495 | 0.4672 | 0.9434 |
| 0.009 | 16.0 | 6928 | 0.5178 | 0.9445 |
| 0.0021 | 17.0 | 7361 | 0.5107 | 0.9467 |
| 0.0042 | 18.0 | 7794 | 0.5101 | 0.9445 |
| 0.0053 | 19.0 | 8227 | 0.5043 | 0.9462 |
| 0.0017 | 20.0 | 8660 | 0.5107 | 0.9462 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hkivancoral/hushem_5x_beit_base_rms_00001_fold2
|
hkivancoral
| 2023-11-29T09:57:08Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-29T09:24:55Z |
---
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_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
---
<!-- 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_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8184
- Accuracy: 0.8667
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6865 | 1.0 | 27 | 0.7969 | 0.7556 |
| 0.1615 | 2.0 | 54 | 0.9353 | 0.7778 |
| 0.041 | 3.0 | 81 | 1.0745 | 0.6444 |
| 0.0119 | 4.0 | 108 | 1.0481 | 0.7333 |
| 0.0095 | 5.0 | 135 | 0.6063 | 0.8667 |
| 0.0013 | 6.0 | 162 | 0.6520 | 0.8444 |
| 0.0015 | 7.0 | 189 | 0.7604 | 0.8667 |
| 0.0013 | 8.0 | 216 | 0.7595 | 0.8444 |
| 0.0008 | 9.0 | 243 | 0.8299 | 0.8444 |
| 0.0008 | 10.0 | 270 | 0.6509 | 0.8444 |
| 0.0009 | 11.0 | 297 | 0.7989 | 0.8444 |
| 0.0002 | 12.0 | 324 | 0.8458 | 0.8444 |
| 0.0005 | 13.0 | 351 | 0.6321 | 0.8667 |
| 0.0002 | 14.0 | 378 | 0.6972 | 0.8444 |
| 0.0002 | 15.0 | 405 | 0.7426 | 0.8667 |
| 0.0005 | 16.0 | 432 | 0.9776 | 0.8 |
| 0.0023 | 17.0 | 459 | 1.0180 | 0.8 |
| 0.0003 | 18.0 | 486 | 1.1105 | 0.7778 |
| 0.0006 | 19.0 | 513 | 0.9919 | 0.7556 |
| 0.0002 | 20.0 | 540 | 1.0177 | 0.8 |
| 0.0012 | 21.0 | 567 | 0.9992 | 0.8444 |
| 0.0003 | 22.0 | 594 | 0.9760 | 0.8444 |
| 0.0047 | 23.0 | 621 | 0.9891 | 0.8 |
| 0.0061 | 24.0 | 648 | 0.9730 | 0.8222 |
| 0.0002 | 25.0 | 675 | 0.8247 | 0.8222 |
| 0.0001 | 26.0 | 702 | 0.8270 | 0.8667 |
| 0.0001 | 27.0 | 729 | 0.7978 | 0.8222 |
| 0.0 | 28.0 | 756 | 0.8136 | 0.8444 |
| 0.0001 | 29.0 | 783 | 0.8553 | 0.8444 |
| 0.0001 | 30.0 | 810 | 0.9423 | 0.8444 |
| 0.0001 | 31.0 | 837 | 0.9286 | 0.8222 |
| 0.0001 | 32.0 | 864 | 0.9464 | 0.8222 |
| 0.0002 | 33.0 | 891 | 0.8713 | 0.8444 |
| 0.0001 | 34.0 | 918 | 0.8762 | 0.8444 |
| 0.0001 | 35.0 | 945 | 0.9092 | 0.8667 |
| 0.0 | 36.0 | 972 | 0.9547 | 0.8444 |
| 0.0 | 37.0 | 999 | 0.9283 | 0.8444 |
| 0.0 | 38.0 | 1026 | 0.8639 | 0.8444 |
| 0.0001 | 39.0 | 1053 | 0.8477 | 0.8667 |
| 0.0 | 40.0 | 1080 | 0.8432 | 0.8667 |
| 0.0 | 41.0 | 1107 | 0.8325 | 0.8667 |
| 0.0 | 42.0 | 1134 | 0.7851 | 0.8667 |
| 0.0003 | 43.0 | 1161 | 0.7875 | 0.8667 |
| 0.0 | 44.0 | 1188 | 0.7888 | 0.8667 |
| 0.0001 | 45.0 | 1215 | 0.8006 | 0.8889 |
| 0.0001 | 46.0 | 1242 | 0.8075 | 0.8889 |
| 0.0001 | 47.0 | 1269 | 0.8158 | 0.8889 |
| 0.0 | 48.0 | 1296 | 0.8184 | 0.8667 |
| 0.0002 | 49.0 | 1323 | 0.8184 | 0.8667 |
| 0.0001 | 50.0 | 1350 | 0.8184 | 0.8667 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Realgon/distilbert_twitterfin_padding10model
|
Realgon
| 2023-11-29T09:54:48Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:34:56Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding10model
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. -->
# distilbert_twitterfin_padding10model
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:
- Loss: 1.0996
- Accuracy: 0.8815
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5889 | 1.0 | 597 | 0.3931 | 0.8513 |
| 0.3408 | 2.0 | 1194 | 0.3420 | 0.8819 |
| 0.2458 | 3.0 | 1791 | 0.3978 | 0.8807 |
| 0.1516 | 4.0 | 2388 | 0.5900 | 0.8819 |
| 0.1098 | 5.0 | 2985 | 0.6569 | 0.8781 |
| 0.0499 | 6.0 | 3582 | 0.7546 | 0.8756 |
| 0.042 | 7.0 | 4179 | 0.7920 | 0.8765 |
| 0.0348 | 8.0 | 4776 | 0.8574 | 0.8673 |
| 0.0341 | 9.0 | 5373 | 0.8166 | 0.8781 |
| 0.0235 | 10.0 | 5970 | 0.9018 | 0.8760 |
| 0.0154 | 11.0 | 6567 | 0.9876 | 0.8727 |
| 0.0115 | 12.0 | 7164 | 0.9529 | 0.8836 |
| 0.0104 | 13.0 | 7761 | 0.9724 | 0.8765 |
| 0.0076 | 14.0 | 8358 | 1.0292 | 0.8735 |
| 0.0089 | 15.0 | 8955 | 1.0013 | 0.8723 |
| 0.0033 | 16.0 | 9552 | 1.0696 | 0.8840 |
| 0.0076 | 17.0 | 10149 | 1.1262 | 0.8681 |
| 0.001 | 18.0 | 10746 | 1.1008 | 0.8773 |
| 0.0016 | 19.0 | 11343 | 1.0923 | 0.8811 |
| 0.0039 | 20.0 | 11940 | 1.0996 | 0.8815 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
WebraftAI/synapsellm-7b-mistral-v0.2
|
WebraftAI
| 2023-11-29T09:54:20Z | 9 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T08:12:03Z |
# SynapseLLM:
SynapseLLM, a significant achievement by WebraftAI, represents a series of large language AI models designed to create robust, generalized, and decentralized information systems. This repository specifically houses the SynapseLLM finetuned version of Mistral. The finetuning process is conducted on a custom dataset, albeit limited in scope, focusing on code and normal question-answering scenarios. This adaptation showcases the model's versatility and applicability within specific domains, contributing to the broader landscape of AI advancements.
## Model Details
**SynapseLLM:**
- Parameters: 7B
- Learning rate: 2e-4
- Adapter used: Qlora
- Precision: float16
- Batch size: 32
- Maximum gradient normal: 0.3
- Optimizer: paged_adamw_32bit
- Warmup Ratio: 0.03
- Step(s) (trained): 100
- Epoch(s) (trained): 1
### Model Description
This is a 7b parameter, decoder only transformer based finetuned model on Chat Q/A and Code instructions. It's a preview finetune on Mistral 7B v0.1 on a sample dataset of 140k rows comprising of 73k Code and 67k General Q/A (Through GPT-4). This is a full model merged and compiled with trained adapters, so you can easily load this through transformers.
- **Developed by:** WebraftAI
- **Funded by:** Webraft Cloud
- **Shared by:** WebraftAI
- **Model type:** Decoder-only Transformer
- **Language(s):** English Only
- **License:** Apache 2.0
- **Finetuned from model:** Mistral-7b-v0.1
|
rika37/rl_course_vizdoom_health_gathering_supreme
|
rika37
| 2023-11-29T09:41:21Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T09:41:12Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.60 +/- 3.51
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r rika37/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
PhucMap/ViSenSum
|
PhucMap
| 2023-11-29T09:41:13Z | 112 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"mbart",
"text2text-generation",
"summarization",
"vi",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2023-10-09T17:34:17Z |
---
license: mit
language:
- vi
metrics:
- rouge
pipeline_tag: summarization
---
|
MadzM/ppo-LunarLander-v2
|
MadzM
| 2023-11-29T09:30:27Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T09:28:52Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 255.89 +/- 21.10
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hkivancoral/hushem_5x_beit_base_rms_00001_fold1
|
hkivancoral
| 2023-11-29T09:24:04Z | 6 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-29T08:51:44Z |
---
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_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8222222222222222
---
<!-- 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_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5839
- Accuracy: 0.8222
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5474 | 1.0 | 27 | 0.5910 | 0.8444 |
| 0.0818 | 2.0 | 54 | 0.5720 | 0.7778 |
| 0.0238 | 3.0 | 81 | 0.8576 | 0.7111 |
| 0.0074 | 4.0 | 108 | 0.5321 | 0.8667 |
| 0.0032 | 5.0 | 135 | 0.4605 | 0.8667 |
| 0.0017 | 6.0 | 162 | 0.6849 | 0.7778 |
| 0.0024 | 7.0 | 189 | 0.4973 | 0.8667 |
| 0.0008 | 8.0 | 216 | 0.4640 | 0.8667 |
| 0.0044 | 9.0 | 243 | 0.6817 | 0.8222 |
| 0.0005 | 10.0 | 270 | 0.5671 | 0.8222 |
| 0.0004 | 11.0 | 297 | 0.5195 | 0.8444 |
| 0.0002 | 12.0 | 324 | 0.7506 | 0.8222 |
| 0.0007 | 13.0 | 351 | 0.4960 | 0.8667 |
| 0.0004 | 14.0 | 378 | 0.4879 | 0.8667 |
| 0.0002 | 15.0 | 405 | 0.2878 | 0.8889 |
| 0.0004 | 16.0 | 432 | 0.5723 | 0.7778 |
| 0.0038 | 17.0 | 459 | 0.8796 | 0.8 |
| 0.0011 | 18.0 | 486 | 0.4544 | 0.8444 |
| 0.001 | 19.0 | 513 | 0.2346 | 0.8889 |
| 0.0001 | 20.0 | 540 | 0.6421 | 0.8444 |
| 0.0001 | 21.0 | 567 | 0.5172 | 0.8667 |
| 0.0012 | 22.0 | 594 | 0.4729 | 0.8222 |
| 0.0001 | 23.0 | 621 | 0.4318 | 0.8222 |
| 0.0001 | 24.0 | 648 | 0.4087 | 0.8222 |
| 0.0004 | 25.0 | 675 | 0.4267 | 0.8889 |
| 0.0001 | 26.0 | 702 | 0.4250 | 0.8667 |
| 0.0001 | 27.0 | 729 | 0.3081 | 0.8889 |
| 0.0001 | 28.0 | 756 | 0.4008 | 0.8222 |
| 0.0 | 29.0 | 783 | 0.3766 | 0.8444 |
| 0.0001 | 30.0 | 810 | 0.3622 | 0.9111 |
| 0.0 | 31.0 | 837 | 0.4006 | 0.8222 |
| 0.0001 | 32.0 | 864 | 0.4743 | 0.8444 |
| 0.0001 | 33.0 | 891 | 0.3292 | 0.8889 |
| 0.0001 | 34.0 | 918 | 1.1554 | 0.7556 |
| 0.0002 | 35.0 | 945 | 0.6888 | 0.8 |
| 0.0003 | 36.0 | 972 | 0.4504 | 0.8667 |
| 0.0001 | 37.0 | 999 | 0.4287 | 0.8667 |
| 0.0 | 38.0 | 1026 | 0.4528 | 0.8667 |
| 0.0001 | 39.0 | 1053 | 0.4353 | 0.8667 |
| 0.0 | 40.0 | 1080 | 0.4656 | 0.8444 |
| 0.0044 | 41.0 | 1107 | 0.4571 | 0.8222 |
| 0.0 | 42.0 | 1134 | 0.4813 | 0.8222 |
| 0.0004 | 43.0 | 1161 | 0.5618 | 0.8444 |
| 0.0 | 44.0 | 1188 | 0.5635 | 0.8444 |
| 0.0 | 45.0 | 1215 | 0.5635 | 0.8444 |
| 0.0061 | 46.0 | 1242 | 0.5733 | 0.8444 |
| 0.0 | 47.0 | 1269 | 0.5697 | 0.8444 |
| 0.0001 | 48.0 | 1296 | 0.5838 | 0.8222 |
| 0.0001 | 49.0 | 1323 | 0.5839 | 0.8222 |
| 0.0 | 50.0 | 1350 | 0.5839 | 0.8222 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Norod78/SDXL-JojosoStyle-Lora-v2
|
Norod78
| 2023-11-29T09:23:49Z | 538 | 6 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"style",
"jojo's bizarre adventure",
"jojo",
"stone ocean",
"sdxl style lora",
"jojo's bizarre adventure stone ocean",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:other",
"region:us"
] |
text-to-image
| 2023-11-29T09:23:45Z |
---
license: other
license_name: bespoke-lora-trained-license
license_link: https://multimodal.art/civitai-licenses?allowNoCredit=True&allowCommercialUse=Rent&allowDerivatives=True&allowDifferentLicense=False
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
- style
- jojo's bizarre adventure
- jojo
- stone ocean
- sdxl style lora
- jojo's bizarre adventure stone ocean
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: Jojosostyle
widget:
- text: 'Spiderman kissing a mutant alien JojosoStyle '
output:
url: >-
4002501.jpeg
- text: 'American gothic by Grant Wood JojosoStyle '
output:
url: >-
4002504.jpeg
- text: 'Insane zombie santa JojosoStyle '
output:
url: >-
4002511.jpeg
- text: 'The cookie monster JojosoStyle '
output:
url: >-
4002518.jpeg
- text: 'The Eiffel tower JojosoStyle , Very detailed, clean, high quality, sharp image'
output:
url: >-
4002520.jpeg
- text: 'A magical landscape JojosoStyle '
output:
url: >-
4002508.jpeg
- text: 'The girl with a pearl earring by vermeer Jojosostyle , Very detailed, clean, high quality, sharp image'
output:
url: >-
4002519.jpeg
- text: 'Pikachu blasting electricity Jojosostyle , Very detailed, clean, high quality, sharp image'
output:
url: >-
4002523.jpeg
- text: 'Snoop Dogg JojosoStyle '
output:
url: >-
4002507.jpeg
- text: 'Mr. Potato head Jojosostyle , Very detailed, clean, high quality, sharp image'
output:
url: >-
4002521.jpeg
---
# JoJo Stone-Ocean Style XL
<Gallery />
([CivitAI](https://civitai.com/models/129551))
## Model description
<p>A LoRA model for SDXL trained upon images from <em>JoJo's Bizarre Adventure: Stone Ocean</em></p>
## Trigger words
You should use `Jojosostyle` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Norod78/jojo-stone-ocean-style-xl/tree/main) them in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('stabilityai/stable-diffusion-xl-base-1.0', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('Norod78/jojo-stone-ocean-style-xl', weight_name='SDXL-JojosoStyle-Lora-v2-r16.safetensors')
image = pipeline('Mr. Potato head Jojosostyle , Very detailed, clean, high quality, sharp image').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
Realgon/distilbert_sst5_padding90model
|
Realgon
| 2023-11-29T09:23:04Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:33:22Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding90model
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. -->
# distilbert_sst5_padding90model
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:
- Loss: 3.9832
- Accuracy: 0.4959
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3043 | 1.0 | 534 | 1.2710 | 0.4100 |
| 1.0413 | 2.0 | 1068 | 1.1063 | 0.5149 |
| 0.8567 | 3.0 | 1602 | 1.1613 | 0.5217 |
| 0.692 | 4.0 | 2136 | 1.3199 | 0.5154 |
| 0.539 | 5.0 | 2670 | 1.5717 | 0.4914 |
| 0.4233 | 6.0 | 3204 | 1.6996 | 0.5036 |
| 0.3198 | 7.0 | 3738 | 1.9868 | 0.5041 |
| 0.2373 | 8.0 | 4272 | 2.1062 | 0.4995 |
| 0.2003 | 9.0 | 4806 | 2.4530 | 0.4887 |
| 0.16 | 10.0 | 5340 | 2.6820 | 0.4986 |
| 0.1309 | 11.0 | 5874 | 2.8490 | 0.5072 |
| 0.1208 | 12.0 | 6408 | 3.2394 | 0.4982 |
| 0.0872 | 13.0 | 6942 | 3.3667 | 0.4964 |
| 0.0776 | 14.0 | 7476 | 3.5361 | 0.5 |
| 0.0562 | 15.0 | 8010 | 3.7808 | 0.4887 |
| 0.0502 | 16.0 | 8544 | 3.7929 | 0.4991 |
| 0.0369 | 17.0 | 9078 | 3.9267 | 0.4887 |
| 0.0311 | 18.0 | 9612 | 3.8721 | 0.5 |
| 0.0235 | 19.0 | 10146 | 3.9710 | 0.4986 |
| 0.033 | 20.0 | 10680 | 3.9832 | 0.4959 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
DingDongBell/ddbldh
|
DingDongBell
| 2023-11-29T09:14:55Z | 2 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:adapter:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-11-29T09:14:36Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: ddbldh
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - ddbldh
These are LoRA adaption weights for [stabilityai/stable-diffusion-2-1-base](https://huggingface.co/stabilityai/stable-diffusion-2-1-base). The weights were trained on the instance prompt "ddbldh" using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
Test prompt: portrait photo of ddbldh
|
Realgon/distilbert_sst5_padding80model
|
Realgon
| 2023-11-29T09:10:18Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:32:49Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding80model
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. -->
# distilbert_sst5_padding80model
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:
- Loss: 3.9171
- Accuracy: 0.5054
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3494 | 1.0 | 534 | 1.2564 | 0.4181 |
| 1.0617 | 2.0 | 1068 | 1.1176 | 0.4982 |
| 0.8787 | 3.0 | 1602 | 1.1842 | 0.5149 |
| 0.7134 | 4.0 | 2136 | 1.2718 | 0.5163 |
| 0.5548 | 5.0 | 2670 | 1.4860 | 0.5104 |
| 0.4256 | 6.0 | 3204 | 1.7135 | 0.4986 |
| 0.3318 | 7.0 | 3738 | 1.8871 | 0.4928 |
| 0.2476 | 8.0 | 4272 | 2.0403 | 0.5127 |
| 0.1946 | 9.0 | 4806 | 2.2836 | 0.4982 |
| 0.1737 | 10.0 | 5340 | 2.5511 | 0.5027 |
| 0.1482 | 11.0 | 5874 | 2.8407 | 0.5036 |
| 0.1166 | 12.0 | 6408 | 3.1513 | 0.4959 |
| 0.0901 | 13.0 | 6942 | 3.2833 | 0.5027 |
| 0.0825 | 14.0 | 7476 | 3.4168 | 0.5041 |
| 0.0551 | 15.0 | 8010 | 3.5105 | 0.5032 |
| 0.0492 | 16.0 | 8544 | 3.6475 | 0.5014 |
| 0.0367 | 17.0 | 9078 | 3.7859 | 0.5045 |
| 0.0351 | 18.0 | 9612 | 3.8480 | 0.4977 |
| 0.0316 | 19.0 | 10146 | 3.9006 | 0.5023 |
| 0.0264 | 20.0 | 10680 | 3.9171 | 0.5054 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
nateraw/llama-2-7b-english-to-hinglish
|
nateraw
| 2023-11-29T09:07:42Z | 15 | 9 |
peft
|
[
"peft",
"hinglish",
"en-to-hi",
"text-generation",
"en",
"hi",
"dataset:findnitai/english-to-hinglish",
"dataset:nateraw/english-to-hinglish",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-hf",
"base_model:adapter:NousResearch/Llama-2-7b-hf",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-11-29T08:13:22Z |
---
library_name: peft
base_model: NousResearch/Llama-2-7b-hf
license: apache-2.0
widget:
- text: |
Translate from english to hinglish:
Where is the bathroom?
---
Translation:
example_title: Nature Calls
output:
text: "bathroom kaha hai?"
- text: |
Translate from english to hinglish:
Can I pet your dog?
---
Translation:
example_title: Pet a Dog
output:
text: "kya mai apke dog ko pet kar sakta hoon?"
datasets:
- findnitai/english-to-hinglish
- nateraw/english-to-hinglish
language:
- en
- hi
pipeline_tag: text-generation
tags:
- hinglish
- en-to-hi
---
# Model Card for Model ID
Lora fine-tune of Llama-2-7b for english to hinglish translation.
```python
import torch
from transformers import AutoModelForCausalLM, pipeline
PROMPT_TEMPLATE = (
f"Translate from english to hinglish:\n{{en}}\n---\nTranslation:\n"
)
model_id = "nousresearch/llama-2-7b-hf"
peft_model_id = "nateraw/llama-2-7b-english-to-hinglish"
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
torch_dtype=torch.bfloat16
)
model.load_adapter(peft_model_id)
pipe = pipeline(
"text-generation",
model=model,
tokenizer=model_id,
)
out = pipe(
PROMPT_TEMPLATE.format(en="Can I pet your dog?"),
return_full_text=False,
do_sample=False,
max_new_tokens=256
)[0]['generated_text']
print(out)
```
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [@nateraw](https://huggingface.co/nateraw)
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [nousresearch/llama-2-7b-hf](https://huggingface.co/nousresearch/llama-2-7b-hf)
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
## Training procedure
### Framework versions
- PEFT 0.6.2
## Training procedure
### Framework versions
- PEFT 0.6.2
|
rika37/ppo-CartPole-v1
|
rika37
| 2023-11-29T09:06:31Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T09:02:07Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -171.42 +/- 100.70
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 50000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'rika37/ppo-CartPole-v1'
'batch_size': 512
'minibatch_size': 128}
```
|
jellyconsumer/new_model_falcon_large
|
jellyconsumer
| 2023-11-29T08:57:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"falcon",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"base_model:finetune:vilsonrodrigues/falcon-7b-instruct-sharded",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T04:00:16Z |
---
license: apache-2.0
base_model: vilsonrodrigues/falcon-7b-instruct-sharded
tags:
- generated_from_trainer
model-index:
- name: new_model_falcon_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. -->
# new_model_falcon_large
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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.2
|
Bpole/lora_mistral_saiga_sberhack_v1.0
|
Bpole
| 2023-11-29T08:55:47Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:Bpole/lora_sberhack_v1.0",
"base_model:adapter:Bpole/lora_sberhack_v1.0",
"region:us"
] | null | 2023-11-29T08:55:44Z |
---
library_name: peft
base_model: Bpole/lora_sberhack_v1.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.2
|
KaeriJenti/kaori-70b-v1
|
KaeriJenti
| 2023-11-29T08:55:13Z | 1,398 | 1 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-29T02:23:55Z |
---
license: llama2
---
<h1>kaori-70b-v1 Model Card</h1>
<h3>Datasets:</h3>
- Open-Platypus
- dolphin
- OpenOrca
This Model Finetuned By Kaeri and Jenti.
<h3>Framework:</h3>
- https://github.com/hiyouga/LLaMA-Efficient-Tuning
<h3>Parameters:</h3>
- Finetune_Type : QLoRA
- GPUs : A100x4(80GB)
- Epochs : 1
- Batchsize : 8
|
nimrita/Reinforce-cartpolev1
|
nimrita
| 2023-11-29T08:47:59Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T08:47:49Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpolev1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
pranjal01/fine_tuned_gpt2_clm-model
|
pranjal01
| 2023-11-29T08:39:09Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"en",
"dataset:eli5",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-22T07:04:36Z |
---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: fine_tuned_gpt2_clm-model
results: []
datasets:
- eli5
language:
- en
metrics:
- perplexity
pipeline_tag: text-generation
---
<!-- 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. -->
# fine_tuned_gpt2_clm-model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the eli5 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3066
### 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 142 | 3.3422 |
| No log | 2.0 | 284 | 3.3226 |
| No log | 3.0 | 426 | 3.3148 |
| 3.4352 | 4.0 | 568 | 3.3095 |
| 3.4352 | 5.0 | 710 | 3.3074 |
| 3.4352 | 6.0 | 852 | 3.3066 |
| 3.4352 | 7.0 | 994 | 3.3046 |
| 3.3068 | 8.0 | 1136 | 3.3049 |
| 3.3068 | 9.0 | 1278 | 3.3048 |
| 3.3068 | 10.0 | 1420 | 3.3050 |
| 3.2433 | 11.0 | 1562 | 3.3062 |
| 3.2433 | 12.0 | 1704 | 3.3059 |
| 3.2433 | 13.0 | 1846 | 3.3062 |
| 3.2433 | 14.0 | 1988 | 3.3065 |
| 3.2113 | 15.0 | 2130 | 3.3066 |
### Inference:
- prompt = "dna phosphorylation is the process of"
- generated Text: dna phosphorylation is the process of forming the deoxygenated product. For example, in a protein phosphorylation inhibitor, it occurs to deoxygenate the phosphorylated protein by binding a phosphate molecule and preventing it from being destroyed by a nonenzymatic process.
In a phosphorylation inhibitor like dna, the product is phosphorylated by the phosphocreatine, a phosphorylated phosphocreatine molecule that can bind to other phosphocreatine molecules that bind to phosphocreatine. This interaction helps to separate the phosphocreatine molecule that is phosphorylated from the phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine-glucose molecule that is phosphocreatine-phosphocreatine-glucose-phosphocreatine-phosphocreatine-glucose.
In anoxidase inhibitors like dna, they are a bit more specific, more specific, and have a more complicated interaction with the phosphocreatine molecule that can bind to phosphocreatine molecules.
I would argue that both dna-and phosphocreatine-phosphocreatine-glucose will not be able to bind to phosphocreatine because the phosphocreatine-phosphocreatine-phosphocreatine-glucose-phosphocreatine molecule that was phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine-glucose, is phosphocreatine.
That is, dna-and phosphocreatine-glucose will be able to bind to phosphocreatine because the phosphocreatine molecule that was phosphocreatine-glucose will not be phosphocreatine because the phosphocreatine-phosphocreatine-glucose molecule that was phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine-glucose, is phosphocreatine.
Edit: Added: The final point is that it can't bind phosphocreatine because that phosphocreatine molecule (a phosphocreatine-phosphocreatine-phosphocreatine-phosphocreatine molecule) can not be phosphoc
### Evaluation metric:
Perplexity: 27.29
### GPU:
- CUDA Version: 12.1
- 4x Tesla T4
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
|
User1115/whisper-large-v2-test-singleWord-small-30steps
|
User1115
| 2023-11-29T08:36:42Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openai/whisper-large-v2",
"base_model:adapter:openai/whisper-large-v2",
"region:us"
] | null | 2023-11-29T08:36:30Z |
---
library_name: peft
base_model: openai/whisper-large-v2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.3.dev0
|
eek/zephyr-7b-sft-lora
|
eek
| 2023-11-29T08:29:58Z | 4 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"en",
"base_model:HuggingFaceH4/zephyr-7b-alpha",
"base_model:adapter:HuggingFaceH4/zephyr-7b-alpha",
"license:mit",
"region:us"
] | null | 2023-11-27T15:23:10Z |
---
license: mit
base_model: HuggingFaceH4/zephyr-7b-alpha
tags:
- generated_from_trainer
model-index:
- name: zephyr-7b-sft-lora
results: []
language:
- en
library_name: adapter-transformers
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-7b-sft-lora
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1445
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 128
- total_train_batch_size: 4096
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1612 | 0.36 | 16 | 2.1446 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
hkivancoral/hushem_5x_beit_base_rms_0001_fold5
|
hkivancoral
| 2023-11-29T08:28:49Z | 7 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-11-29T07:56:06Z |
---
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_0001_fold5
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7073170731707317
---
<!-- 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_0001_fold5
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4047
- Accuracy: 0.7073
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.4155 | 1.0 | 28 | 1.3777 | 0.2683 |
| 1.3848 | 2.0 | 56 | 1.2989 | 0.2927 |
| 1.3314 | 3.0 | 84 | 1.2733 | 0.4878 |
| 1.2486 | 4.0 | 112 | 1.0811 | 0.5122 |
| 1.2007 | 5.0 | 140 | 0.9236 | 0.5854 |
| 1.05 | 6.0 | 168 | 1.1380 | 0.5122 |
| 1.0162 | 7.0 | 196 | 0.9574 | 0.5854 |
| 0.9476 | 8.0 | 224 | 1.4400 | 0.4878 |
| 0.903 | 9.0 | 252 | 0.9012 | 0.6341 |
| 0.9351 | 10.0 | 280 | 1.0183 | 0.6829 |
| 0.8113 | 11.0 | 308 | 0.9612 | 0.6585 |
| 0.8131 | 12.0 | 336 | 1.6631 | 0.4878 |
| 0.7921 | 13.0 | 364 | 0.9316 | 0.6829 |
| 0.8114 | 14.0 | 392 | 1.3372 | 0.5854 |
| 0.7382 | 15.0 | 420 | 1.4796 | 0.6341 |
| 0.7119 | 16.0 | 448 | 1.9753 | 0.5366 |
| 0.6933 | 17.0 | 476 | 1.3458 | 0.7073 |
| 0.591 | 18.0 | 504 | 1.3968 | 0.6585 |
| 0.6986 | 19.0 | 532 | 1.4904 | 0.6829 |
| 0.6832 | 20.0 | 560 | 1.7362 | 0.6585 |
| 0.5173 | 21.0 | 588 | 1.5475 | 0.7317 |
| 0.5116 | 22.0 | 616 | 1.9547 | 0.6585 |
| 0.4833 | 23.0 | 644 | 2.1246 | 0.6341 |
| 0.4295 | 24.0 | 672 | 1.9058 | 0.7317 |
| 0.4431 | 25.0 | 700 | 2.4495 | 0.6585 |
| 0.3801 | 26.0 | 728 | 1.6867 | 0.7561 |
| 0.4263 | 27.0 | 756 | 2.1056 | 0.6585 |
| 0.3209 | 28.0 | 784 | 2.6127 | 0.6098 |
| 0.29 | 29.0 | 812 | 2.2833 | 0.6341 |
| 0.2306 | 30.0 | 840 | 2.6477 | 0.6341 |
| 0.2318 | 31.0 | 868 | 2.2205 | 0.6829 |
| 0.1766 | 32.0 | 896 | 2.1057 | 0.8293 |
| 0.1861 | 33.0 | 924 | 2.9102 | 0.6341 |
| 0.2172 | 34.0 | 952 | 2.3319 | 0.7317 |
| 0.1336 | 35.0 | 980 | 2.7931 | 0.7073 |
| 0.128 | 36.0 | 1008 | 3.2544 | 0.6098 |
| 0.1009 | 37.0 | 1036 | 2.3057 | 0.7805 |
| 0.1495 | 38.0 | 1064 | 2.9047 | 0.7317 |
| 0.0845 | 39.0 | 1092 | 3.1290 | 0.7317 |
| 0.064 | 40.0 | 1120 | 2.9682 | 0.7561 |
| 0.0399 | 41.0 | 1148 | 2.9364 | 0.7561 |
| 0.0198 | 42.0 | 1176 | 4.0340 | 0.6585 |
| 0.0179 | 43.0 | 1204 | 3.2313 | 0.7317 |
| 0.0799 | 44.0 | 1232 | 3.4340 | 0.7317 |
| 0.0495 | 45.0 | 1260 | 3.8737 | 0.6829 |
| 0.041 | 46.0 | 1288 | 3.5139 | 0.6829 |
| 0.0058 | 47.0 | 1316 | 3.4146 | 0.7073 |
| 0.0141 | 48.0 | 1344 | 3.4016 | 0.7073 |
| 0.0316 | 49.0 | 1372 | 3.4047 | 0.7073 |
| 0.0269 | 50.0 | 1400 | 3.4047 | 0.7073 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Realgon/distilbert_sst5_padding40model
|
Realgon
| 2023-11-29T08:24:56Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:30:44Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding40model
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. -->
# distilbert_sst5_padding40model
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:
- Loss: 4.1102
- Accuracy: 0.5032
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2973 | 1.0 | 534 | 1.2639 | 0.4226 |
| 1.0311 | 2.0 | 1068 | 1.1147 | 0.5122 |
| 0.8351 | 3.0 | 1602 | 1.2030 | 0.5086 |
| 0.68 | 4.0 | 2136 | 1.3100 | 0.5213 |
| 0.5243 | 5.0 | 2670 | 1.5365 | 0.4995 |
| 0.3959 | 6.0 | 3204 | 1.7658 | 0.4946 |
| 0.304 | 7.0 | 3738 | 1.9953 | 0.5050 |
| 0.2223 | 8.0 | 4272 | 2.1893 | 0.4991 |
| 0.1758 | 9.0 | 4806 | 2.5567 | 0.4986 |
| 0.1431 | 10.0 | 5340 | 2.8571 | 0.4982 |
| 0.1208 | 11.0 | 5874 | 3.0516 | 0.5027 |
| 0.1104 | 12.0 | 6408 | 3.3050 | 0.5095 |
| 0.0792 | 13.0 | 6942 | 3.5054 | 0.5014 |
| 0.0761 | 14.0 | 7476 | 3.6607 | 0.5009 |
| 0.0447 | 15.0 | 8010 | 3.8772 | 0.5032 |
| 0.0377 | 16.0 | 8544 | 3.9335 | 0.5050 |
| 0.0297 | 17.0 | 9078 | 4.0339 | 0.5059 |
| 0.0255 | 18.0 | 9612 | 4.0308 | 0.5095 |
| 0.0258 | 19.0 | 10146 | 4.0368 | 0.5086 |
| 0.0182 | 20.0 | 10680 | 4.1102 | 0.5032 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
lensonbocko/Reinforce-V1
|
lensonbocko
| 2023-11-29T08:18:45Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T08:18:34Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-V1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
openchat/openchat_v3.2
|
openchat
| 2023-11-29T08:16:32Z | 1,494 | 42 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2309.11235",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-30T10:12:00Z |
---
license: llama2
---
# OpenChat: Advancing Open-source Language Models with Imperfect Data</h1>
<div align="center">
<img src="https://raw.githubusercontent.com/imoneoi/openchat/master/assets/logo_new.png" style="width: 65%">
</div>
[OpenChat](https://github.com/imoneoi/openchat) is a series of open-source language models based on supervised fine-tuning (SFT). We leverage the ~80k ShareGPT conversations with a conditioning strategy and weighted loss to achieve remarkable performance despite our simple methods. Our final vision is to develop a high-performance, open-source, and commercially available large language model, and we are continuously making progress.
**🔥 Rank #1 of 13B open-source models | 89.5% win-rate on [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) | 7.01 score on [MT-bench](https://chat.lmsys.org/?leaderboard)**
**💲 FREE for commercial use under [Llama 2 Community License](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)**
**🕒 Super efficient padding-free finetuning for applications, only 10 hours on 8xA100 80G**
## <a id="models"></a> Usage
To use these models, we highly recommend installing the OpenChat package by following the [installation guide](https://github.com/imoneoi/openchat/#installation) and using the OpenChat OpenAI-compatible API server by running the serving command from the table below. The server is optimized for high-throughput deployment using [vLLM](https://github.com/vllm-project/vllm) and can run on a GPU with at least 48GB RAM or two consumer GPUs with tensor parallelism. To enable tensor parallelism, append `--tensor-parallel-size 2` to the serving command.
When started, the server listens at `localhost:18888` for requests and is compatible with the [OpenAI ChatCompletion API specifications](https://platform.openai.com/docs/api-reference/chat). See the example request below for reference. Additionally, you can access the [OpenChat Web UI](#web-ui) for a user-friendly experience.
To deploy the server as an online service, use `--api-keys sk-KEY1 sk-KEY2 ...` to specify allowed API keys and `--disable-log-requests --disable-log-stats --log-file openchat.log` for logging only to a file. We recommend using a [HTTPS gateway](https://fastapi.tiangolo.com/es/deployment/concepts/#security-https) in front of the server for security purposes.
*Note:* If IPv6 address errors occur, which is a [vLLM issue](https://github.com/vllm-project/vllm/issues/570), please run `export NCCL_IGNORE_DISABLED_P2P=1` before starting the server.
<details>
<summary>Example request (click to expand)</summary>
```bash
curl http://localhost:18888/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "openchat_v3.2",
"messages": [{"role": "user", "content": "You are a large language model named OpenChat. Write a poem to describe yourself"}]
}'
```
</details>
| Model | Size | Context | Weights | Serving |
|--------------|------|---------|--------------------------------------------------------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| OpenChat 3.2 | 13B | 4096 | [Huggingface](https://huggingface.co/openchat/openchat_v3.2) | `python -m ochat.serving.openai_api_server --model-type openchat_v3.2 --model openchat/openchat_v3.2 --engine-use-ray --worker-use-ray --max-num-batched-tokens 5120` |
| OpenChat 3.1 | 13B | 4096 | [Huggingface](https://huggingface.co/openchat/openchat_v3.1) | `python -m ochat.serving.openai_api_server --model-type openchat_v3.1_llama2 --model openchat/openchat_v3.1 --engine-use-ray --worker-use-ray --max-num-batched-tokens 5120` |
For inference with Huggingface Transformers (slow and not recommended), follow the conversation template provided below:
<details>
<summary>Conversation templates (click to expand)</summary>
V3.2
```python
# Single-turn V3.2
tokenize("GPT4 User: Hello<|end_of_turn|>GPT4 Assistant:")
# Result: [1, 402, 7982, 29946, 4911, 29901, 15043, 32000, 402, 7982, 29946, 4007, 22137, 29901]
# Multi-turn V3.2
tokenize("GPT4 User: Hello<|end_of_turn|>GPT4 Assistant: Hi<|end_of_turn|>GPT4 User: How are you today?<|end_of_turn|>GPT4 Assistant:")
# Result: [1, 402, 7982, 29946, 4911, 29901, 15043, 32000, 402, 7982, 29946, 4007, 22137, 29901, 6324, 32000, 402, 7982, 29946, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 402, 7982, 29946, 4007, 22137, 29901]
```
V3.1
```python
# Single-turn V3.1
tokenize("Assistant is GPT4<|end_of_turn|>User: Hello<|end_of_turn|>Assistant:")
# Result: [1, 4007, 22137, 338, 402, 7982, 29946, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901]
# Multi-turn V3.1
tokenize("Assistant is GPT4<|end_of_turn|>User: Hello<|end_of_turn|>Assistant: Hi<|end_of_turn|>User: How are you today?<|end_of_turn|>Assistant:")
# Result: [1, 4007, 22137, 338, 402, 7982, 29946, 32000, 4911, 29901, 15043, 32000, 4007, 22137, 29901, 6324, 32000, 4911, 29901, 1128, 526, 366, 9826, 29973, 32000, 4007, 22137, 29901]
```
</details>
## <a id="benchmarks"></a> Benchmarks
We have evaluated our models using the two most popular evaluation benchmarks **, including AlpacaEval and MT-bench. Here we list the top models with our released versions, sorted by model size in descending order. The full version can be found on the [MT-bench](https://chat.lmsys.org/?leaderboard) and [AlpacaEval](https://tatsu-lab.github.io/alpaca_eval/) leaderboards.
To ensure consistency, we used the same routine as ChatGPT / GPT-4 to run these benchmarks. We started the OpenAI API-compatible server and set the `openai.api_base` to `http://localhost:18888/v1` in the benchmark program.
| **Model** | **Size** | **Context** | **💲Free** | **AlpacaEval (win rate %)** | **MT-bench (win rate adjusted %)** | **MT-bench (score)** |
|------------------|----------|-------------|------------|-----------------------------|------------------------------------|----------------------|
| | | | | **v.s. text-davinci-003** | **v.s. ChatGPT** | |
| GPT-4 | 1.8T* | 8K | ❌ | 95.3 | 82.5 | 8.99 |
| ChatGPT | 175B* | 4K | ❌ | 89.4 | 50.0 | 7.94 |
| Llama-2-70B-Chat | 70B | 4K | ✅ | 92.7 | | 6.86 |
| **OpenChat 3.2** | **13B** | **4K** | ✅ | **89.1** | **51.6** | **7.01** |
| **OpenChat 3.1** | **13B** | **4K** | ✅ | **89.5** | **50.0** | **6.65** |
| Llama-2-13B-Chat | 13B | 4K | ✅ | 81.0 | | 6.65 |
| Vicuna 1.3 | 13B | 2K | ❌ | 82.1 | 37.5 | 6.00 |
*: Estimated model size
**: The benchmark metrics represent a quantified measure of a subset of the model's capabilities. A win-rate greater than 50% does not necessarily indicate that the model is better than ChatGPT in all scenarios or for all use cases. It is essential to consider the specific tasks or applications for which the model was evaluated and compare the results accordingly.
## Limitations
**Foundation Model Limitations**
Despite its advanced capabilities, OpenChat is still bound by the limitations inherent in its foundation models. These limitations may impact the model's performance in areas such as:
- Complex reasoning
- Mathematical and arithmetic tasks
- Programming and coding challenges
**Hallucination of Non-existent Information**
OpenChat may sometimes generate information that does not exist or is not accurate, also known as "hallucination". Users should be aware of this possibility and verify any critical information obtained from the model.
## License
Our OpenChat V3 models are licensed under the [Llama 2 Community License](https://ai.meta.com/resources/models-and-libraries/llama-downloads/).
```
@article{wang2023openchat,
title={OpenChat: Advancing Open-source Language Models with Mixed-Quality Data},
author={Wang, Guan and Cheng, Sijie and Zhan, Xianyuan and Li, Xiangang and Song, Sen and Liu, Yang},
journal={arXiv preprint arXiv:2309.11235},
year={2023}
}
```
|
Realgon/distilbert_sst5_padding30model
|
Realgon
| 2023-11-29T08:14:43Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:30:16Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding30model
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. -->
# distilbert_sst5_padding30model
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:
- Loss: 3.9313
- Accuracy: 0.5027
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2856 | 1.0 | 534 | 1.3049 | 0.4190 |
| 1.03 | 2.0 | 1068 | 1.1134 | 0.5154 |
| 0.8354 | 3.0 | 1602 | 1.1725 | 0.5154 |
| 0.6773 | 4.0 | 2136 | 1.3321 | 0.5158 |
| 0.5157 | 5.0 | 2670 | 1.6081 | 0.5009 |
| 0.3964 | 6.0 | 3204 | 1.7841 | 0.4950 |
| 0.3171 | 7.0 | 3738 | 1.8784 | 0.5158 |
| 0.2289 | 8.0 | 4272 | 2.1902 | 0.5072 |
| 0.1917 | 9.0 | 4806 | 2.5022 | 0.5 |
| 0.153 | 10.0 | 5340 | 2.7217 | 0.5014 |
| 0.147 | 11.0 | 5874 | 2.9098 | 0.4995 |
| 0.1279 | 12.0 | 6408 | 3.1218 | 0.5136 |
| 0.08 | 13.0 | 6942 | 3.5110 | 0.4982 |
| 0.0688 | 14.0 | 7476 | 3.4982 | 0.4959 |
| 0.0552 | 15.0 | 8010 | 3.6294 | 0.5041 |
| 0.0517 | 16.0 | 8544 | 3.7352 | 0.4968 |
| 0.0337 | 17.0 | 9078 | 3.8002 | 0.4986 |
| 0.0332 | 18.0 | 9612 | 3.8191 | 0.5081 |
| 0.0272 | 19.0 | 10146 | 3.9221 | 0.5032 |
| 0.0237 | 20.0 | 10680 | 3.9313 | 0.5027 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
michaelsungboklee/bert-finetuned-ner
|
michaelsungboklee
| 2023-11-29T08:13:35Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-11-29T07:24:35Z |
---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9315520369454066
- name: Recall
type: recall
value: 0.9505217098619994
- name: F1
type: f1
value: 0.9409412744689714
- name: Accuracy
type: accuracy
value: 0.9870194854889033
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0284
- Precision: 0.9316
- Recall: 0.9505
- F1: 0.9409
- Accuracy: 0.9870
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0349 | 1.0 | 1756 | 0.0347 | 0.9085 | 0.9362 | 0.9222 | 0.9810 |
| 0.0184 | 2.0 | 3512 | 0.0269 | 0.9281 | 0.9495 | 0.9387 | 0.9869 |
| 0.009 | 3.0 | 5268 | 0.0284 | 0.9316 | 0.9505 | 0.9409 | 0.9870 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Amiran13/wav2vec2-large-xlsr-georgian_v1
|
Amiran13
| 2023-11-29T08:10:19Z | 21 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-large-xlsr-53",
"base_model:finetune:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-11-20T20:45:45Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-georgian_v1
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-large-xlsr-georgian_v1
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0992
- Wer: 0.2605
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 3.1412 | 0.16 | 100 | 3.0855 | 1.0 |
| 3.0569 | 0.33 | 200 | 3.0369 | 1.0 |
| 2.9625 | 0.49 | 300 | 2.9778 | 1.0 |
| 0.7715 | 0.65 | 400 | 0.5113 | 0.7185 |
| 0.4725 | 0.81 | 500 | 0.3072 | 0.5138 |
| 0.4103 | 0.98 | 600 | 0.2447 | 0.4337 |
| 0.2775 | 1.14 | 700 | 0.2055 | 0.3769 |
| 0.2554 | 1.3 | 800 | 0.1950 | 0.3603 |
| 0.263 | 1.46 | 900 | 0.1813 | 0.3372 |
| 0.2294 | 1.63 | 1000 | 0.1664 | 0.3132 |
| 0.2296 | 1.79 | 1100 | 0.1565 | 0.2962 |
| 0.2183 | 1.95 | 1200 | 0.1474 | 0.2986 |
| 0.1822 | 2.12 | 1300 | 0.1546 | 0.2811 |
| 0.1798 | 2.28 | 1400 | 0.1442 | 0.2811 |
| 0.179 | 2.44 | 1500 | 0.1411 | 0.2686 |
| 0.1593 | 2.6 | 1600 | 0.1408 | 0.2739 |
| 0.2652 | 2.77 | 1700 | 0.2074 | 0.4499 |
| 0.1834 | 2.93 | 1800 | 0.1570 | 0.3942 |
| 0.2015 | 3.09 | 1900 | 0.1516 | 0.3859 |
| 0.1696 | 3.26 | 2000 | 0.1452 | 0.3826 |
| 0.1782 | 3.42 | 2100 | 0.1413 | 0.3763 |
| 0.1636 | 3.58 | 2200 | 0.1350 | 0.3761 |
| 0.173 | 3.74 | 2300 | 0.1323 | 0.3622 |
| 0.1704 | 3.91 | 2400 | 0.1289 | 0.3644 |
| 0.1418 | 4.07 | 2500 | 0.1266 | 0.3481 |
| 0.1403 | 4.23 | 2600 | 0.1274 | 0.3482 |
| 0.1353 | 4.4 | 2700 | 0.1287 | 0.3489 |
| 0.1432 | 4.56 | 2800 | 0.1293 | 0.3532 |
| 0.1283 | 4.72 | 2900 | 0.1226 | 0.3416 |
| 0.1367 | 4.88 | 3000 | 0.1206 | 0.3426 |
| 0.1162 | 5.05 | 3100 | 0.1222 | 0.3394 |
| 0.1267 | 5.21 | 3200 | 0.1183 | 0.3313 |
| 0.1126 | 5.37 | 3300 | 0.1180 | 0.3299 |
| 0.1127 | 5.53 | 3400 | 0.1177 | 0.3305 |
| 0.1155 | 5.7 | 3500 | 0.1185 | 0.3317 |
| 0.1086 | 5.86 | 3600 | 0.1129 | 0.3227 |
| 0.1135 | 6.02 | 3700 | 0.1118 | 0.3266 |
| 0.1112 | 6.19 | 3800 | 0.1142 | 0.3228 |
| 0.0866 | 6.35 | 3900 | 0.1172 | 0.3284 |
| 0.1003 | 6.51 | 4000 | 0.1133 | 0.3244 |
| 0.4366 | 6.68 | 4100 | 0.2436 | 0.4587 |
| 0.1216 | 6.84 | 4200 | 0.1344 | 0.3386 |
| 0.1165 | 7.0 | 4300 | 0.1280 | 0.3324 |
| 0.131 | 7.17 | 4400 | 0.1252 | 0.3245 |
| 0.1407 | 7.33 | 4500 | 0.1234 | 0.3252 |
| 0.1394 | 7.49 | 4600 | 0.1208 | 0.3177 |
| 0.1449 | 7.65 | 4700 | 0.1180 | 0.3165 |
| 0.1295 | 7.82 | 4800 | 0.1170 | 0.3152 |
| 0.1228 | 7.98 | 4900 | 0.1182 | 0.3160 |
| 0.0913 | 8.14 | 5000 | 0.1122 | 0.3086 |
| 0.1014 | 8.3 | 5100 | 0.1118 | 0.3100 |
| 0.0861 | 8.47 | 5200 | 0.1126 | 0.3074 |
| 0.1442 | 8.63 | 5300 | 0.1373 | 0.3311 |
| 0.1054 | 8.79 | 5400 | 0.1225 | 0.3143 |
| 0.104 | 8.96 | 5500 | 0.1190 | 0.3157 |
| 0.0972 | 9.12 | 5600 | 0.1140 | 0.3076 |
| 0.0948 | 9.28 | 5700 | 0.1090 | 0.3067 |
| 0.1067 | 9.45 | 5800 | 0.1117 | 0.3074 |
| 0.0798 | 9.61 | 5900 | 0.1097 | 0.3040 |
| 0.089 | 9.77 | 6000 | 0.1049 | 0.3005 |
| 0.0829 | 9.93 | 6100 | 0.1056 | 0.3006 |
| 0.0687 | 10.1 | 6200 | 0.1102 | 0.3018 |
| 0.0844 | 10.26 | 6300 | 0.1056 | 0.2985 |
| 0.0862 | 10.42 | 6400 | 0.1073 | 0.2990 |
| 0.0936 | 10.58 | 6500 | 0.1049 | 0.2949 |
| 0.0821 | 10.75 | 6600 | 0.1053 | 0.2966 |
| 0.0797 | 10.91 | 6700 | 0.1043 | 0.2939 |
| 0.0802 | 11.07 | 6800 | 0.1057 | 0.2911 |
| 0.0895 | 11.24 | 6900 | 0.1029 | 0.2934 |
| 0.073 | 11.4 | 7000 | 0.1042 | 0.2897 |
| 0.0842 | 11.56 | 7100 | 0.1023 | 0.2902 |
| 0.0825 | 11.72 | 7200 | 0.1024 | 0.2911 |
| 0.0958 | 11.89 | 7300 | 0.1018 | 0.2888 |
| 0.0698 | 12.05 | 7400 | 0.1030 | 0.2883 |
| 0.0693 | 12.21 | 7500 | 0.1019 | 0.2872 |
| 0.0736 | 12.37 | 7600 | 0.1003 | 0.2871 |
| 0.0683 | 12.54 | 7700 | 0.1004 | 0.2865 |
| 0.0649 | 12.7 | 7800 | 0.1005 | 0.2835 |
| 0.0669 | 12.86 | 7900 | 0.0985 | 0.2846 |
| 0.069 | 13.03 | 8000 | 0.0999 | 0.2844 |
| 0.0674 | 13.19 | 8100 | 0.1002 | 0.2835 |
| 0.0695 | 13.35 | 8200 | 0.1013 | 0.2829 |
| 0.0578 | 13.51 | 8300 | 0.1019 | 0.2821 |
| 0.0614 | 13.68 | 8400 | 0.0978 | 0.2815 |
| 0.0554 | 13.84 | 8500 | 0.0984 | 0.2813 |
| 0.0763 | 14.0 | 8600 | 0.1001 | 0.2813 |
| 0.0877 | 14.16 | 8700 | 0.1000 | 0.2808 |
| 0.0882 | 14.33 | 8800 | 0.0979 | 0.2803 |
| 0.0864 | 14.49 | 8900 | 0.0981 | 0.2788 |
| 0.0828 | 14.65 | 9000 | 0.0975 | 0.2790 |
| 0.3052 | 14.82 | 9100 | 0.2150 | 0.4175 |
| 0.1478 | 14.98 | 9200 | 0.1325 | 0.3027 |
| 1.0386 | 15.15 | 9300 | 0.4375 | 0.6793 |
| 0.116 | 15.31 | 9400 | 0.1266 | 0.3042 |
| 0.1226 | 15.47 | 9500 | 0.1206 | 0.3000 |
| 0.0885 | 15.63 | 9600 | 0.1173 | 0.2958 |
| 0.091 | 15.8 | 9700 | 0.1145 | 0.2929 |
| 0.0886 | 15.96 | 9800 | 0.1112 | 0.2908 |
| 0.0783 | 16.12 | 9900 | 0.1075 | 0.2873 |
| 0.069 | 16.28 | 10000 | 0.1072 | 0.2876 |
| 0.0783 | 16.45 | 10100 | 0.1070 | 0.2876 |
| 0.0669 | 16.61 | 10200 | 0.1055 | 0.2848 |
| 0.072 | 16.77 | 10300 | 0.1043 | 0.2846 |
| 0.0721 | 16.94 | 10400 | 0.1020 | 0.2821 |
| 0.0694 | 17.1 | 10500 | 0.1047 | 0.2803 |
| 0.0574 | 17.26 | 10600 | 0.1053 | 0.2830 |
| 0.0578 | 17.42 | 10700 | 0.1042 | 0.2806 |
| 0.0663 | 17.59 | 10800 | 0.1035 | 0.2801 |
| 0.0615 | 17.75 | 10900 | 0.1025 | 0.2785 |
| 0.0706 | 17.91 | 11000 | 0.1028 | 0.2792 |
| 0.2373 | 18.08 | 11100 | 0.1686 | 0.3372 |
| 0.1137 | 18.24 | 11200 | 0.1202 | 0.2938 |
| 0.1008 | 18.4 | 11300 | 0.1143 | 0.2895 |
| 0.1004 | 18.57 | 11400 | 0.1127 | 0.2874 |
| 0.0874 | 18.73 | 11500 | 0.1108 | 0.2861 |
| 0.0926 | 18.89 | 11600 | 0.1108 | 0.2838 |
| 0.0703 | 19.05 | 11700 | 0.1101 | 0.2834 |
| 0.0893 | 19.22 | 11800 | 0.1097 | 0.2824 |
| 0.0681 | 19.38 | 11900 | 0.1099 | 0.2822 |
| 0.0668 | 19.54 | 12000 | 0.1086 | 0.2813 |
| 0.069 | 19.7 | 12100 | 0.1087 | 0.2810 |
| 0.0683 | 19.87 | 12200 | 0.1085 | 0.2807 |
| 0.1116 | 20.03 | 12300 | 0.1221 | 0.2978 |
| 0.0752 | 20.19 | 12400 | 0.1161 | 0.2956 |
| 0.0787 | 20.36 | 12500 | 0.1128 | 0.2927 |
| 0.0741 | 20.52 | 12600 | 0.1100 | 0.2922 |
| 0.0764 | 20.68 | 12700 | 0.1081 | 0.2906 |
| 0.0747 | 20.85 | 12800 | 0.1082 | 0.2896 |
| 0.0876 | 21.01 | 12900 | 0.1052 | 0.2896 |
| 0.0878 | 21.17 | 13000 | 0.1110 | 0.2950 |
| 0.0895 | 21.33 | 13100 | 0.1095 | 0.2934 |
| 0.0953 | 21.5 | 13200 | 0.1122 | 0.2981 |
| 0.0787 | 21.66 | 13300 | 0.1072 | 0.2896 |
| 0.0774 | 21.82 | 13400 | 0.1076 | 0.2880 |
| 0.0908 | 21.98 | 13500 | 0.1113 | 0.2916 |
| 0.0737 | 22.15 | 13600 | 0.1067 | 0.2870 |
| 0.0714 | 22.31 | 13700 | 0.1096 | 0.2864 |
| 0.0775 | 22.47 | 13800 | 0.1085 | 0.2868 |
| 0.0761 | 22.64 | 13900 | 0.1040 | 0.2852 |
| 0.0675 | 22.8 | 14000 | 0.1090 | 0.2836 |
| 0.0829 | 22.96 | 14100 | 0.1066 | 0.2814 |
| 0.0731 | 23.12 | 14200 | 0.1057 | 0.2835 |
| 0.058 | 23.29 | 14300 | 0.1059 | 0.2834 |
| 0.0833 | 23.45 | 14400 | 0.1056 | 0.2847 |
| 0.1007 | 23.62 | 14500 | 0.1225 | 0.3059 |
| 0.0896 | 23.78 | 14600 | 0.1088 | 0.2899 |
| 0.084 | 23.94 | 14700 | 0.1056 | 0.2834 |
| 0.0684 | 24.1 | 14800 | 0.1070 | 0.2865 |
| 0.0646 | 24.27 | 14900 | 0.1109 | 0.2862 |
| 0.0728 | 24.43 | 15000 | 0.1081 | 0.2876 |
| 0.0615 | 24.59 | 15100 | 0.1077 | 0.2846 |
| 0.0642 | 24.75 | 15200 | 0.1062 | 0.2842 |
| 0.0736 | 24.92 | 15300 | 0.1058 | 0.2864 |
| 0.0801 | 25.08 | 15400 | 0.1106 | 0.2844 |
| 0.0687 | 25.24 | 15500 | 0.1104 | 0.2836 |
| 0.0852 | 25.41 | 15600 | 0.1055 | 0.2826 |
| 0.078 | 25.57 | 15700 | 0.1069 | 0.2817 |
| 0.0815 | 25.73 | 15800 | 0.1040 | 0.2799 |
| 0.0863 | 25.89 | 15900 | 0.1074 | 0.2801 |
| 0.0603 | 26.06 | 16000 | 0.1044 | 0.2779 |
| 0.0625 | 26.22 | 16100 | 0.1036 | 0.2796 |
| 0.057 | 26.38 | 16200 | 0.1086 | 0.2802 |
| 0.0632 | 26.54 | 16300 | 0.1057 | 0.2790 |
| 0.0644 | 26.71 | 16400 | 0.1022 | 0.2750 |
| 0.0645 | 26.87 | 16500 | 0.1003 | 0.2766 |
| 0.0536 | 27.03 | 16600 | 0.1051 | 0.2786 |
| 0.058 | 27.2 | 16700 | 0.1051 | 0.2790 |
| 0.052 | 27.36 | 16800 | 0.1034 | 0.2748 |
| 0.0514 | 27.52 | 16900 | 0.1027 | 0.2751 |
| 0.0593 | 27.68 | 17000 | 0.1036 | 0.2795 |
| 0.0577 | 27.85 | 17100 | 0.1025 | 0.2770 |
| 0.0694 | 28.01 | 17200 | 0.1008 | 0.2733 |
| 0.0641 | 28.17 | 17300 | 0.1088 | 0.2760 |
| 0.0566 | 28.33 | 17400 | 0.1092 | 0.2759 |
| 0.073 | 28.5 | 17500 | 0.1120 | 0.2788 |
| 0.0632 | 28.66 | 17600 | 0.1056 | 0.2764 |
| 0.0674 | 28.82 | 17700 | 0.1021 | 0.2739 |
| 0.0663 | 28.99 | 17800 | 0.1033 | 0.2733 |
| 0.0544 | 29.15 | 17900 | 0.1053 | 0.2721 |
| 0.0583 | 29.31 | 18000 | 0.1033 | 0.2732 |
| 0.0652 | 29.47 | 18100 | 0.1015 | 0.2728 |
| 0.0577 | 29.64 | 18200 | 0.1029 | 0.2730 |
| 0.1068 | 29.8 | 18300 | 0.1297 | 0.2950 |
| 0.0805 | 29.97 | 18400 | 0.1113 | 0.2792 |
| 0.0689 | 30.13 | 18500 | 0.1077 | 0.2789 |
| 0.0688 | 30.29 | 18600 | 0.1069 | 0.2777 |
| 0.0589 | 30.45 | 18700 | 0.1071 | 0.2757 |
| 0.049 | 30.62 | 18800 | 0.1077 | 0.2749 |
| 0.0534 | 30.78 | 18900 | 0.1046 | 0.2703 |
| 0.0506 | 30.94 | 19000 | 0.1039 | 0.2728 |
| 0.0534 | 31.11 | 19100 | 0.1036 | 0.2719 |
| 0.0453 | 31.27 | 19200 | 0.1064 | 0.2717 |
| 0.0514 | 31.43 | 19300 | 0.1034 | 0.2712 |
| 0.0579 | 31.59 | 19400 | 0.1065 | 0.2726 |
| 0.0491 | 31.76 | 19500 | 0.1054 | 0.2749 |
| 0.0547 | 31.92 | 19600 | 0.1023 | 0.2720 |
| 0.08 | 32.08 | 19700 | 0.1037 | 0.2707 |
| 0.0649 | 32.24 | 19800 | 0.1037 | 0.2702 |
| 0.064 | 32.41 | 19900 | 0.1053 | 0.2714 |
| 0.064 | 32.57 | 20000 | 0.1035 | 0.2691 |
| 0.0658 | 32.73 | 20100 | 0.1017 | 0.2663 |
| 0.0636 | 32.9 | 20200 | 0.1031 | 0.2680 |
| 0.0439 | 33.06 | 20300 | 0.1010 | 0.2668 |
| 0.0518 | 33.22 | 20400 | 0.1016 | 0.2691 |
| 0.0498 | 33.38 | 20500 | 0.1028 | 0.2682 |
| 0.0516 | 33.55 | 20600 | 0.1009 | 0.2679 |
| 0.0534 | 33.71 | 20700 | 0.1022 | 0.2672 |
| 0.0464 | 33.87 | 20800 | 0.1029 | 0.2661 |
| 0.0522 | 34.03 | 20900 | 0.1002 | 0.2668 |
| 0.0458 | 34.2 | 21000 | 0.0981 | 0.2644 |
| 0.0425 | 34.36 | 21100 | 0.1004 | 0.2659 |
| 0.0461 | 34.52 | 21200 | 0.1009 | 0.2650 |
| 0.0436 | 34.69 | 21300 | 0.1007 | 0.2652 |
| 0.0507 | 34.85 | 21400 | 0.1005 | 0.2655 |
| 0.0437 | 35.01 | 21500 | 0.0992 | 0.2648 |
| 0.0492 | 35.17 | 21600 | 0.1022 | 0.2655 |
| 0.0456 | 35.34 | 21700 | 0.1030 | 0.2639 |
| 0.0421 | 35.5 | 21800 | 0.1054 | 0.2639 |
| 0.0759 | 35.67 | 21900 | 0.1253 | 0.2760 |
| 0.059 | 35.83 | 22000 | 0.1125 | 0.2710 |
| 0.0515 | 35.99 | 22100 | 0.1073 | 0.2667 |
| 0.0583 | 36.16 | 22200 | 0.1085 | 0.2671 |
| 0.0603 | 36.32 | 22300 | 0.1047 | 0.2658 |
| 0.0575 | 36.48 | 22400 | 0.1034 | 0.2652 |
| 0.0605 | 36.64 | 22500 | 0.1044 | 0.2656 |
| 0.0545 | 36.81 | 22600 | 0.1057 | 0.2649 |
| 0.0583 | 36.97 | 22700 | 0.1033 | 0.2641 |
| 0.0492 | 37.13 | 22800 | 0.1039 | 0.2641 |
| 0.0561 | 37.29 | 22900 | 0.1027 | 0.2640 |
| 0.0447 | 37.46 | 23000 | 0.1023 | 0.2631 |
| 0.0521 | 37.62 | 23100 | 0.1010 | 0.2636 |
| 0.0482 | 37.78 | 23200 | 0.1021 | 0.2635 |
| 0.0468 | 37.95 | 23300 | 0.0999 | 0.2631 |
| 0.0473 | 38.11 | 23400 | 0.1016 | 0.2629 |
| 0.0416 | 38.27 | 23500 | 0.1003 | 0.2621 |
| 0.0491 | 38.43 | 23600 | 0.1022 | 0.2618 |
| 0.0394 | 38.6 | 23700 | 0.1017 | 0.2622 |
| 0.0389 | 38.76 | 23800 | 0.1011 | 0.2620 |
| 0.0381 | 38.92 | 23900 | 0.0992 | 0.2608 |
| 0.0557 | 39.08 | 24000 | 0.0999 | 0.2613 |
| 0.0545 | 39.25 | 24100 | 0.1002 | 0.2608 |
| 0.0633 | 39.41 | 24200 | 0.0997 | 0.2607 |
| 0.0471 | 39.57 | 24300 | 0.0994 | 0.2609 |
| 0.0672 | 39.74 | 24400 | 0.0991 | 0.2606 |
| 0.066 | 39.9 | 24500 | 0.0992 | 0.2605 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
Realgon/distilbert_sst5_padding20model
|
Realgon
| 2023-11-29T08:05:08Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:29:48Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding20model
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. -->
# distilbert_sst5_padding20model
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:
- Loss: 4.0295
- Accuracy: 0.5081
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3053 | 1.0 | 534 | 1.2846 | 0.4262 |
| 1.04 | 2.0 | 1068 | 1.1241 | 0.5281 |
| 0.8475 | 3.0 | 1602 | 1.1618 | 0.5199 |
| 0.6826 | 4.0 | 2136 | 1.3165 | 0.5204 |
| 0.5346 | 5.0 | 2670 | 1.6460 | 0.4959 |
| 0.4044 | 6.0 | 3204 | 1.7466 | 0.5032 |
| 0.3145 | 7.0 | 3738 | 1.9172 | 0.5014 |
| 0.2246 | 8.0 | 4272 | 2.1789 | 0.5014 |
| 0.1915 | 9.0 | 4806 | 2.4467 | 0.5 |
| 0.1632 | 10.0 | 5340 | 2.7215 | 0.4928 |
| 0.1238 | 11.0 | 5874 | 3.0191 | 0.4964 |
| 0.1245 | 12.0 | 6408 | 3.2605 | 0.5068 |
| 0.0853 | 13.0 | 6942 | 3.5142 | 0.5050 |
| 0.0763 | 14.0 | 7476 | 3.5273 | 0.5036 |
| 0.0448 | 15.0 | 8010 | 3.7739 | 0.5100 |
| 0.0527 | 16.0 | 8544 | 3.8209 | 0.5081 |
| 0.0372 | 17.0 | 9078 | 3.9001 | 0.5081 |
| 0.028 | 18.0 | 9612 | 3.8935 | 0.5063 |
| 0.0262 | 19.0 | 10146 | 3.9926 | 0.5095 |
| 0.0285 | 20.0 | 10680 | 4.0295 | 0.5081 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
KnutJaegersberg/Yi-34B-200K-MiniOrca
|
KnutJaegersberg
| 2023-11-29T08:05:02Z | 1,444 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:TinyPixel/orca-mini",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-27T19:33:49Z |
---
license: other
license_name: yi-license
license_link: LICENSE
pipeline_tag: text-generation
datasets:
- TinyPixel/orca-mini
---
Trained for 2.7 epochs on the 50k shortest records of miniorca dataset with NEFTune.
The base model is the official yi-34b-200k model.
Prompt Example:
```
### System:
You are an AI assistant. You will be given a task. You must generate a detailed and long answer.
### User:
What is AGI?
### Assistant:
```
License
The source code in this repo is licensed under the Apache 2.0 license. The Yi series models are fully open for academic research and free commercial usage with permission via applications. All usage must adhere to the Model License Agreement 2.0. To apply for the official commercial license, please contact us (yi@01.ai).
|
zostrich/frozenLake_agent
|
zostrich
| 2023-11-29T07:58:53Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-11-29T07:58:49Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: frozenLake_agent
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="zostrich/frozenLake_agent", 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"])
```
|
csukuangfj/vits-piper-en_US-lessac-medium
|
csukuangfj
| 2023-11-29T07:58:07Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2023-10-26T05:19:13Z |
# Introduction
Please refer to
https://colab.research.google.com/drive/1PScLJV3sbUUAOiptLO7Ixlzh9XnWWoYZ?usp=sharing
about how files in the repo are generated.
|
Realgon/distilbert_sst5_padding10model
|
Realgon
| 2023-11-29T07:55:53Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-11-28T18:29:22Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding10model
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. -->
# distilbert_sst5_padding10model
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:
- Loss: 4.0781
- Accuracy: 0.5036
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2838 | 1.0 | 534 | 1.2476 | 0.4281 |
| 1.0188 | 2.0 | 1068 | 1.1170 | 0.5231 |
| 0.8166 | 3.0 | 1602 | 1.2120 | 0.5131 |
| 0.6648 | 4.0 | 2136 | 1.3360 | 0.5172 |
| 0.501 | 5.0 | 2670 | 1.6498 | 0.4896 |
| 0.3858 | 6.0 | 3204 | 1.7835 | 0.5023 |
| 0.2878 | 7.0 | 3738 | 2.0075 | 0.5063 |
| 0.2136 | 8.0 | 4272 | 2.1988 | 0.4982 |
| 0.1796 | 9.0 | 4806 | 2.4884 | 0.5086 |
| 0.1443 | 10.0 | 5340 | 2.8428 | 0.4946 |
| 0.1189 | 11.0 | 5874 | 3.0841 | 0.4923 |
| 0.096 | 12.0 | 6408 | 3.2575 | 0.5041 |
| 0.0774 | 13.0 | 6942 | 3.5018 | 0.4977 |
| 0.064 | 14.0 | 7476 | 3.6108 | 0.5086 |
| 0.0469 | 15.0 | 8010 | 3.7317 | 0.5054 |
| 0.0364 | 16.0 | 8544 | 3.9036 | 0.4923 |
| 0.0292 | 17.0 | 9078 | 3.9546 | 0.5036 |
| 0.0244 | 18.0 | 9612 | 4.0218 | 0.5054 |
| 0.0256 | 19.0 | 10146 | 4.0257 | 0.5068 |
| 0.021 | 20.0 | 10680 | 4.0781 | 0.5036 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
|
Shamik/whisper-base.en-finetuned-gtzan
|
Shamik
| 2023-11-29T07:55:43Z | 105 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:openai/whisper-base.en",
"base_model:finetune:openai/whisper-base.en",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2023-11-29T07:55:38Z |
---
license: apache-2.0
base_model: openai/whisper-base.en
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: whisper-base.en-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.92
---
<!-- 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. -->
# whisper-base.en-finetuned-gtzan
This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3412
- Accuracy: 0.92
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5696 | 0.99 | 56 | 1.3573 | 0.62 |
| 0.9913 | 2.0 | 113 | 0.7820 | 0.77 |
| 0.4771 | 2.99 | 169 | 0.4873 | 0.84 |
| 0.4411 | 4.0 | 226 | 0.3367 | 0.91 |
| 0.1615 | 4.99 | 282 | 0.3412 | 0.92 |
| 0.1339 | 6.0 | 339 | 0.4125 | 0.91 |
| 0.0331 | 6.99 | 395 | 0.4773 | 0.89 |
| 0.0382 | 8.0 | 452 | 0.4282 | 0.88 |
| 0.049 | 8.99 | 508 | 0.4634 | 0.9 |
| 0.0312 | 9.91 | 560 | 0.4444 | 0.9 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
|
spokkazo/codeparrot-ds
|
spokkazo
| 2023-11-29T07:53:20Z | 111 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-27T11:17:17Z |
---
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: codeparrot-ds
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. -->
# codeparrot-ds
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0625
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.5676 | 0.08 | 5000 | 1.7415 |
| 1.6815 | 0.15 | 10000 | 1.5287 |
| 1.5345 | 0.23 | 15000 | 1.4235 |
| 1.4547 | 0.31 | 20000 | 1.3586 |
| 1.3972 | 0.38 | 25000 | 1.3040 |
| 1.3449 | 0.46 | 30000 | 1.2580 |
| 1.3003 | 0.54 | 35000 | 1.2138 |
| 1.2541 | 0.61 | 40000 | 1.1734 |
| 1.2114 | 0.69 | 45000 | 1.1337 |
| 1.1762 | 0.77 | 50000 | 1.1006 |
| 1.145 | 0.84 | 55000 | 1.0776 |
| 1.1265 | 0.92 | 60000 | 1.0654 |
| 1.1164 | 1.0 | 65000 | 1.0625 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
knnkrt/Realistic-Vision-V5.1-Inpainting
|
knnkrt
| 2023-11-29T07:52:58Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-11-29T07:04:20Z |
---
license: creativeml-openrail-m
---
|
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