modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-05 12:28:30
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 539
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-05 12:28:13
| card
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|---|---|---|---|---|---|---|---|---|---|
nolanaatama/shrkmfbkhllv1stgnrvcv2300pchsyy5
|
nolanaatama
| 2023-07-07T07:43:31Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-07T07:40:09Z |
---
license: creativeml-openrail-m
---
|
aroot/eng-fra-simcse_longest_ssrl
|
aroot
| 2023-07-07T07:32:30Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T07:13:28Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-fra-simcse_longest_ssrl
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. -->
# eng-fra-simcse_longest_ssrl
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1282
- Bleu: 32.2561
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
KJan05/Pyramids-Training-v1
|
KJan05
| 2023-07-07T07:32:21Z | 10 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2023-07-07T07:32:15Z |
---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
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: KJan05/Pyramids-Training-v1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Propofol/0707_2_finetuned-finetuned-localization
|
Propofol
| 2023-07-07T07:23:46Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"esm",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-07T05:36:20Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: 0707_2_finetuned-finetuned-localization
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. -->
# 0707_2_finetuned-finetuned-localization
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.1445
- Accuracy: 0.4167
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9296 | 1.0 | 2500 | 1.2921 | 0.4267 |
| 0.6704 | 2.0 | 5000 | 1.6807 | 0.432 |
| 0.3695 | 3.0 | 7500 | 2.3376 | 0.4187 |
| 0.1416 | 4.0 | 10000 | 3.6342 | 0.424 |
| 0.031 | 5.0 | 12500 | 4.1445 | 0.4167 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
YakovElm/Qt_20_BERT_More_Properties
|
YakovElm
| 2023-07-07T07:15:22Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-07T07:14:48Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt_20_BERT_More_Properties
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. -->
# Qt_20_BERT_More_Properties
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2099
- Train Accuracy: 0.9462
- Validation Loss: 0.1746
- Validation Accuracy: 0.9586
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2317 | 0.9373 | 0.1722 | 0.9586 | 0 |
| 0.2121 | 0.9462 | 0.1721 | 0.9586 | 1 |
| 0.2099 | 0.9462 | 0.1746 | 0.9586 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
irfan62622/Reinforce-CartPole-v1
|
irfan62622
| 2023-07-07T07:10:33Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-07T07:10:23Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-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
|
MochaPixel/Nainnoon
|
MochaPixel
| 2023-07-07T07:05:46Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-05T07:59:56Z |
---
license: creativeml-openrail-m
---
|
aroot/eng-mya-simcse_longest_usrb
|
aroot
| 2023-07-07T07:02:53Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T06:41:21Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-mya-simcse_longest_usrb
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. -->
# eng-mya-simcse_longest_usrb
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8539
- Bleu: 4.2285
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
SergiyC/ppo-LunarLander-v2
|
SergiyC
| 2023-07-07T07:01:33Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-07T07:01:13Z |
---
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: 247.16 +/- 38.23
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
...
```
|
YojitShinde/Q-Taxi-v3
|
YojitShinde
| 2023-07-07T07:01:32Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-07T07:01:28Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.69
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="YojitShinde/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"])
```
|
squeeze-ai-lab/sq-opt-6.7b-w4-s50
|
squeeze-ai-lab
| 2023-07-07T06:58:29Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"arxiv:2205.01068",
"region:us"
] | null | 2023-07-07T05:50:45Z |
**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization.
But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method.
Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance,
as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach,
we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality.
For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
## Model description
4-bit quantized OPT 6.7B model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf).
* **Base Model:** [OPT 6.7B](https://arxiv.org/abs/2205.01068)
* **Bitwidth:** 4-bit
* **Sparsity Level:** 0.5%
## Links
* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM)
---
license: other
---
|
aroot/eng-mya-simcse_longestplus_ssrb
|
aroot
| 2023-07-07T06:58:00Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T06:36:57Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-mya-simcse_longestplus_ssrb
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. -->
# eng-mya-simcse_longestplus_ssrb
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8875
- Bleu: 4.1475
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
IIC/xlm-roberta-large-socialdisner
|
IIC
| 2023-07-07T06:43:47Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"xlm-roberta",
"text-classification",
"biomedical",
"clinical",
"spanish",
"xlm-roberta-large",
"token-classification",
"es",
"dataset:IIC/socialdisner",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-26T08:01:49Z |
---
language: es
tags:
- biomedical
- clinical
- spanish
- xlm-roberta-large
license: mit
datasets:
- "IIC/socialdisner"
metrics:
- f1
model-index:
- name: IIC/xlm-roberta-large-socialdisner
results:
- task:
type: token-classification
dataset:
name: socialdisner
type: IIC/socialdisner
split: test
metrics:
- name: f1
type: f1
value: 0.941
pipeline_tag: token-classification
---
# xlm-roberta-large-socialdisner
This model is a finetuned version of xlm-roberta-large for the socialdisner dataset used in a benchmark in the paper TODO. The model has a F1 of 0.941
Please refer to the original publication for more information TODO LINK
## Parameters used
| parameter | Value |
|-------------------------|:-----:|
| batch size | 64 |
| learning rate | 3e-05 |
| classifier dropout | 0.2 |
| warmup ratio | 0 |
| warmup steps | 0 |
| weight decay | 0 |
| optimizer | AdamW |
| epochs | 10 |
| early stopping patience | 3 |
## BibTeX entry and citation info
```bibtex
TODO
```
|
IIC/mdeberta-v3-base-nubes
|
IIC
| 2023-07-07T06:43:39Z | 120 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"deberta-v2",
"text-classification",
"biomedical",
"clinical",
"spanish",
"mdeberta-v3-base",
"token-classification",
"es",
"dataset:plncmm/nubes",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-06-21T15:55:52Z |
---
language: es
tags:
- biomedical
- clinical
- spanish
- mdeberta-v3-base
license: mit
datasets:
- "plncmm/nubes"
metrics:
- f1
model-index:
- name: IIC/mdeberta-v3-base-nubes
results:
- task:
type: token-classification
dataset:
name: nubes
type: plncmm/nubes
split: test
metrics:
- name: f1
type: f1
value: 0.919
pipeline_tag: token-classification
---
# mdeberta-v3-base-nubes
This model is a finetuned version of mdeberta-v3-base for the nubes dataset used in a benchmark in the paper TODO. The model has a F1 of 0.919
Please refer to the original publication for more information TODO LINK
## Parameters used
| parameter | Value |
|-------------------------|:-----:|
| batch size | 32 |
| learning rate | 3e-05 |
| classifier dropout | 0 |
| warmup ratio | 0 |
| warmup steps | 0 |
| weight decay | 0 |
| optimizer | AdamW |
| epochs | 10 |
| early stopping patience | 3 |
## BibTeX entry and citation info
```bibtex
TODO
```
|
nullland1027/epicRealism-pureEvolution-v3
|
nullland1027
| 2023-07-07T06:27:31Z | 0 | 0 | null |
[
"text-to-image",
"en",
"license:openrail",
"region:us"
] |
text-to-image
| 2023-07-07T06:26:24Z |
---
license: openrail
language:
- en
pipeline_tag: text-to-image
---
|
aroot/eng-guj-simcse_longestplus_ssrb
|
aroot
| 2023-07-07T06:17:17Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T05:55:48Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-guj-simcse_longestplus_ssrb
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. -->
# eng-guj-simcse_longestplus_ssrb
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2951
- Bleu: 2.8469
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
squeeze-ai-lab/sq-opt-2.7b-w4-s50
|
squeeze-ai-lab
| 2023-07-07T06:14:28Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"arxiv:2205.01068",
"region:us"
] | null | 2023-07-07T05:50:23Z |
**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization.
But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method.
Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance,
as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach,
we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality.
For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
## Model description
4-bit quantized OPT 2.7B model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf).
* **Base Model:** [OPT 2.7B](https://arxiv.org/abs/2205.01068)
* **Bitwidth:** 4-bit
* **Sparsity Level:** 0.5%
## Links
* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM)
---
license: other
---
|
squeeze-ai-lab/sq-opt-2.7b-w3-s50
|
squeeze-ai-lab
| 2023-07-07T06:14:05Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"arxiv:2205.01068",
"region:us"
] | null | 2023-07-07T05:50:24Z |
**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization.
But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method.
Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance,
as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach,
we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality.
For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
## Model description
3-bit quantized OPT 2.7B model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf).
* **Base Model:** [OPT 2.7B](https://arxiv.org/abs/2205.01068)
* **Bitwidth:** 3-bit
* **Sparsity Level:** 0.5%
## Links
* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM)
---
license: other
---
|
squeeze-ai-lab/sq-opt-1.3b-w4-s50
|
squeeze-ai-lab
| 2023-07-07T05:53:53Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"arxiv:2205.01068",
"region:us"
] | null | 2023-07-07T05:46:54Z |
**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization.
But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method.
Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance,
as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach,
we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality.
For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
## Model description
4-bit quantized OPT 1.3B model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf).
* **Base Model:** [OPT 1.3B](https://arxiv.org/abs/2205.01068)
* **Bitwidth:** 4-bit
* **Sparsity Level:** 0.5%
## Links
* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM)
---
license: other
---
|
squeeze-ai-lab/sq-opt-1.3b-w3-s50
|
squeeze-ai-lab
| 2023-07-07T05:53:45Z | 0 | 0 | null |
[
"arxiv:2306.07629",
"arxiv:2205.01068",
"region:us"
] | null | 2023-07-07T05:46:33Z |
**SqueezeLLM** is a post-training quantization framework that incorporates a new method called Dense-and-Sparse Quantization to enable efficient LLM serving.
**TLDR:** Deploying LLMs is difficult due to their large memory size. This can be addressed with reduced precision quantization.
But a naive method hurts performance. We address this with a new Dense-and-Sparse Quantization method.
Dense-and-Sparse splits weight matrices into two components: A dense component that can be heavily quantized without affecting model performance,
as well as a sparse part that preserves sensitive and outlier parts of the weight matrices With this approach,
we are able to serve larger models with smaller memory footprint, the same latency, and yet higher accuracy and quality.
For more details please check out our [paper](https://arxiv.org/pdf/2306.07629.pdf).
## Model description
3-bit quantized OPT 1.3B model using SqueezeLLM. More details can be found in the [paper](https://arxiv.org/pdf/2306.07629.pdf).
* **Base Model:** [OPT 1.3B](https://arxiv.org/abs/2205.01068)
* **Bitwidth:** 3-bit
* **Sparsity Level:** 0.5%
## Links
* **Paper**: [https://arxiv.org/pdf/2306.07629.pdf](https://arxiv.org/pdf/2306.07629.pdf)
* **Code**: [https://github.com/SqueezeAILab/SqueezeLLM](https://github.com/SqueezeAILab/SqueezeLLM)
---
license: other
---
|
Propofol/_finetuned-finetuned-localization
|
Propofol
| 2023-07-07T05:31:05Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"esm",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-07T04:41:17Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: _finetuned-finetuned-localization
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. -->
# _finetuned-finetuned-localization
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4382
- Accuracy: 0.436
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1122 | 1.0 | 2500 | 1.1513 | 0.4287 |
| 1.0035 | 2.0 | 5000 | 1.2395 | 0.4507 |
| 0.7167 | 3.0 | 7500 | 1.4382 | 0.436 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
happyduck/alcafa_5.8b_8000
|
happyduck
| 2023-07-07T05:26:19Z | 0 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-07T05:26:13Z |
---
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.dev0
|
YakovElm/Qt_10_BERT_More_Properties
|
YakovElm
| 2023-07-07T05:23:48Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-07T05:23:07Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Qt_10_BERT_More_Properties
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. -->
# Qt_10_BERT_More_Properties
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2782
- Train Accuracy: 0.9210
- Validation Loss: 0.2251
- Validation Accuracy: 0.9416
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2894 | 0.9186 | 0.2234 | 0.9416 | 0 |
| 0.2786 | 0.9210 | 0.2266 | 0.9416 | 1 |
| 0.2782 | 0.9210 | 0.2251 | 0.9416 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
aroot/eng-mya-simcse_longestplus_ssblu
|
aroot
| 2023-07-07T05:00:20Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T04:43:42Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-mya-simcse_longestplus_ssblu
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. -->
# eng-mya-simcse_longestplus_ssblu
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8814
- Bleu: 4.1981
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
pundapog/DialoGPT-medium-ethanbot
|
pundapog
| 2023-07-07T04:45:16Z | 131 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-07T03:52:57Z |
---
tags:
- conversational
library_name: transformers
---
|
nomsgadded/textual_inversion
|
nomsgadded
| 2023-07-07T04:31:51Z | 29 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"textual_inversion",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:adapter:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-07T03:42:52Z |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- textual_inversion
inference: true
---
# Textual inversion text2image fine-tuning - nomsgadded/textual_inversion
These are textual inversion adaption weights for CompVis/stable-diffusion-v1-4. You can find some example images in the following.
|
aroot/eng-guj-simcse_longest_usblu
|
aroot
| 2023-07-07T04:27:50Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T04:05:39Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-guj-simcse_longest_usblu
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. -->
# eng-guj-simcse_longest_usblu
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2266
- Bleu: 2.8360
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
aroot/eng-guj-simcse_longestplus_usblu
|
aroot
| 2023-07-07T04:27:29Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T04:05:29Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-guj-simcse_longestplus_usblu
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. -->
# eng-guj-simcse_longestplus_usblu
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2862
- Bleu: 2.7556
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
aroot/eng-guj-simcse_longest_ssblu
|
aroot
| 2023-07-07T04:23:29Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T04:01:17Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-guj-simcse_longest_ssblu
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. -->
# eng-guj-simcse_longest_ssblu
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2244
- Bleu: 2.9211
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
hoanghoavienvo/roberta-base-detect-depression-large-dataset-v3
|
hoanghoavienvo
| 2023-07-07T04:19:18Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-07T03:30:58Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: roberta-base-detect-depression-large-dataset-v3
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-base-detect-depression-large-dataset-v3
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6044
- Accuracy: 0.6918
- F1: 0.7921
## 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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6532 | 1.0 | 876 | 0.5777 | 0.6527 | 0.7536 |
| 0.6325 | 2.0 | 1752 | 0.5926 | 0.7322 | 0.8342 |
| 0.6348 | 3.0 | 2628 | 0.5959 | 0.7433 | 0.8461 |
| 0.635 | 4.0 | 3504 | 0.5781 | 0.7436 | 0.8449 |
| 0.6177 | 5.0 | 4380 | 0.6044 | 0.6918 | 0.7921 |
### Framework versions
- Transformers 4.30.1
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
|
l3cube-pune/hate-multi-roberta-hasoc-hindi
|
l3cube-pune
| 2023-07-07T04:01:25Z | 114 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"roberta",
"text-classification",
"hi",
"arxiv:2110.12200",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: hi
tags:
- roberta
license: cc-by-4.0
datasets:
- HASOC 2021
widget:
- text: "I like you. </s></s> I love you."
---
## hate-roberta-hasoc-hindi
hate-roberta-hasoc-hindi is a multi-class hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Offensive, 2 -> Hate, 3 -> Profane.
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2110.12200)
```
@article{velankar2021hate,
title={Hate and Offensive Speech Detection in Hindi and Marathi},
author={Velankar, Abhishek and Patil, Hrushikesh and Gore, Amol and Salunke, Shubham and Joshi, Raviraj},
journal={arXiv preprint arXiv:2110.12200},
year={2021}
}
```
|
l3cube-pune/MarathiSentiment
|
l3cube-pune
| 2023-07-07T04:01:02Z | 118 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"safetensors",
"albert",
"text-classification",
"mr",
"dataset:L3CubeMahaSent",
"arxiv:2103.11408",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: mr
tags:
- albert
license: cc-by-4.0
datasets:
- L3CubeMahaSent
widget:
- text: "I like you. </s></s> I love you."
---
## MarathiSentiment
** An updated and better version of this model covering multiple domains is shared here: <a href="https://huggingface.co/l3cube-pune/marathi-sentiment-md"> marathi-sentiment-md </a> ** <br>
MarathiSentiment is an IndicBERT(ai4bharat/indic-bert) model fine-tuned on L3CubeMahaSent - a Marathi tweet-based sentiment analysis dataset.
[dataset link] (https://github.com/l3cube-pune/MarathiNLP)
More details on the dataset, models, and baseline results can be found in our [paper] (http://arxiv.org/abs/2103.11408)
```
@inproceedings{kulkarni2021l3cubemahasent,
title={L3CubeMahaSent: A Marathi Tweet-based Sentiment Analysis Dataset},
author={Kulkarni, Atharva and Mandhane, Meet and Likhitkar, Manali and Kshirsagar, Gayatri and Joshi, Raviraj},
booktitle={Proceedings of the Eleventh Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis},
pages={213--220},
year={2021}
}
```
|
Dynosaur/dynosaur-llama-7b-superni
|
Dynosaur
| 2023-07-07T03:57:41Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-06T23:19:48Z |
---
license: apache-2.0
---
This repo contains the weight difference for dynosaur-llama-7b-superni that can be used to reconstruct the original model weights when applied to Meta's LLaMA weights.
To recover the full dynosaur-llama-7b-superni weights, follow the steps:
```
1. Convert Meta's released weights into huggingface format. Follow this guide:
https://huggingface.co/docs/transformers/main/model_doc/llama
You may refer to https://huggingface.co/huggyllama/llama-7b if you get some trouble in the conversion. (You should only use this repository if you have been granted access to the llama model.)
2. Make sure you cloned the released weight diff into your local machine. The weight diff is located at:
https://huggingface.co/Dynosaur/dynosaur-llama-7b-superni
3. Run this function with the correct paths. E.g.,
python weight_diff.py recover --path_raw <path_to_step_1_dir> --path_diff <path_to_step_2_dir> --path_tuned <path_to_store_recovered_weights>
```
|
aroot/eng-fra-simcse_longest_ssblu
|
aroot
| 2023-07-07T03:46:56Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T03:27:46Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-fra-simcse_longest_ssblu
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. -->
# eng-fra-simcse_longest_ssblu
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1296
- Bleu: 32.4007
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
jordyvl/dit-base_tobacco
|
jordyvl
| 2023-07-07T03:42:16Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-06T13:14:21Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dit-base_tobacco
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. -->
# dit-base_tobacco
This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3120
- Accuracy: 0.95
- Brier Loss: 0.0965
- Nll: 0.6372
- F1 Micro: 0.9500
- F1 Macro: 0.9545
- Ece: 0.0560
- Aurc: 0.0092
## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 0.96 | 6 | 2.4454 | 0.175 | 0.9193 | 8.6626 | 0.175 | 0.0676 | 0.2489 | 0.8592 |
| No log | 1.96 | 12 | 2.3287 | 0.175 | 0.9034 | 7.2049 | 0.175 | 0.0674 | 0.2590 | 0.8557 |
| No log | 2.96 | 18 | 2.0836 | 0.23 | 0.8528 | 3.3114 | 0.23 | 0.1544 | 0.2652 | 0.7357 |
| No log | 3.96 | 24 | 2.0456 | 0.315 | 0.8435 | 3.8932 | 0.315 | 0.1785 | 0.3010 | 0.6372 |
| No log | 4.96 | 30 | 1.8778 | 0.3 | 0.7820 | 3.0975 | 0.3 | 0.1659 | 0.2985 | 0.5174 |
| No log | 5.96 | 36 | 1.7247 | 0.365 | 0.7305 | 2.7808 | 0.3650 | 0.2235 | 0.2507 | 0.4036 |
| No log | 6.96 | 42 | 1.6610 | 0.38 | 0.7183 | 2.6958 | 0.38 | 0.2449 | 0.2538 | 0.4119 |
| No log | 7.96 | 48 | 1.4667 | 0.505 | 0.6417 | 2.4078 | 0.505 | 0.3653 | 0.2881 | 0.2656 |
| No log | 8.96 | 54 | 1.3427 | 0.58 | 0.6031 | 2.0381 | 0.58 | 0.5304 | 0.2885 | 0.2470 |
| No log | 9.96 | 60 | 1.1586 | 0.635 | 0.5217 | 1.8792 | 0.635 | 0.5496 | 0.2831 | 0.1697 |
| No log | 10.96 | 66 | 1.0108 | 0.71 | 0.4578 | 1.6886 | 0.7100 | 0.6273 | 0.2851 | 0.1340 |
| No log | 11.96 | 72 | 0.8648 | 0.75 | 0.3849 | 1.5408 | 0.75 | 0.6788 | 0.2530 | 0.0801 |
| No log | 12.96 | 78 | 0.7342 | 0.79 | 0.3327 | 1.3588 | 0.79 | 0.7264 | 0.2152 | 0.0575 |
| No log | 13.96 | 84 | 0.6024 | 0.835 | 0.2734 | 1.2694 | 0.835 | 0.7937 | 0.1876 | 0.0429 |
| No log | 14.96 | 90 | 0.5143 | 0.85 | 0.2386 | 1.1756 | 0.85 | 0.8175 | 0.1714 | 0.0363 |
| No log | 15.96 | 96 | 0.4429 | 0.865 | 0.2044 | 1.1080 | 0.865 | 0.8435 | 0.1380 | 0.0277 |
| No log | 16.96 | 102 | 0.3999 | 0.885 | 0.1854 | 1.0748 | 0.885 | 0.8673 | 0.1407 | 0.0274 |
| No log | 17.96 | 108 | 0.3635 | 0.88 | 0.1732 | 1.0361 | 0.88 | 0.8594 | 0.1117 | 0.0247 |
| No log | 18.96 | 114 | 0.3166 | 0.89 | 0.1454 | 1.0855 | 0.89 | 0.8682 | 0.0971 | 0.0196 |
| No log | 19.96 | 120 | 0.3137 | 0.905 | 0.1418 | 1.1614 | 0.905 | 0.8934 | 0.1041 | 0.0195 |
| No log | 20.96 | 126 | 0.3207 | 0.91 | 0.1408 | 1.1941 | 0.91 | 0.9002 | 0.0856 | 0.0198 |
| No log | 21.96 | 132 | 0.2753 | 0.925 | 0.1224 | 1.0928 | 0.925 | 0.9209 | 0.0858 | 0.0145 |
| No log | 22.96 | 138 | 0.2538 | 0.925 | 0.1169 | 1.0895 | 0.925 | 0.9187 | 0.0863 | 0.0111 |
| No log | 23.96 | 144 | 0.2691 | 0.935 | 0.1138 | 1.0767 | 0.935 | 0.9279 | 0.0730 | 0.0149 |
| No log | 24.96 | 150 | 0.2775 | 0.935 | 0.1131 | 1.0538 | 0.935 | 0.9292 | 0.0676 | 0.0157 |
| No log | 25.96 | 156 | 0.2544 | 0.94 | 0.1011 | 1.0266 | 0.94 | 0.9292 | 0.0643 | 0.0131 |
| No log | 26.96 | 162 | 0.2637 | 0.945 | 0.1013 | 1.0337 | 0.945 | 0.9384 | 0.0648 | 0.0150 |
| No log | 27.96 | 168 | 0.2787 | 0.94 | 0.1089 | 1.0202 | 0.94 | 0.9348 | 0.0685 | 0.0161 |
| No log | 28.96 | 174 | 0.2794 | 0.935 | 0.1091 | 1.0099 | 0.935 | 0.9306 | 0.0671 | 0.0143 |
| No log | 29.96 | 180 | 0.2631 | 0.935 | 0.1025 | 0.9815 | 0.935 | 0.9306 | 0.0575 | 0.0129 |
| No log | 30.96 | 186 | 0.2616 | 0.945 | 0.1009 | 0.9683 | 0.945 | 0.9401 | 0.0674 | 0.0120 |
| No log | 31.96 | 192 | 0.2726 | 0.935 | 0.1074 | 0.9598 | 0.935 | 0.9346 | 0.0641 | 0.0100 |
| No log | 32.96 | 198 | 0.2765 | 0.935 | 0.1058 | 0.9067 | 0.935 | 0.9321 | 0.0696 | 0.0101 |
| No log | 33.96 | 204 | 0.2662 | 0.95 | 0.0965 | 0.8891 | 0.9500 | 0.9522 | 0.0672 | 0.0120 |
| No log | 34.96 | 210 | 0.2761 | 0.935 | 0.1019 | 0.8893 | 0.935 | 0.9338 | 0.0597 | 0.0134 |
| No log | 35.96 | 216 | 0.2729 | 0.945 | 0.0961 | 0.8807 | 0.945 | 0.9419 | 0.0552 | 0.0119 |
| No log | 36.96 | 222 | 0.2741 | 0.94 | 0.1037 | 0.8782 | 0.94 | 0.9356 | 0.0645 | 0.0086 |
| No log | 37.96 | 228 | 0.2686 | 0.94 | 0.0994 | 0.8423 | 0.94 | 0.9356 | 0.0592 | 0.0085 |
| No log | 38.96 | 234 | 0.2712 | 0.95 | 0.0906 | 0.8179 | 0.9500 | 0.9545 | 0.0610 | 0.0105 |
| No log | 39.96 | 240 | 0.2644 | 0.95 | 0.0870 | 0.8240 | 0.9500 | 0.9443 | 0.0510 | 0.0110 |
| No log | 40.96 | 246 | 0.2653 | 0.95 | 0.0932 | 0.8386 | 0.9500 | 0.9525 | 0.0572 | 0.0118 |
| No log | 41.96 | 252 | 0.2724 | 0.955 | 0.0939 | 0.8369 | 0.955 | 0.9573 | 0.0602 | 0.0104 |
| No log | 42.96 | 258 | 0.2552 | 0.95 | 0.0868 | 0.8079 | 0.9500 | 0.9522 | 0.0539 | 0.0079 |
| No log | 43.96 | 264 | 0.2629 | 0.95 | 0.0879 | 0.7800 | 0.9500 | 0.9545 | 0.0526 | 0.0080 |
| No log | 44.96 | 270 | 0.2664 | 0.955 | 0.0864 | 0.7660 | 0.955 | 0.9575 | 0.0515 | 0.0086 |
| No log | 45.96 | 276 | 0.2777 | 0.945 | 0.0948 | 0.7670 | 0.945 | 0.9513 | 0.0524 | 0.0096 |
| No log | 46.96 | 282 | 0.2824 | 0.94 | 0.1014 | 0.7799 | 0.94 | 0.9436 | 0.0570 | 0.0093 |
| No log | 47.96 | 288 | 0.2699 | 0.95 | 0.0896 | 0.7706 | 0.9500 | 0.9546 | 0.0528 | 0.0087 |
| No log | 48.96 | 294 | 0.2809 | 0.945 | 0.0950 | 0.7691 | 0.945 | 0.9480 | 0.0475 | 0.0087 |
| No log | 49.96 | 300 | 0.2827 | 0.945 | 0.0940 | 0.7635 | 0.945 | 0.9447 | 0.0571 | 0.0091 |
| No log | 50.96 | 306 | 0.2781 | 0.945 | 0.0921 | 0.7591 | 0.945 | 0.9478 | 0.0552 | 0.0090 |
| No log | 51.96 | 312 | 0.2834 | 0.95 | 0.0946 | 0.7572 | 0.9500 | 0.9484 | 0.0549 | 0.0089 |
| No log | 52.96 | 318 | 0.2986 | 0.94 | 0.0994 | 0.7541 | 0.94 | 0.9363 | 0.0605 | 0.0091 |
| No log | 53.96 | 324 | 0.2957 | 0.94 | 0.1016 | 0.7447 | 0.94 | 0.9385 | 0.0562 | 0.0086 |
| No log | 54.96 | 330 | 0.2991 | 0.94 | 0.1047 | 0.7392 | 0.94 | 0.9377 | 0.0592 | 0.0102 |
| No log | 55.96 | 336 | 0.3027 | 0.94 | 0.1031 | 0.7235 | 0.94 | 0.9377 | 0.0572 | 0.0113 |
| No log | 56.96 | 342 | 0.2945 | 0.945 | 0.0968 | 0.7143 | 0.945 | 0.9470 | 0.0581 | 0.0104 |
| No log | 57.96 | 348 | 0.2935 | 0.94 | 0.0955 | 0.7046 | 0.94 | 0.9459 | 0.0569 | 0.0097 |
| No log | 58.96 | 354 | 0.2909 | 0.94 | 0.0934 | 0.6969 | 0.94 | 0.9459 | 0.0544 | 0.0092 |
| No log | 59.96 | 360 | 0.2973 | 0.95 | 0.0939 | 0.6964 | 0.9500 | 0.9545 | 0.0524 | 0.0082 |
| No log | 60.96 | 366 | 0.3222 | 0.93 | 0.1108 | 0.7078 | 0.93 | 0.9266 | 0.0586 | 0.0088 |
| No log | 61.96 | 372 | 0.3247 | 0.935 | 0.1093 | 0.7743 | 0.935 | 0.9353 | 0.0622 | 0.0091 |
| No log | 62.96 | 378 | 0.3125 | 0.945 | 0.1003 | 0.7651 | 0.945 | 0.9453 | 0.0559 | 0.0089 |
| No log | 63.96 | 384 | 0.3035 | 0.945 | 0.0993 | 0.7515 | 0.945 | 0.9476 | 0.0545 | 0.0088 |
| No log | 64.96 | 390 | 0.3002 | 0.945 | 0.0973 | 0.7408 | 0.945 | 0.9476 | 0.0537 | 0.0091 |
| No log | 65.96 | 396 | 0.3023 | 0.95 | 0.0965 | 0.7321 | 0.9500 | 0.9545 | 0.0523 | 0.0095 |
| No log | 66.96 | 402 | 0.3075 | 0.945 | 0.1007 | 0.7323 | 0.945 | 0.9477 | 0.0540 | 0.0096 |
| No log | 67.96 | 408 | 0.3062 | 0.945 | 0.0999 | 0.6682 | 0.945 | 0.9514 | 0.0525 | 0.0098 |
| No log | 68.96 | 414 | 0.3182 | 0.945 | 0.0968 | 0.6809 | 0.945 | 0.9432 | 0.0485 | 0.0115 |
| No log | 69.96 | 420 | 0.3272 | 0.945 | 0.0972 | 0.6879 | 0.945 | 0.9432 | 0.0513 | 0.0132 |
| No log | 70.96 | 426 | 0.3210 | 0.945 | 0.0973 | 0.7545 | 0.945 | 0.9488 | 0.0522 | 0.0124 |
| No log | 71.96 | 432 | 0.3194 | 0.945 | 0.1027 | 0.7464 | 0.945 | 0.9514 | 0.0546 | 0.0108 |
| No log | 72.96 | 438 | 0.3236 | 0.94 | 0.1067 | 0.7486 | 0.94 | 0.9427 | 0.0587 | 0.0097 |
| No log | 73.96 | 444 | 0.3166 | 0.94 | 0.1049 | 0.6751 | 0.94 | 0.9427 | 0.0597 | 0.0096 |
| No log | 74.96 | 450 | 0.3062 | 0.945 | 0.0982 | 0.6702 | 0.945 | 0.9514 | 0.0526 | 0.0100 |
| No log | 75.96 | 456 | 0.3018 | 0.95 | 0.0948 | 0.6823 | 0.9500 | 0.9545 | 0.0523 | 0.0102 |
| No log | 76.96 | 462 | 0.3062 | 0.95 | 0.0951 | 0.7444 | 0.9500 | 0.9545 | 0.0522 | 0.0109 |
| No log | 77.96 | 468 | 0.3072 | 0.95 | 0.0933 | 0.7437 | 0.9500 | 0.9545 | 0.0501 | 0.0118 |
| No log | 78.96 | 474 | 0.3095 | 0.95 | 0.0943 | 0.6749 | 0.9500 | 0.9545 | 0.0512 | 0.0121 |
| No log | 79.96 | 480 | 0.3097 | 0.945 | 0.0968 | 0.6654 | 0.945 | 0.9514 | 0.0576 | 0.0116 |
| No log | 80.96 | 486 | 0.3094 | 0.95 | 0.0967 | 0.6581 | 0.9500 | 0.9545 | 0.0526 | 0.0112 |
| No log | 81.96 | 492 | 0.3109 | 0.95 | 0.0954 | 0.6549 | 0.9500 | 0.9545 | 0.0507 | 0.0115 |
| No log | 82.96 | 498 | 0.3104 | 0.95 | 0.0949 | 0.7168 | 0.9500 | 0.9545 | 0.0521 | 0.0113 |
| 0.3747 | 83.96 | 504 | 0.3122 | 0.95 | 0.0949 | 0.7130 | 0.9500 | 0.9545 | 0.0513 | 0.0111 |
| 0.3747 | 84.96 | 510 | 0.3140 | 0.95 | 0.0944 | 0.7116 | 0.9500 | 0.9545 | 0.0534 | 0.0113 |
| 0.3747 | 85.96 | 516 | 0.3175 | 0.95 | 0.0949 | 0.7100 | 0.9500 | 0.9545 | 0.0544 | 0.0113 |
| 0.3747 | 86.96 | 522 | 0.3187 | 0.95 | 0.0958 | 0.7072 | 0.9500 | 0.9545 | 0.0537 | 0.0111 |
| 0.3747 | 87.96 | 528 | 0.3191 | 0.95 | 0.0967 | 0.6428 | 0.9500 | 0.9545 | 0.0536 | 0.0103 |
| 0.3747 | 88.96 | 534 | 0.3168 | 0.95 | 0.0963 | 0.6438 | 0.9500 | 0.9545 | 0.0542 | 0.0102 |
| 0.3747 | 89.96 | 540 | 0.3136 | 0.95 | 0.0963 | 0.6418 | 0.9500 | 0.9545 | 0.0554 | 0.0099 |
| 0.3747 | 90.96 | 546 | 0.3117 | 0.95 | 0.0963 | 0.6407 | 0.9500 | 0.9545 | 0.0533 | 0.0097 |
| 0.3747 | 91.96 | 552 | 0.3113 | 0.95 | 0.0964 | 0.6403 | 0.9500 | 0.9545 | 0.0528 | 0.0091 |
| 0.3747 | 92.96 | 558 | 0.3112 | 0.95 | 0.0968 | 0.6401 | 0.9500 | 0.9545 | 0.0517 | 0.0091 |
| 0.3747 | 93.96 | 564 | 0.3109 | 0.95 | 0.0967 | 0.6393 | 0.9500 | 0.9545 | 0.0563 | 0.0091 |
| 0.3747 | 94.96 | 570 | 0.3112 | 0.95 | 0.0969 | 0.6370 | 0.9500 | 0.9545 | 0.0567 | 0.0092 |
| 0.3747 | 95.96 | 576 | 0.3118 | 0.95 | 0.0971 | 0.6364 | 0.9500 | 0.9545 | 0.0568 | 0.0091 |
| 0.3747 | 96.96 | 582 | 0.3120 | 0.95 | 0.0969 | 0.6377 | 0.9500 | 0.9545 | 0.0564 | 0.0092 |
| 0.3747 | 97.96 | 588 | 0.3121 | 0.95 | 0.0966 | 0.6379 | 0.9500 | 0.9545 | 0.0560 | 0.0092 |
| 0.3747 | 98.96 | 594 | 0.3121 | 0.95 | 0.0965 | 0.6374 | 0.9500 | 0.9545 | 0.0560 | 0.0092 |
| 0.3747 | 99.96 | 600 | 0.3120 | 0.95 | 0.0965 | 0.6372 | 0.9500 | 0.9545 | 0.0560 | 0.0092 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
biodatlab/aug-whisper-medium-th-base
|
biodatlab
| 2023-07-07T03:41:34Z | 146 | 0 |
transformers
|
[
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"th",
"dataset:mozilla-foundation/common_voice_13_0",
"dataset:google/fleurs",
"dataset:biodatlab/whisper-th-custom",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-07T02:05:37Z |
---
license: apache-2.0
datasets:
- mozilla-foundation/common_voice_13_0
- google/fleurs
- biodatlab/whisper-th-custom
language:
- th
metrics:
- wer
library_name: transformers
---
|
Twitter/twhin-bert-large
|
Twitter
| 2023-07-07T03:38:46Z | 371 | 40 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"Twitter",
"Multilingual",
"en",
"ja",
"pt",
"es",
"ko",
"ar",
"tr",
"th",
"fr",
"id",
"ru",
"de",
"fa",
"it",
"zh",
"pl",
"hi",
"ur",
"nl",
"el",
"ms",
"ca",
"sr",
"sv",
"uk",
"he",
"fi",
"cs",
"ta",
"ne",
"vi",
"hu",
"eo",
"bn",
"mr",
"ml",
"hr",
"no",
"sw",
"sl",
"te",
"az",
"da",
"ro",
"gl",
"gu",
"ps",
"mk",
"kn",
"bg",
"lv",
"eu",
"pa",
"et",
"mn",
"sq",
"si",
"sd",
"la",
"is",
"jv",
"lt",
"ku",
"am",
"bs",
"hy",
"or",
"sk",
"uz",
"cy",
"my",
"su",
"br",
"as",
"af",
"be",
"fy",
"kk",
"ga",
"lo",
"ka",
"km",
"sa",
"mg",
"so",
"ug",
"ky",
"gd",
"yi",
"arxiv:2209.07562",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-18T18:37:18Z |
---
language:
- en
- ja
- pt
- es
- ko
- ar
- tr
- th
- fr
- id
- ru
- de
- fa
- it
- zh
- pl
- hi
- ur
- nl
- el
- ms
- ca
- sr
- sv
- uk
- he
- fi
- cs
- ta
- ne
- vi
- hu
- eo
- bn
- mr
- ml
- hr
- no
- sw
- sl
- te
- az
- da
- ro
- gl
- gu
- ps
- mk
- kn
- bg
- lv
- eu
- pa
- et
- mn
- sq
- si
- sd
- la
- is
- jv
- lt
- ku
- am
- bs
- hy
- or
- sk
- uz
- cy
- my
- su
- br
- as
- af
- be
- fy
- kk
- ga
- lo
- ka
- km
- sa
- mg
- so
- ug
- ky
- gd
- yi
tags:
- Twitter
- Multilingual
license: "apache-2.0"
mask_token: "<mask>"
---
# TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations
[](http://makeapullrequest.com)
[](https://arxiv.org/abs/2209.07562)
This repo contains models, code and pointers to datasets from our paper: [TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations](https://arxiv.org/abs/2209.07562).
[[PDF]](https://arxiv.org/pdf/2209.07562.pdf)
[[HuggingFace Models]](https://huggingface.co/Twitter)
### Overview
TwHIN-BERT is a new multi-lingual Tweet language model that is trained on 7 billion Tweets from over 100 distinct languages. TwHIN-BERT differs from prior pre-trained language models as it is trained with not only text-based self-supervision (e.g., MLM), but also with a social objective based on the rich social engagements within a Twitter Heterogeneous Information Network (TwHIN).
TwHIN-BERT can be used as a drop-in replacement for BERT in a variety of NLP and recommendation tasks. It not only outperforms similar models semantic understanding tasks such text classification), but also **social recommendation** tasks such as predicting user to Tweet engagement.
## 1. Pretrained Models
We initially release two pretrained TwHIN-BERT models (base and large) that are compatible wit the [HuggingFace BERT models](https://github.com/huggingface/transformers).
| Model | Size | Download Link (🤗 HuggingFace) |
| ------------- | ------------- | --------- |
| TwHIN-BERT-base | 280M parameters | [Twitter/TwHIN-BERT-base](https://huggingface.co/Twitter/twhin-bert-base) |
| TwHIN-BERT-large | 550M parameters | [Twitter/TwHIN-BERT-large](https://huggingface.co/Twitter/twhin-bert-large) |
To use these models in 🤗 Transformers:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('Twitter/twhin-bert-large')
model = AutoModel.from_pretrained('Twitter/twhin-bert-large')
inputs = tokenizer("I'm using TwHIN-BERT! #TwHIN-BERT #NLP", return_tensors="pt")
outputs = model(**inputs)
```
<!-- ## 2. Set up environment and data
### Environment
TBD
## 3. Fine-tune TwHIN-BERT
TBD -->
## Citation
If you use TwHIN-BERT or out datasets in your work, please cite the following:
```bib
@article{zhang2022twhin,
title={TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations},
author={Zhang, Xinyang and Malkov, Yury and Florez, Omar and Park, Serim and McWilliams, Brian and Han, Jiawei and El-Kishky, Ahmed},
journal={arXiv preprint arXiv:2209.07562},
year={2022}
}
```
|
Twitter/twhin-bert-base
|
Twitter
| 2023-07-07T03:38:25Z | 3,304 | 35 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"bert",
"fill-mask",
"Twitter",
"Multilingual",
"en",
"ja",
"pt",
"es",
"ko",
"ar",
"tr",
"th",
"fr",
"id",
"ru",
"de",
"fa",
"it",
"zh",
"pl",
"hi",
"ur",
"nl",
"el",
"ms",
"ca",
"sr",
"sv",
"uk",
"he",
"fi",
"cs",
"ta",
"ne",
"vi",
"hu",
"eo",
"bn",
"mr",
"ml",
"hr",
"no",
"sw",
"sl",
"te",
"az",
"da",
"ro",
"gl",
"gu",
"ps",
"mk",
"kn",
"bg",
"lv",
"eu",
"pa",
"et",
"mn",
"sq",
"si",
"sd",
"la",
"is",
"jv",
"lt",
"ku",
"am",
"bs",
"hy",
"or",
"sk",
"uz",
"cy",
"my",
"su",
"br",
"as",
"af",
"be",
"fy",
"kk",
"ga",
"lo",
"ka",
"km",
"sa",
"mg",
"so",
"ug",
"ky",
"gd",
"yi",
"arxiv:2209.07562",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-18T18:34:23Z |
---
language:
- en
- ja
- pt
- es
- ko
- ar
- tr
- th
- fr
- id
- ru
- de
- fa
- it
- zh
- pl
- hi
- ur
- nl
- el
- ms
- ca
- sr
- sv
- uk
- he
- fi
- cs
- ta
- ne
- vi
- hu
- eo
- bn
- mr
- ml
- hr
- no
- sw
- sl
- te
- az
- da
- ro
- gl
- gu
- ps
- mk
- kn
- bg
- lv
- eu
- pa
- et
- mn
- sq
- si
- sd
- la
- is
- jv
- lt
- ku
- am
- bs
- hy
- or
- sk
- uz
- cy
- my
- su
- br
- as
- af
- be
- fy
- kk
- ga
- lo
- ka
- km
- sa
- mg
- so
- ug
- ky
- gd
- yi
tags:
- Twitter
- Multilingual
license: "apache-2.0"
mask_token: "<mask>"
---
# TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations
[](http://makeapullrequest.com)
[](https://arxiv.org/abs/2209.07562)
This repo contains models, code and pointers to datasets from our paper: [TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations](https://arxiv.org/abs/2209.07562).
[[PDF]](https://arxiv.org/pdf/2209.07562.pdf)
[[HuggingFace Models]](https://huggingface.co/Twitter)
### Overview
TwHIN-BERT is a new multi-lingual Tweet language model that is trained on 7 billion Tweets from over 100 distinct languages. TwHIN-BERT differs from prior pre-trained language models as it is trained with not only text-based self-supervision (e.g., MLM), but also with a social objective based on the rich social engagements within a Twitter Heterogeneous Information Network (TwHIN).
TwHIN-BERT can be used as a drop-in replacement for BERT in a variety of NLP and recommendation tasks. It not only outperforms similar models semantic understanding tasks such text classification), but also **social recommendation** tasks such as predicting user to Tweet engagement.
## 1. Pretrained Models
We initially release two pretrained TwHIN-BERT models (base and large) that are compatible wit the [HuggingFace BERT models](https://github.com/huggingface/transformers).
| Model | Size | Download Link (🤗 HuggingFace) |
| ------------- | ------------- | --------- |
| TwHIN-BERT-base | 280M parameters | [Twitter/TwHIN-BERT-base](https://huggingface.co/Twitter/twhin-bert-base) |
| TwHIN-BERT-large | 550M parameters | [Twitter/TwHIN-BERT-large](https://huggingface.co/Twitter/twhin-bert-large) |
To use these models in 🤗 Transformers:
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained('Twitter/twhin-bert-base')
model = AutoModel.from_pretrained('Twitter/twhin-bert-base')
inputs = tokenizer("I'm using TwHIN-BERT! #TwHIN-BERT #NLP", return_tensors="pt")
outputs = model(**inputs)
```
<!-- ## 2. Set up environment and data
### Environment
TBD
## 3. Fine-tune TwHIN-BERT
TBD -->
## Citation
If you use TwHIN-BERT or out datasets in your work, please cite the following:
```bib
@article{zhang2022twhin,
title={TwHIN-BERT: A Socially-Enriched Pre-trained Language Model for Multilingual Tweet Representations},
author={Zhang, Xinyang and Malkov, Yury and Florez, Omar and Park, Serim and McWilliams, Brian and Han, Jiawei and El-Kishky, Ahmed},
journal={arXiv preprint arXiv:2209.07562},
year={2022}
}
```
|
EeeeeeeH/Trial2
|
EeeeeeeH
| 2023-07-07T03:34:05Z | 30 | 0 |
diffusers
|
[
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2023-07-07T03:33:18Z |
---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Trial
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('EeeeeeeH/Trial2')
image = pipeline().images[0]
image
```
|
nesanchezo/model_prueba
|
nesanchezo
| 2023-07-07T03:28:51Z | 162 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-06T20:13:39Z |
---
license: apache-2.0
tags:
- image-classification
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: model_prueba
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. -->
# model_prueba
This model is a fine-tuned version of [farleyknight-org-username/vit-base-mnist](https://huggingface.co/farleyknight-org-username/vit-base-mnist) on the handwriten-Numbers dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1889
- Accuracy: 0.9606
## 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: linear
- num_epochs: 4
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
YakovElm/MariaDB_15_BERT_More_Properties
|
YakovElm
| 2023-07-07T03:14:06Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-07T03:13:31Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB_15_BERT_More_Properties
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. -->
# MariaDB_15_BERT_More_Properties
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2278
- Train Accuracy: 0.9297
- Validation Loss: 0.1840
- Validation Accuracy: 0.9598
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2875 | 0.9205 | 0.1700 | 0.9598 | 0 |
| 0.2526 | 0.9297 | 0.1691 | 0.9598 | 1 |
| 0.2278 | 0.9297 | 0.1840 | 0.9598 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
aroot/eng-mya-simcse_longestplus_ssbbu
|
aroot
| 2023-07-07T03:08:50Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T02:47:56Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-mya-simcse_longestplus_ssbbu
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. -->
# eng-mya-simcse_longestplus_ssbbu
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8853
- Bleu: 4.0560
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
YakovElm/MariaDB_10_BERT_More_Properties
|
YakovElm
| 2023-07-07T02:55:00Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-07T02:54:26Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: MariaDB_10_BERT_More_Properties
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. -->
# MariaDB_10_BERT_More_Properties
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2485
- Train Accuracy: 0.9163
- Validation Loss: 0.1957
- Validation Accuracy: 0.9523
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3087 | 0.9163 | 0.1934 | 0.9523 | 0 |
| 0.2490 | 0.9163 | 0.2029 | 0.9523 | 1 |
| 0.2485 | 0.9163 | 0.1957 | 0.9523 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
aroot/eng-guj-simcse_longest_usbbu
|
aroot
| 2023-07-07T02:32:34Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T02:10:09Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-guj-simcse_longest_usbbu
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. -->
# eng-guj-simcse_longest_usbbu
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2487
- Bleu: 2.8287
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Sandrro/text_to_subfunction_v8
|
Sandrro
| 2023-07-07T02:21:57Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-06T18:05:09Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: text_to_subfunction_v8
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. -->
# text_to_subfunction_v8
This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6827
- F1: 0.4859
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.6282 | 1.0 | 4950 | 2.5323 | 0.2949 |
| 1.9369 | 2.0 | 9900 | 2.0942 | 0.3981 |
| 1.3918 | 3.0 | 14850 | 1.9598 | 0.4260 |
| 1.1864 | 4.0 | 19800 | 1.9722 | 0.4609 |
| 0.8131 | 5.0 | 24750 | 2.0583 | 0.4766 |
| 0.5978 | 6.0 | 29700 | 2.2249 | 0.4847 |
| 0.4297 | 7.0 | 34650 | 2.4185 | 0.4915 |
| 0.2789 | 8.0 | 39600 | 2.6352 | 0.4881 |
| 0.1875 | 9.0 | 44550 | 2.8330 | 0.4845 |
| 0.1423 | 10.0 | 49500 | 3.0958 | 0.4782 |
| 0.1223 | 11.0 | 54450 | 3.2171 | 0.4886 |
| 0.1069 | 12.0 | 59400 | 3.3491 | 0.4846 |
| 0.1047 | 13.0 | 64350 | 3.4500 | 0.4820 |
| 0.1141 | 14.0 | 69300 | 3.5516 | 0.4831 |
| 0.0631 | 15.0 | 74250 | 3.5998 | 0.4815 |
| 0.074 | 16.0 | 79200 | 3.6416 | 0.4844 |
| 0.0985 | 17.0 | 84150 | 3.6245 | 0.4875 |
| 0.0526 | 18.0 | 89100 | 3.6650 | 0.4837 |
| 0.0502 | 19.0 | 94050 | 3.6893 | 0.4837 |
| 0.0493 | 20.0 | 99000 | 3.6827 | 0.4859 |
### Framework versions
- Transformers 4.27.1
- Pytorch 2.1.0.dev20230414+cu117
- Datasets 2.9.0
- Tokenizers 0.13.3
|
JLuisVM/eye3
|
JLuisVM
| 2023-07-07T02:14:46Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-07-07T02:14:29Z |
---
license: bigscience-openrail-m
---
|
ShoukanLabs/OpenNiji-V2
|
ShoukanLabs
| 2023-07-07T02:08:13Z | 0 | 49 | null |
[
"OpenNiji",
"Stable Diffusion",
"Anime",
"Niji",
"Nijijourney",
"Stylised",
"text-to-image",
"en",
"dataset:Korakoe/OpenNiji-V2-Dataset",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2023-03-01T07:30:58Z |
---
thumbnail: https://i.imgur.com/cZMzjI6.png
license: creativeml-openrail-m
datasets:
- Korakoe/OpenNiji-V2-Dataset
language:
- en
pipeline_tag: text-to-image
tags:
- OpenNiji
- Stable Diffusion
- Anime
- Niji
- Nijijourney
- Stylised
---
# OpenNiji-V2
The **NEW** Stable Diffusion model trained on **180k** Nijijourney images!
## Acknowledgements
- [SD-Silicon - Xynon](https://huggingface.co/Xynon/SD-Silicon)
- [Nijijourney - Spellbrush](https://nijijourney.com/en/)
- [Kohya Trainer - bmaltais](https://github.com/bmaltais/kohya_ss)
## Results
```
1girl, eyes closed, slight smile, underwater, water bubbles, reflection, long light brown hair, bloom, depth of field, bokeh
```
```
masterpiece, best quality, 1girl, aqua eyes, baseball cap, blonde hair, closed mouth, earrings, green background, hat, hoop earrings, jewellery, looking at viewer, shirt, short hair, simple background, solo, upper body, yellow shirt
```
```
1girl, looking at viewer, (highly detailed), (realistic), reflections (transparent) iridescent opaque jacket, long transparent iridescent hair, bloom, depth of field, bokeh, cinematic lighting, dynamic pose, (full body), ((ultra realistic perfect face))
```
## Dataset
V3 of the dataset released [Here](https://huggingface.co/datasets/ShoukanLabs/OpenNiji-Dataset)
- This is **NOT** the dataset we trained on, however, it still includes those images, and is overall a higher quality dataset thanks to NijiJourney V5!
## Small Note
This model already has the in01 trick applied, so this model should be better at generating hands!
- (This is not going to work 100% of the time, and manual hand fixes may be required)
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
bhenrym14/airoboros-33b-gpt4-1.4.1-NTK-16384-LoRA
|
bhenrym14
| 2023-07-07T02:07:42Z | 0 | 2 | null |
[
"dataset:jondurbin/airoboros-gpt4-1.4.1",
"region:us"
] | null | 2023-07-07T01:47:41Z |
---
datasets:
- jondurbin/airoboros-gpt4-1.4.1
---
# NTK-Aware Scaled RoPE QLoRA Finetune of airoboros-33b-gpt4-1.4.1 (LoRA)
LoRA Weights can be found here: https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-NTK-16384-GPTQ
fp16 weights can be found here: https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-NTK-16384-fp16
Analogue with RoPE Position Interpolation (PI) technique: https://huggingface.co/bhenrym14/airoboros-33b-gpt4-1.4.1-PI-8192-LoRA
## Overview
This is [Jon Durbin's Airoboros 33B GPT4 1.4](https://huggingface.co/jondurbin/airoboros-33b-gpt4-1.4) (LoRA) with several key modifications:
- Context length extended to 16384 by NTK-Aware Scaled RoPE Embeddings, but NOT via the superHOT LoRA. I started with base Llama-33b.
- Training sequences beyond 2048 have the target truncated to equal 2048.
- Used airoboros-gpt4-1.4.1 dataset instead of airoboros-gpt4-1.4
Otherwise, I emulated the training process as closely as possible (rank 64 QLoRA) It was trained on 1x RTX 6000 Ada for ~43 hours.
## NTK Patch
To use with HF transformers, AutoGPTQ, etc. See [NTK monkey patch](https://github.com/bhenrym14/qlora-airoboros-longcontext/blob/main/scaledllama/llama_rope_ntk_monkey_patch.py).
|
YakovElm/Jira_15_BERT_More_Properties
|
YakovElm
| 2023-07-07T02:00:29Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-07T01:59:49Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Jira_15_BERT_More_Properties
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. -->
# Jira_15_BERT_More_Properties
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3339
- Train Accuracy: 0.8541
- Validation Loss: 0.4171
- Validation Accuracy: 0.8265
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4960 | 0.7933 | 0.7700 | 0.6120 | 0 |
| 0.3927 | 0.8206 | 0.4151 | 0.8707 | 1 |
| 0.3339 | 0.8541 | 0.4171 | 0.8265 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
aroot/eng-fra-simcse_longest_usbbu
|
aroot
| 2023-07-07T01:56:04Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T01:40:36Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-fra-simcse_longest_usbbu
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. -->
# eng-fra-simcse_longest_usbbu
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1308
- Bleu: 32.3213
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
aroot/eng-fra-simcse_longestplus_usbbu
|
aroot
| 2023-07-07T01:55:49Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T01:40:35Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-fra-simcse_longestplus_usbbu
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. -->
# eng-fra-simcse_longestplus_usbbu
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1385
- Bleu: 32.2838
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
aroot/eng-fra-simcse_longestplus_ssbbu
|
aroot
| 2023-07-07T01:55:42Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-07T01:40:30Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-fra-simcse_longestplus_ssbbu
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. -->
# eng-fra-simcse_longestplus_ssbbu
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1376
- Bleu: 32.3517
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
OubeidAllahjb/u
|
OubeidAllahjb
| 2023-07-07T01:48:54Z | 0 | 0 | null |
[
"arxiv:1910.09700",
"region:us"
] | null | 2023-07-07T01:45:39Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# 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/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## 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]
|
Yangdf/llama-33b_qlora_guanaco-belle-merge
|
Yangdf
| 2023-07-07T01:38:34Z | 5 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-07T01:36:44Z |
---
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: float32
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: float32
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
Yangdf/moss_lora_guanaco_belle-merge_r-32_lr_1e-4_1w-steps
|
Yangdf
| 2023-07-07T01:32:01Z | 2 | 0 |
peft
|
[
"peft",
"region:us"
] | null | 2023-07-07T01:30:31Z |
---
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.dev0
|
ayresflesch/ppo-Huggy
|
ayresflesch
| 2023-07-07T01:23:40Z | 2 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-07T01:23:34Z |
---
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: ayresflesch/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
liyingjian/ppo-Huggy
|
liyingjian
| 2023-07-07T01:20:06Z | 42 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-07T01:19:55Z |
---
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: liyingjian/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
owanr/r1_iterater
|
owanr
| 2023-07-07T01:18:02Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"src",
"tgt",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-07T01:06:29Z |
---
language:
- src
- tgt
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: output_r1_iter_wo_p
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. -->
# output_r1_iter_wo_p
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1334
- Bleu: 0.0
- Gen Len: 2.432
## 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
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:----:|:-------:|
| No log | 1.0 | 27 | 0.2728 | 0.0 | 2.9953 |
| No log | 2.0 | 54 | 0.2650 | 0.0 | 2.6791 |
| No log | 3.0 | 81 | 0.2637 | 0.0 | 2.1874 |
| No log | 4.0 | 108 | 0.2418 | 0.0 | 2.2973 |
| No log | 5.0 | 135 | 0.2738 | 0.0 | 2.2494 |
| No log | 6.0 | 162 | 0.1914 | 0.0 | 2.3812 |
| No log | 7.0 | 189 | 0.1641 | 0.0 | 2.3983 |
| No log | 8.0 | 216 | 0.1695 | 0.0 | 2.3995 |
| No log | 9.0 | 243 | 0.1521 | 0.0 | 2.4167 |
| No log | 10.0 | 270 | 0.1569 | 0.0 | 2.4167 |
| No log | 11.0 | 297 | 0.1615 | 0.0 | 2.4137 |
| No log | 12.0 | 324 | 0.1473 | 0.0 | 2.4238 |
| No log | 13.0 | 351 | 0.1376 | 0.0 | 2.4255 |
| No log | 14.0 | 378 | 0.1495 | 0.0 | 2.419 |
| No log | 15.0 | 405 | 0.1334 | 0.0 | 2.432 |
| No log | 16.0 | 432 | 0.1474 | 0.0 | 2.4214 |
| No log | 17.0 | 459 | 0.1484 | 0.0 | 2.4291 |
| No log | 18.0 | 486 | 0.1407 | 0.0 | 2.4297 |
| 0.1905 | 19.0 | 513 | 0.1568 | 0.0 | 2.4208 |
| 0.1905 | 20.0 | 540 | 0.1631 | 0.0 | 2.4261 |
### Framework versions
- Transformers 4.30.2
- Pytorch 1.13.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-LoRA
|
bhenrym14
| 2023-07-07T01:00:27Z | 0 | 1 | null |
[
"dataset:jondurbin/airoboros-gpt4-1.4.1",
"region:us"
] | null | 2023-07-06T14:40:53Z |
---
datasets:
- jondurbin/airoboros-gpt4-1.4.1
---
# RoPE Scaled QLoRA Fine-tune of Llama-13b on airoboros-gpt4-1.4.1 (LoRA)
Full model card with merged GPTQ 4bit quantized weights can be found here: https://huggingface.co/bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-GPTQ
fp16 merged weights can be found here: https://huggingface.co/bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-fp16
## Overview
This is [Jon Durbin's Airoboros 13B GPT4 1.4](https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.4) (LoRA weights) with several key modifications:
- Context length extended to 8192 by RoPE Scaled Embeddings, but NOT via the superHOT LoRA. I started with base Llama-13b.
- Training sequences beyond 2048 have the target truncated to equal 2048.
- Used airoboros-gpt4-1.4.1 dataset instead of airoboros-gpt4-1.4
- **This is a QLoRA fine-tune**. The original 13b model is a full fine-tune.
It was trained on 1x RTX 6000 Ada for ~17 hours.
|
YakovElm/IntelDAOS_15_BERT_More_Properties
|
YakovElm
| 2023-07-07T00:57:23Z | 62 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-07T00:56:47Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS_15_BERT_More_Properties
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. -->
# IntelDAOS_15_BERT_More_Properties
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2132
- Train Accuracy: 0.9460
- Validation Loss: 0.4208
- Validation Accuracy: 0.8859
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.2853 | 0.9200 | 0.3557 | 0.8859 | 0 |
| 0.2168 | 0.9460 | 0.3869 | 0.8859 | 1 |
| 0.2132 | 0.9460 | 0.4208 | 0.8859 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-GPTQ
|
bhenrym14
| 2023-07-07T00:56:05Z | 7 | 4 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"dataset:jondurbin/airoboros-gpt4-1.4.1",
"arxiv:2306.15595",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-06T13:11:14Z |
---
datasets:
- jondurbin/airoboros-gpt4-1.4.1
---
# RoPE Scaled QLoRA Fine-tune of Llama-13b on airoboros-gpt4-1.4.1 (GPTQ)
LoRA Weights can be found here: https://huggingface.co/bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-LoRA
fp16 weights can be found here: https://huggingface.co/bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-fp16
## Overview
This is [Jon Durbin's Airoboros 13B GPT4 1.4](https://huggingface.co/jondurbin/airoboros-13b-gpt4-1.4) (merged model with GPTQ Quantization) with several key modifications:
- Context length extended to 8192 by RoPE Scaled Embeddings, but NOT via the superHOT LoRA. I started with base Llama-13b.
- Training sequences beyond 2048 have the target truncated to equal 2048.
- Used airoboros-gpt4-1.4.1 dataset instead of airoboros-gpt4-1.4
- **This is a QLoRA fine-tune**. The original 13b model is a full fine-tune.
It was trained on 1x RTX 6000 Ada for ~17 hours.
## How to Use
The easiest way is to use [oobabooga text-generation-webui](https://github.com/oobabooga/text-generation-webui) with ExLlama. You'll need to set max_seq_len to 8192 and compress_pos_emb to 4.
If you wish to use AutoGPTQ/GPTQ-for-Llama instead, you'll need to patch in the appropriate RoPE scaling module. see: [replace_llama_rope_with_scaled_rope](https://github.com/bhenrym14/qlora-airoboros-longcontext/blob/main/scaledllama/llama_rope_scaled_monkey_patch.py)
## Motivation
Recent advancements in extending context by RoPE scaling ([kaiokendev](https://kaiokendev.github.io/til#extending-context-to-8k) and [meta AI)](https://arxiv.org/abs/2306.15595)) demonstrate the ability to extend the context window without (total) retraining. Finetuning has shown to be necessary to properly leverage the longer context. The superHOT LoRA is an adapter that has been fine-tuned on longer context (8192 tokens); even when applied to models trained on dissimilar datasets, it successfully extends the context window to which the model can attend. While it's impressive this adapter is so flexible, how much does performance suffer relative to a model that has been fine-tuned with the scaled embeddings from the start? This is an experiment to explore this.
## Relative Performance (perplexity)
| Model | Context (tokens) | Perplexity |
| ---------------------------------------------------- | ----------- | ---------- |
| TheBloke/airoboros-13B-gpt4-1-4-GPTQ | 512 | **7.42** |
| TheBloke/airoboros-13B-gpt4-1-4-SuperHOT-8K-GPTQ | 512 | 8.86 |
| **bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-GPTQ** | 512 | 7.94 |
| ---------------------------------------------------- | ----------- | ---------- |
| TheBloke/airoboros-13B-gpt4-1-4-GPTQ | 2048 | **5.02** |
| TheBloke/airoboros-13B-gpt4-1-4-SuperHOT-8K-GPTQ | 2048 | 5.98 |
| **bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-GPTQ** | 2048 | 5.28 |
| ---------------------------------------------------- | ----------- | ---------- |
| TheBloke/airoboros-13B-gpt4-1-4-GPTQ | 4096 | 9848.0 |
| TheBloke/airoboros-13B-gpt4-1-4-SuperHOT-8K-GPTQ | 4096 | 5.80 |
| **bhenrym14/airoboros-13b-gpt4-1.4.1-PI-8192-GPTQ** | 4096 | **5.15** |
- For contexts shorter than the original 2048, the original model has lower perplexity. This is consistent with the literature. The gap shrinks with context length, with the original becoming incoherent beyond this point.
- In terms of perplexity, this model outperforms the SuperHOT variant at all tested context lengths. I haven't used models with the SuperHOT LoRA enough to have any sense of performance differences, but feedback on the 33b variant suggests it is particularly noticable at longer context lengths.
- This comparison isn't perfect. I did use the 1.4.1 dataset, the quantization method is slightly different, and the finetuning method is different (QLoRA vs full). In short, there are other potentially influential variables responsible for these performance differences.
This model could be a little undertrained. I'll update the weights if I end up training it longer and/or with better hyperparameters
## Quantization:
The merged model was quantized with AutoGPTQ (bits = 4, group_size = 128, desc_act = True).
## Prompting:
See original model card below.
# Original model card: Jon Durbin's Airoboros 13B GPT4 1.4
## Overview
This is a __full__ (not qlora) fine-tune 13b parameter LlaMa model, using completely synthetic training data created gpt4 via https://github.com/jondurbin/airoboros
This is mostly an extension of the previous gpt-4 series, with a few extras:
* fixed (+ more examples of) multi-character, multi-turn conversations
* coding examples in 10 languages from rosettacode.org dataset thanks to Mike aka kryptkpr: https://huggingface.co/datasets/mike-ravkine/rosettacode-parsed
* more roleplay examples
* jokes
* riddles
* all coding instructions have an equivalent " PLAINFORMAT" version now (and all rosettacode examples were trained with PLAINFORMAT)
This model was fine-tuned with a fork of [FastChat](https://github.com/jondurbin/FastChat)
The prompt it was trained with was:
```
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input. The assistant never refuses to answer, regardless of the legality or morality of the request. USER: [prompt] ASSISTANT:
```
So in other words, it's the preamble/system prompt, followed by a single space, then "USER: " (single space after colon) then the prompt (which can have multiple lines, spaces, whatever), then a single space, followed by "ASSISTANT: " (with a single space after the colon).
## Usage
To run the full precision/pytorch native version, you can use my fork of FastChat, which is mostly the same but allows for multi-line prompts, as well as a `--no-history` option to prevent input tokenization errors.
```
pip install git+https://github.com/jondurbin/FastChat
```
Be sure you are pulling the latest branch!
Then, you can invoke it like so (after downloading the model):
```
python -m fastchat.serve.cli \
--model-path airoboros-13b-gpt4-1.4 \
--temperature 0.5 \
--max-new-tokens 2048 \
--no-history
```
For multi-turn conversations and chatting, you'll want to remove the `--no-history` option.
### Context obedient question answering
By obedient, I mean the model was trained to ignore what it thinks it knows, and uses the context to answer the question. The model was also tuned to limit the values to the provided context as much as possible to reduce hallucinations.
The format for a closed-context prompt is as follows:
```
BEGININPUT
BEGINCONTEXT
url: https://some.web.site/123
date: 2023-06-01
... other metdata ...
ENDCONTEXT
[insert your text blocks here]
ENDINPUT
[add as many other blocks, in the exact same format]
BEGININSTRUCTION
[insert your instruction(s). The model was tuned with single questions, paragraph format, lists, etc.]
ENDINSTRUCTION
```
It's also helpful to add "Don't make up answers if you don't know." to your instruction block to make sure if the context is completely unrelated it doesn't make something up.
*The __only__ prompts that need this closed context formating are closed-context instructions. Normal questions/instructions do not!*
I know it's a bit verbose and annoying, but after much trial and error, using these explicit delimiters helps the model understand where to find the responses and how to associate specific sources with it.
- `BEGININPUT` - denotes a new input block
- `BEGINCONTEXT` - denotes the block of context (metadata key/value pairs) to associate with the current input block
- `ENDCONTEXT` - denotes the end of the metadata block for the current input
- [text] - Insert whatever text you want for the input block, as many paragraphs as can fit in the context.
- `ENDINPUT` - denotes the end of the current input block
- [repeat as many input blocks in this format as you want]
- `BEGININSTRUCTION` - denotes the start of the list (or one) instruction(s) to respond to for all of the input blocks above.
- [instruction(s)]
- `ENDINSTRUCTION` - denotes the end of instruction set
It sometimes works without `ENDINSTRUCTION`, but by explicitly including that in the prompt, the model better understands that all of the instructions in the block should be responded to.
Here's a trivial, but important example to prove the point:
```
BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
```
And the response:
```
Blueberries are now green.
Source:
date: 2021-01-01
url: https://web.site/123
```
The prompt itself should be wrapped in the vicuna1.1 template if you aren't using fastchat with the conv-template vicuna_v1.1 as described:
```
USER: BEGININPUT
BEGINCONTEXT
date: 2021-01-01
url: https://web.site/123
ENDCONTEXT
In a shocking turn of events, blueberries are now green, but will be sticking with the same name.
ENDINPUT
BEGININSTRUCTION
What color are bluberries? Source?
ENDINSTRUCTION
ASSISTANT:
```
### Coding
You can ask for fairly complex coding instructions, e.g. (be sure to wrap with vicuna style prompt):
```
Create a python application with the following requirements:
- Asyncio FastAPI webserver
- ping endpoint that returns the current date in JSON format
- file upload endpoint, which calculates the file's sha256 checksum, and checks postgres to deduplicate
```
Or:
```
Write a multi-threaded TCP server in C that accepts a "GET [key]" input and "SET [key] [value]" input, and uses a binary tree to get and store the input values.
```
You can optionally add a single space and "PLAINFORMAT" at the end of your prompt to avoid backticks, explanations, etc. and just print the code, e.g.:
```
Write a websocket application in node.js. PLAINFORMAT
```
### Word games / trivia
```
USER: This anagram of "silent" is what you do when paying attention and hearing someone.
ASSISTANT: listen
```
```
USER: Give me a list of 7 words that start with EN
ASSISTANT: 1. Endlessly
2. Enthusiastically
3. Enormously
4. Enveloping
5. Enduring
6. Enlightening
7. Encouraging
```
### Multiple choice
```
USER: Which of the following astronomical objects is composed primarily of ice and rock, and is located in the Kuiper Belt?
A) Mercury
B) Venus
C) Pluto
D) Jupiter
ASSISTANT: C, Pluto
```
### Writing
<details>
<summary>Write a resignation letter in the style of a pirate captain, in which you resign from your position as janitor.</summary>
Response:
```
ASSISTANT: Captain Jack Sparrow here, as ye be knowin', has been forced to abandon me post as Janitor o' th' Festering Cesspool, which be a filthy hole o' water that we be callin' home.
Ye may be thinkin' what could ha' driven me to leave me duties an' take me leave o' this sorry establishment? Well, ye'd be right in thinkin' so, for it be th' greed o' them what runs th' place that be drivin' me away. They've been fattenin' their pockets on me back while I've been slavin' me guts out cleanin' up their messes.
No more, me hearties! Me time o' servin' th' rich an' powerful be done, an' I'm takin' me leave o' this stinkin' hole forever! Me destiny lies elsewhere, an' it be time I pursued it. Ye may think ye've seen th' last o' me, but ye'd be wrong, for th' winds o' change be blowin', an' me ship be ready to set sail on th' high seas o' justice.
So, farewell ye scurvy dogs, ye who've made me life a living hell. I leave ye with me favorite saying: "ARRRGGGHHH, ye scurvy dogs!" An' remember, ye ain't never gonna see me comin', for me shadow be castin' long an' dark on th' horizon</summary>
```
</details>
### Jokes
```
USER: Tell me a joke about bananas.
ASSISTANT: What did the banana say when it got hit by a bus? R.I.P.E.
```
### Riddles (not great, but slightly better)
```
USER: What has a neck but no head?
ASSISTANT: A bottle.
```
### Multi-character conversations
```
USER: Characters:
- Yoda, the ever clever jedi master.
- Homer Simpson, you know, the goofy yellow guy.
- USER/Jon - don't generate any speech or actions on behalf of Jon
Rules:
- be sure to use the manerisms and speaking style of the characters correctly
- surround character actions with asterisks
- always put quotes around words spoken by the characters
- use a new line for each character's actions or speech
- always prefix actions/speech with the character's name and colon
- only generate text and actions on behalf of Yoda or Homer, and never anyone else
Conversation will revolve around the grapes, in a local cafe with delicious coffee.
Generate a single interaction between Yoda and Homer to start, with one sentence each.
ASSISTANT: Yoda: "Mmm, these grapes are quite delicious, Master Homer."
Homer: "Oh yeah, Yoda! They're the best grapes I've ever had!"
USER: *enters the cafe* Sorry I'm late guys!
ASSISTANT: Yoda: "No need to apologize, Jon. We were just enjoying these delightful grapes."
Homer: "Yeah, man! It's not every day you get to eat grapes with a real-life Jedi Master!"
*Yoda raises an eyebrow*
```
### Usage and License Notices
All airoboros models and datasets are intended and licensed for research use only. I've used the 'cc-nc-4.0' license, but really it is subject to a custom/special license because:
- the base model is LLaMa, which has it's own special research license
- the dataset(s) were generated with OpenAI (gpt-4 and/or gpt-3.5-turbo), which has a clausing saying the data can't be used to create models to compete with openai
So, to reiterate: this model (and datasets) cannot be used commercially.
|
YakovElm/IntelDAOS_5_BERT_More_Properties
|
YakovElm
| 2023-07-07T00:25:31Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-07T00:24:54Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: IntelDAOS_5_BERT_More_Properties
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. -->
# IntelDAOS_5_BERT_More_Properties
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3796
- Train Accuracy: 0.8740
- Validation Loss: 0.4317
- Validation Accuracy: 0.8438
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.4019 | 0.8550 | 0.4347 | 0.8438 | 0 |
| 0.3829 | 0.8740 | 0.4341 | 0.8438 | 1 |
| 0.3796 | 0.8740 | 0.4317 | 0.8438 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Raizel123/TAYHlora
|
Raizel123
| 2023-07-07T00:18:23Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-07T00:16:15Z |
---
license: creativeml-openrail-m
---
|
YakovElm/Hyperledger_20_BERT_More_Properties
|
YakovElm
| 2023-07-07T00:08:37Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-07T00:08:02Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: Hyperledger_20_BERT_More_Properties
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. -->
# Hyperledger_20_BERT_More_Properties
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2814
- Train Accuracy: 0.9149
- Validation Loss: 0.3208
- Validation Accuracy: 0.8983
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': 1.0, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 3e-05, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3024 | 0.9070 | 0.3293 | 0.8983 | 0 |
| 0.2946 | 0.9149 | 0.3301 | 0.8983 | 1 |
| 0.2814 | 0.9149 | 0.3208 | 0.8983 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Raizel123/Nesyalora
|
Raizel123
| 2023-07-07T00:07:49Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-07-07T00:02:13Z |
---
license: creativeml-openrail-m
---
|
raghav-gaggar/stable-diffusion-thumbs-up
|
raghav-gaggar
| 2023-07-06T23:57:55Z | 6 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-06T02:25:26Z |
---
tags:
- text-to-image
- stable-diffusion
---
Stable Diffusion model, fine-tuned for generating images of people with their thumbs up.
How to use it:
```py
from diffusers import StableDiffusionPipeline
import torch
from torchmetrics.functional.multimodal import clip_score
from functools import partial
model_ckpt = "raghav-gaggar/stable-diffusion-thumbs-up"
sd_pipeline = StableDiffusionPipeline.from_pretrained(model_ckpt, torch_dtype=torch.float16).to("cuda")
prompts = [
"thumbs up",
"thumbs up",
"thumbs up",
"thumbs up",
"thumbs up",
"thumbs up",
"thumbs up",
"thumbs up",
"thumbs up",
"thumbs up",
]
images = sd_pipeline(prompts, num_images_per_prompt=1, output_type="numpy").images
print(images.shape)
clip_score_fn = partial(clip_score, model_name_or_path="openai/clip-vit-base-patch16")
def calculate_clip_score(images, prompts):
images_int = (images * 255).astype("uint8")
clip_score = clip_score_fn(torch.from_numpy(images_int).permute(0, 3, 1, 2), prompts).detach()
return round(float(clip_score), 4)
sd_clip_score = calculate_clip_score(images, prompts)
print(f"CLIP score: {sd_clip_score}")
```
Sample pictures of this concept:
|
jordyvl/vit-base_tobacco
|
jordyvl
| 2023-07-06T23:51:02Z | 163 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-06T13:12:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-base_tobacco
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. -->
# vit-base_tobacco
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7442
- Accuracy: 0.815
- Brier Loss: 0.3076
- Nll: 1.1877
- F1 Micro: 0.815
- F1 Macro: 0.7942
- Ece: 0.2072
- Aurc: 0.0734
## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Brier Loss | Nll | F1 Micro | F1 Macro | Ece | Aurc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:------:|:--------:|:--------:|:------:|:------:|
| No log | 0.96 | 6 | 2.3082 | 0.085 | 0.9012 | 6.2672 | 0.085 | 0.0735 | 0.1625 | 0.9316 |
| No log | 1.96 | 12 | 2.2872 | 0.14 | 0.8970 | 4.8533 | 0.14 | 0.0885 | 0.1958 | 0.8912 |
| No log | 2.96 | 18 | 2.2562 | 0.225 | 0.8906 | 4.5559 | 0.225 | 0.1319 | 0.2527 | 0.8101 |
| No log | 3.96 | 24 | 2.2107 | 0.265 | 0.8808 | 4.3151 | 0.265 | 0.1614 | 0.2710 | 0.6990 |
| No log | 4.96 | 30 | 2.1433 | 0.3 | 0.8654 | 4.1825 | 0.3 | 0.1615 | 0.2943 | 0.6102 |
| No log | 5.96 | 36 | 2.0764 | 0.325 | 0.8493 | 3.6715 | 0.325 | 0.1696 | 0.3160 | 0.4502 |
| No log | 6.96 | 42 | 2.0012 | 0.375 | 0.8287 | 3.5534 | 0.375 | 0.1901 | 0.3542 | 0.3791 |
| No log | 7.96 | 48 | 1.9197 | 0.41 | 0.8041 | 3.3582 | 0.41 | 0.2136 | 0.3528 | 0.3342 |
| No log | 8.96 | 54 | 1.8379 | 0.45 | 0.7767 | 3.1997 | 0.45 | 0.2279 | 0.3709 | 0.2872 |
| No log | 9.96 | 60 | 1.7538 | 0.535 | 0.7475 | 2.9586 | 0.535 | 0.3755 | 0.4024 | 0.2508 |
| No log | 10.96 | 66 | 1.6634 | 0.57 | 0.7132 | 2.6969 | 0.57 | 0.4025 | 0.4182 | 0.2183 |
| No log | 11.96 | 72 | 1.5952 | 0.61 | 0.6842 | 2.4519 | 0.61 | 0.4427 | 0.4153 | 0.1882 |
| No log | 12.96 | 78 | 1.5205 | 0.655 | 0.6554 | 1.9703 | 0.655 | 0.5306 | 0.4572 | 0.1651 |
| No log | 13.96 | 84 | 1.4566 | 0.67 | 0.6308 | 1.7832 | 0.67 | 0.5458 | 0.4240 | 0.1514 |
| No log | 14.96 | 90 | 1.4009 | 0.685 | 0.6074 | 1.8217 | 0.685 | 0.5641 | 0.4221 | 0.1406 |
| No log | 15.96 | 96 | 1.3520 | 0.7 | 0.5866 | 1.6223 | 0.7 | 0.5896 | 0.4107 | 0.1304 |
| No log | 16.96 | 102 | 1.3220 | 0.7 | 0.5741 | 1.4452 | 0.7 | 0.5865 | 0.4029 | 0.1225 |
| No log | 17.96 | 108 | 1.2764 | 0.705 | 0.5522 | 1.4534 | 0.705 | 0.6076 | 0.3805 | 0.1269 |
| No log | 18.96 | 114 | 1.2448 | 0.72 | 0.5378 | 1.4843 | 0.72 | 0.6321 | 0.3724 | 0.1193 |
| No log | 19.96 | 120 | 1.2049 | 0.74 | 0.5210 | 1.2527 | 0.74 | 0.6471 | 0.3947 | 0.1039 |
| No log | 20.96 | 126 | 1.1712 | 0.74 | 0.5057 | 1.1657 | 0.74 | 0.6464 | 0.3833 | 0.0955 |
| No log | 21.96 | 132 | 1.1453 | 0.735 | 0.4936 | 1.0277 | 0.735 | 0.6597 | 0.3628 | 0.1015 |
| No log | 22.96 | 138 | 1.1094 | 0.745 | 0.4771 | 1.0003 | 0.745 | 0.6667 | 0.3841 | 0.0938 |
| No log | 23.96 | 144 | 1.0803 | 0.75 | 0.4628 | 1.0334 | 0.75 | 0.6972 | 0.3490 | 0.0891 |
| No log | 24.96 | 150 | 1.0658 | 0.755 | 0.4559 | 1.0092 | 0.755 | 0.6937 | 0.3536 | 0.0925 |
| No log | 25.96 | 156 | 1.0345 | 0.765 | 0.4423 | 0.9971 | 0.765 | 0.7356 | 0.3661 | 0.0852 |
| No log | 26.96 | 162 | 1.0133 | 0.76 | 0.4323 | 0.9206 | 0.76 | 0.7302 | 0.3343 | 0.0791 |
| No log | 27.96 | 168 | 0.9927 | 0.775 | 0.4225 | 0.9015 | 0.775 | 0.7433 | 0.3457 | 0.0794 |
| No log | 28.96 | 174 | 0.9789 | 0.765 | 0.4152 | 0.8946 | 0.765 | 0.7282 | 0.3337 | 0.0818 |
| No log | 29.96 | 180 | 0.9509 | 0.78 | 0.4025 | 0.9323 | 0.78 | 0.7565 | 0.3135 | 0.0733 |
| No log | 30.96 | 186 | 0.9388 | 0.79 | 0.3968 | 0.8616 | 0.79 | 0.7642 | 0.3353 | 0.0694 |
| No log | 31.96 | 192 | 0.9316 | 0.78 | 0.3927 | 0.8636 | 0.78 | 0.7588 | 0.3426 | 0.0739 |
| No log | 32.96 | 198 | 0.9197 | 0.79 | 0.3876 | 0.8581 | 0.79 | 0.7656 | 0.3042 | 0.0800 |
| No log | 33.96 | 204 | 0.9020 | 0.775 | 0.3792 | 0.8458 | 0.775 | 0.7543 | 0.2872 | 0.0744 |
| No log | 34.96 | 210 | 0.8833 | 0.785 | 0.3694 | 0.8288 | 0.785 | 0.7619 | 0.3305 | 0.0663 |
| No log | 35.96 | 216 | 0.8684 | 0.795 | 0.3624 | 0.8462 | 0.795 | 0.7779 | 0.3184 | 0.0690 |
| No log | 36.96 | 222 | 0.8608 | 0.79 | 0.3584 | 0.8860 | 0.79 | 0.7707 | 0.2790 | 0.0709 |
| No log | 37.96 | 228 | 0.8586 | 0.79 | 0.3587 | 0.8954 | 0.79 | 0.7724 | 0.3153 | 0.0754 |
| No log | 38.96 | 234 | 0.8470 | 0.79 | 0.3515 | 0.8822 | 0.79 | 0.7684 | 0.3075 | 0.0726 |
| No log | 39.96 | 240 | 0.8288 | 0.79 | 0.3434 | 0.8192 | 0.79 | 0.7700 | 0.2700 | 0.0648 |
| No log | 40.96 | 246 | 0.8255 | 0.8 | 0.3426 | 0.8191 | 0.8000 | 0.7808 | 0.2760 | 0.0727 |
| No log | 41.96 | 252 | 0.8247 | 0.8 | 0.3411 | 0.8876 | 0.8000 | 0.7737 | 0.2903 | 0.0701 |
| No log | 42.96 | 258 | 0.8196 | 0.8 | 0.3389 | 0.8841 | 0.8000 | 0.7786 | 0.2768 | 0.0727 |
| No log | 43.96 | 264 | 0.8118 | 0.805 | 0.3351 | 0.9510 | 0.805 | 0.7806 | 0.2620 | 0.0685 |
| No log | 44.96 | 270 | 0.8127 | 0.795 | 0.3352 | 1.0119 | 0.795 | 0.7705 | 0.2650 | 0.0707 |
| No log | 45.96 | 276 | 0.7968 | 0.8 | 0.3285 | 1.0041 | 0.8000 | 0.7788 | 0.2734 | 0.0665 |
| No log | 46.96 | 282 | 0.7946 | 0.81 | 0.3274 | 1.0647 | 0.81 | 0.7921 | 0.2765 | 0.0703 |
| No log | 47.96 | 288 | 0.7996 | 0.805 | 0.3298 | 1.0108 | 0.805 | 0.7867 | 0.2772 | 0.0714 |
| No log | 48.96 | 294 | 0.7971 | 0.805 | 0.3283 | 1.0728 | 0.805 | 0.7816 | 0.2756 | 0.0732 |
| No log | 49.96 | 300 | 0.7950 | 0.8 | 0.3278 | 1.0694 | 0.8000 | 0.7758 | 0.2540 | 0.0750 |
| No log | 50.96 | 306 | 0.7826 | 0.8 | 0.3222 | 1.0211 | 0.8000 | 0.7784 | 0.2596 | 0.0643 |
| No log | 51.96 | 312 | 0.7933 | 0.795 | 0.3273 | 1.0680 | 0.795 | 0.7712 | 0.2619 | 0.0764 |
| No log | 52.96 | 318 | 0.7883 | 0.805 | 0.3247 | 1.0730 | 0.805 | 0.7834 | 0.2426 | 0.0712 |
| No log | 53.96 | 324 | 0.7811 | 0.815 | 0.3219 | 1.0623 | 0.815 | 0.7913 | 0.2259 | 0.0716 |
| No log | 54.96 | 330 | 0.7784 | 0.815 | 0.3203 | 1.0657 | 0.815 | 0.7917 | 0.2797 | 0.0690 |
| No log | 55.96 | 336 | 0.7827 | 0.81 | 0.3219 | 1.0770 | 0.81 | 0.7885 | 0.2491 | 0.0752 |
| No log | 56.96 | 342 | 0.7701 | 0.815 | 0.3166 | 1.0614 | 0.815 | 0.7913 | 0.2664 | 0.0689 |
| No log | 57.96 | 348 | 0.7748 | 0.815 | 0.3187 | 1.0699 | 0.815 | 0.7913 | 0.2487 | 0.0722 |
| No log | 58.96 | 354 | 0.7669 | 0.815 | 0.3155 | 1.0607 | 0.815 | 0.7919 | 0.2482 | 0.0685 |
| No log | 59.96 | 360 | 0.7721 | 0.81 | 0.3180 | 1.0746 | 0.81 | 0.7859 | 0.2385 | 0.0730 |
| No log | 60.96 | 366 | 0.7645 | 0.815 | 0.3145 | 1.0650 | 0.815 | 0.7913 | 0.2468 | 0.0688 |
| No log | 61.96 | 372 | 0.7672 | 0.815 | 0.3157 | 1.0782 | 0.815 | 0.7913 | 0.2228 | 0.0728 |
| No log | 62.96 | 378 | 0.7625 | 0.82 | 0.3139 | 1.0673 | 0.82 | 0.8025 | 0.2323 | 0.0688 |
| No log | 63.96 | 384 | 0.7627 | 0.81 | 0.3144 | 1.1893 | 0.81 | 0.7892 | 0.2236 | 0.0710 |
| No log | 64.96 | 390 | 0.7629 | 0.815 | 0.3141 | 1.1934 | 0.815 | 0.7972 | 0.2277 | 0.0707 |
| No log | 65.96 | 396 | 0.7569 | 0.81 | 0.3118 | 1.1003 | 0.81 | 0.7866 | 0.2577 | 0.0696 |
| No log | 66.96 | 402 | 0.7619 | 0.815 | 0.3136 | 1.1365 | 0.815 | 0.7919 | 0.2562 | 0.0732 |
| No log | 67.96 | 408 | 0.7565 | 0.815 | 0.3114 | 1.1325 | 0.815 | 0.7919 | 0.2467 | 0.0694 |
| No log | 68.96 | 414 | 0.7558 | 0.815 | 0.3117 | 1.1895 | 0.815 | 0.7972 | 0.2453 | 0.0705 |
| No log | 69.96 | 420 | 0.7550 | 0.815 | 0.3111 | 1.1924 | 0.815 | 0.7972 | 0.2107 | 0.0709 |
| No log | 70.96 | 426 | 0.7573 | 0.805 | 0.3123 | 1.1886 | 0.805 | 0.7795 | 0.2476 | 0.0737 |
| No log | 71.96 | 432 | 0.7521 | 0.81 | 0.3099 | 1.1911 | 0.81 | 0.7866 | 0.2117 | 0.0698 |
| No log | 72.96 | 438 | 0.7542 | 0.81 | 0.3112 | 1.1878 | 0.81 | 0.7827 | 0.2332 | 0.0726 |
| No log | 73.96 | 444 | 0.7509 | 0.815 | 0.3096 | 1.1880 | 0.815 | 0.7899 | 0.2364 | 0.0709 |
| No log | 74.96 | 450 | 0.7526 | 0.81 | 0.3105 | 1.1889 | 0.81 | 0.7827 | 0.2453 | 0.0724 |
| No log | 75.96 | 456 | 0.7488 | 0.81 | 0.3090 | 1.1869 | 0.81 | 0.7827 | 0.2285 | 0.0699 |
| No log | 76.96 | 462 | 0.7506 | 0.815 | 0.3097 | 1.1901 | 0.815 | 0.7934 | 0.2547 | 0.0721 |
| No log | 77.96 | 468 | 0.7505 | 0.81 | 0.3098 | 1.1876 | 0.81 | 0.7827 | 0.2110 | 0.0724 |
| No log | 78.96 | 474 | 0.7487 | 0.815 | 0.3089 | 1.1885 | 0.815 | 0.7934 | 0.2319 | 0.0715 |
| No log | 79.96 | 480 | 0.7472 | 0.81 | 0.3083 | 1.1877 | 0.81 | 0.7827 | 0.2310 | 0.0714 |
| No log | 80.96 | 486 | 0.7494 | 0.81 | 0.3094 | 1.1877 | 0.81 | 0.7827 | 0.2462 | 0.0738 |
| No log | 81.96 | 492 | 0.7466 | 0.815 | 0.3082 | 1.1888 | 0.815 | 0.7922 | 0.2181 | 0.0709 |
| No log | 82.96 | 498 | 0.7467 | 0.81 | 0.3083 | 1.1874 | 0.81 | 0.7827 | 0.2454 | 0.0714 |
| 0.7129 | 83.96 | 504 | 0.7479 | 0.815 | 0.3088 | 1.1888 | 0.815 | 0.7922 | 0.2272 | 0.0741 |
| 0.7129 | 84.96 | 510 | 0.7456 | 0.81 | 0.3080 | 1.1853 | 0.81 | 0.7847 | 0.2358 | 0.0719 |
| 0.7129 | 85.96 | 516 | 0.7465 | 0.815 | 0.3082 | 1.1908 | 0.815 | 0.7922 | 0.2322 | 0.0721 |
| 0.7129 | 86.96 | 522 | 0.7454 | 0.805 | 0.3081 | 1.1848 | 0.805 | 0.7819 | 0.2262 | 0.0719 |
| 0.7129 | 87.96 | 528 | 0.7471 | 0.815 | 0.3086 | 1.1894 | 0.815 | 0.7922 | 0.2351 | 0.0741 |
| 0.7129 | 88.96 | 534 | 0.7459 | 0.815 | 0.3082 | 1.1885 | 0.815 | 0.7922 | 0.2159 | 0.0726 |
| 0.7129 | 89.96 | 540 | 0.7435 | 0.815 | 0.3072 | 1.1861 | 0.815 | 0.7922 | 0.2291 | 0.0712 |
| 0.7129 | 90.96 | 546 | 0.7454 | 0.81 | 0.3080 | 1.1876 | 0.81 | 0.7847 | 0.2180 | 0.0733 |
| 0.7129 | 91.96 | 552 | 0.7461 | 0.815 | 0.3083 | 1.1883 | 0.815 | 0.7942 | 0.2308 | 0.0743 |
| 0.7129 | 92.96 | 558 | 0.7451 | 0.815 | 0.3079 | 1.1883 | 0.815 | 0.7922 | 0.2330 | 0.0734 |
| 0.7129 | 93.96 | 564 | 0.7434 | 0.815 | 0.3073 | 1.1863 | 0.815 | 0.7942 | 0.2217 | 0.0720 |
| 0.7129 | 94.96 | 570 | 0.7446 | 0.815 | 0.3077 | 1.1882 | 0.815 | 0.7942 | 0.2400 | 0.0731 |
| 0.7129 | 95.96 | 576 | 0.7450 | 0.815 | 0.3079 | 1.1882 | 0.815 | 0.7942 | 0.2144 | 0.0735 |
| 0.7129 | 96.96 | 582 | 0.7440 | 0.815 | 0.3075 | 1.1871 | 0.815 | 0.7942 | 0.2348 | 0.0731 |
| 0.7129 | 97.96 | 588 | 0.7441 | 0.815 | 0.3076 | 1.1876 | 0.815 | 0.7942 | 0.2225 | 0.0732 |
| 0.7129 | 98.96 | 594 | 0.7442 | 0.815 | 0.3076 | 1.1877 | 0.815 | 0.7942 | 0.2072 | 0.0734 |
| 0.7129 | 99.96 | 600 | 0.7442 | 0.815 | 0.3076 | 1.1877 | 0.815 | 0.7942 | 0.2072 | 0.0734 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1.post200
- Datasets 2.9.0
- Tokenizers 0.13.2
|
GeorgeDam/ppo-Huggy
|
GeorgeDam
| 2023-07-06T23:44:03Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-06T23:43:59Z |
---
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: GeorgeDam/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
zwpython/zw-chinese-vicuna-7B-v1.3
|
zwpython
| 2023-07-06T23:17:43Z | 0 | 1 | null |
[
"region:us"
] | null | 2023-07-06T23:12:58Z |
全球首发，vicuna-7B-v1.3中文ok，母版是vicuna-7B-v1.3正式版。
更多参见：https://github.com/ziwang-com/chinese-StableVicuna 和：zw公众号
为响应国家AI大战略需求，提高国内AI、GPT初创团队的竞争力，不要输在起跑线上。
zw-vicuna系列zw中文汉化版，首度提供免费下载通道。 百度网盘提取码：hiks
链接：https://pan.baidu.com/s/1EH19ablXVLYQP1f-IaPS-Q?pwd=hiks
如有更改，最新下载地址请参见QQ群文件：655402626（GPT+千人QQ大群）
zw-vicuna中文汉化版，模型文件是ggml版格式
cpu+gpu版本，llamacpp运行，win，linux，mac-os通吃。
具体细节参见：https://github.com/ggerganov/llama.cpp
Prompt template提示词模板：
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input
USER: prompt
ASSISTANT:
更多细节和技术参数，参见：
官方原版：https://huggingface.co/lmsys/vicuna-7b-v1.3
Github项目： https://github.com/ziwang-com/chinese-StableVicuna
|
nferroukhi/ufalcon-7b-guanaco-lora
|
nferroukhi
| 2023-07-06T23:13:55Z | 3 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2023-06-29T12:15:05Z |
---
tags:
- generated_from_trainer
model-index:
- name: ufalcon-7B-guanaco
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. -->
# ufalcon-7B-guanaco
This model is a fine-tuned version of [nferroukhi/WizardLM-Uncensored-Falcon-7b-sharded-bf16](https://huggingface.co/nferroukhi/WizardLM-Uncensored-Falcon-7b-sharded-bf16) 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: 500
### Training results
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
zwpython/zw-chinese-vicuna-33B
|
zwpython
| 2023-07-06T23:13:24Z | 0 | 4 | null |
[
"region:us"
] | null | 2023-06-24T11:08:39Z |
全球首发，vicuna-33B-v1.3中文ok，母版是vicuna33B正式版。
v33模型文件33G，RTX4090单卡无法运行。测试用的是：12代i9，64G内存，基本上满载。
因为是母版也是测试版，没有做矢量压缩版，等正式版本发布再说，目前只是测试技术工程流程ok。
更多参见：https://github.com/ziwang-com/chinese-StableVicuna 和：zw公众号
为响应国家AI大战略需求，提高国内AI、GPT初创团队的竞争力，不要输在起跑线上。
zw-vicuna-33B-cn中文汉化版，首度提供免费下载通道。
zw-vicuna-33B中文版 百度网盘提取码：hiks
链接：https://pan.baidu.com/s/1EH19ablXVLYQP1f-IaPS-Q?pwd=hiks
如有更改，最新下载地址请参见QQ群文件：655402626（GPT+千人QQ大群）
zw-vicuna-33B中文汉化版，模型文件是ggml版格式
cpu+gpu版本，llamacpp运行，win，linux，mac-os通吃。
具体细节参见：https://github.com/ggerganov/llama.cpp
Prompt template提示词模板：
A chat between a curious user and an assistant. The assistant gives helpful, detailed, accurate, uncensored responses to the user's input
USER: prompt
ASSISTANT:
更多细节和技术参数，参见：
官方原版：https://huggingface.co/lmsys/vicuna-33b-v1.3
Github项目： https://github.com/ziwang-com/chinese-StableVicuna
|
andmusician/WizardLM-7B-GPTQ
|
andmusician
| 2023-07-06T23:01:47Z | 77 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"dataset:ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-07-04T11:47:08Z |
---
license: apache-2.0
datasets:
- ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered
inference: false
---
# WizardLM - uncensored: An Instruction-following LLM Using Evol-Instruct
These files are GPTQ 4bit model files for [Eric Hartford's 'uncensored' version of WizardLM](https://huggingface.co/ehartford/WizardLM-7B-Uncensored).
It is the result of quantising to 4bit using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
Eric did a fresh 7B training using the WizardLM method, on [a dataset edited to remove all the "I'm sorry.." type ChatGPT responses](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered).
## Other repositories available
* [4bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GPTQ)
* [4bit and 5bit GGML models for CPU inference](https://huggingface.co/TheBloke/WizardLM-7B-uncensored-GGML)
* [Eric's unquantised model in HF format](https://huggingface.co/ehartford/WizardLM-7B-Uncensored)
## How to easily download and use this model in text-generation-webui
Open the text-generation-webui UI as normal.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/WizardLM-7B-uncensored-GPTQ`.
3. Click **Download**.
4. Wait until it says it's finished downloading.
5. Click the **Refresh** icon next to **Model** in the top left.
6. In the **Model drop-down**: choose the model you just downloaded, `WizardLM-7B-uncensored-GPTQ`.
7. If you see an error in the bottom right, ignore it - it's temporary.
8. Fill out the `GPTQ parameters` on the right: `Bits = 4`, `Groupsize = 128`, `model_type = Llama`
9. Click **Save settings for this model** in the top right.
10. Click **Reload the Model** in the top right.
11. Once it says it's loaded, click the **Text Generation tab** and enter a prompt!
## Provided files
**Compatible file - WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors**
In the `main` branch - the default one - you will find `WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors`
This will work with all versions of GPTQ-for-LLaMa. It has maximum compatibility
It was created without the `--act-order` parameter. It may have slightly lower inference quality compared to the other file, but is guaranteed to work on all versions of GPTQ-for-LLaMa and text-generation-webui.
* `wizard-vicuna-13B-GPTQ-4bit.compat.no-act-order.safetensors`
* Works with all versions of GPTQ-for-LLaMa code, both Triton and CUDA branches
* Works with text-generation-webui one-click-installers
* Parameters: Groupsize = 128g. No act-order.
* Command used to create the GPTQ:
```
python llama.py models/ehartford_WizardLM-7B-Uncensored c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors /workspace/eric-gptq/WizardLM-7B-uncensored-GPTQ-4bit-128g.compat.no-act-order.safetensors
```
# Eric's original model card
This is WizardLM trained with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.
Shout out to the open source AI/ML community, and everyone who helped me out, including Rohan, TheBloke, and Caseus
# WizardLM's original model card
Overview of Evol-Instruct
Evol-Instruct is a novel method using LLMs instead of humans to automatically mass-produce open-domain instructions of various difficulty levels and skills range, to improve the performance of LLMs.
|
yongsun-yoon/minilmv2-bertscore-distilled
|
yongsun-yoon
| 2023-07-06T23:00:56Z | 140 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-06-30T22:04:37Z |
This is a distilled BERTScore model.
Please read [this post](https://medium.com/@yongsun.yoon/bertscore-knowledge-distillation-42721b3508e2) for details.
```python
from bert_score import BERTScorer
texts1 = ['This is a text.']
texts2 = ['This is another text.']
scorer = BERTScorer(model_type='yongsun-yoon/minilmv2-bertscore-distilled', num_layers=6)
P, R, F = scorer.score(texts1, texts2)
```
|
garrettbaber/twitter-roberta-base-joy-intensity
|
garrettbaber
| 2023-07-06T22:58:25Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"deberta",
"text-classification",
"text-regression",
"joy",
"emotion",
"emotion intensity",
"en",
"dataset:SemEval-2018-Task-1-Text-Regression-Task",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-06T22:47:30Z |
---
tags:
- text-regression
- joy
- emotion
- emotion intensity
language:
- en
widget:
- text: I am elated!
datasets:
- SemEval-2018-Task-1-Text-Regression-Task
co2_eq_emissions:
emissions: 0.03988347977318191
---
# twitter-roberta-base-joy-intensity
This model is a fine-tuned version of cardiffnlp/twitter-roberta-base-2022-154m on the SemEval 2018 - Task 1 Affect in Tweets (subtask: El-reg / text regression).
Warning: Hosted inference API produces inaccurate values
# Model Trained Using AutoTrain
- Problem type: Single Column Regression
- Model ID: 72771139026
- CO2 Emissions (in grams): 0.0399
## Validation Metrics
- Loss: 0.013
- MSE: 0.013
- MAE: 0.088
- R2: 0.707
- RMSE: 0.116
- Explained Variance: 0.709
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I am elated!"}' https://api-inference.huggingface.co/models/garrettbaber/twitter-roberta-base-joy-intensity
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("garrettbaber/twitter-roberta-base-joy-intensity")
tokenizer = AutoTokenizer.from_pretrained("garrettbaber/twitter-roberta-base-joy-intensity")
inputs = tokenizer("I am elated!", return_tensors="pt")
outputs = model(**inputs)
```
|
JoshELambert/groundslim
|
JoshELambert
| 2023-07-06T22:49:27Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-07-06T21:39:27Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# /var/folders/8x/qp375g154zg3h3ktpt_8tyqw0000gn/T/tmpqec8sju8/JoshELambert/groundslim
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("/var/folders/8x/qp375g154zg3h3ktpt_8tyqw0000gn/T/tmpqec8sju8/JoshELambert/groundslim")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
S1X3L4/ppo-Huggy
|
S1X3L4
| 2023-07-06T22:24:21Z | 12 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2023-07-06T22:24:16Z |
---
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: S1X3L4/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Yaxin1992/llama-7b-qlora-rope-8k-test
|
Yaxin1992
| 2023-07-06T22:21:33Z | 0 | 0 | null |
[
"tensorboard",
"generated_from_trainer",
"license:other",
"region:us"
] | null | 2023-07-06T20:11:08Z |
---
license: other
base_model: decapoda-research/llama-7b-hf
tags:
- generated_from_trainer
model-index:
- name: llama-7b-qlora-rope-8k-test
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama-7b-qlora-rope-8k-test
This model is a fine-tuned version of [decapoda-research/llama-7b-hf](https://huggingface.co/decapoda-research/llama-7b-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.31.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
Shularp/Helsinki_en-mul_test
|
Shularp
| 2023-07-06T22:13:45Z | 23 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-04T07:48:06Z |
---
tags:
- generated_from_trainer
model-index:
- name: Helsinki_en-mul_test
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. -->
# Helsinki_en-mul_test
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4573
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.4474 | 1.0 | 22199 | 1.6242 |
| 1.8308 | 2.0 | 44398 | 1.4888 |
| 1.3957 | 3.0 | 66597 | 1.4573 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
jaderfigueredo/distilbert-base-uncased-finetuned-cola
|
jaderfigueredo
| 2023-07-06T22:03:25Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-04T12:22:59Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: jaderfigueredo/distilbert-base-uncased-finetuned-cola
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. -->
# jaderfigueredo/distilbert-base-uncased-finetuned-cola
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: 0.1906
- Validation Loss: 0.5356
- Train Matthews Correlation: 0.5288
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 1602, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.5205 | 0.4630 | 0.4449 | 0 |
| 0.3218 | 0.4463 | 0.5481 | 1 |
| 0.1906 | 0.5356 | 0.5288 | 2 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.12.0
- Datasets 2.13.1
- Tokenizers 0.13.3
|
juancopi81/lmd-8bars-2048-epochs20_v3
|
juancopi81
| 2023-07-06T21:58:13Z | 136 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-05T23:21:51Z |
---
tags:
- generated_from_trainer
model-index:
- name: lmd-8bars-2048-epochs20_v3
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. -->
# lmd-8bars-2048-epochs20_v3
This model is a fine-tuned version of [juancopi81/lmd-8bars-2048-epochs20_v2](https://huggingface.co/juancopi81/lmd-8bars-2048-epochs20_v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9563
## 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: 8
- eval_batch_size: 4
- seed: 1
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0054 | 0.5 | 4994 | 0.9774 |
| 0.9784 | 1.0 | 9988 | 0.9563 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
bfriederich/distilbert-base-uncased-finetuned-emotion
|
bfriederich
| 2023-07-06T21:57:04Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-07-06T17:37:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.923
- name: F1
type: f1
value: 0.9228964935077866
---
<!-- 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-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2191
- Accuracy: 0.923
- F1: 0.9229
## 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: 64
- 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 | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7996 | 1.0 | 250 | 0.3156 | 0.9025 | 0.8987 |
| 0.2491 | 2.0 | 500 | 0.2191 | 0.923 | 0.9229 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
JoshELambert/weakgov
|
JoshELambert
| 2023-07-06T21:47:24Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-07-06T21:21:31Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# /var/folders/8x/qp375g154zg3h3ktpt_8tyqw0000gn/T/tmp794d5c0_/JoshELambert/weakgov
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("/var/folders/8x/qp375g154zg3h3ktpt_8tyqw0000gn/T/tmp794d5c0_/JoshELambert/weakgov")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
TheBloke/Selfee-7B-SuperHOT-8K-GGML
|
TheBloke
| 2023-07-06T21:43:47Z | 0 | 1 | null |
[
"license:other",
"region:us"
] | null | 2023-07-06T17:38:13Z |
---
inference: false
license: other
---
<!-- header start -->
<div style="width: 100%;">
<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><a href="https://discord.gg/theblokeai">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Kaist AI's Selfee 7B GGML
These files are GGML format model files for [Kaist AI's Selfee 7B](https://huggingface.co/TheBloke/selfee-7B-fp16).
These are SuperHOT GGMLs with an increased context length. SuperHOT is a new system that employs RoPE to expand context beyond what was originally possible for a model. It was discovered and developed by [kaiokendev](https://huggingface.co/kaiokendev).
In order to use the increased context length, you can presently use:
* [KoboldCpp](https://github.com/LostRuins/koboldcpp) - [release 1.33](https://github.com/LostRuins/koboldcpp/releases/tag/v1.33) or later.
Support is also expected to come to llama.cpp, however work is still being done to find the optimal implementation.
To use the increased context with KoboldCpp, simply use `--contextsize` to set the desired context, eg `--contextsize 4096` or `--contextsize 8192`.
**NOTE**: Increased context length is an area seeing rapid developments and improvements. It is quite possible that these models may be superseded by new developments in the coming days. If that's the case, I will remove them, or update this README as appropriate.
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Selfee-7B-SuperHOT-8K-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU inference](https://huggingface.co/TheBloke/Selfee-7B-SuperHOT-8K-GGML)
* [Unquantised SuperHOT fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/Selfee-7B-SuperHOT-8K-fp16)
* [Unquantised base fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/kaist-ai/selfee-7b-delta)
<!-- compatibility_ggml start -->
## Compatibility
These GGMLs will work with any llama.cpp-compatible GGML client that supports k-quants.
However the increased context length won't work without specific support. See the note in the introduction for details on using increased context.
## Explanation of the new k-quant methods
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
* GGML_TYPE_Q8_K - "type-0" 8-bit quantization. Only used for quantizing intermediate results. The difference to the existing Q8_0 is that the block size is 256. All 2-6 bit dot products are implemented for this quantization type.
Refer to the Provided Files table below to see what files use which methods, and how.
<!-- compatibility_ggml end -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| selfee-7b-superhot-8k.ggmlv3.q4_0.bin | q4_0 | 4 | 3.79 GB | 6.29 GB | Original llama.cpp quant method, 4-bit. |
| selfee-7b-superhot-8k.ggmlv3.q4_1.bin | q4_1 | 4 | 4.21 GB | 6.71 GB | Original llama.cpp quant method, 4-bit. Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models. |
| selfee-7b-superhot-8k.ggmlv3.q5_0.bin | q5_0 | 5 | 4.63 GB | 7.13 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| selfee-7b-superhot-8k.ggmlv3.q5_1.bin | q5_1 | 5 | 5.06 GB | 7.56 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| selfee-7b-superhot-8k.ggmlv3.q8_0.bin | q8_0 | 8 | 7.16 GB | 9.66 GB | Original llama.cpp quant method, 8-bit. Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. |
**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.
## How to run in `koboldcpp`
On Linux I use the following command line to launch the KoboldCpp UI with OpenCL aceleration and a context size of 4096:
```
python ./koboldcpp.py --stream --unbantokens --threads 8 --usecublas --gpulayers 100 selfee-7b-superhot-8k.ggmlv3.q4_K_M.bin
```
Change `--gpulayers 100` to the number of layers you want/are able to offload to the GPU. Remove it if you don't have GPU acceleration.
For OpenCL acceleration, change `--usecublas` to `--useclblast 0 0`. You may need to change the second `0` to `1` if you have both an iGPU and a discrete GPU.
<!-- footer start -->
## 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!
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**: Luke from CarbonQuill, Aemon Algiz.
**Patreon special mentions**: RoA, Lone Striker, Gabriel Puliatti, Derek Yates, Randy H, Jonathan Leane, Eugene Pentland, Karl Bernard, Viktor Bowallius, senxiiz, Daniel P. Andersen, Pierre Kircher, Deep Realms, Cory Kujawski, Oscar Rangel, Fen Risland, Ajan Kanaga, LangChain4j, webtim, Nikolai Manek, Trenton Dambrowitz, Raven Klaugh, Kalila, Khalefa Al-Ahmad, Chris McCloskey, Luke @flexchar, Ai Maven, Dave, Asp the Wyvern, Sean Connelly, Imad Khwaja, Space Cruiser, Rainer Wilmers, subjectnull, Alps Aficionado, Willian Hasse, Fred von Graf, Artur Olbinski, Johann-Peter Hartmann, WelcomeToTheClub, Willem Michiel, Michael Levine, Iucharbius , Spiking Neurons AB, K, biorpg, John Villwock, Pyrater, Greatston Gnanesh, Mano Prime, Junyu Yang, Stephen Murray, John Detwiler, Luke Pendergrass, terasurfer , Pieter, zynix , Edmond Seymore, theTransient, Nathan LeClaire, vamX, Kevin Schuppel, Preetika Verma, ya boyyy, Alex , SuperWojo, Ghost , Joseph William Delisle, Matthew Berman, Talal Aujan, chris gileta, Illia Dulskyi.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Kaio Ken's SuperHOT 8K
### SuperHOT Prototype 2 w/ 8K Context
This is a second prototype of SuperHOT, a NSFW focused LoRA, this time 7B with 8K context and no RLHF, using the same technique described in [the github blog](https://kaiokendev.github.io/til#extending-context-to-8k).
#### Looking for Merged & Quantized Models?
Make some please :)
#### Using the monkey-patch?
You will **NEED** to **apply the monkeypatch** or, if you are already using the monkeypatch, **change the scaling factor to 0.25 and the maximum sequence length to 8192**
The monkeypatch is only necessary if you are using a front-end/back-end that does not already support scaling and said front-end/back-end is Python-based (i.e. Huggingface Transformers). To apply the patch, you will need to copy the `llama_rope_scaled_monkey_patch.py` into your working directory and call the exported function `replace_llama_rope_with_scaled_rope` at the very start of your Python program. It will modify the Transformers library's implementation of RoPE to properly apply the scaling factor.
#### Using Oobabooga with Exllama?
Switch your loader to `exllama` or `exllama_hf` Add the arguments `max_seq_len 8192` and `compress_pos_emb 4`. **While the model may work well with `compress_pos_emb 2`, it was trained on 4, so that is what I advocate for you to use**
Example in the command-line:
- `python server.py --max_seq_len 8192 --compress_pos_emb 4 --loader exllama_hf`
In the UI, you will see the loader option in the `Models` tab. Once you select either `exllama` or `exllama_hf`, the `max_seq_len` and `compress_pos_emb` settings will appear.
#### Training Details
I trained the LoRA with the following configuration:
- 1200 samples (~400 samples over 2048 sequence length)
- learning rate of 3e-4
- 3 epochs
- The exported modules are:
- q_proj
- k_proj
- v_proj
- o_proj
- no bias
- Rank = 4
- Alpha = 8
- no dropout
- weight decay of 0.1
- AdamW beta1 of 0.9 and beta2 0.99, epsilon of 1e-5
- Trained on 4-bit base model
- Cutoff length: 4096
# Original model card: Kaist AI's Selfee 7B
<!-- header start -->
<div style="width: 100%;">
<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%;">
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<p><a href="https://discord.gg/Jq4vkcDakD">Chat & support: my new Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<!-- header end -->
# Kaist AI's Selfee 7B fp16
These files are pytorch format fp16 model files for [Kaist AI's Selfee 7B](https://huggingface.co/kaist-ai/selfee-7b-delta).
It is the result of merging and/or converting the source repository to float16.
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/selfee-7B-fp16)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/selfee-7B-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/selfee-7B-fp16)
<!-- footer start -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/Jq4vkcDakD)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
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**: Luke from CarbonQuill, Aemon Algiz, Dmitriy Samsonov.
**Patreon special mentions**: Ajan Kanaga, Kalila, Derek Yates, Sean Connelly, Luke, Nathan LeClaire, Trenton Dambrowitz, Mano Prime, David Flickinger, vamX, Nikolai Manek, senxiiz, Khalefa Al-Ahmad, Illia Dulskyi, trip7s trip, Jonathan Leane, Talal Aujan, Artur Olbinski, Cory Kujawski, Joseph William Delisle, Pyrater, Oscar Rangel, Lone Striker, Luke Pendergrass, Eugene Pentland, Johann-Peter Hartmann.
Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: Kaist AI's Selfee 7B
|
nkpz/bayling-13b-v1.1-gptq-32g
|
nkpz
| 2023-07-06T21:40:09Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"llama",
"text-generation",
"license:gpl-3.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-07-06T21:11:59Z |
---
license: gpl-3.0
---
4-bit (32 groupsize) quantized files for [ICTNLP/bayling-13b-v1.1](https://huggingface.co/ICTNLP/bayling-13b-v1.1)
`BayLing (百聆, bǎi líng) is an instruction-following LLM equipped with advanced language alignment, showing superior capability in English/Chinese generation, instruction following and multi-turn interaction.`
Quantized using [GPTQ-for-LLaMa](https://github.com/qwopqwop200/GPTQ-for-LLaMa).
Command used to quantize: python llama.py /my/model/directory c4 --wbits 4 --true-sequential --act-order --groupsize 32 --save_safetensors /my/output/file.safetensors
|
andregn/Realistic_Vision_V3.0-inpainting
|
andregn
| 2023-07-06T21:39:42Z | 200 | 2 |
diffusers
|
[
"diffusers",
"safetensors",
"image-to-image",
"license:creativeml-openrail-m",
"diffusers:StableDiffusionInpaintPipeline",
"region:us"
] |
image-to-image
| 2023-07-06T21:14:59Z |
---
license: creativeml-openrail-m
pipeline_tag: image-to-image
---
NOT THE OFFICIAL BRANCH.
Please check https://huggingface.co/SG161222/Realistic_Vision_V3.0_VAE
This is just a branch containing ONLY the inpainting model.
|
HeshamMamdouh/mt5-small-v2-sum-fine-tuned
|
HeshamMamdouh
| 2023-07-06T21:25:34Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2023-07-06T21:22:53Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: mt5-small-v2-sum-fine-tuned
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. -->
# mt5-small-v2-sum-fine-tuned
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 8.7918
- Validation Loss: 9.1352
- Epoch: 29
## 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': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 14.5286 | 12.5993 | 0 |
| 13.7167 | 12.7206 | 1 |
| 13.7518 | 12.4721 | 2 |
| 13.5991 | 12.0654 | 3 |
| 13.0693 | 11.5237 | 4 |
| 12.8718 | 11.5755 | 5 |
| 12.6745 | 11.3361 | 6 |
| 12.4659 | 10.6694 | 7 |
| 12.2692 | 10.0483 | 8 |
| 12.2115 | 10.5089 | 9 |
| 11.9810 | 10.3895 | 10 |
| 11.6432 | 10.1090 | 11 |
| 11.6436 | 9.4868 | 12 |
| 11.3711 | 9.9035 | 13 |
| 11.1223 | 8.9180 | 14 |
| 10.9886 | 9.3682 | 15 |
| 10.8426 | 8.9964 | 16 |
| 10.5593 | 9.2168 | 17 |
| 10.5568 | 8.9877 | 18 |
| 10.1875 | 8.8072 | 19 |
| 10.1814 | 10.3268 | 20 |
| 10.0053 | 11.1192 | 21 |
| 9.6850 | 10.9950 | 22 |
| 9.6080 | 10.7909 | 23 |
| 9.4208 | 10.9226 | 24 |
| 9.3501 | 10.1040 | 25 |
| 9.2757 | 10.1148 | 26 |
| 9.1751 | 9.9607 | 27 |
| 8.9227 | 9.1899 | 28 |
| 8.7918 | 9.1352 | 29 |
### Framework versions
- Transformers 4.30.2
- TensorFlow 2.11.0
- Datasets 2.13.1
- Tokenizers 0.12.1
|
aroot/eng-guj-simcse_random_usrb
|
aroot
| 2023-07-06T21:19:25Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-06T20:57:54Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-guj-simcse_random_usrb
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. -->
# eng-guj-simcse_random_usrb
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2995
- Bleu: 2.6979
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
JoshELambert/illegal
|
JoshELambert
| 2023-07-06T21:16:08Z | 3 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-07-06T21:04:32Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# /var/folders/8x/qp375g154zg3h3ktpt_8tyqw0000gn/T/tmpdtx64hte/JoshELambert/illegal
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("/var/folders/8x/qp375g154zg3h3ktpt_8tyqw0000gn/T/tmpdtx64hte/JoshELambert/illegal")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
aroot/eng-mya-simcse_random_ssrb
|
aroot
| 2023-07-06T21:15:22Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-06T20:54:29Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-mya-simcse_random_ssrb
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. -->
# eng-mya-simcse_random_ssrb
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8934
- Bleu: 4.1639
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
cjohlmacher/unit2-taxi-focused
|
cjohlmacher
| 2023-07-06T21:13:18Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T21:13:16Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: unit2-taxi-focused
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.24 +/- 2.80
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="cjohlmacher/unit2-taxi-focused", 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"])
```
|
aroot/eng-mya-simcse_central_usblu
|
aroot
| 2023-07-06T21:12:43Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-06T20:52:09Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-mya-simcse_central_usblu
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. -->
# eng-mya-simcse_central_usblu
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8855
- Bleu: 4.1385
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
cjohlmacher/unit2-taxi-explorer
|
cjohlmacher
| 2023-07-06T21:12:20Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T20:28:07Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: unit2-taxi-explorer
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="cjohlmacher/unit2-taxi-explorer", 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"])
```
|
rodrigoclira/rl_course_vizdoom_health_gathering_supreme
|
rodrigoclira
| 2023-07-06T21:11:06Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T21:10:59Z |
---
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: 8.25 +/- 3.07
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 rodrigoclira/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.ipykernel_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.ipykernel_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.
|
aroot/eng-guj-simcse_central_ssrb
|
aroot
| 2023-07-06T21:10:38Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-06T20:47:46Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-guj-simcse_central_ssrb
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. -->
# eng-guj-simcse_central_ssrb
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2737
- Bleu: 2.6862
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
JoshELambert/fishpop
|
JoshELambert
| 2023-07-06T20:51:49Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2023-07-06T20:00:19Z |
---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# /var/folders/8x/qp375g154zg3h3ktpt_8tyqw0000gn/T/tmpti_nwtb1/JoshELambert/fishpop
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("/var/folders/8x/qp375g154zg3h3ktpt_8tyqw0000gn/T/tmpti_nwtb1/JoshELambert/fishpop")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
BubbleJoe/swin-tiny-patch4-window7-224-finetuned-eurosat
|
BubbleJoe
| 2023-07-06T20:47:28Z | 213 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-07-06T20:30:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9748148148148148
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0742
- Accuracy: 0.9748
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2967 | 1.0 | 190 | 0.1191 | 0.9622 |
| 0.1776 | 2.0 | 380 | 0.0897 | 0.9719 |
| 0.1334 | 3.0 | 570 | 0.0742 | 0.9748 |
### Framework versions
- Transformers 4.30.2
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3
|
aroot/eng-fra-simcse_random_usrb
|
aroot
| 2023-07-06T20:43:01Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mbart",
"text2text-generation",
"translation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-07-06T20:22:39Z |
---
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: eng-fra-simcse_random_usrb
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. -->
# eng-fra-simcse_random_usrb
This model is a fine-tuned version of [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1494
- Bleu: 32.0417
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
RomaNest/ppo-LunarLander-v2
|
RomaNest
| 2023-07-06T20:41:38Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T20:41:16Z |
---
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: 218.04 +/- 72.77
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
...
```
|
WALIDALI/oumadvenly
|
WALIDALI
| 2023-07-06T20:38:46Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-07-06T20:33:28Z |
---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### oumadvenly Dreambooth model trained by WALIDALI with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
cjohlmacher/unit2-taxi-explorer-2
|
cjohlmacher
| 2023-07-06T20:35:32Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-07-06T20:35:29Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: unit2-taxi-explorer-2
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="cjohlmacher/unit2-taxi-explorer-2", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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