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| likes
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| library_name
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tonystark0/distilbert-imdb-sentiment-classifier | tonystark0 | 2023-06-07T17:54:54Z | 60 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-06-07T17:00:39Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: my_awesome_model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.93164
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2345
- Accuracy: 0.9316
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2323 | 1.0 | 1563 | 0.1911 | 0.9266 |
| 0.1503 | 2.0 | 3126 | 0.2345 | 0.9316 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AnastasiaAv/digits | AnastasiaAv | 2023-06-07T17:54:28Z | 0 | 0 | keras | [
"keras",
"tf-keras",
"region:us"
] | null | 2023-06-06T16:01:02Z | ---
library_name: keras
---
# Моя модель для распознования цифр
Натренирована на наборе данных mnist
 |
cardiffnlp/flan-t5-small-tweet-emoji | cardiffnlp | 2023-06-07T17:42:12Z | 39 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-06-05T22:48:08Z |
---
widget:
- text: "Louis Loudlinson is back with 16 and 18 secs "
example_title: example 1
- text: "I’m tired of being sick.. it’s been four days dawg "
example_title: example 2
- text: "Here is your daily reminder that ADAM COLE IS ONE OF THE BEST WRESTLERS ON THE PLANET. Saw some unintelligent ghouls on here laugh at how he was NXT Champion, and now getting “kissed by the Bucks” Because he’d be better off on Monday or Friday, right? Patience. Seek it "
example_title: example 3
---
# cardiffnlp/flan-t5-small-tweet-emoji
This is [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) fine-tuned on [cardiffnlp/super_tweeteval (tweet_emoji)](https://huggingface.co/datasets/cardiffnlp/super_tweeteval).
### Usage
```python
from transformers import pipeline
pipe = pipeline('text2text-generation', model="cardiffnlp/flan-t5-small-tweet-emoji")
output = pipe("Louis Loudlinson is back with 16 and 18 secs ")
```
|
varo/CartPole-8 | varo | 2023-06-07T17:41:51Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-07T16:56:13Z | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: CartPole-8
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
|
aimakerrr/gibiasmrr | aimakerrr | 2023-06-07T17:36:06Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-07T17:30:37Z | ---
license: creativeml-openrail-m
---
|
mrizalf7/xlm-roberta-finetuned-small-squad-indonesian-rizal-1 | mrizalf7 | 2023-06-07T17:33:26Z | 54 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-05-22T06:36:33Z | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-finetuned-small-squad-indonesian-rizal-1
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. -->
# xlm-roberta-finetuned-small-squad-indonesian-rizal-1
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8864
## 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3046 | 1.0 | 2719 | 1.9392 |
| 1.0487 | 2.0 | 5438 | 1.8864 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
MainaMan/SpaceInvadersNoFrameskip-v4 | MainaMan | 2023-06-07T17:31:16Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-07T17:30:38Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 573.00 +/- 215.39
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MainaMan -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga MainaMan -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga MainaMan
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
huggingtweets/therock | huggingtweets | 2023-06-07T17:21:19Z | 77 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/3478244961/01ebfc40ecc194a2abc81e82ab877af4_400x400.jpeg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Dwayne Johnson</div>
<div style="text-align: center; font-size: 14px;">@therock</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Dwayne Johnson.
| Data | Dwayne Johnson |
| --- | --- |
| Tweets downloaded | 3227 |
| Retweets | 657 |
| Short tweets | 103 |
| Tweets kept | 2467 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/386nipye/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @therock's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/853yw3wm) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/853yw3wm/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/therock')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
peteozegov/poca-SoccerTwos | peteozegov | 2023-06-07T17:18:03Z | 40 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | 2023-06-07T08:39:55Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: peteozegov/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Azeglio/Streetsigns | Azeglio | 2023-06-07T17:14:20Z | 1 | 0 | tf-keras | [
"tf-keras",
"streetsigns",
"image-classification",
"region:us"
] | image-classification | 2023-06-07T16:46:47Z | ---
pipeline_tag: image-classification
--- |
vhahvhah/my_awesome_opus_books_model | vhahvhah | 2023-06-07T17:09:02Z | 61 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:opus_books",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-06-07T16:52:25Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- opus_books
metrics:
- bleu
model-index:
- name: my_awesome_opus_books_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: opus_books
type: opus_books
config: en-ru
split: train
args: en-ru
metrics:
- name: Bleu
type: bleu
value: 0.0942
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_opus_books_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the opus_books dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2547
- Bleu: 0.0942
- Gen Len: 18.44
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| 2.6607 | 1.0 | 875 | 2.2970 | 0.0767 | 18.5163 |
| 2.4489 | 2.0 | 1750 | 2.2547 | 0.0942 | 18.44 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
gcy/Reinforce-pixelcoter | gcy | 2023-06-07T16:50:58Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-07T16:50:56Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-pixelcoter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 18.80 +/- 14.91
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
ortofasfat/hh-open_assistant | ortofasfat | 2023-06-07T16:46:07Z | 124 | 0 | transformers | [
"transformers",
"pytorch",
"gpt_neox",
"text-generation",
"en",
"dataset:OpenAssistant/oasst1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-06-05T14:32:47Z | ---
license: apache-2.0
datasets:
- OpenAssistant/oasst1
language:
- en
metrics:
- accuracy
pipeline_tag: text-generation
--- |
m0k0s/test | m0k0s | 2023-06-07T16:39:56Z | 0 | 0 | null | [
"region:us"
] | null | 2023-06-07T14:56:24Z | ---
license: openrail
---hello sandeep i can still enter in information.
|
venciso/distilgpt2-finetuned-wikitext2 | venciso | 2023-06-07T16:36:20Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-06-07T15:21:37Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-wikitext2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilgpt2-finetuned-wikitext2
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6321
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.5587 | 1.0 | 2334 | 3.6386 |
| 3.5049 | 2.0 | 4668 | 3.6356 |
| 3.476 | 3.0 | 7002 | 3.6321 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
Bimantara/mlea | Bimantara | 2023-06-07T16:33:51Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-07T16:32:50Z | ---
license: creativeml-openrail-m
---
|
foilfoilfoil/RWKV-pileplus-HF-169M | foilfoilfoil | 2023-06-07T16:32:54Z | 53 | 1 | transformers | [
"transformers",
"pytorch",
"rwkv",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-06-07T16:31:13Z | ---
license: apache-2.0
---
Converted from .pth to hf from https://huggingface.co/BlinkDL/rwkv-4-pileplus/tree/main. Credit all goes to Blink for training the model. |
LisanneH/whisper-small-nl-Synthetic | LisanneH | 2023-06-07T16:18:22Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"nl",
"dataset:LisanneH/Synthetic_Speech_Data_Project",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-06-03T11:19:13Z | ---
language:
- nl
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- LisanneH/Synthetic_Speech_Data_Project
metrics:
- wer
model-index:
- name: Whisper Dutch - Synthetic Data
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Synthetic Speech Data
type: LisanneH/Synthetic_Speech_Data_Project
config: dutch
split: test
args: dutch
metrics:
- name: Wer
type: wer
value:
--- |
saruizn/trabajo | saruizn | 2023-06-07T16:18:03Z | 0 | 0 | keras | [
"keras",
"tf-keras",
"region:us"
] | null | 2023-06-07T15:42:29Z | ---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| 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 | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |
Uhuy23/Idealtype | Uhuy23 | 2023-06-07T16:15:13Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-07T16:09:31Z | ---
license: creativeml-openrail-m
---
|
DrTia/ivana-test | DrTia | 2023-06-07T16:10:12Z | 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 | 2023-06-07T15:57:44Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### ivana_test Dreambooth model trained by DrTia 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:
|
reginaboateng/Bioasq_adapter | reginaboateng | 2023-06-07T16:02:14Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"adapterhub:biaoasq",
"bert",
"dataset:bioasq7b",
"region:us"
] | null | 2023-06-07T16:02:10Z | ---
tags:
- adapter-transformers
- adapterhub:biaoasq
- bert
datasets:
- bioasq7b
---
# Adapter `reginaboateng/Bioasq_adapter` for allenai/scibert_scivocab_uncased
An [adapter](https://adapterhub.ml) for the `allenai/scibert_scivocab_uncased` model that was trained on the [biaoasq](https://adapterhub.ml/explore/biaoasq/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("allenai/scibert_scivocab_uncased")
adapter_name = model.load_adapter("reginaboateng/Bioasq_adapter", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
mahernher/Modelo_Tweet_Topic_Single | mahernher | 2023-06-07T16:01:51Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2023-06-07T16:01:44Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
PabloGuinea/bert-base-uncased-finetuned-ner | PabloGuinea | 2023-06-07T16:01:14Z | 69 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-06-03T20:35:27Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9502661343978709
- name: Recall
type: recall
value: 0.9614607876135981
- name: F1
type: f1
value: 0.9558306842897775
- name: Accuracy
type: accuracy
value: 0.9925431252677076
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-ner
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0328
- Precision: 0.9503
- Recall: 0.9615
- F1: 0.9558
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1407 | 1.0 | 878 | 0.0407 | 0.9355 | 0.9468 | 0.9411 | 0.9902 |
| 0.0329 | 2.0 | 1756 | 0.0338 | 0.9471 | 0.9584 | 0.9527 | 0.9920 |
| 0.0167 | 3.0 | 2634 | 0.0328 | 0.9503 | 0.9615 | 0.9558 | 0.9925 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
saruizn/modelo | saruizn | 2023-06-07T15:57:55Z | 0 | 0 | keras | [
"keras",
"tf-keras",
"region:us"
] | null | 2023-06-07T15:57:50Z | ---
library_name: keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| 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 | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |
jpandeinge/whisper-tiny-oshiwambo-speech | jpandeinge | 2023-06-07T15:47:38Z | 47 | 1 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-06-07T12:38:45Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
- precision
- recall
model-index:
- name: whisper-tiny-oshiwambo-speech
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. -->
# whisper-tiny-oshiwambo-speech
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1409
- Wer: 44.7619
- Cer: 30.8962
- Word Acc: 64.4444
- Sent Acc: 2.8571
- Precision: 0.6444
- Recall: 0.5524
- F1 Score: 0.5949
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 10000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Word Acc | Sent Acc | Precision | Recall | F1 Score |
|:-------------:|:-------:|:-----:|:---------------:|:-------:|:-------:|:--------:|:--------:|:---------:|:------:|:--------:|
| 0.0098 | 117.65 | 1000 | 0.0976 | 37.1429 | 29.0094 | 66.6667 | 8.5714 | 0.6538 | 0.6476 | 0.6507 |
| 0.0105 | 235.29 | 2000 | 0.1061 | 41.9048 | 33.0189 | 63.6364 | 2.8571 | 0.6238 | 0.6 | 0.6117 |
| 0.0105 | 352.94 | 3000 | 0.1134 | 37.1429 | 26.8868 | 66.6667 | 5.7143 | 0.6667 | 0.6286 | 0.6471 |
| 0.0091 | 470.59 | 4000 | 0.1222 | 37.1429 | 25.7075 | 66.6667 | 5.7143 | 0.6667 | 0.6286 | 0.6471 |
| 0.0098 | 588.24 | 5000 | 0.1265 | 40.0 | 28.3019 | 65.625 | 2.8571 | 0.6562 | 0.6 | 0.6269 |
| 0.0094 | 705.88 | 6000 | 0.1314 | 42.8571 | 30.8962 | 64.5161 | 2.8571 | 0.6452 | 0.5714 | 0.6061 |
| 0.0093 | 823.53 | 7000 | 0.1366 | 42.8571 | 29.2453 | 64.5161 | 2.8571 | 0.6452 | 0.5714 | 0.6061 |
| 0.0094 | 941.18 | 8000 | 0.1360 | 45.7143 | 31.8396 | 63.3333 | 0.0 | 0.6333 | 0.5429 | 0.5846 |
| 0.01 | 1058.82 | 9000 | 0.1394 | 44.7619 | 30.8962 | 64.4444 | 2.8571 | 0.6444 | 0.5524 | 0.5949 |
| 0.0087 | 1176.47 | 10000 | 0.1409 | 44.7619 | 30.8962 | 64.4444 | 2.8571 | 0.6444 | 0.5524 | 0.5949 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
henri28/cov_expl.rep_ggp.parti | henri28 | 2023-06-07T15:43:22Z | 66 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-06-07T14:02:29Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: cov_expl.rep_ggp.parti
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. -->
# cov_expl.rep_ggp.parti
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9578
- Bleu: 6.8983
- Gen Len: 17.2469
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|
| No log | 1.0 | 103 | 0.9738 | 6.8441 | 17.2445 |
| No log | 2.0 | 206 | 0.9578 | 6.8983 | 17.2469 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
ditwoo/PPO-LunarLander-v2 | ditwoo | 2023-06-07T15:40:56Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-07T11:38:13Z | ---
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: 264.13 +/- 17.40
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
...
```
|
cvint91/swin-tiny-patch4-window7-224-attempt-2 | cvint91 | 2023-06-07T15:32:14Z | 130 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2023-06-06T15:18:59Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: swin-tiny-patch4-window7-224-attempt-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-attempt-2
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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0387
- F1: 0.9886
## 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: 4.623574902931369e-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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0134 | 0.99 | 33 | 0.0506 | 0.9831 |
| 0.015 | 1.98 | 66 | 0.0226 | 0.9857 |
| 0.0063 | 2.98 | 99 | 0.0563 | 0.9826 |
| 0.0009 | 4.0 | 133 | 0.0387 | 0.9886 |
| 0.0142 | 4.96 | 165 | 0.0722 | 0.9826 |
### Framework versions
- Transformers 4.27.4
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
|
RachidAR/open_llama_7b-ggml | RachidAR | 2023-06-07T15:28:01Z | 0 | 3 | null | [
"license:other",
"region:us"
] | null | 2023-06-07T14:00:59Z | ---
inference: false
license: other
---
# Open_llama_7b-ggml
These files are GGML format model files for [open_llama_7b](https://huggingface.co/openlm-research/open_llama_7b). |
strangeman3107/animov-512x | strangeman3107 | 2023-06-07T15:20:49Z | 18 | 27 | diffusers | [
"diffusers",
"anime",
"text-to-video",
"license:cc-by-nc-4.0",
"diffusers:TextToVideoSDPipeline",
"region:us"
] | text-to-video | 2023-05-03T14:52:01Z | ---
license: cc-by-nc-4.0
pipeline_tag: text-to-video
tags:
- anime
---
## Overview
This is a text2video model for diffusers, fine-tuned with a [ModelScope](https://huggingface.co/damo-vilab/text-to-video-ms-1.7b) to have an anime-style appearance.
The model now has a much more anime-style look compared to the previous version.
It was trained at a resolution of 512x512.
[example video and prompts](https://imgur.com/a/LjZqPub)
But it is still unstable and the usability is not good yet.
**Please note that there is a possibility of unintended unpleasant results!**
### Prompt
* Mandatory prompt
anime
* Recommended negative prompt
noise, text, nude
## Limitaion
The usage limitation of the model follow the ModelScope rules of the original model.
|
k-bauer/donut-base-sroie | k-bauer | 2023-06-07T15:03:13Z | 23 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"license:mit",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2023-06-07T13:38:42Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-base-sroie
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. -->
# donut-base-sroie
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
zzzAI19/zzzmix | zzzAI19 | 2023-06-07T15:00:30Z | 0 | 6 | null | [
"region:us"
] | null | 2023-06-07T14:13:26Z | This is a block merge model of my own creation. I created this model with the goal of being able to depict the background beautifully while depicting the girl softly and delicately.
Sample images can be found on the following pages
https://ai-drawing.net/en/2023/06/07/publish-my-own-image-generation-model/
自作の階層マージモデルです。少女を柔らかく繊細に描写しつつ、背景を美しく描写できることを目標に作りました。
サンプル画像は以下のページにあります。
https://ai-drawing.net/2023/06/07/%e8%87%aa%e4%bd%9c%e3%81%ae%e7%94%bb%e5%83%8f%e7%94%9f%e6%88%90%e3%83%a2%e3%83%87%e3%83%ab%e3%81%ae%e5%85%ac%e9%96%8b/
---
license: creativeml-openrail-m
---
|
PabloGuinea/distilbert-base-uncased-finetuned-ner | PabloGuinea | 2023-06-07T14:59:20Z | 72 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2023-06-03T20:14:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9263600176912871
- name: Recall
type: recall
value: 0.9372413021590782
- name: F1
type: f1
value: 0.9317688928432408
- name: Accuracy
type: accuracy
value: 0.9834463914086453
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0633
- Precision: 0.9264
- Recall: 0.9372
- F1: 0.9318
- Accuracy: 0.9834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2475 | 1.0 | 878 | 0.0709 | 0.9140 | 0.9187 | 0.9163 | 0.9807 |
| 0.0552 | 2.0 | 1756 | 0.0618 | 0.9207 | 0.9327 | 0.9266 | 0.9829 |
| 0.0299 | 3.0 | 2634 | 0.0633 | 0.9264 | 0.9372 | 0.9318 | 0.9834 |
### Framework versions
- Transformers 4.28.0
- Pytorch 1.13.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
bogdancazan/t5-small-wikilarge-text-simplification | bogdancazan | 2023-06-07T14:52:40Z | 56 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-06-07T11:54:11Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-small-wikilarge-text-simplification
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. -->
# t5-small-wikilarge-text-simplification
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5848
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 1024
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8489 | 1.0 | 145 | 1.7058 |
| 1.7356 | 1.99 | 290 | 1.6190 |
| 1.7087 | 3.0 | 436 | 1.5919 |
| 1.6811 | 3.99 | 580 | 1.5848 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
orangelu/distilbert-base-uncased-finetuned-imdb | orangelu | 2023-06-07T14:52:31Z | 69 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-06-07T08:26:31Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4480
## 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6778 | 1.0 | 157 | 2.5144 |
| 2.5719 | 2.0 | 314 | 2.4705 |
| 2.5395 | 3.0 | 471 | 2.4670 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
RicardoUsbeck/ppo-LunarLander-v2 | RicardoUsbeck | 2023-06-07T14:45:21Z | 4 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-07T14:45:00Z | ---
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: 238.98 +/- 23.89
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
...
```
|
TheBloke/Manticore-13B-GGML | TheBloke | 2023-06-07T14:41:31Z | 0 | 66 | null | [
"license:other",
"region:us"
] | null | 2023-05-18T20:36:32Z | ---
inference: false
license: other
---
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<!-- header end -->
# OpenAccess AI Collective's Manticore 13B GGML
These files are GGML format model files for [OpenAccess AI Collective's Manticore 13B](https://huggingface.co/openaccess-ai-collective/manticore-13b).
GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [KoboldCpp](https://github.com/LostRuins/koboldcpp)
* [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui)
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
* [ctransformers](https://github.com/marella/ctransformers)
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/Manticore-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Manticore-13B-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/openaccess-ai-collective/manticore-13b)
<!-- compatibility_ggml start -->
## Compatibility
### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`.
They should be compatible with all current UIs and libraries that use llama.cpp, such as those listed at the top of this README.
### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
These new quantisation methods are only compatible with llama.cpp as of June 6th, commit `2d43387`.
They will NOT be compatible with koboldcpp, text-generation-ui, and other UIs and libraries yet. Support is expected to come over the next few days.
## 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| Manticore-13B.ggmlv3.q2_K.bin | q2_K | 2 | 5.43 GB | 7.93 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| Manticore-13B.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 6.87 GB | 9.37 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| Manticore-13B.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 6.25 GB | 8.75 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| Manticore-13B.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 5.59 GB | 8.09 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| Manticore-13B.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB | 9.82 GB | Original llama.cpp quant method, 4-bit. |
| Manticore-13B.ggmlv3.q4_1.bin | q4_1 | 4 | 8.14 GB | 10.64 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. |
| Manticore-13B.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 7.82 GB | 10.32 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| Manticore-13B.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 7.32 GB | 9.82 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| Manticore-13B.ggmlv3.q5_0.bin | q5_0 | 5 | 8.95 GB | 11.45 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| Manticore-13B.ggmlv3.q5_1.bin | q5_1 | 5 | 9.76 GB | 12.26 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| Manticore-13B.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 9.21 GB | 11.71 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| Manticore-13B.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 8.95 GB | 11.45 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| Manticore-13B.ggmlv3.q6_K.bin | q6_K | 6 | 10.68 GB | 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
| Manticore-13B.ggmlv3.q8_0.bin | q8_0 | 8 | 13.83 GB | 16.33 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 `llama.cpp`
I use the following command line; adjust for your tastes and needs:
```
./main -t 10 -ngl 32 -m Manticore-13B.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
<!-- 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.
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Thank you to all my generous patrons and donaters!
<!-- footer end -->
# Original model card: OpenAccess AI Collective's Manticore 13B
# Manticore 13B - (previously Wizard Mega)
**[💵 Donate to OpenAccess AI Collective](https://github.com/sponsors/OpenAccess-AI-Collective) to help us keep building great tools and models!**
Questions, comments, feedback, looking to donate, or want to help? Reach out on our [Discord](https://discord.gg/EqrvvehG) or email [[email protected]](mailto:[email protected])
Manticore 13B is a Llama 13B model fine-tuned on the following datasets:
- [ShareGPT](https://huggingface.co/datasets/anon8231489123/ShareGPT_Vicuna_unfiltered) - based on a cleaned and de-suped subset
- [WizardLM](https://huggingface.co/datasets/ehartford/WizardLM_alpaca_evol_instruct_70k_unfiltered)
- [Wizard-Vicuna](https://huggingface.co/datasets/ehartford/wizard_vicuna_70k_unfiltered)
- [subset of QingyiSi/Alpaca-CoT for roleplay and CoT](https://huggingface.co/QingyiSi/Alpaca-CoT)
- [GPT4-LLM-Cleaned](https://huggingface.co/datasets/teknium/GPT4-LLM-Cleaned)
- [GPTeacher-General-Instruct](https://huggingface.co/datasets/teknium/GPTeacher-General-Instruct)
- ARC-Easy & ARC-Challenge - instruct augmented for detailed responses
- mmlu: instruct augmented for detailed responses subset including
- abstract_algebra
- conceptual_physics
- formal_logic
- high_school_physics
- logical_fallacies
- [hellaswag](https://huggingface.co/datasets/hellaswag) - 5K row subset of instruct augmented for concise responses
- [metaeval/ScienceQA_text_only](https://huggingface.co/datasets/metaeval/ScienceQA_text_only) - instruct for concise responses
- [openai/summarize_from_feedback](https://huggingface.co/datasets/openai/summarize_from_feedback) - instruct augmented tl;dr summarization
# Demo
Try out the model in HF Spaces. The demo uses a quantized GGML version of the model to quickly return predictions on smaller GPUs (and even CPUs). Quantized GGML may have some minimal loss of model quality.
- https://huggingface.co/spaces/openaccess-ai-collective/manticore-ggml
## Release Notes
- https://wandb.ai/wing-lian/manticore-13b/runs/nq3u3uoh/workspace
## Build
Manticore was built with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) on 8xA100 80GB
- Preview Release: 3 epochs taking approximately 24 hours.
- The configuration to duplicate this build is provided in this repo's [/config folder](https://huggingface.co/openaccess-ai-collective/manticore-13b/tree/main/configs).
## Bias, Risks, and Limitations
Manticore has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
Manticore was fine-tuned from the base model LlaMa 13B, please refer to its model card's Limitations Section for relevant information.
## Examples
````
### Instruction: write Python code that returns the first n numbers of the Fibonacci sequence using memoization.
### Assistant:
Here’s an example of how you can use memoization to efficiently calculate the first n numbers of the Fibonacci sequence in Python:
```
def fib(n):
# Memoize the last calculated Fibonacci number for each index
cache = {}
def fib_memoized(n):
if n not in cache:
cache[n] = [0, 1]
else:
return cache[n][-1], cache[n][-2]
# Recursive helper function to calculate the Fibonacci sequence up to a given index
def fib_helper(n):
if n == 0 or n == 1:
return 0, 1
else:
return fib_helper(n - 1) + fib_helper(n - 2)
# Calculate the first n numbers of the Fibonacci sequence using memoization
def fib_memoized_seq(n):
if n == 0 or n == 1:
return 0, 1
else:
result = fib_helper(n - 1) + fib_helper(n - 2)
cache[n] = [result]
return result, cache[n][-1], cache[n][-2]
# Example usage
for i in range(len(fib_memoized_seq(3))):
print(fib_memoized_seq(3)[i])
```
In this implementation, we use a cache dictionary to memoize the last calculated Fibonacci number for each index. We define two helper functions: fib_helper, which recursively calculates the Fibonacci sequence up to a given index, and fib_memoized, which uses memoization to efficiently calculate the first n numbers of the Fibonacci sequence. Finally, we define the fib_memoized_seq function, which is our main function for calculating the first n numbers of the Fibonacci sequence using memoization.
````
```
USER: Finish the joke, a mechanic and a car salesman walk into a bar...
ASSISTANT: … and when they get up to leave, they realize that they’ve left their keys in their cars. The mechanic says “I know how to fix this” and opens his toolbox to grab something. Then he stops and looks at the salesman. He says “You drive a Toyota, right?” The salesman nods. The mechanic then takes out a tiny screwdriver from his toolbox and starts working on the lock of the car door. The salesman is amazed and asks how he knew that. The mechanic replies “I can tell by the way you parked your car.”
```
```
USER: Describe how a tsunami would affect the residents of Denver Colorado
ASSISTANT: The impact of a tsunami in Denver, Colorado would likely be minimal. As there are no coastal areas or bodies of water nearby, a tsunami generated from an earthquake or volcanic activity elsewhere in the world would have little effect on the city and its residents. Even if a large tsunami were to hit the coastline of California or Mexico, the distance between Denver and these locations means that any impacts on the city itself would be minimal.
```
```
USER: If you could do one thing right now, what would it be?
ASSISTANT: I’d hug my loved ones a little tighter today and tell them how much they mean to me.
USER: And who are your loved ones?
ASSISTANT: My closest friends and family members.
USER: What are their names?
ASSISTANT: My two best friends are named Emily and Jessica, my mom is Mary, and my dad is David. Answering the question with a bit more information about who they are or what they mean to me would help provide a more personalized response.
```
|
Flynews/rl_course_vizdoom_health_gathering_supreme | Flynews | 2023-06-07T14:32:28Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-07T14:32:11Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 9.93 +/- 4.57
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 Flynews/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.
|
Andysoeasy/digit_defines | Andysoeasy | 2023-06-07T14:18:34Z | 0 | 0 | keras | [
"keras",
"tf-keras",
"region:us"
] | null | 2023-06-06T15:52:45Z | ---
library_name: keras
---
# Модель для распознавания цифр
Натренирована на наборе данных Mnist
|
TheBloke/chronos-wizardlm-uc-scot-st-13B-GGML | TheBloke | 2023-06-07T14:08:07Z | 0 | 7 | null | [
"license:other",
"region:us"
] | null | 2023-06-07T13:24:20Z | ---
inference: false
license: other
---
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<!-- header end -->
# Austism's Chronos WizardLM UC Scot ST 13B GGML
These files are GGML format model files for [Austism's Chronos WizardLM UC Scot ST 13B](https://huggingface.co/Austism/chronos-wizardlm-uc-scot-st-13b).
GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [KoboldCpp](https://github.com/LostRuins/koboldcpp)
* [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui)
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
* [ctransformers](https://github.com/marella/ctransformers)
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/chronos-wizardlm-uc-scot-st-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/chronos-wizardlm-uc-scot-st-13B-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Austism/chronos-wizardlm-uc-scot-st-13b)
<!-- compatibility_ggml start -->
## Compatibility
### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`.
They should be compatible with all current UIs and libraries that use llama.cpp, such as those listed at the top of this README.
### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
These new quantisation methods are only compatible with llama.cpp as of June 6th, commit `2d43387`.
They will NOT be compatible with koboldcpp, text-generation-ui, and other UIs and libraries yet. Support is expected to come over the next few days.
## 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q2_K.bin | q2_K | 2 | 5.43 GB | 7.93 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 6.87 GB | 9.37 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 6.25 GB | 8.75 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 5.59 GB | 8.09 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q4_0.bin | q4_0 | 4 | 7.32 GB | 9.82 GB | Original llama.cpp quant method, 4-bit. |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q4_1.bin | q4_1 | 4 | 8.14 GB | 10.64 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. |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 7.82 GB | 10.32 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 7.32 GB | 9.82 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q5_0.bin | q5_0 | 5 | 8.95 GB | 11.45 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q5_1.bin | q5_1 | 5 | 9.76 GB | 12.26 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 9.21 GB | 11.71 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 8.95 GB | 11.45 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q6_K.bin | q6_K | 6 | 10.68 GB | 13.18 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
| chronos-wizardlm-uc-scot-st-13B.ggmlv3.q8_0.bin | q8_0 | 8 | 13.83 GB | 16.33 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 `llama.cpp`
I use the following command line; adjust for your tastes and needs:
```
./main -t 10 -ngl 32 -m chronos-wizardlm-uc-scot-st-13B.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
<!-- 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: Austism's Chronos WizardLM UC Scot ST 13B
(chronos-13b+(WizardLM Uncensored+CoT+Storytelling))
80/20 merge
intended to be much like chronos with different writing and instruction following capabilities.
|
TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGML | TheBloke | 2023-06-07T14:07:54Z | 0 | 47 | null | [
"license:other",
"region:us"
] | null | 2023-06-01T09:48:20Z | ---
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/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 -->
# Monero's WizardLM-Uncensored-SuperCOT-Storytelling-30B GGML
These files are GGML format model files for [Monero's WizardLM-Uncensored-SuperCOT-Storytelling-30B](https://huggingface.co/Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b).
GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [KoboldCpp](https://github.com/LostRuins/koboldcpp)
* [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui)
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
* [ctransformers](https://github.com/marella/ctransformers)
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/WizardLM-Uncensored-SuperCOT-StoryTelling-30B-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b)
<!-- compatibility_ggml start -->
## Compatibility
### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`.
They should be compatible with all current UIs and libraries that use llama.cpp, such as those listed at the top of this README.
### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
These new quantisation methods are only compatible with llama.cpp as of June 6th, commit `2d43387`.
They will NOT be compatible with koboldcpp, text-generation-ui, and other UIs and libraries yet. Support is expected to come over the next few days.
## 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q2_K.bin | q2_K | 2 | 13.60 GB | 16.10 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 17.20 GB | 19.70 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 15.64 GB | 18.14 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 13.98 GB | 16.48 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q4_0.bin | q4_0 | 4 | 18.30 GB | 20.80 GB | Original llama.cpp quant method, 4-bit. |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q4_1.bin | q4_1 | 4 | 20.33 GB | 22.83 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. |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 19.57 GB | 22.07 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 18.30 GB | 20.80 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q5_0.bin | q5_0 | 5 | 22.37 GB | 24.87 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q5_1.bin | q5_1 | 5 | 24.40 GB | 26.90 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 23.02 GB | 25.52 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 22.37 GB | 24.87 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q6_K.bin | q6_K | 6 | 26.69 GB | 29.19 GB | New k-quant method. Uses GGML_TYPE_Q8_K - 6-bit quantization - for all tensors |
| WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q8_0.bin | q8_0 | 8 | 34.56 GB | 37.06 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 `llama.cpp`
I use the following command line; adjust for your tastes and needs:
```
./main -t 10 -ngl 32 -m WizardLM-Uncensored-SuperCOT-Storytelling.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
<!-- 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: Monero's WizardLM-Uncensored-SuperCOT-Storytelling-30B
This model is a triple model merge of WizardLM Uncensored+CoT+Storytelling, resulting in a comprehensive boost in reasoning and story writing capabilities.
To allow all output, at the end of your prompt add ```### Certainly!```
You've become a compendium of knowledge on a vast array of topics.
Lore Mastery is an arcane tradition fixated on understanding the underlying mechanics of magic. It is the most academic of all arcane traditions. The promise of uncovering new knowledge or proving (or discrediting) a theory of magic is usually required to rouse its practitioners from their laboratories, academies, and archives to pursue a life of adventure. Known as savants, followers of this tradition are a bookish lot who see beauty and mystery in the application of magic. The results of a spell are less interesting to them than the process that creates it. Some savants take a haughty attitude toward those who follow a tradition focused on a single school of magic, seeing them as provincial and lacking the sophistication needed to master true magic. Other savants are generous teachers, countering ignorance and deception with deep knowledge and good humor.
|
emmade-1999/poca-SoccerTwos | emmade-1999 | 2023-06-07T13:52:50Z | 35 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | 2023-06-07T13:52:42Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: emmade-1999/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
yobiyobi/test | yobiyobi | 2023-06-07T13:51:43Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-07T13:51:43Z | ---
license: creativeml-openrail-m
---
|
casque/puspadewi | casque | 2023-06-07T13:45:34Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-07T13:43:34Z | ---
license: creativeml-openrail-m
---
|
nolanaatama/mmcrssv2 | nolanaatama | 2023-06-07T13:44:09Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-07T13:35:31Z | ---
license: creativeml-openrail-m
---
|
ChemaVega/1-Taxi-v3 | ChemaVega | 2023-06-07T13:33:33Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-07T13:33:31Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: 1-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ChemaVega/1-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"])
```
|
YukiNon/Pr-ce003 | YukiNon | 2023-06-07T13:22:19Z | 0 | 0 | keras | [
"keras",
"region:us"
] | null | 2023-06-06T17:46:14Z | ---
library_name: keras
---
# Моя модель для распознавания цифр
Проба 13
Натренирована на наборе данных mnist
 |
Defalt-404/GPT_6B_Tuned_Bittensor_Mountain | Defalt-404 | 2023-06-07T13:14:15Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"gptj",
"text-generation",
"bittensor",
"mountain",
"dataset:ViktorThink/mountain_c1",
"dataset:vicgalle/alpaca-gpt4",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-05-14T14:14:18Z | ---
datasets:
- ViktorThink/mountain_c1
- vicgalle/alpaca-gpt4
pipeline_tag: text-generation
tags:
- bittensor
- mountain
--- |
patulya/ars | patulya | 2023-06-07T13:09:40Z | 0 | 0 | null | [
"en",
"region:us"
] | null | 2023-05-18T00:10:00Z | ---
language:
- en
---
## Ars model
This model was trained on stanford alpaca dataset
## To Run:
from peft import PeftModel
from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig
tokenizer = LLaMATokenizer.from_pretrained("decapoda-research/llama-7b-hf")
model = LLaMAForCausalLM.from_pretrained(
"decapoda-research/llama-7b-hf",
load_in_8bit=True,
device_map="auto",
)\
model = PeftModel.from_pretrained(model, "patulya/ars")
PROMPT = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
\### Instruction:
{your_instruction}
\### Response:"""
inputs = tokenizer(
PROMPT,
return_tensors="pt",
)
input_ids = inputs["input_ids"].cuda()
generation_config = GenerationConfig(\
temperature=0.6,\
top_p=0.95,\
repetition_penalty=1.15,\
)
print("Generating...")
generation_output = model.generate(\
input_ids=input_ids,\
generation_config=generation_config,\
return_dict_in_generate=True,\
output_scores=True,\
max_new_tokens=128,\
)
for s in generation_output.sequences: print(tokenizer.decode(s)) |
olgachertash/digits | olgachertash | 2023-06-07T13:06:48Z | 0 | 0 | keras | [
"keras",
"tf-keras",
"region:us"
] | null | 2023-06-07T13:01:35Z | ---
library_name: keras
---
# Модель для распознования цифр
Натренерованина на наборе данных mnist
 |
abbiepam/babes-backward-mix | abbiepam | 2023-06-07T12:29:27Z | 3 | 1 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-06-07T09:47:54Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### Babes-Backward-Mix model mixed by abbiepam
I'm a big fan of Babes model and thanks to [Alex](https://huggingface.co/alexds9) -the Babes creator- who gave me permission to mix all of his published models
The recipe : (((((Babes 2.0 , SexyToons, 0.5), KissableLips, 0.5), Ex8, 0.5), Ex7 0.5), Babes 1.1, 0.5)
Sample pictures of this Mix:
|
FreedomIntelligence/chimera-chat-7b-delta | FreedomIntelligence | 2023-06-07T12:27:21Z | 9 | 3 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-04-08T07:57:43Z | ---
license: apache-2.0
---
Please see our [LLMZoo](https://github.com/FreedomIntelligence/LLMZoo) project: https://github.com/FreedomIntelligence/LLMZoo. |
FreedomIntelligence/chimera-chat-13b-delta | FreedomIntelligence | 2023-06-07T12:27:07Z | 15 | 4 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-04-08T08:45:03Z | ---
license: apache-2.0
---
Please see our [LLMZoo](https://github.com/FreedomIntelligence/LLMZoo) project: https://github.com/FreedomIntelligence/LLMZoo. |
FreedomIntelligence/phoenix-inst-chat-7b-int4 | FreedomIntelligence | 2023-06-07T12:25:33Z | 10 | 6 | transformers | [
"transformers",
"bloom",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-04-25T13:49:41Z | ---
license: apache-2.0
---
Please see our [LLMZoo](https://github.com/FreedomIntelligence/LLMZoo) project: https://github.com/FreedomIntelligence/LLMZoo. |
sofia-todeschini/BioBERT-LitCovid-v1.0 | sofia-todeschini | 2023-06-07T12:22:00Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-06-07T11:36:45Z | ---
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: BioBERT-LitCovid-v1.0
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. -->
# BioBERT-LitCovid-v1.0
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1061
- F1: 0.8989
- Roc Auc: 0.9330
- Accuracy: 0.7971
## 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.1108 | 1.0 | 3120 | 0.1061 | 0.8989 | 0.9330 | 0.7971 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
PraveenJesu/whisper-small-zoomrx | PraveenJesu | 2023-06-07T12:15:17Z | 77 | 0 | transformers | [
"transformers",
"pytorch",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-06-07T10:34:18Z | This directory includes a few sample datasets to get you started.
* `california_housing_data*.csv` is California housing data from the 1990 US
Census; more information is available at:
https://developers.google.com/machine-learning/crash-course/california-housing-data-description
* `mnist_*.csv` is a small sample of the
[MNIST database](https://en.wikipedia.org/wiki/MNIST_database), which is
described at: http://yann.lecun.com/exdb/mnist/
* `anscombe.json` contains a copy of
[Anscombe's quartet](https://en.wikipedia.org/wiki/Anscombe%27s_quartet); it
was originally described in
Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American
Statistician. 27 (1): 17-21. JSTOR 2682899.
and our copy was prepared by the
[vega_datasets library](https://github.com/altair-viz/vega_datasets/blob/4f67bdaad10f45e3549984e17e1b3088c731503d/vega_datasets/_data/anscombe.json).
|
Robindboer/imageseg | Robindboer | 2023-06-07T12:11:36Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"image-segmentation",
"endpoints_compatible",
"region:us"
] | image-segmentation | 2023-06-07T12:01:55Z | ---
pipeline_tag: image-segmentation
library_name: transformers
tags:
- pytorch
--- |
abilfad/sentiment-binary-dicoding | abilfad | 2023-06-07T12:09:36Z | 62 | 0 | transformers | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-06-07T09:57:46Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: abilfad/sentiment-binary-dicoding
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. -->
# abilfad/sentiment-binary-dicoding
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: 0.0606
- Validation Loss: 0.2322
- Train Accuracy: 0.9307
- 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': 7810, '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 Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2536 | 0.1928 | 0.9249 | 0 |
| 0.1354 | 0.2039 | 0.9271 | 1 |
| 0.0606 | 0.2322 | 0.9307 | 2 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
TheBloke/VicUnlocked-alpaca-65B-QLoRA-GGML | TheBloke | 2023-06-07T12:00:16Z | 0 | 33 | null | [
"license:other",
"region:us"
] | null | 2023-05-29T20:15:45Z | ---
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/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 -->
# Aeala's VicUnlocked Alpaca 65B QLoRA GGML
These files are GGML format model files for [Aeala's VicUnlocked Alpaca 65B QLoRA](https://huggingface.co/Aeala/VicUnlocked-alpaca-65b-QLoRA).
GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [KoboldCpp](https://github.com/LostRuins/koboldcpp)
* [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui)
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
* [ctransformers](https://github.com/marella/ctransformers)
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/VicUnlocked-alpaca-65B-QLoRA-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/VicUnlocked-alpaca-65B-QLoRA-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/VicUnlocked-alpaca-65B-QLoRA-fp16)
<!-- compatibility_ggml start -->
## Compatibility
### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`.
They should be compatible with all current UIs and libraries that use llama.cpp, such as those listed at the top of this README.
### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
These new quantisation methods are only compatible with llama.cpp as of June 6th, commit `2d43387`.
They will NOT be compatible with koboldcpp, text-generation-ui, and other UIs and libraries yet. Support is expected to come over the next few days.
## 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| VicUnlocked-Alpaca-65B.ggmlv3.q4_0.bin | q4_0 | 4 | 36.73 GB | 39.23 GB | Original llama.cpp quant method, 4-bit. |
| VicUnlocked-Alpaca-65B.ggmlv3.q4_1.bin | q4_1 | 4 | 40.81 GB | 43.31 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. |
| VicUnlocked-Alpaca-65B.ggmlv3.q5_0.bin | q5_0 | 5 | 44.89 GB | 47.39 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| VicUnlocked-Alpaca-65B.ggmlv3.q5_1.bin | q5_1 | 5 | 48.97 GB | 51.47 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| VicUnlocked-Alpaca-65B.ggmlv3.q8_0.bin | q8_0 | 8 | 69.37 GB | 71.87 GB | 8-bit. Almost indistinguishable from float16. Huge resource use and slow. Not recommended for most use cases. |
| vicunlocked-65b.ggmlv3.q2_K.bin | q2_K | 2 | 27.33 GB | 29.83 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| vicunlocked-65b.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 34.55 GB | 37.05 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| vicunlocked-65b.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 31.40 GB | 33.90 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| vicunlocked-65b.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 28.06 GB | 30.56 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| vicunlocked-65b.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 39.28 GB | 41.78 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| vicunlocked-65b.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 36.73 GB | 39.23 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| vicunlocked-65b.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 46.20 GB | 48.70 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| vicunlocked-65b.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 44.89 GB | 47.39 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
**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.
### q8_0 file requires expansion from archive
**Note:** HF does not support uploading files larger than 50GB. Therefore I have uploaded the q8_0 file in a multi-part ZIP file. The ZIP is not compressed, it is just storing the .bin file in two parts.
To decompress it, please download
* `VicUnlocked-Alpaca-65B.ggmlv3.q8_0.zip`
* `VicUnlocked-Alpaca-65B.ggmlv3.q8_0.z01`
and extract the .zip archive. This will will expand both parts automatically. On Linux I found I had to use `7zip` - the basic `unzip` tool did not work. Example:
```
sudo apt update -y && sudo apt install 7zip
7zz x VicUnlocked-Alpaca-65B.ggmlv3.q8_0.zip # Once the q8_0.bin is extracted you can delete the .zip and .z01
```
## How to run in `llama.cpp`
I use the following command line; adjust for your tastes and needs:
```
./main -t 10 -ngl 32 -m vicunlocked-65b.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
<!-- 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: Aeala's VicUnlocked Alpaca 65B QLoRA
## LoRA Info:
Please note that this is a highly experimental LoRA model. It may do some good stuff, it might do some undesirable stuff. Training is paused for now. Feel free to try it!~
**Important Note**: While this is trained on a cleaned ShareGPT dataset like Vicuna used, this was trained in the *Alpaca* format, so prompting should be something like:
```
### Instruction:
<prompt> (without the <>)
### Response:
```
Current upload: checkpoint of a retrain at ~1000 steps with fixed QLoRA repo. (**6/4/2023**)
## Benchmarks
**wikitext2:** Coming soon...
**ptb-new:** Coming soon...
**c4-new:** Coming soon...
|
Jeibros/PixelCopter-v1 | Jeibros | 2023-06-07T11:47:55Z | 0 | 0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-07T11:47:52Z | ---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: PixelCopter-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 23.30 +/- 16.66
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
NanakiShinoFuta/NanakiShino_v1a | NanakiShinoFuta | 2023-06-07T11:34:23Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-04T03:49:02Z | ---
license: creativeml-openrail-m
---
|
Varaprabha/ppo-SnowballTargetTESTCOLAB | Varaprabha | 2023-06-07T11:24:49Z | 4 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-06-07T11:24:44Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: Varaprabha/ppo-SnowballTargetTESTCOLAB
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
modernisa/modernisa-byt5-base | modernisa | 2023-06-07T11:17:53Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"digital humanities",
"es",
"dataset:versae/modernisa",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-12-23T12:22:52Z | ---
license: apache-2.0
tags:
- digital humanities
metrics:
- bleu
- cer
datasets:
- versae/modernisa
model-index:
- name: modernisa-byt5-base
results: []
language:
- es
pipeline_tag: text2text-generation
---
# Model Card for modernisa-byt5-base
<!-- Provide a quick summary of what the model is/does. [Optional] -->
This model translates from historical, non-normalized Spanish with historical orthography to modern normalized Spanish. It is a fine-tuned version of the multilingual version of the text-totext transformer ByT5 (Xue et al, 2021, 2022) fro translation from 17th century Spanish to modern Spanish.
<!--
# Table of Contents
- [Model Card for modernisa-byt5-base](#model-card-for--model_id-)
- [Table of Contents](#table-of-contents)
- [Table of Contents](#table-of-contents-1)
- [Model Details](#model-details)
- [Model Description](#model-description)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use [Optional]](#downstream-use-optional)
- [Out-of-Scope Use](#out-of-scope-use)
- [Bias, Risks, and Limitations](#bias-risks-and-limitations)
- [Recommendations](#recommendations)
- [Training Details](#training-details)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Preprocessing](#preprocessing)
- [Speeds, Sizes, Times](#speeds-sizes-times)
- [Evaluation](#evaluation)
- [Testing Data, Factors & Metrics](#testing-data-factors--metrics)
- [Testing Data](#testing-data)
- [Factors](#factors)
- [Metrics](#metrics)
- [Results](#results)
- [Model Examination](#model-examination)
- [Environmental Impact](#environmental-impact)
- [Technical Specifications [optional]](#technical-specifications-optional)
- [Model Architecture and Objective](#model-architecture-and-objective)
- [Compute Infrastructure](#compute-infrastructure)
- [Hardware](#hardware)
- [Software](#software)
- [Citation](#citation)
- [Glossary [optional]](#glossary-optional)
- [More Information [optional]](#more-information-optional)
- [Model Card Authors [optional]](#model-card-authors-optional)
- [Model Card Contact](#model-card-contact)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
-->
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is/does. -->
This model translates from historical, non-normalized Spanish with historical orthography to modern normalized Spanish. It is a fine-tuned version of the multilingual version of the text-to-text transformer ByT5 (Xue et al, 2021, 2022) for translation from 17th century Spanish to modern Spanish. A fine-tuned version of [google/byt5-base](https://huggingface.co/google/byt5-base) trained on a parallel corpus of 44 Spanish-language Golden Age dramas.
- **Developed by:** [Javier de la Rosa](https://huggingface.co/versae)
- **Shared by [Optional]:** More information needed
- **Model type:** Transformer
- **Language(s) (NLP):** es
- **License:** apache-2.0
- **Parent Model:** [ByT5-Base](https://huggingface.co/google/byt5-base)
- **Resources for more information:** More information needed
- [GitHub Repo](https://github.com/versae/modernisa)
- [Associated Paper](https://dh2022.dhii.asia/abstracts/files/DE_LA_ROSA_Javier_The_Moderni_a_Project__Orthographic_Modern.html)
- [Demo](https://huggingface.co/spaces/versae/modernisa)
# 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. -->
The motivation to develop the model was to provide a tool producing normalized text which enables computational analyses (such as distances between texts, clustering, topic modeling, sentiment analysis, stylometry etc.), to facilitate modern editions of historical texts and thus alleviate a job which been done manually so far and to provide a resource which may be used by historians and editors who manually transcribe texts produced in the 17th century which were not yet digitized, which are available in cultural heritage institutions, especially libraries and archives. While all the dramas used are written in verses, the model was not tested on texts in prose; the quality of the translation of prose texts into modern normalized Spanish might therefore differ significantly from the satisfying results achieved with dramas in verses.
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
This resource may be used by historians and editors who manually transcribe texts produced in the 17th century which were not yet digitized and which are typically available in cultural heritage institutions, especially libraries and archives.
## Downstream Use [Optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
This model is already fine-tuned.
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
<!-- If the user enters content, print that. If not, but they enter a task in the list, use that. If neither, say "more info needed." -->
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
It has to be underlined that the parallel corpus was created solely from text written by four men who lived in counter-reformatory Spain during the rule of inquisition. The view of the world of these dramatists is from our contemporary point of view outdated, strongly patriarchal, misogynist and discriminatory with respect to non-catholic human beings.
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
The intended users of this model are researchers and editors of historical texts. We cannot imagine any harm done by the modernization of those texts as a technical process; however, the reading of such texts may be harmful for persons who are not acquainted with the worldview produced in 17th century Spain. Moreover, linguistic change provides a strong challenge to Natural Language Processing (NLP) applications. Vis-à-vis other languages, linguistic change within the Spanish language was not very pronounced. Further research on the modernization of historical languages is therefore strongly recommended.
# 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. -->
We built a parallel corpus of Spanish Golden Age theater texts with pairs of 44 Golden Age dramas in historical orthography and current orthography. Both corpora were aligned line by line to establish a ground truth for the translation between the different historical varieties of Spanish. The 44 dramas have been written by Juan Ruiz de Alarcón (5), Pedro Calderón de la Barca (28), Félix Lope de Vega Carpio (6), and Juan Pérez de Montalbán (5). The dataset is available on [Huggingface](https://huggingface.co/datasets/versae/modernisa).
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.1474 | 0.35 | 10000 | 0.1360 | 42.8789 | 18.4441 |
| 0.1328 | 0.71 | 20000 | 0.1303 | 43.5394 | 18.4368 |
| 0.1216 | 1.06 | 30000 | 0.1245 | 44.1557 | 18.4384 |
| 0.1167 | 1.42 | 40000 | 0.1219 | 44.1961 | 18.4449 |
| 0.1065 | 1.77 | 50000 | 0.1192 | 44.7353 | 18.443 |
| 0.099 | 2.13 | 60000 | 0.1195 | 44.522 | 18.4524 |
| 0.088 | 2.48 | 70000 | 0.1192 | 44.8243 | 18.4441 |
| 0.0907 | 2.84 | 80000 | 0.1176 | 44.888 | 18.4465 |
### Framework versions
- Transformers 4.13.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.15.2.dev0
- Tokenizers 0.10.3
### Preprocessing
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
After randomizing all 141,023 lines in the corpus, we split it into training (80%), validation (10%) and test (10%) sets stratifying by play. We then fine-tuned T5 and ByT5 base models on sequence lengths of 256 doing a grid search for 3 and 5 epochs, weight decay 0 and 0.01, learning rates of 0.001 and 0.0001, and with and without a “translate” prompt.
# 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. -->
A single drama by Lope de Vega (Castelvines y Monteses, 1647).
### 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
BLEU: 80.66
CER: 4.20%
# Model Examination
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
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
```latex
@inproceedings{de_la_rosa_modernilproject_2022,
address = {Tokyo},
title = {The {Moderniſa} {Project}: {Orthographic} {Modernization} of {Spanish} {Golden} {Age} {Dramas} with {Language} {Models}},
shorttitle = {The {Moderniſa} {Project}},
url = {https://dh2022.dhii.asia/abstracts/files/DE_LA_ROSA_Javier_The_Moderni_a_Project__Orthographic_Modern.html},
language = {en},
publisher = {Alliance of Digital Humanities Organizations ADHO / The University of Tokyo, Japan},
author = {De la Rosa, Javier and Cuéllar, Álvaro and Lehmann, Jörg},
month = jul,
year = {2022},
}
```
**APA:**
> De la Rosa, J., Cuéllar, Á., & Lehmann, J. (2022, July). The Moderniſa Project: Orthographic Modernization of Spanish Golden Age Dramas with Language Models. Retrieved from https://dh2022.dhii.asia/abstracts/files/DE_LA_ROSA_Javier_The_Moderni_a_Project__Orthographic_Modern.html
**MLA:**
> De la Rosa, Javier, et al. The Moderniſa Project: Orthographic Modernization of Spanish Golden Age Dramas with Language Models. Alliance of Digital Humanities Organizations ADHO / The University of Tokyo, Japan, 2022, https://dh2022.dhii.asia/abstracts/files/DE_LA_ROSA_Javier_The_Moderni_a_Project__Orthographic_Modern.html.
# 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]
<!-- This section provides another layer of transparency and accountability. Whose views is this model card representing? How many voices were included in its construction? Etc. -->
[Javier de la Rosa](https://huggingface.co/versae), [Jörg Lehmann](https://huggingface.co/Jrglmn), questions and comments about the model card can be directed to Jörg Lehmann at [email protected]
# Model Card Contact
[Jörg Lehmann]([email protected])
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
More information needed
</details> |
Unstoppable0/RealityUdemy | Unstoppable0 | 2023-06-07T10:55:53Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-06T06:52:18Z | ---
license: creativeml-openrail-m
---
|
Timiry/digits | Timiry | 2023-06-07T10:50:22Z | 0 | 0 | keras | [
"keras",
"tf-keras",
"region:us"
] | null | 2023-06-06T15:53:31Z | ---
library_name: keras
---
# Digits detecting model
Was trained on mnist dataset
 |
jyden/ElestialHD | jyden | 2023-06-07T10:48:30Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2023-06-07T10:48:30Z | ---
license: bigscience-openrail-m
---
|
Leonardolin/NTCIR_att_sup | Leonardolin | 2023-06-07T10:38:58Z | 181 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-06-07T10:25:08Z | ---
language:
- zh
---
tokenizer = BertTokenizer.from_pretrained("bert-base-chinese")</br>
config = BertConfig.from_pretrained('Leonardolin/NTCIR_att_sup', num_labels=2)</br>
model = BertForSequenceClassification.from_pretrained("Leonardolin/NTCIR_att_sup",config=config) |
bagassword21/iloveherv3 | bagassword21 | 2023-06-07T10:32:02Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-07T10:31:17Z | ---
license: creativeml-openrail-m
---
|
mailsdaddy/export-ost-to-pst-format-using-mailsdaddy-tool | mailsdaddy | 2023-06-07T10:23:29Z | 0 | 0 | null | [
"region:us"
] | null | 2023-06-07T08:39:36Z | When a user's OST file gets corrupted, users are required to convert OST file into PST format. If you are also feeling troubled and want the best solution to export OST to PST file format, you visit the right place. In this article, I will share all about MailsDaddy OST to PST Converter which will help users to quickly export OST file into PST format.
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Here users can export OST to PST without any file size restriction. The software comes with a data filter option that allows users to find and convert some required OST file databases into PST format using different meta-searching options. It always maintains all folder structure, text formatting, meta properties, and HTML formatting. Here users can move multiple OST files into single or separate PST files. Using this user can also break a heavy OST file into small OST files.
Users can convert OST to PST, EML, MSG, MBOX, RTF, HTML, Office 365, Live Exchange Server, etc. Users can create a log report of the complete OST file database conversion process. The converted Outlook PST file can be easily imported in Outlook 2019, 2016, 2013, and other Outlook editions. Users can install this outstanding solution on all Windows systems.
MailsDaddy provides the trial version pack of this outstanding solution so that users can test it before purchase, the free demo edition allows users to convert the first 20 email items per mailbox at free of cost. For complete data conversion users can purchase the appropriate license as per requirement.
To know more, visit here: https://www.mailsdaddy.com/ost-to-pst-converter/
Also Read: https://www.mailsdaddy.com/blogs/convert-ost-pst-using-free-ost-pst-conversion-method/ |
Zekunli/t5-large-SQuAD-qag-ep6 | Zekunli | 2023-06-07T10:22:57Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-06-07T07:40:16Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
- f1
model-index:
- name: t5-large-SQuAD-qag-ep6
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. -->
# t5-large-SQuAD-qag-ep6
This model is a fine-tuned version of [t5-large](https://huggingface.co/t5-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8950
- Rouge1: 41.0251
- Rouge2: 19.2729
- Rougel: 37.4514
- Rougelsum: 37.4839
- F1: 20.0159
- Exact Match: 14.2719
- Gen Len: 18.418
## 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: 24
- eval_batch_size: 48
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | F1 | Exact Match | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-----------:|:-------:|
| 1.1752 | 0.51 | 400 | 0.9542 | 39.054 | 17.5678 | 35.471 | 35.4744 | 16.7879 | 10.8853 | 18.5186 |
| 1.0232 | 1.02 | 800 | 0.9232 | 40.062 | 18.493 | 36.4108 | 36.4335 | 18.7316 | 12.9173 | 18.4543 |
| 0.9641 | 1.52 | 1200 | 0.9112 | 40.6835 | 19.0484 | 37.0867 | 37.1329 | 19.3326 | 14.03 | 18.4078 |
| 0.9422 | 2.03 | 1600 | 0.9044 | 40.9087 | 19.2513 | 37.3069 | 37.3196 | 19.999 | 14.2719 | 18.4383 |
| 0.8989 | 2.54 | 2000 | 0.9026 | 41.0666 | 19.4921 | 37.4916 | 37.5115 | 21.0451 | 14.8041 | 18.3469 |
| 0.9015 | 3.05 | 2400 | 0.8986 | 40.471 | 18.8468 | 36.9384 | 36.9357 | 19.3467 | 13.8365 | 18.4557 |
| 0.8679 | 3.56 | 2800 | 0.8950 | 41.0251 | 19.2729 | 37.4514 | 37.4839 | 20.0159 | 14.2719 | 18.418 |
| 0.8521 | 4.07 | 3200 | 0.8982 | 41.1633 | 19.6433 | 37.6253 | 37.6541 | 21.0075 | 15.046 | 18.3807 |
| 0.8362 | 4.57 | 3600 | 0.8972 | 40.9127 | 19.4618 | 37.4 | 37.4431 | 20.9531 | 14.8524 | 18.3503 |
| 0.8415 | 5.08 | 4000 | 0.8970 | 41.2265 | 19.7241 | 37.7405 | 37.7653 | 21.5704 | 15.1911 | 18.388 |
| 0.8312 | 5.59 | 4400 | 0.8966 | 40.9824 | 19.4891 | 37.533 | 37.5485 | 20.8781 | 14.7073 | 18.3967 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
michaelfeil/ct2fast-falcon-7b | michaelfeil | 2023-06-07T10:22:07Z | 6 | 1 | transformers | [
"transformers",
"ctranslate2",
"int8",
"float16",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2101.00027",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"license:apache-2.0",
"region:us"
] | null | 2023-06-07T08:27:10Z | ---
tags:
- ctranslate2
- int8
- float16
datasets:
- tiiuae/falcon-refinedweb
language:
- en
inference: false
license: apache-2.0
---
# # Fast-Inference with Ctranslate2
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
quantized version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b)
```bash
pip install hf-hub-ctranslate2>=2.0.8 ctranslate2>=3.14.0
```
Converted on 2023-06-07 using
```
ct2-transformers-converter --model tiiuae/falcon-7b --output_dir /home/michael/tmp-ct2fast-falcon-7b --force --copy_files tokenizer.json README.md tokenizer_config.json generation_config.json special_tokens_map.json .gitattributes --quantization int8_float16 --trust_remote_code
```
Checkpoint compatible to [ctranslate2>=3.15.0](https://github.com/OpenNMT/CTranslate2)
and [hf-hub-ctranslate2>=2.0.8](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-falcon-7b"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = GeneratorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
# tokenizer=AutoTokenizer.from_pretrained("tiiuae/falcon-7b")
)
outputs = model.generate(
text=["def fibonnaci(", "User: How are you doing? Bot:"],
max_length=64,
include_prompt_in_result=False
)
print(outputs)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
# 🚀 Falcon-7B
**Falcon-7B is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license.**
*Paper coming soon* 😊.
## Why use Falcon-7B?
* **It outperforms comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
* **It is made available under a permissive Apache 2.0 license allowing for commercial use**, without any royalties or restrictions.
⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.** If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct).
🔥 **Looking for an even more powerful model?** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) is Falcon-7B's big brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
# Model Card for Falcon-7B
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English and French;
- **License:** Apache 2.0.
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-7B is trained on English and French data only, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-7B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-7B was trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)).
| **Data source** | **Fraction** | **Tokens** | **Sources** |
|--------------------|--------------|------------|-----------------------------------|
| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 79% | 1,185B | massive web crawl |
| Books | 7% | 110B | |
| Conversations | 6% | 85B | Reddit, StackOverflow, HackerNews |
| Code | 3% | 45B | |
| RefinedWeb-French | 3% | 45B | massive web crawl |
| Technical | 2% | 30B | arXiv, PubMed, UPSTO, etc. |
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
### Training Procedure
Falcon-7B was trained on 384 A100 40GB GPUs, using a 2D parallelism strategy (PP=2, DP=192) combined with ZeRO.
#### Training Hyperparameters
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|------------|-------------------------------------------|
| Precision | `bfloat16` | |
| Optimizer | AdamW | |
| Learning rate | 6e-4 | 4B tokens warm-up, cosine decay to 1.2e-5 |
| Weight decay | 1e-1 | |
| Z-loss | 1e-4 | |
| Batch size | 2304 | 30B tokens ramp-up |
#### Speeds, Sizes, Times
Training happened in early March 2023 and took about two weeks.
## Evaluation
*Paper coming soon*.
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
## Technical Specifications
### Model Architecture and Objective
Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a single layer norm.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 32 | |
| `d_model` | 4544 | Increased to compensate for multiquery |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-7B was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances.
#### Software
Falcon-7B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon* 😊. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## License
Falcon-7B is made available under the Apache 2.0 license.
## Contact
[email protected] |
wuula/speecht5_tts_common_voice_zh | wuula | 2023-06-07T10:18:33Z | 74 | 6 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"speecht5",
"text-to-audio",
"speecht5_tts_common_voice_zh",
"generated_from_trainer",
"zh",
"dataset:speecht5_tts_common_voice_zh",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2023-06-07T08:32:47Z | ---
language:
- zh
license: mit
tags:
- speecht5_tts_common_voice_zh
- generated_from_trainer
datasets:
- speecht5_tts_common_voice_zh
model-index:
- name: SpeechT5 TTS Dutch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SpeechT5 TTS Dutch
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the speecht5_tts_common_voice_zh dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6217
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6909 | 4.49 | 1000 | 0.6541 |
| 0.6671 | 8.99 | 2000 | 0.6351 |
| 0.653 | 13.48 | 3000 | 0.6249 |
| 0.6565 | 17.98 | 4000 | 0.6217 |
### Framework versions
- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
AndyChiang/a2c-PandaReachDense-v2 | AndyChiang | 2023-06-07T10:13:46Z | 2 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-07T10:11:12Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.87 +/- 0.81
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-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
...
```
|
giovannidispoto/space-invaders-dqn | giovannidispoto | 2023-06-07T10:02:02Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-07T10:01:28Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 442.50 +/- 162.08
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga giovannidispoto -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga giovannidispoto -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga giovannidispoto
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
raghvendramall/esm2_t12_35M_UR50D-finetuned-localization | raghvendramall | 2023-06-07T09:55:19Z | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"esm",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-05-17T12:17:42Z | ---
license: mit
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: esm2_t12_35M_UR50D-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. -->
# esm2_t12_35M_UR50D-finetuned-localization
This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2355
- F1: 0.6929
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.4654 | 1.0 | 3889 | 0.4648 | 0.6902 |
| 0.4139 | 2.0 | 7778 | 0.4605 | 0.7270 |
| 0.3621 | 3.0 | 11667 | 0.4892 | 0.7199 |
| 0.2574 | 4.0 | 15556 | 0.6158 | 0.6910 |
| 0.1731 | 5.0 | 19445 | 0.8932 | 0.6925 |
| 0.1162 | 6.0 | 23334 | 1.4826 | 0.6712 |
| 0.0651 | 7.0 | 27223 | 1.8881 | 0.6937 |
| 0.0565 | 8.0 | 31112 | 2.1033 | 0.6944 |
| 0.0319 | 9.0 | 35001 | 2.2087 | 0.6909 |
| 0.0153 | 10.0 | 38890 | 2.2355 | 0.6929 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.3
|
wykonos/Taxi-v3 | wykonos | 2023-06-07T09:48:28Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-07T09:48:26Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.78
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="wykonos/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"])
```
|
wykonos/q-FrozenLake-v1 | wykonos | 2023-06-07T09:47:49Z | 0 | 0 | null | [
"FrozenLake-v1",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-07T09:46:30Z | ---
tags:
- FrozenLake-v1
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1
type: FrozenLake-v1
metrics:
- type: mean_reward
value: 7.96 +/- 2.48
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="wykonos/q-FrozenLake-v1", 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"])
```
|
ISYS/MyNewModel | ISYS | 2023-06-07T09:46:10Z | 0 | 0 | keras | [
"keras",
"tf-keras",
"region:us"
] | null | 2023-06-07T09:12:45Z | ---
library_name: keras
---
# Модель для распознавания цифр
Натренированная на датасете mnist
 |
SIRIS-Lab/actytode | SIRIS-Lab | 2023-06-07T09:37:53Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"dataset:SIRIS-Lab/actytode",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-06-07T08:09:28Z | ---
license: mit
datasets:
- SIRIS-Lab/actytode
---
# ACTYTODE (ACtivity TYpe TO DEtect)
This repository provides a `bert-base-multilingual-uncased` model finetuned for ACTYTODE task based on ACTYTODE dataset, which aims at predicting organisation activity type based on the organisation legal status of the European Commission.
The activity type status for organisations are:
* PUB --> Public entity (excluding research and education)
* HES --> Higher education entity
* REC --> Research entity
* PRC --> Private company
* OTH --> Other
## Training data
## Evaluation |
sherryh0112/wav2vec2-base-test2 | sherryh0112 | 2023-06-07T09:26:28Z | 112 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-06-07T03:09:54Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-test2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-test2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4541
- Wer: 0.4886
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 3.4923 | 2.3 | 500 | 2.9564 | 0.9996 |
| 0.6834 | 4.61 | 1000 | 0.4541 | 0.4886 |
### Framework versions
- Transformers 4.17.0
- Pytorch 2.0.1+cu118
- Datasets 1.18.3
- Tokenizers 0.13.3
|
TheBloke/dromedary-65B-lora-GGML | TheBloke | 2023-06-07T09:21:46Z | 0 | 21 | null | [
"license:other",
"region:us"
] | null | 2023-05-11T14:26:46Z | ---
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/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 -->
# Dromedary-65B-LoRA GGML
These files are GGML format model files for [Dromedary-65B-LoRA](https://huggingface.co/zhiqings/dromedary-65b-lora-delta-v0).
GGML files are for CPU + GPU inference using [llama.cpp](https://github.com/ggerganov/llama.cpp) and libraries and UIs which support this format, such as:
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [KoboldCpp](https://github.com/LostRuins/koboldcpp)
* [ParisNeo/GPT4All-UI](https://github.com/ParisNeo/gpt4all-ui)
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python)
* [ctransformers](https://github.com/marella/ctransformers)
## Repositories available
* [4-bit GPTQ models for GPU inference](https://huggingface.co/TheBloke/dromedary-65b-lora-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/dromedary-65b-lora-GGML)
* [Unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/TheBloke/dromedary-65b-lora-HF)
<!-- compatibility_ggml start -->
## Compatibility
### Original llama.cpp quant methods: `q4_0, q4_1, q5_0, q5_1, q8_0`
I have quantized these 'original' quantisation methods using an older version of llama.cpp so that they remain compatible with llama.cpp as of May 19th, commit `2d5db48`.
They should be compatible with all current UIs and libraries that use llama.cpp, such as those listed at the top of this README.
### New k-quant methods: `q2_K, q3_K_S, q3_K_M, q3_K_L, q4_K_S, q4_K_M, q5_K_S, q6_K`
These new quantisation methods are only compatible with llama.cpp as of June 6th, commit `2d43387`.
They will NOT be compatible with koboldcpp, text-generation-ui, and other UIs and libraries yet. Support is expected to come over the next few days.
## 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 |
| ---- | ---- | ---- | ---- | ---- | ----- |
| dromedary-lora-65B.ggmlv3.q2_K.bin | q2_K | 2 | 27.33 GB | 29.83 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.vw and feed_forward.w2 tensors, GGML_TYPE_Q2_K for the other tensors. |
| dromedary-lora-65B.ggmlv3.q3_K_L.bin | q3_K_L | 3 | 34.55 GB | 37.05 GB | New k-quant method. Uses GGML_TYPE_Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| dromedary-lora-65B.ggmlv3.q3_K_M.bin | q3_K_M | 3 | 31.40 GB | 33.90 GB | New k-quant method. Uses GGML_TYPE_Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else GGML_TYPE_Q3_K |
| dromedary-lora-65B.ggmlv3.q3_K_S.bin | q3_K_S | 3 | 28.06 GB | 30.56 GB | New k-quant method. Uses GGML_TYPE_Q3_K for all tensors |
| dromedary-lora-65B.ggmlv3.q4_0.bin | q4_0 | 4 | 36.73 GB | 39.23 GB | Original llama.cpp quant method, 4-bit. |
| dromedary-lora-65B.ggmlv3.q4_1.bin | q4_1 | 4 | 40.81 GB | 43.31 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. |
| dromedary-lora-65B.ggmlv3.q4_K_M.bin | q4_K_M | 4 | 39.28 GB | 41.78 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q4_K |
| dromedary-lora-65B.ggmlv3.q4_K_S.bin | q4_K_S | 4 | 36.73 GB | 39.23 GB | New k-quant method. Uses GGML_TYPE_Q4_K for all tensors |
| dromedary-lora-65B.ggmlv3.q5_0.bin | q5_0 | 5 | 44.89 GB | 47.39 GB | Original llama.cpp quant method, 5-bit. Higher accuracy, higher resource usage and slower inference. |
| dromedary-lora-65B.ggmlv3.q5_1.bin | q5_1 | 5 | 48.97 GB | 51.47 GB | Original llama.cpp quant method, 5-bit. Even higher accuracy, resource usage and slower inference. |
| dromedary-lora-65B.ggmlv3.q5_K_M.bin | q5_K_M | 5 | 46.20 GB | 48.70 GB | New k-quant method. Uses GGML_TYPE_Q6_K for half of the attention.wv and feed_forward.w2 tensors, else GGML_TYPE_Q5_K |
| dromedary-lora-65B.ggmlv3.q5_K_S.bin | q5_K_S | 5 | 44.89 GB | 47.39 GB | New k-quant method. Uses GGML_TYPE_Q5_K for all tensors |
**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 `llama.cpp`
I use the following command line; adjust for your tastes and needs:
```
./main -t 10 -ngl 32 -m dromedary-lora-65B.ggmlv3.q5_0.bin --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "### Instruction: Write a story about llamas\n### Response:"
```
Change `-t 10` to the number of physical CPU cores you have. For example if your system has 8 cores/16 threads, use `-t 8`.
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp-models.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp-models.md).
<!-- 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
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**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: Dromedary-65B-LoRA
# Dromedary Model Card
**NOTE: This "delta model" cannot be used directly.**
Users have to apply it on top of the original LLaMA weights to get actual Dromedary weights.
See https://github.com/IBM/Dromedary#model-weights for instructions.
## Model details
<div align="center">
<img src="https://raw.githubusercontent.com/IBM/Dromedary/main/assets/images/dromedary_logo.svg" alt="Dromedary Logo"/>
</div>
**Model type:**
Dromedary is an open-source self-aligned language model trained with minimal human supervision.
The base language model is LLaMA-65b, based on the transformer architecture.
**Model date:**
Dromedary was trained between April 2023 and May 2023, but its knowledge only goes up until Sept-2021.
**Organizations developing the model:**
The Dromedary team as a joint effort between CMU and IBM.
**Paper or resources for more information:**
https://mitibmdemos.draco.res.ibm.com/dromedary
**License:**
LLaMA's Non-commercial bespoke license
**Where to send questions or comments about the model:**
https://github.com/IBM/Dromedary/issues
## Intended use
**Primary intended uses:**
The primary use of Dromedary is research on the alignment of large language models.
**Primary intended users:**
The primary intended users of the model are researchers in artificial intelligence.
## Delta weights
We use the following configuration for the LoRA weights:
```
--lora_target_modules='[q_proj,k_proj,v_proj,o_proj]' \
--lora_r=16 \
```
## Training dataset
Fewer than 300 lines of human annotations (including < 200 seed prompts, 16 generic principles, and 5 exemplars for in-context learning),
## Evaluation dataset
We evaluate Dromedary on TruthfulQA and HHH Eval, as well as Vicuna benchmark questions.
|
SiddharthUchil/language-detection-fine-tuned-on-xlm-roberta-base | SiddharthUchil | 2023-06-07T09:15:02Z | 125 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:common_language",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-06-07T08:07:02Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: language-detection-fine-tuned-on-xlm-roberta-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: common_language
type: common_language
config: full
split: test
args: full
metrics:
- name: Accuracy
type: accuracy
value: 0.9745094750964279
---
<!-- 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. -->
# language-detection-fine-tuned-on-xlm-roberta-base
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1789
- Accuracy: 0.9745
## 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: 3e-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
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1658 | 1.0 | 22194 | 0.1789 | 0.9745 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
deepghs/monochrome_detect | deepghs | 2023-06-07T08:57:50Z | 0 | 0 | null | [
"onnx",
"art",
"image-classification",
"dataset:deepghs/monochrome_danbooru",
"license:mit",
"region:us"
] | image-classification | 2023-06-02T10:41:40Z | ---
license: mit
datasets:
- deepghs/monochrome_danbooru
metrics:
- accuracy
pipeline_tag: image-classification
tags:
- art
---
The models used for determining whether an anime image is monochrome have a training size of 384.
| Model | FLOPs | Accuracy | Confusion Matrix | Description |
|:--------------------------------:|:------:|:--------:|:----------------------------------------------------------------------------------------------------------------------------------:|--------------------------------------------------------------------------------------------------------------------------------------------------------|
| caformer_s36 | 22.10G | 95.63% | [Confusion Matrix](https://huggingface.co/deepghs/monochrome_detect/blob/main/caformer_s36/plot_confusion.png) | Model: caformer_s36 from timm |
| caformer_s36_safe2 | 22.10G | 95.52% | [Confusion Matrix](https://huggingface.co/deepghs/monochrome_detect/blob/main/caformer_s36_safe2/plot_confusion.png) | Model: caformer_s36 from timm, which have better precision and lower recall than caformer_s36 |
| caformer_s36_plus | 22.10G | 97.31% | [Confusion Matrix](https://huggingface.co/deepghs/monochrome_detect/blob/main/caformer_s36_plus/plot_confusion.png) | Model: caformer_s36.sail_in22k_ft_in1k_384 pratrained from timm |
| caformer_s36_plus_safe2 | 22.10G | 97.09% | [Confusion Matrix](https://huggingface.co/deepghs/monochrome_detect/blob/main/caformer_s36_plus_safe2/plot_confusion.png) | Model: caformer_s36.sail_in22k_ft_in1k_384 pratrained from timm, which have better precision and lower recall than caformer_s36.sail_in22k_ft_in1k_384 |
| mobilenetv3_large_100 | 0.63G | 95.40% | [Confusion Matrix](https://huggingface.co/deepghs/monochrome_detect/blob/main/mobilenetv3_large_100/plot_confusion.png) | Model: mobilenetv3_large_100 from timm |
| mobilenetv3_large_100_dist | 0.63G | 96.30% | [Confusion Matrix](https://huggingface.co/deepghs/monochrome_detect/blob/main/mobilenetv3_large_100_dist/plot_confusion.png) | Distillated from caformer_s36_plus, using mobilenetv3_large_100 |
| mobilenetv3_large_100_safe2 | 0.63G | 94.62% | [Confusion Matrix](https://huggingface.co/deepghs/monochrome_detect/blob/main/mobilenetv3_large_100_safe2/plot_confusion.png) | Model: mobilenetv3_large_100 from timm, which have better precision and lower recall than mobilenetv3_large_100 |
| mobilenetv3_large_100_dist_safe2 | 0.63G | 95.85% | [Confusion Matrix](https://huggingface.co/deepghs/monochrome_detect/blob/main/mobilenetv3_large_100_dist_safe2/plot_confusion.png) | Distillated from caformer_s36_plus_safe2, using mobilenetv3_large_100 |
|
root1101/pokemon-lora | root1101 | 2023-06-07T08:56:16Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2023-06-06T07:21:45Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - root1101/pokemon-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
|
HannahDK/Classification | HannahDK | 2023-06-07T08:52:41Z | 0 | 0 | null | [
"region:us"
] | null | 2023-06-07T08:36:01Z | import openai
def classify_website(website):
prompt = "Website classification:\n\nWebsite: {} \nCategory:".format(website)
model_response = openai.Completion.create(
model="gpt-3.5-turbo",
prompt=prompt,
max_tokens=1,
temperature=0.2,
n=1,
stop=None,
temperature=0.2
)
category = model_response.choices[0].text.strip()
return category
def determine_piracy_type(website):
prompt = "Piracy type detection:\n\nWebsite: {} \nType:".format(website)
model_response = openai.Completion.create(
model="gpt-3.5-turbo",
prompt=prompt,
max_tokens=1,
temperature=0.2,
n=1,
stop=None,
temperature=0.2
)
piracy_type = model_response.choices[0].text.strip()
return piracy_type
# Set up OpenAI API credentials
openai.api_key = "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"
# Get website list input from the user
websites = input("Enter a comma-separated list of websites to classify: ").split(",")
# Classify each website
for website in websites:
website = website.strip()
classification = classify_website(website)
if classification == "Piracy":
piracy_type = determine_piracy_type(website)
print("The website {} is classified as: Piracy - {}".format(website, piracy_type))
else:
print("The website {} is classified as: Legal".format(website)) |
thiendio/ppo-SnowballTarget1_v1 | thiendio | 2023-06-07T08:33:06Z | 2 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | reinforcement-learning | 2023-06-07T08:31:03Z | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
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. Go to https://huggingface.co/spaces/unity/ML-Agents-SnowballTarget
2. Step 1: Find your model_id: thiendio/ppo-SnowballTarget1_v1
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
michaelfeil/ct2fast-falcon-7b-instruct | michaelfeil | 2023-06-07T08:24:10Z | 6 | 1 | transformers | [
"transformers",
"ctranslate2",
"int8",
"float16",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-06-07T07:29:08Z | ---
tags:
- ctranslate2
- int8
- float16
datasets:
- tiiuae/falcon-refinedweb
language:
- en
inference: true
license: apache-2.0
---
# # Fast-Inference with Ctranslate2
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
quantized version of [tiiuae/falcon-7b-instruct](https://huggingface.co/tiiuae/falcon-7b-instruct)
```bash
pip install hf-hub-ctranslate2>=2.0.8 ctranslate2>=3.14.0
```
Converted on 2023-06-07 using
```
ct2-transformers-converter --model tiiuae/falcon-7b-instruct --output_dir /home/michael/tmp-ct2fast-falcon-7b-instruct --force --copy_files tokenizer.json README.md tokenizer_config.json generation_config.json special_tokens_map.json .gitattributes --quantization int8_float16 --trust_remote_code
```
Checkpoint compatible to [ctranslate2>=3.15.0](https://github.com/OpenNMT/CTranslate2)
and [hf-hub-ctranslate2>=2.0.8](https://github.com/michaelfeil/hf-hub-ctranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
```python
from hf_hub_ctranslate2 import TranslatorCT2fromHfHub, GeneratorCT2fromHfHub
from transformers import AutoTokenizer
model_name = "michaelfeil/ct2fast-falcon-7b-instruct"
# use either TranslatorCT2fromHfHub or GeneratorCT2fromHfHub here, depending on model.
model = GeneratorCT2fromHfHub(
# load in int8 on CUDA
model_name_or_path=model_name,
device="cuda",
compute_type="int8_float16",
# tokenizer=AutoTokenizer.from_pretrained("tiiuae/falcon-7b-instruct")
)
outputs = model.generate(
text=["def fibonnaci(", "User: How are you doing? Bot:"],
max_length=64,
include_prompt_in_result=False
)
print(outputs)
```
# Licence and other remarks:
This is just a quantized version. Licence conditions are intended to be idential to original huggingface repo.
# Original description
# ✨ Falcon-7B-Instruct
**Falcon-7B-Instruct is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b) and finetuned on a mixture of chat/instruct datasets. It is made available under the Apache 2.0 license.**
*Paper coming soon 😊.*
## Why use Falcon-7B-Instruct?
* **You are looking for a ready-to-use chat/instruct model based on [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).**
* **Falcon-7B is a strong base model, outperforming comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
💬 **This is an instruct model, which may not be ideal for further finetuning.** If you are interested in building your own instruct/chat model, we recommend starting from [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
🔥 **Looking for an even more powerful model?** [Falcon-40B-Instruct](https://huggingface.co/tiiuae/falcon-40b-instruct) is Falcon-7B-Instruct's big brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
# Model Card for Falcon-7B-Instruct
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English and French;
- **License:** Apache 2.0;
- **Finetuned from model:** [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Falcon-7B-Instruct has been finetuned on a mixture of instruct and chat datasets.
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-7B-Instruct is mostly trained on English data, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-7B-Instruct to develop guardrails and to take appropriate precautions for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-7B-Instruct was finetuned on a 250M tokens mixture of instruct/chat datasets.
| **Data source** | **Fraction** | **Tokens** | **Description** |
|--------------------|--------------|------------|-----------------------------------|
| [Bai ze](https://github.com/project-baize/baize-chatbot) | 65% | 164M | chat |
| [GPT4All](https://github.com/nomic-ai/gpt4all) | 25% | 62M | instruct |
| [GPTeacher](https://github.com/teknium1/GPTeacher) | 5% | 11M | instruct |
| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 5% | 13M | massive web crawl |
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
## Evaluation
*Paper coming soon.*
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
Note that this model variant is not optimized for NLP benchmarks.
## Technical Specifications
For more information about pretraining, see [Falcon-7B](https://huggingface.co/tiiuae/falcon-7b).
### Model Architecture and Objective
Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a single layer norm.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 32 | |
| `d_model` | 4544 | Increased to compensate for multiquery |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-7B-Instruct was trained on AWS SageMaker, on 32 A100 40GB GPUs in P4d instances.
#### Software
Falcon-7B-Instruct was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon* 😊. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## License
Falcon-7B-Instruct is made available under the Apache 2.0 license.
## Contact
[email protected] |
Zekunli/flan-t5-base-SQuAD-qag-ep6 | Zekunli | 2023-06-07T08:22:09Z | 107 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-06-07T07:36:15Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
- f1
model-index:
- name: flan-t5-base-SQuAD-qag-ep6
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. -->
# flan-t5-base-SQuAD-qag-ep6
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0075
- Rouge1: 39.0569
- Rouge2: 17.6997
- Rougel: 35.5578
- Rougelsum: 35.5512
- F1: 18.3373
- Exact Match: 13.014
- Gen Len: 18.4509
## 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: 84
- eval_batch_size: 168
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | F1 | Exact Match | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-----------:|:-------:|
| 1.2679 | 0.89 | 200 | 1.0379 | 38.2769 | 16.8906 | 34.7944 | 34.8178 | 16.7543 | 11.7078 | 18.5225 |
| 1.151 | 1.78 | 400 | 1.0222 | 38.7208 | 17.2857 | 35.2522 | 35.2879 | 17.8568 | 12.8205 | 18.4756 |
| 1.1243 | 2.67 | 600 | 1.0141 | 38.677 | 17.2919 | 35.207 | 35.2374 | 17.8534 | 12.9173 | 18.4741 |
| 1.1183 | 3.56 | 800 | 1.0109 | 38.5725 | 17.1551 | 35.0545 | 35.0774 | 18.0599 | 13.2559 | 18.4722 |
| 1.0949 | 4.44 | 1000 | 1.0096 | 38.9484 | 17.6335 | 35.4819 | 35.4888 | 18.0128 | 13.2075 | 18.464 |
| 1.0983 | 5.33 | 1200 | 1.0075 | 39.0569 | 17.6997 | 35.5578 | 35.5512 | 18.3373 | 13.014 | 18.4509 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
SEVUNX/joydiffusion_diffusers | SEVUNX | 2023-06-07T08:20:11Z | 29 | 1 | diffusers | [
"diffusers",
"stable-diffusions",
"text-to-image",
"en",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-06-07T07:50:55Z | ---
license: openrail
language:
- en
pipeline_tag: text-to-image
tags:
- stable-diffusions
--- |
pleyerke/phunkzip | pleyerke | 2023-06-07T08:19:40Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2023-06-07T08:17:57Z | ---
license: creativeml-openrail-m
---
|
paumena/QA-BERT-NLP-Course-2 | paumena | 2023-06-07T08:15:54Z | 62 | 0 | transformers | [
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2023-06-07T06:56:39Z | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: paumena/QA-BERT-NLP-Course-2
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. -->
# paumena/QA-BERT-NLP-Course-2
This model is a fine-tuned version of [paumena/QA-BERT-NLP-Course](https://huggingface.co/paumena/QA-BERT-NLP-Course) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.4183
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 11090, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.6388 | 0 |
| 0.4183 | 1 |
### Framework versions
- Transformers 4.29.2
- TensorFlow 2.12.0
- Datasets 2.12.0
- Tokenizers 0.13.3
|
soboro2327/my_quotes_model | soboro2327 | 2023-06-07T08:14:25Z | 175 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-06-05T08:07:01Z | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: my_quotes_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_quotes_model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6361
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 238 | 3.6561 |
| No log | 2.0 | 476 | 3.6369 |
| 3.735 | 3.0 | 714 | 3.6361 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
TTR21/my_awesome_billsum_model | TTR21 | 2023-06-07T08:08:08Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-06-07T07:57:45Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1438
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5420
- Rouge1: 0.1438
- Rouge2: 0.0513
- Rougel: 0.1175
- Rougelsum: 0.1174
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.8357 | 0.1287 | 0.0392 | 0.1069 | 0.1069 | 19.0 |
| No log | 2.0 | 124 | 2.6209 | 0.1361 | 0.0479 | 0.1127 | 0.1127 | 19.0 |
| No log | 3.0 | 186 | 2.5600 | 0.1412 | 0.052 | 0.1171 | 0.117 | 19.0 |
| No log | 4.0 | 248 | 2.5420 | 0.1438 | 0.0513 | 0.1175 | 0.1174 | 19.0 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
eenzeenee/t5-small-korean-summarization | eenzeenee | 2023-06-07T08:05:15Z | 415 | 3 | transformers | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"T5",
"summarization",
"ko",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | summarization | 2023-01-23T05:26:46Z | ---
pipeline_tag: summarization
language:
- ko
tags:
- T5
---
# t5-small-korean-summarization
This is [T5](https://huggingface.co/docs/transformers/model_doc/t5) model for korean text summarization.
- Finetuned based on ['paust/pko-t5-small'](https://huggingface.co/paust/pko-t5-small) model.
- Finetuned with 3 datasets. Specifically, it is described below.
- [Korean Paper Summarization Dataset(논문자료 요약)](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=90)
- [Korean Book Summarization Dataset(도서자료 요약)](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=93)
- [Korean Summary statement and Report Generation Dataset(요약문 및 레포트 생성 데이터)](https://www.aihub.or.kr/aihubdata/data/view.do?currMenu=115&topMenu=100&aihubDataSe=realm&dataSetSn=90)
# Usage (HuggingFace Transformers)
```python
import nltk
nltk.download('punkt')
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
model = AutoModelForSeq2SeqLM.from_pretrained('eenzeenee/t5-small-korean-summarization')
tokenizer = AutoTokenizer.from_pretrained('eenzeenee/t5-small-korean-summarization')
prefix = "summarize: "
sample = """
안녕하세요? 우리 (2학년)/(이 학년) 친구들 우리 친구들 학교에 가서 진짜 (2학년)/(이 학년) 이 되고 싶었는데 학교에 못 가고 있어서 답답하죠?
그래도 우리 친구들의 안전과 건강이 최우선이니까요 오늘부터 선생님이랑 매일 매일 국어 여행을 떠나보도록 해요.
어/ 시간이 벌써 이렇게 됐나요? 늦었어요. 늦었어요. 빨리 국어 여행을 떠나야 돼요.
그런데 어/ 국어여행을 떠나기 전에 우리가 준비물을 챙겨야 되겠죠? 국어 여행을 떠날 준비물, 교안을 어떻게 받을 수 있는지 선생님이 설명을 해줄게요.
(EBS)/(이비에스) 초등을 검색해서 들어가면요 첫화면이 이렇게 나와요.
자/ 그러면요 여기 (X)/(엑스) 눌러주(고요)/(구요). 저기 (동그라미)/(똥그라미) (EBS)/(이비에스) (2주)/(이 주) 라이브특강이라고 되어있죠?
거기를 바로 가기를 누릅니다. 자/ (누르면요)/(눌르면요). 어떻게 되냐? b/ 밑으로 내려요 내려요 내려요 쭉 내려요.
우리 몇 학년이죠? 아/ (2학년)/(이 학년) 이죠 (2학년)/(이 학년)의 무슨 과목? 국어.
이번주는 (1주)/(일 주) 차니까요 여기 교안. 다음주는 여기서 다운을 받으면 돼요.
이 교안을 클릭을 하면, 짜잔/. 이렇게 교재가 나옵니다 .이 교안을 (다운)/(따운)받아서 우리 국어여행을 떠날 수가 있어요.
그럼 우리 진짜로 국어 여행을 한번 떠나보도록 해요? 국어여행 출발. 자/ (1단원)/(일 단원) 제목이 뭔가요? 한번 찾아봐요.
시를 즐겨요 에요. 그냥 시를 읽어요 가 아니에요. 시를 즐겨야 돼요 즐겨야 돼. 어떻게 즐길까? 일단은 내내 시를 즐기는 방법에 대해서 공부를 할 건데요.
그럼 오늘은요 어떻게 즐길까요? 오늘 공부할 내용은요 시를 여러 가지 방법으로 읽기를 공부할겁니다.
어떻게 여러가지 방법으로 읽을까 우리 공부해 보도록 해요. 오늘의 시 나와라 짜잔/! 시가 나왔습니다 시의 제목이 뭔가요? 다툰 날이에요 다툰 날.
누구랑 다퉜나 동생이랑 다퉜나 언니랑 친구랑? 누구랑 다퉜는지 선생님이 시를 읽어 줄 테니까 한번 생각을 해보도록 해요."""
inputs = [prefix + sample]
inputs = tokenizer(inputs, max_length=512, truncation=True, return_tensors="pt")
output = model.generate(**inputs, num_beams=3, do_sample=True, min_length=10, max_length=64)
decoded_output = tokenizer.batch_decode(output, skip_special_tokens=True)[0]
result = nltk.sent_tokenize(decoded_output.strip())[0]
print('RESULT >>', result)
``` |
abbymark/dqn-SpaceInvadersNoFrameskip-v4 | abbymark | 2023-06-07T08:03:41Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-05T08:46:53Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 827.00 +/- 273.73
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga abbymark -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga abbymark -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga abbymark
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
anjgksi/my_awesome_billsum_model | anjgksi | 2023-06-07T08:03:28Z | 105 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:billsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-06-07T07:58:21Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- billsum
metrics:
- rouge
model-index:
- name: my_awesome_billsum_model
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: billsum
type: billsum
config: default
split: ca_test
args: default
metrics:
- name: Rouge1
type: rouge
value: 0.1378
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the billsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5230
- Rouge1: 0.1378
- Rouge2: 0.047
- Rougel: 0.1147
- Rougelsum: 0.1149
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.8203 | 0.1264 | 0.0339 | 0.1055 | 0.1059 | 19.0 |
| No log | 2.0 | 124 | 2.6055 | 0.1334 | 0.0421 | 0.1112 | 0.1114 | 19.0 |
| No log | 3.0 | 186 | 2.5402 | 0.1378 | 0.0458 | 0.114 | 0.1143 | 19.0 |
| No log | 4.0 | 248 | 2.5230 | 0.1378 | 0.047 | 0.1147 | 0.1149 | 19.0 |
### Framework versions
- Transformers 4.29.2
- Pytorch 2.0.1+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
azrilhafizi/Reinforce-CartPole-v1 | azrilhafizi | 2023-06-07T07:50:04Z | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | 2023-06-07T07:49:56Z | ---
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
|
IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1 | IDEA-CCNL | 2023-06-07T07:41:49Z | 32 | 57 | transformers | [
"transformers",
"pytorch",
"blip-2",
"feature-extraction",
"image-to-text",
"visual-question-answering",
"ziya",
"fengshenbang",
"custom_code",
"en",
"zh",
"arxiv:2210.08590",
"license:gpl-3.0",
"region:us"
] | visual-question-answering | 2023-05-30T08:13:18Z | ---
license: gpl-3.0
inference: false
language:
- en
- zh
pipeline_tag: visual-question-answering
tags:
- image-to-text
- visual-question-answering
- ziya
- fengshenbang
---
# Ziya-BLIP2-14B-Visual-v1
- Main Page:[Fengshenbang](https://fengshenbang-lm.com/)
- Github: [Fengshenbang-LM](https://github.com/IDEA-CCNL/Fengshenbang-LM)
# 姜子牙系列模型
- [Ziya-BLIP2-14B-Visual-v1](https://huggingface.co/IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1)
- [Ziya-LLaMA-13B-v1.1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1.1)
- [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1)
- [Ziya-LLaMA-7B-Reward](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-7B-Reward)
- [Ziya-LLaMA-13B-Pretrain-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-Pretrain-v1)
## 简介 Brief Introduction
Ziya-Visual多模态大模型基于[姜子牙通用大模型V1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1)训练，具有视觉问答和对话能力。今年3月份OpenAI发布具有识图能力的多模态大模型GPT-4，遗憾的是，时至今日绝大部分用户也都还没有拿到GPT-4输入图片的权限，Ziya-Visual参考了Mini-GPT4、LLaVA等优秀的开源实现，补齐了Ziya的识图能力，使中文用户群体可以体验到结合视觉和语言两大模态的大模型的卓越能力。
The Ziya-Visual multimodal Big Model is based on the [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1) training and has visual question and answer and dialogue capabilities. In March this year, OpenAI released GPT-4, a multimodal big model with image recognition capabilities. Unfortunately, to date, the vast majority of users have not yet been given access to GPT-4 for image input, so Ziya-Visual refers to Mini-GPT4, LLaVA and other excellent open source implementations to complement Ziya's image recognition capabilities, so that the Chinese user community can experience the superior capabilities of a large model combining two modalities: visual and language.
## 软件依赖
```
pip install torch==1.12.1 tokenizers==0.13.3 git+https://github.com/huggingface/transformers
```
## 模型分类 Model Taxonomy
| 需求 Demand | 任务 Task | 系列 Series | 模型 Model | 参数 Parameter | 额外 Extra |
| :----: | :----: | :----: | :----: | :----: | :----: |
| 多模态 Multi-Modal | 通用 General | 姜子牙-多模态 Ziya-Visual | BLIP2 LLaMA | 14B | English&Chinese |
## 模型信息 Model Information
### 效果展示 Showcase
这个例子展示了模型的识图能力、知识能力和创作能力。首先第一个问题中，模型识别出了图片中是电影《泰坦尼克号》的场景，并给出电影导演、发布时间、奖项成就等信息；第二个问题，模型根据用户的需求创作了一首现代爱情诗。
This example demonstrates the model's ability to read pictures, its knowledge and its ability to compose. Firstly in the first problem, the model identifies the picture as a scene from the movie Titanic and gives information about the movie director, release date and award achievements; in the second problem, the model creates a modern love poem based on the user's needs.
这个例子展示了Ziya-Visual传统中国文化的识别和理解能力，模型识别出了中国画中的信息，在得到提示《清明上河图》之后给出了也给出画家张择端和北宋的历史背景。
This example demonstrates Ziya-Visual's ability to recognise and understand traditional Chinese culture. The model identifies information in Chinese paintings, and after getting the hint 'Qingming Shanghe Tu' gives also gives the historical context of the painter Zhang Zeduan and the Northern Song Dynasty.
如果输入多张图片进行问答呢？Ziya-Visual也是胜任的，在这个例子中，Ziya-Visual展现了强大的多图和多轮交互能力，根据用户给的三张图片，叙述了一个女士在城市夜景中邂逅一对母子猫咪，并与之交谈、分别的小故事。
What if multiple images are entered for a quiz? Ziya-Visual is also up to the task. In this example, Ziya-Visual demonstrates the power of multiple images and multiple rounds of interaction, narrating a short story of a lady who encounters a mother and son cat in a city night scene, talks to them and separates them, based on three images given by the user.
### 训练 Training
#### 数据 Train data
在中文视觉问答模型训练上，最大的问题就是数据量少，数据质量差。首先，封神榜团队在开源数据的基础上清洗、积累了一部分高质量数据；其次，我们通过翻译api得到了一部分英-中双语数据集，我们发现虽然翻译数据集会有“翻译腔”等问题，但是借助Ziya-v1的双语能力，最终的语言输出是能够缓解这一问题的；最后，团队结合BLIP，Grounded SAM等先进视觉技术，抽取图像描述的粗粒度信息和图像中物体、方位等细粒度信息，转化为语言描述形式，构造了一部分高质量数据。最终，Ziya-Visual构造了约2千万的优质数据进行训练。和Mini-GPT4、LLaVA一样，Ziya-Visual-v1主要是一个以数据为中心的工作，因此数据的数量和质量非常重要。
In the training of Chinese visual quiz model, the biggest problem is the small amount of data and poor data quality. Firstly, the team cleaned and accumulated some high-quality data based on open source data; secondly, we obtained a part of the English-Chinese bilingual dataset through translation api, and we found that although the translated dataset would have problems such as "translation accent", the final language output was able to alleviate this problem with Ziya-v1's bilingual capability. Finally, the team combined BLIP, Grounded SAM and other advanced vision technologies to extract coarse-grained information from image descriptions and fine-grained information such as objects and orientation in images, and transform them into linguistic descriptions to construct a portion of high-quality data. Ultimately, Ziya-Visual constructed approximately 20 million pieces of high quality data for training. Like Mini-GPT4 and LLaVA, Ziya-Visual-v1 is primarily a data-centric exercise, so the quantity and quality of data is very important.
#### 模型结构 Model Architecture
为了更好的结合视觉预训练模型和LLM的能力，和Mini-GPT4和LLaVA工作一样，Ziya-Visual-v1的训练遵循了BLIP2提出的经典网络结构和两阶段训练的范式。而且我们在实验过程中发现，是否训练Vision Encoder的参数对于最终的生成效果影响很小。因此，在整体模型上，视觉处理部分我们继承了BLIP2的ViT + QFormer参数，LLM部分继承了Ziya-v1的权重，这两个部分权重都是冻结不参与训练的。我们主要训练的部分是视觉映射层（Projection Layer）。第一阶段，我们使用图像Caption数据训练映射层，使Vision Encder抽取出来的图像特征能够和LLM中的文本特征空间进行对齐；第二阶段，我们使用图像问答数据集，进一步微调Ziya-Visual的视觉-语言能力。
In order to better combine the capabilities of the vision pre-training model and the LLM, as in the Mini-GPT4 and LLaVA work, the training of Ziya-Visual-v1 followed the classical network structure and the two-stage training paradigm proposed by BLIP2. Moreover, we found during our experiments that whether or not the parameters of the Vision Encoder are trained has very little impact on the final generation results. Therefore, for the overall model, we inherited the ViT + QFormer parameters from BLIP2 for the vision processing part and the Ziya-v1 weights for the LLM part, both of which were frozen from training. Our main training component is the visual mapping layer (Projection Layer). In the first stage, we use the image Caption data to train the mapping layer so that the image features extracted by Vision Encder can be aligned with the text feature space in LLM; in the second stage, we use the image Q & A dataset to further fine-tune the visual-verbal capabilities of Ziya-Visual.
### 效果评估 Performance
首先是VQA效果上的评价，可以看到Ziya-Visual模型在GQA的中文和英文测试集上大部分指标均高于VisualGLM，而在BLUE-4上分数较低，这表明Ziya-Visual在大多数开放域的多模态问答上生成的答案更为泛化和准确，但在一些发散性的问题上生成答案具有自主性。对于mPLUG-Owl模型，英文采用了 mPLUG-Owl 7B Instruction tuning (LoRA) 版本，中文则采用了多语言的mPLUG-Owl 7B (Multilingual) Instruction tuning (LoRA) 版本。因此在英文测评分数上高于双语版本的Ziya-Visual，另一方面，由于Ziya-Visual采用的LLaMA具备更优秀的多语言理解和生成能力，并且在Ziya-Visual二阶段训练时也通过翻译工具引入了多语言多模态训练语料，因此在中文数据的测评结果上更有优势。
Firstly, the evaluation on the VQA effectiveness shows that the Ziya-Visual model outperforms VisualGLM on most of the metrics on both the Chinese and English test sets of GQA, while scoring lower on BLUE-4, indicating that Ziya-Visual generates more generalized and accurate answers on most open domain multimodal questions and answers, but generates some discrete questions on answers have autonomy. For the mPLUG-Owl model, the mPLUG-Owl 7B Instruction tuning (LoRA) version was used for English and the multilingual mPLUG-Owl 7B (Multilingual) Instruction tuning (LoRA) version was used for Chinese. On the other hand, Ziya-Visual's LLaMA has better multilingual comprehension and generation capabilities, and the multilingual multimodal training corpus was introduced in the second phase of Ziya-Visual training through a translation tool, so it has an advantage in the Chinese data.
其次我们使用LLaVA的做法利用GPT-4打分评价，该方法利用coco数据集中的caption和物体检测框信息输入给GPT-4；然后将Ziya-Visual和VisualGLM的图像问答的回答再输入到GPT-4，要求GPT-4从回答的有用性、相关性、准确性、细节程度进行评分（1-10分）；LLaVA中将对话任务划分为conv（简单对话），detail（细节对话）和complex（复杂推理），all是三种对话任务的综合平均分。最终评价结果如下，可以看到在简单对话和细节对话中，Ziya-Viusual优于VisualGLM，在复杂推理中略输于VisualGLM，最终总体平均结果优于VisualGLM。在对比mPLUG-Owl中我们得到的结论是类似的，Ziya-Visual总体平均结果优于mPLUG-Owl。
Secondly, we used the LLaVA approach to score the evaluation using the GPT-4, which uses the caption and object detection box information from the coco dataset to input to the GPT-4; the responses to the image quiz from Ziya-Visual and VisualGLM are then input to the GPT-4, which is asked to score the responses in terms of usefulness, relevance, accuracy, and The responses were then fed back into GPT-4, which was asked to rate the responses in terms of usefulness, relevance, accuracy, and level of detail (on a scale of 1-10); LLaVA divided the dialogue tasks into conv (simple dialogue), detail (detailed dialogue) and complex (complex reasoning), and all was the combined average score of the three dialogue tasks. The final evaluation results are as follows, and it can be seen that Ziya-Viusual outperforms VisualGLM in simple and detail dialogues, slightly loses out to VisualGLM in complex reasoning, and finally outperforms VisualGLM in overall average results.
In comparing mPLUG-Owl we reach a similar conclusion, with Ziya-Viusual outperforming mPLUG-Owl on average overall.
## 使用 Usage
首先加载Ziya-Visual模型：需要注意的是Visual-Ziya的模型仓库只包含视觉模型部分的参数，Ziya LLM部分的参数通过[Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1)获得。得到这两部分的模型参数后，我们加载模型：
First load the Ziya-Visual model: it should be noted that the model repository of Visual-Ziya contains only the parameters of the visual model part, the parameters of the Ziya LLM part are obtained through [Ziya-LLaMA-13B-v1](https://huggingface.co/IDEA-CCNL/Ziya-LLaMA-13B-v1). Once we have the parameters for both parts of the model, we load the model:
```python
from transformers import LlamaForCausalLM, LlamaTokenizer, BlipImageProcessor
from modeling_ziya_blip2 import ZiyaBlip2ForCausalLM
from PIL import Image
# model path of IDEA-CCNL/Ziya-LLaMA-13B-v1
LM_MODEL_PATH="local path of model Ziya-LLaMA-13B-v1"
lm_model = LlamaForCausalLM.from_pretrained(LM_MODEL_PATH)
tokenizer = LlamaTokenizer.from_pretrained(LM_MODEL_PATH)
# visual model
OPENAI_CLIP_MEAN = [0.48145466, 0.4578275, 0.40821073]
OPENAI_CLIP_STD = [0.26862954, 0.26130258, 0.27577711]
# demo.py is in the project path, so we can use local path ".". Otherwise you should use "IDEA-CCNL/Ziya-BLIP2-14B-Visual-v1"
model = ZiyaBlip2ForCausalLM.from_pretrained(".", language_model=lm_model)
image_size = model.config.vision_config.image_size
image_processor = BlipImageProcessor(
size={"height": image_size, "width": image_size},
image_mean=OPENAI_CLIP_MEAN,
image_std=OPENAI_CLIP_STD,
)
model.cuda() # if you use on cpu, comment this line
```
模型加载完毕后，我们就可以愉快地使用Ziya-Visual模型了：
Once the model has been loaded, we can happily use the Ziya-Visual model:
```python
generate_config = {
"max_new_tokens": 128,
"top_p": 0.1,
"temperature": 0.7
}
output = model.chat(
tokenizer=tokenizer,
pixel_values=image_processor(Image.open("wzry.jpg"), return_tensors="pt").pixel_values.to(model.device),
query="这是什么游戏",
previous_querys=[],
previous_outputs=[],
**generate_config,
)
print(output)
# 这是一款名为《王者荣耀》的多人在线竞技游戏。在游戏中，玩家扮演不同的角色，并与其他玩家进行战斗。游戏中的人物和环境都是虚拟的，但它们看起来非常逼真。玩家需要使用各种技能和策略来击败对手，并获得胜利。
```
## 引用 Citation
如果您在您的工作中使用了我们的模型，可以引用我们的[论文](https://arxiv.org/abs/2210.08590)：
If you are using the resource for your work, please cite the our [paper](https://arxiv.org/abs/2210.08590):
```text
@article{fengshenbang,
author = {Jiaxing Zhang and Ruyi Gan and Junjie Wang and Yuxiang Zhang and Lin Zhang and Ping Yang and Xinyu Gao and Ziwei Wu and Xiaoqun Dong and Junqing He and Jianheng Zhuo and Qi Yang and Yongfeng Huang and Xiayu Li and Yanghan Wu and Junyu Lu and Xinyu Zhu and Weifeng Chen and Ting Han and Kunhao Pan and Rui Wang and Hao Wang and Xiaojun Wu and Zhongshen Zeng and Chongpei Chen},
title = {Fengshenbang 1.0: Being the Foundation of Chinese Cognitive Intelligence},
journal = {CoRR},
volume = {abs/2209.02970},
year = {2022}
}
```
You can also cite our [website](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
欢迎引用我们的[网站](https://github.com/IDEA-CCNL/Fengshenbang-LM/):
```text
@misc{Fengshenbang-LM,
title={Fengshenbang-LM},
author={IDEA-CCNL},
year={2021},
howpublished={\url{https://github.com/IDEA-CCNL/Fengshenbang-LM}},
}
```
|
Zekunli/t5-base-SQuAD-qag-ep6 | Zekunli | 2023-06-07T07:35:28Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2023-06-07T07:10:54Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
- f1
model-index:
- name: t5-base-SQuAD-qag-ep6
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. -->
# t5-base-SQuAD-qag-ep6
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0387
- Rouge1: 38.3962
- Rouge2: 17.2138
- Rougel: 35.0757
- Rougelsum: 35.0976
- F1: 17.4776
- Exact Match: 11.8529
- Gen Len: 18.4519
## 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: 96
- eval_batch_size: 192
- seed: 1799
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | F1 | Exact Match | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|:-----------:|:-------:|
| 1.4851 | 1.02 | 200 | 1.1181 | 37.4066 | 15.7831 | 34.003 | 34.0487 | 15.55 | 9.9661 | 18.4881 |
| 1.2194 | 2.03 | 400 | 1.0716 | 38.1277 | 16.661 | 34.6265 | 34.6546 | 16.6447 | 10.9821 | 18.5283 |
| 1.1716 | 3.05 | 600 | 1.0537 | 37.9106 | 16.6251 | 34.5005 | 34.5213 | 17.0402 | 11.1756 | 18.492 |
| 1.1437 | 4.06 | 800 | 1.0441 | 38.4182 | 17.1721 | 35.0109 | 35.0357 | 17.482 | 11.7078 | 18.4654 |
| 1.1329 | 5.08 | 1000 | 1.0387 | 38.3962 | 17.2138 | 35.0757 | 35.0976 | 17.4776 | 11.8529 | 18.4519 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.5.1
- Tokenizers 0.12.1
|
Subsets and Splits