modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
conlan/a2c-cartpole-v1 | 2023-11-03T02:55:33.000Z | [
"stable-baselines3",
"CartPole-v1",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | conlan | null | null | conlan/a2c-cartpole-v1 | 0 | 2 | stable-baselines3 | 2023-11-03T02:53:01 | ---
library_name: stable-baselines3
tags:
- CartPole-v1
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
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
---
# **A2C** Agent playing **CartPole-v1**
This is a trained model of a **A2C** agent playing **CartPole-v1**
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
...
```
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makwingchi/PPO-LunarLander-v2 | 2023-11-03T02:56:45.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | makwingchi | null | null | makwingchi/PPO-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-03T02:56:28 | ---
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: 266.94 +/- 22.83
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
...
```
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] |
Jack-cpu/Falcon_7_AIACT_Qlora | 2023-11-03T03:41:57.000Z | [
"peft",
"region:us"
] | null | Jack-cpu | null | null | Jack-cpu/Falcon_7_AIACT_Qlora | 0 | 2 | peft | 2023-11-03T03:41:42 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
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samYahoo/phi-1_5-finetuned-gsm8k | 2023-11-03T20:08:52.000Z | [
"transformers",
"tensorboard",
"safetensors",
"mixformer-sequential",
"text-generation",
"generated_from_trainer",
"custom_code",
"license:other",
"region:us"
] | text-generation | samYahoo | null | null | samYahoo/phi-1_5-finetuned-gsm8k | 0 | 2 | transformers | 2023-11-03T03:49:54 | ---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
model-index:
- name: phi-1_5-finetuned-gsm8k
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. -->
# phi-1_5-finetuned-gsm8k
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 300
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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sflanker/ppo-LunarLander-v2 | 2023-11-03T05:42:43.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | sflanker | null | null | sflanker/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-03T05:42:26 | ---
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: 171.41 +/- 114.61
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
...
```
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knslee07/distilbert-base-uncased-finetuned-emotion | 2023-11-03T06:03:34.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | knslee07 | null | null | knslee07/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-11-03T05:54:52 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9195
- name: F1
type: f1
value: 0.9194973648458569
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2267
- Accuracy: 0.9195
- F1: 0.9195
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8207 | 1.0 | 250 | 0.3304 | 0.9045 | 0.9019 |
| 0.2591 | 2.0 | 500 | 0.2267 | 0.9195 | 0.9195 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3
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shuangzhiaishang/ppo-LunarLander-v2 | 2023-11-03T06:37:05.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | shuangzhiaishang | null | null | shuangzhiaishang/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-03T06:36:43 | ---
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: 254.11 +/- 23.76
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
...
```
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Midnight-22/ppo-LunarLander-v2 | 2023-11-03T06:55:02.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Midnight-22 | null | null | Midnight-22/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-03T06:53:54 | ---
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: 249.57 +/- 25.65
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
...
```
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bunbohue/BART-base_readme_summarizer_2 | 2023-11-03T08:34:10.000Z | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | bunbohue | null | null | bunbohue/BART-base_readme_summarizer_2 | 0 | 2 | transformers | 2023-11-03T07:00:13 | ---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: BART-base_readme_summarizer_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. -->
# BART-base_readme_summarizer_2
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3492
- Rouge1: 0.6101
- Rouge2: 0.4961
- Rougel: 0.5899
- Rougelsum: 0.591
- Gen Len: 14.9109
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.6598 | 1.0 | 786 | 1.9606 | 0.4853 | 0.3392 | 0.4533 | 0.4549 | 14.0738 |
| 1.8432 | 2.0 | 1572 | 1.7326 | 0.5073 | 0.3651 | 0.4762 | 0.4776 | 15.4097 |
| 1.5567 | 3.0 | 2358 | 1.5870 | 0.5302 | 0.3826 | 0.4985 | 0.4995 | 15.0102 |
| 1.244 | 4.0 | 3144 | 1.4617 | 0.5657 | 0.4319 | 0.5373 | 0.5385 | 14.9746 |
| 1.112 | 5.0 | 3930 | 1.4030 | 0.5825 | 0.4529 | 0.5553 | 0.5562 | 15.5191 |
| 0.9389 | 6.0 | 4716 | 1.3690 | 0.5943 | 0.4714 | 0.5711 | 0.5714 | 15.1018 |
| 0.8119 | 7.0 | 5502 | 1.3668 | 0.6122 | 0.4977 | 0.5924 | 0.5925 | 14.9822 |
| 0.769 | 8.0 | 6288 | 1.3492 | 0.6101 | 0.4961 | 0.5899 | 0.591 | 14.9109 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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onangeko/ppo-LunarLander-v2 | 2023-11-03T08:42:41.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | onangeko | null | null | onangeko/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-03T08:42:17 | ---
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: 260.47 +/- 20.34
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
...
```
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] |
sainteye/ifoodie-menu-v2 | 2023-11-03T09:09:04.000Z | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | sainteye | null | null | sainteye/ifoodie-menu-v2 | 0 | 2 | transformers | 2023-11-03T09:08:59 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: ifoodie-menu-v2
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.7894737124443054
---
# ifoodie-menu-v2
['優質', '差', '廣告', '普通']
## Example Images
# #### 優質
# 
#
# #### 差
# 
#
# #### 廣告
# 
#
# #### 普通
# 
# | 502 | [
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Capstone-lpx/mistral_b_finance_finetuned_test | 2023-11-03T09:27:32.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | Capstone-lpx | null | null | Capstone-lpx/mistral_b_finance_finetuned_test | 0 | 2 | peft | 2023-11-03T09:27:25 | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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## Glossary [optional]
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## Model Card Contact
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0
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ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing-chat | 2023-11-03T09:29:36.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | ramdhanfirdaus | null | null | ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing-chat | 0 | 2 | peft | 2023-11-03T09:29:27 | ---
library_name: peft
base_model: tiiuae/falcon-rw-1b
---
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## Training procedure
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- PEFT 0.6.0
## Training procedure
### Framework versions
- PEFT 0.6.0
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Eswann/ppo-LunarLander-v2 | 2023-11-03T10:16:22.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Eswann | null | null | Eswann/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-03T10:15:13 | ---
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: 251.21 +/- 14.00
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
...
```
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CADM97/RL-LunarLander-v2-PPO | 2023-11-03T10:33:50.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | CADM97 | null | null | CADM97/RL-LunarLander-v2-PPO | 0 | 2 | stable-baselines3 | 2023-11-03T10:33:28 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 218.47 +/- 82.70
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
...
```
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Juliacnc/ppo-LunarLander-v2 | 2023-11-03T10:50:28.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Juliacnc | null | null | Juliacnc/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-03T10:50:09 | ---
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: 272.62 +/- 17.30
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
...
```
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] |
GBjorn/ppo-LunarLander-v2 | 2023-11-03T11:22:43.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | GBjorn | null | null | GBjorn/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-03T11:22:19 | ---
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: 246.34 +/- 28.52
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
...
```
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] |
LeKyks1/DL_TP1 | 2023-11-03T11:31:28.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | LeKyks1 | null | null | LeKyks1/DL_TP1 | 0 | 2 | stable-baselines3 | 2023-11-03T11:31:10 | ---
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: -1183.55 +/- 1180.88
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
...
```
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LeKyks1/PPO-LunarLander-v2 | 2023-11-03T11:32:17.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | LeKyks1 | null | null | LeKyks1/PPO-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-03T11:32:01 | ---
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: -795.59 +/- 312.71
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
...
```
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saleemtechlabs/my_awesome_model | 2023-11-03T15:51:59.000Z | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | saleemtechlabs | null | null | saleemtechlabs/my_awesome_model | 0 | 2 | transformers | 2023-11-03T11:54:54 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: saleemtechlabs/my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# saleemtechlabs/my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0896
- Validation Loss: 0.2620
- Train Accuracy: 0.9153
- 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', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 735, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.4017 | 0.2537 | 0.8983 | 0 |
| 0.1626 | 0.2695 | 0.8949 | 1 |
| 0.0896 | 0.2620 | 0.9153 | 2 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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sharkMeow/mt5-small-finetuned-b8-e10-1024-128 | 2023-11-03T17:43:04.000Z | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | sharkMeow | null | null | sharkMeow/mt5-small-finetuned-b8-e10-1024-128 | 0 | 2 | transformers | 2023-11-03T12:14:34 | ---
license: apache-2.0
base_model: google/mt5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-b8-e10-1024-128
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. -->
# mt5-small-finetuned-b8-e10-1024-128
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3822
- Rouge1: 13.327
- Rouge2: 4.8244
- Rougel: 13.1978
- Rougelsum: 13.2133
- Gen Len: 17.5592
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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 | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 4.7372 | 1.0 | 1357 | 3.8287 | 9.3951 | 3.6576 | 9.342 | 9.3047 | 12.6653 |
| 4.3162 | 2.0 | 2714 | 3.6750 | 10.9224 | 4.1119 | 10.8209 | 10.8235 | 15.0997 |
| 4.1726 | 3.0 | 4071 | 3.5668 | 11.7438 | 4.2353 | 11.6204 | 11.6087 | 16.5169 |
| 4.0439 | 4.0 | 5428 | 3.5002 | 12.402 | 4.4267 | 12.2785 | 12.2924 | 17.0402 |
| 3.9978 | 5.0 | 6785 | 3.4494 | 12.7762 | 4.5509 | 12.6699 | 12.6829 | 17.2466 |
| 3.9687 | 6.0 | 8142 | 3.4229 | 12.9652 | 4.6727 | 12.8555 | 12.8761 | 17.4303 |
| 3.8639 | 7.0 | 9499 | 3.4058 | 13.4216 | 4.784 | 13.3097 | 13.2988 | 17.4252 |
| 3.8474 | 8.0 | 10856 | 3.3924 | 13.2422 | 4.7672 | 13.1416 | 13.12 | 17.5046 |
| 3.843 | 9.0 | 12213 | 3.3845 | 13.2519 | 4.8713 | 13.1421 | 13.1304 | 17.5371 |
| 3.8545 | 10.0 | 13570 | 3.3822 | 13.327 | 4.8244 | 13.1978 | 13.2133 | 17.5592 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.14.6
- Tokenizers 0.13.3
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] |
espnet/akreal_lh_medium_asr2_e_branchformer_wavlm_large_21_km1k_bpe_rm6k_bpe_ts3k | 2023-11-03T12:24:10.000Z | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:libriheavy_medium",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] | automatic-speech-recognition | espnet | null | null | espnet/akreal_lh_medium_asr2_e_branchformer_wavlm_large_21_km1k_bpe_rm6k_bpe_ts3k | 0 | 2 | espnet | 2023-11-03T12:23:33 | ---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- libriheavy_medium
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/akreal_lh_medium_asr2_e_branchformer_wavlm_large_21_km1k_bpe_rm6k_bpe_ts3k`
This model was trained by Pavel Denisov using libriheavy_medium recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
Follow the [ESPnet installation instructions](https://espnet.github.io/espnet/installation.html)
if you haven't done that already.
```bash
cd espnet
git checkout b62c674b740d148a5e1d07b1bc3eb8e5dddb5839
pip install -e .
cd egs2/libriheavy_medium/asr2
./run.sh --skip_data_prep false --skip_train true --download_model espnet/akreal_lh_medium_asr2_e_branchformer_wavlm_large_21_km1k_bpe_rm6k_bpe_ts3k
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Fri Nov 3 12:31:34 CET 2023`
- python version: `3.10.8 (main, Nov 14 2022, 00:00:00) [GCC 11.3.1 20220421 (Red Hat 11.3.1-3)]`
- espnet version: `espnet 202310`
- pytorch version: `pytorch 2.0.1+cu118`
- Git hash: `b62c674b740d148a5e1d07b1bc3eb8e5dddb5839`
- Commit date: `Mon Oct 30 13:47:12 2023 +0100`
## exp/asr_train_discrete_asr_e_branchformer1_e12_lr1e-3_raw_wavlm_large_21_km1000_bpe_rm6000_bpe_ts3000
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc0.3_asr_model_valid.acc.ave/test_clean|2557|102570|90.9|8.5|0.6|0.4|9.5|88.9|
|decode_ctc0.3_asr_model_valid.acc.ave/test_other|2815|111093|87.4|11.4|1.2|0.6|13.2|91.8|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc0.3_asr_model_valid.acc.ave/test_clean|2557|552463|97.9|1.1|1.0|0.5|2.7|89.0|
|decode_ctc0.3_asr_model_valid.acc.ave/test_other|2815|600913|96.4|1.8|1.7|0.9|4.4|91.8|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_ctc0.3_asr_model_valid.acc.ave/test_clean|2557|160091|92.1|4.9|3.0|1.2|9.0|89.0|
|decode_ctc0.3_asr_model_valid.acc.ave/test_other|2815|181621|88.0|7.0|5.0|1.6|13.6|91.8|
## exp/asr_train_discrete_asr_e_branchformer1_e12_lr1e-3_raw_wavlm_large_21_km1000_bpe_rm6000_bpe_ts3000/decode_ctc0.3_asr_model_valid.acc.ave
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|org/dev|5348|217854|88.9|10.2|0.9|0.5|11.6|91.1|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|org/dev|5348|1177476|97.1|1.5|1.4|0.7|3.6|91.2|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|org/dev|5348|349335|90.0|6.1|3.9|1.4|11.4|91.2|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_discrete_asr_e_branchformer1_e12_lr1e-3.yaml
print_config: false
log_level: INFO
drop_last_iter: false
dry_run: false
iterator_type: sequence
valid_iterator_type: null
output_dir: exp/asr_train_discrete_asr_e_branchformer1_e12_lr1e-3_raw_wavlm_large_21_km1000_bpe_rm6000_bpe_ts3000
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 1
dist_backend: nccl
dist_init_method: env://
dist_world_size: 2
dist_rank: 0
local_rank: 0
dist_master_addr: localhost
dist_master_port: 56451
dist_launcher: null
multiprocessing_distributed: true
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
create_graph_in_tensorboard: false
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
use_lora: false
save_lora_only: true
lora_conf: {}
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 600000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe6000_bpe3000/train/src_text_shape.bpe
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe6000_bpe3000/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe6000_bpe3000/valid/text_shape.bpe
- exp/asr_stats_raw_rm_wavlm_large_21_km1000_bpe6000_bpe3000/valid/src_text_shape.bpe
batch_type: numel
valid_batch_type: null
fold_length:
- 150
- 150
sort_in_batch: descending
shuffle_within_batch: false
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
chunk_excluded_key_prefixes: []
train_data_path_and_name_and_type:
- - dump/raw/train_medium/text.rm.wavlm_large_21_km1000
- src_text
- text
- - dump/raw/train_medium/text.ts.en
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev/text.ts.en
- text
- text
- - dump/raw/dev/text.rm.wavlm_large_21_km1000
- src_text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
allow_multi_rates: false
valid_max_cache_size: null
exclude_weight_decay: false
exclude_weight_decay_conf: {}
optim: adam
optim_conf:
lr: 0.001
weight_decay: 1.0e-06
scheduler: warmuplr
scheduler_conf:
warmup_steps: 15000
token_list:
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- 冴佶侕
- 仺倍仗傷
- 乪仼
- 備乻依
- 亣亐俺兛
- 俤佗倚伬亃
- 伴乀仟偬乱乙
- 侘伻傩
- 僘仪之
- 儌偽仮凉丩
- 倃七倞
- 亣俺丿兛免
- 偢侉偢俓丨
- 侽亲冮伺
- ▁乜倿侉侷
- 专儥仦
- 儦仕僈仨
- 儡儂侼
- 倰乢亝仴
- 冹侟冪
- 僁侍久傼
- 儒伌兤为僞
- 冤乸偎何仸
- 侫余侫
- 亿书俨侃
- 伲乐侮亼
- 偔僈偄义
- 仓位兰
- 傊伝
- 佪乱乙
- 倌冏
- 侨佥
- 側乁個
- 乵丗償伕倓僶
- 侺低侇
- 倻丱凃倧俀
- 于侴像
- 凚乴兰儤
- 倛俬丫倓
- 侖乒仪倷兣
- 伊僠冷佨冷
- 倽
- 些伇凌伮
- 伛冾体
- 仵俭丙伭
- 冬万介佧
- 亿
- 倩丢傽
- 丽俆乡倗冼
- 仺倍傷伪佡
- 五乏傸
- 乞侉兼
- 倊儲倲
- 儬佦
- 儈佱偈兇
- 儮倫
- 偦傪儤也傽
- 侭六亐亱冲伨佩
- 俄丗佣乌伭
- 侹侶乸传
- 倭倉仯
- 二倝丢傽
- 佞仗几傸丝
- 傭乘傭
- 傥
- 伦儧僥
- 傰佘佂
- 侫侥令
- 兢俢侩冋
- 份侺伇乁
- 兘俋伬
- 凛佥兤侀
- 儚丽侉丽
- 処
- 倞业俍
- 儚侉丽
- 仲伈仲余仲伈
- 促万乻况依
- 備乻儭依
- 傤俣世侎俵侱两
- 估儣凃倧俀
- 冭么伇乁
- 僁久傼傉仈
- 伛倗冼儌
- 俭休伭
- 円侜
- 俲僻冃
- 丁凔偹乊佩
- 丱偌佹亶丝
- 侲仳侵債
- 伴乀偬乱乙
- 倭二亘乏
- 伵
- 傀
- 偋
- 倈
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- 亊
- 偭
- 僎
- 克
- 倹
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- 信
- 亨
- 仯
- 傂
- 倷
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- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: null
zero_infinity: true
use_preprocessor: true
token_type: bpe
src_token_type: bpe
bpemodel: data/token_list/tgt_bpe_unigram3000_ts_en/bpe.model
src_bpemodel: data/token_list/src_bpe_unigram6000_rm_wavlm_large_21_km1000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
tokenizer_encode_conf: null
src_tokenizer_encode_conf:
enable_sampling: true
alpha: 0.4
nbest_size: -1
frontend: embed
frontend_conf:
embed_dim: 512
positional_dropout_rate: 0.1
specaug: specaug
specaug_conf:
apply_time_warp: false
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: false
freq_mask_width_range:
- 0
- 10
num_freq_mask: 0
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 10
preencoder: null
preencoder_conf: {}
encoder: e_branchformer
encoder_conf:
output_size: 256
attention_heads: 4
attention_layer_type: rel_selfattn
pos_enc_layer_type: rel_pos
rel_pos_type: latest
cgmlp_linear_units: 1024
cgmlp_conv_kernel: 31
use_linear_after_conv: false
gate_activation: identity
num_blocks: 12
dropout_rate: 0.1
positional_dropout_rate: 0.1
attention_dropout_rate: 0.1
input_layer: conv1d2
layer_drop_rate: 0.0
linear_units: 1024
positionwise_layer_type: linear
use_ffn: true
macaron_ffn: true
merge_conv_kernel: 31
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.1
self_attention_dropout_rate: 0.1
src_attention_dropout_rate: 0.1
layer_drop_rate: 0.0
model: discrete_asr
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
share_decoder_input_output_embed: false
share_encoder_decoder_input_embed: false
required:
- output_dir
- src_token_list
- token_list
version: '202310'
distributed: true
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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] |
HoangHa/llama2-vie-alpaca | 2023-11-03T12:36:12.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | HoangHa | null | null | HoangHa/llama2-vie-alpaca | 0 | 2 | transformers | 2023-11-03T12:28:08 | ---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
---
# Model Trained Using AutoTrain | 120 | [
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] |
TheBloke/Utopia-13B-GPTQ | 2023-11-05T16:07:32.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"text-generation-inference",
"region:us"
] | text-generation | TheBloke | null | null | TheBloke/Utopia-13B-GPTQ | 4 | 2 | transformers | 2023-11-03T12:55:01 | ---
base_model: Undi95/Utopia-13B
inference: false
license: cc-by-nc-4.0
model_creator: Undi
model_name: Utopia 13B
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
tags:
- not-for-all-audiences
- nsfw
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Utopia 13B - GPTQ
- Model creator: [Undi](https://huggingface.co/Undi95)
- Original model: [Utopia 13B](https://huggingface.co/Undi95/Utopia-13B)
<!-- description start -->
## Description
This repo contains GPTQ model files for [Undi's Utopia 13B](https://huggingface.co/Undi95/Utopia-13B).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/Utopia-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Utopia-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Utopia-13B-GGUF)
* [Undi's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Undi95/Utopia-13B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `cc-by-nc-4.0`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Undi's Utopia 13B](https://huggingface.co/Undi95/Utopia-13B).
<!-- licensing end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Utopia-13B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 8.00 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Utopia-13B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.36 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Utopia-13B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.51 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/Utopia-13B-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 14.54 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [main](https://huggingface.co/TheBloke/Utopia-13B-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 7.26 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Utopia-13B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 13.65 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/Utopia-13B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/Utopia-13B-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `Utopia-13B-GPTQ`:
```shell
mkdir Utopia-13B-GPTQ
huggingface-cli download TheBloke/Utopia-13B-GPTQ --local-dir Utopia-13B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir Utopia-13B-GPTQ
huggingface-cli download TheBloke/Utopia-13B-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir Utopia-13B-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir Utopia-13B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/Utopia-13B-GPTQ --local-dir Utopia-13B-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Utopia-13B-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Utopia-13B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Utopia-13B-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Utopia-13B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/Utopia-13B-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/Utopia-13B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Undi's Utopia 13B
<!-- description start -->
## Description
This repo contains fp16 files of Utopia-13B, a merge I have done with the new task_arithmetic merge method from mergekit.
<!-- description end -->
<!-- description start -->
## Models and loras used
- [Xwin-LM/Xwin-LM-13B-V0.2](https://huggingface.co/Xwin-LM/Xwin-LM-13B-V0.2)
- [NeverSleep/Nethena-13B](https://huggingface.co/NeverSleep/Nethena-13B)
- [PygmalionAI/pygmalion-2-13b](https://huggingface.co/PygmalionAI/pygmalion-2-13b)
- [Undi95/Storytelling-v2.1-13B-lora](https://huggingface.co/Undi95/Storytelling-v2.1-13B-lora)
- [zattio770/120-Days-of-LORA-v2-13B](https://huggingface.co/zattio770/120-Days-of-LORA-v2-13B)
- [lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT](https://huggingface.co/lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT)
<!-- description end -->
## The sauce
```
Xwin-LM/Xwin-LM-13B-V0.2
Undi95/Storytelling-v2.1-13B-lora
=> p1
NeverSleep/Nethena-13B
zattio770/120-Days-of-LORA-v2-13B
=> p2
PygmalionAI/pygmalion-2-13b
lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
=> p3
merge_method: task_arithmetic
base_model: TheBloke/Llama-2-13B-fp16
models:
- model: TheBloke/Llama-2-13B-fp16
- model: Undi95/newpart1
parameters:
weight: 1.0
- model: Undi95/newpart2
parameters:
weight: 0.45
- model: Undi95/newpart3
parameters:
weight: 0.33
dtype: float16
```
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
If you want to support me, you can [here](https://ko-fi.com/undiai).
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] |
guoyww/animatediff-motion-lora-rolling-anticlockwise | 2023-11-03T13:06:58.000Z | [
"diffusers",
"animatediff",
"text-to-video",
"region:us"
] | text-to-video | guoyww | null | null | guoyww/animatediff-motion-lora-rolling-anticlockwise | 0 | 2 | diffusers | 2023-11-03T13:06:58 | ---
library_name: diffusers
pipeline_tag: text-to-video
tags:
- animatediff
---
# Motion LoRAs
Motion LoRAs allow adding specific types of motion to your animations.
Currently the following types of motion are available for models using the `guoyww/animatediff-motion-adapter-v1-5-2` checkpoint.
- Zoom In/Out
- Pan Left/Right
- Tilt Up/Down
- Rolling Clockwise/Anticlockwise
Please refer to the [AnimateDiff documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/animatediff) for information on how to use these Motion LoRAs. | 694 | [
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] |
Realgon/left_padding50_model | 2023-11-03T15:25:58.000Z | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | Realgon | null | null | Realgon/left_padding50_model | 0 | 2 | transformers | 2023-11-03T13:26:12 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: left_padding50_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: 0.92952
---
<!-- 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. -->
# left_padding50_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: 0.2353
- Accuracy: 0.9295
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2306 | 1.0 | 1563 | 0.2656 | 0.9027 |
| 0.1599 | 2.0 | 3126 | 0.2353 | 0.9295 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0+cu117
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing-chat-2 | 2023-11-03T13:31:19.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | ramdhanfirdaus | null | null | ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing-chat-2 | 0 | 2 | peft | 2023-11-03T13:31:12 | ---
library_name: peft
base_model: tiiuae/falcon-rw-1b
---
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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**BibTeX:**
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## Glossary [optional]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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## Training procedure
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
### Framework versions
- PEFT 0.7.0.dev0
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SamDNX/ppo-LunarLander-v2 | 2023-11-03T14:12:31.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | SamDNX | null | null | SamDNX/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-03T14:12:12 | ---
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: 269.81 +/- 16.52
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
...
```
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sebastiantrbl/GPT2-input-response-pair | 2023-11-03T15:15:54.000Z | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | sebastiantrbl | null | null | sebastiantrbl/GPT2-input-response-pair | 0 | 2 | transformers | 2023-11-03T14:58:54 | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: GPT2-input-response-pair
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# GPT2-input-response-pair
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing-chat-3 | 2023-11-03T15:02:20.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | ramdhanfirdaus | null | null | ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-testing-chat-3 | 0 | 2 | peft | 2023-11-03T15:02:11 | ---
library_name: peft
base_model: tiiuae/falcon-rw-1b
---
# Model Card for Model ID
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## Model Details
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## How to Get Started with the Model
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## Training Details
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.7.0.dev0
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amyy78/a2c-PandaReachDense-v3 | 2023-11-03T15:08:37.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | amyy78 | null | null | amyy78/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-11-03T15:03:08 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.25 +/- 0.09
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
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jamaya/bert-finetuned-ner | 2023-11-03T17:11:37.000Z | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | jamaya | null | null | jamaya/bert-finetuned-ner | 0 | 2 | transformers | 2023-11-03T16:06:34 | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.927841845140033
- name: Recall
type: recall
value: 0.9478290138000673
- name: F1
type: f1
value: 0.9377289377289377
- name: Accuracy
type: accuracy
value: 0.9855036204156119
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0643
- Precision: 0.9278
- Recall: 0.9478
- F1: 0.9377
- Accuracy: 0.9855
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0781 | 1.0 | 1756 | 0.0789 | 0.9110 | 0.9325 | 0.9217 | 0.9802 |
| 0.0415 | 2.0 | 3512 | 0.0617 | 0.9243 | 0.9472 | 0.9356 | 0.9851 |
| 0.0256 | 3.0 | 5268 | 0.0643 | 0.9278 | 0.9478 | 0.9377 | 0.9855 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Miloou/ppo-LunarLander-v2 | 2023-11-03T16:50:12.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Miloou | null | null | Miloou/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-03T16:49:51 | ---
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: 260.73 +/- 18.88
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
...
```
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ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-non-2 | 2023-11-03T16:54:43.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | ramdhanfirdaus | null | null | ramdhanfirdaus/falcon-1b-finetuned-aings-adapters-non-2 | 0 | 2 | peft | 2023-11-03T16:54:33 | ---
library_name: peft
base_model: tiiuae/falcon-rw-1b
---
# Model Card for Model ID
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## Model Details
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## How to Get Started with the Model
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.7.0.dev0
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shaunck96/sentiment_BERTbaseuncased_finetuned_emotion | 2023-11-03T17:27:00.000Z | [
"transformers",
"tf",
"bert",
"feature-extraction",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] | feature-extraction | shaunck96 | null | null | shaunck96/sentiment_BERTbaseuncased_finetuned_emotion | 0 | 2 | transformers | 2023-11-03T17:16:12 | ---
tags:
- generated_from_keras_callback
model-index:
- name: sentiment_BERTbaseuncased_finetuned_emotion
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. -->
# sentiment_BERTbaseuncased_finetuned_emotion
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
Jack-cpu/Llama2_7_Technical_Qlora | 2023-11-03T17:22:57.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | Jack-cpu | null | null | Jack-cpu/Llama2_7_Technical_Qlora | 0 | 2 | peft | 2023-11-03T17:22:36 | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
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## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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**BibTeX:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0
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damnloveless/a2c-PandaReachDense-v3 | 2023-11-03T18:16:48.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | damnloveless | null | null | damnloveless/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-11-03T18:11:37 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -1.01 +/- 1.67
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
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TheLegendMars/ppo-LunarLander-v2 | 2023-11-03T18:44:14.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | TheLegendMars | null | null | TheLegendMars/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-03T18:43:51 | ---
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: 256.70 +/- 23.41
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
...
```
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Joetib/pythia-finetuned-5-steps | 2023-11-03T19:27:54.000Z | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Joetib | null | null | Joetib/pythia-finetuned-5-steps | 0 | 2 | transformers | 2023-11-03T19:13:03 | ---
license: apache-2.0
base_model: EleutherAI/pythia-410M
tags:
- generated_from_trainer
model-index:
- name: pythia-finetuned-5-steps
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. -->
# pythia-finetuned-5-steps
This model is a fine-tuned version of [EleutherAI/pythia-410M](https://huggingface.co/EleutherAI/pythia-410M) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- training_steps: 5
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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rowdy-store/products-ner | 2023-11-03T19:33:41.000Z | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:ner",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | rowdy-store | null | null | rowdy-store/products-ner | 0 | 2 | transformers | 2023-11-03T19:22:10 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: products-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ner
type: ner
config: ner
split: test
args: ner
metrics:
- name: Precision
type: precision
value: 0.8186813186813187
- name: Recall
type: recall
value: 0.8563218390804598
- name: F1
type: f1
value: 0.8370786516853932
- name: Accuracy
type: accuracy
value: 0.9532710280373832
---
<!-- 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. -->
# products-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1747
- Precision: 0.8187
- Recall: 0.8563
- F1: 0.8371
- Accuracy: 0.9533
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 30 | 0.7395 | 0.3421 | 0.3736 | 0.3571 | 0.7897 |
| No log | 2.0 | 60 | 0.4036 | 0.5842 | 0.6782 | 0.6277 | 0.8863 |
| No log | 3.0 | 90 | 0.2716 | 0.7105 | 0.7759 | 0.7418 | 0.9174 |
| No log | 4.0 | 120 | 0.2286 | 0.7433 | 0.7989 | 0.7701 | 0.9315 |
| No log | 5.0 | 150 | 0.2093 | 0.7760 | 0.8161 | 0.7955 | 0.9377 |
| No log | 6.0 | 180 | 0.1890 | 0.7796 | 0.8333 | 0.8056 | 0.9455 |
| No log | 7.0 | 210 | 0.1772 | 0.8197 | 0.8621 | 0.8403 | 0.9533 |
| No log | 8.0 | 240 | 0.1747 | 0.8187 | 0.8563 | 0.8371 | 0.9533 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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RiversHaveWings/Mistral-7B-v0.1-safetensors | 2023-11-03T20:44:53.000Z | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"pretrained",
"en",
"arxiv:2310.06825",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | RiversHaveWings | null | null | RiversHaveWings/Mistral-7B-v0.1-safetensors | 0 | 2 | transformers | 2023-11-03T20:37:29 | ---
license: apache-2.0
pipeline_tag: text-generation
language:
- en
tags:
- pretrained
inference:
parameters:
temperature: 0.7
---
# Model Card for Mistral-7B-v0.1
The Mistral-7B-v0.1 Large Language Model (LLM) is a pretrained generative text model with 7 billion parameters.
Mistral-7B-v0.1 outperforms Llama 2 13B on all benchmarks we tested.
For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).
## Model Architecture
Mistral-7B-v0.1 is a transformer model, with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Troubleshooting
- If you see the following error:
```
KeyError: 'mistral'
```
- Or:
```
NotImplementedError: Cannot copy out of meta tensor; no data!
```
Ensure you are utilizing a stable version of Transformers, 4.34.0 or newer.
## Notice
Mistral 7B is a pretrained base model and therefore does not have any moderation mechanisms.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. | 1,390 | [
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Ocastano/title_category_model | 2023-11-03T21:52:14.000Z | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | Ocastano | null | null | Ocastano/title_category_model | 0 | 2 | sentence-transformers | 2023-11-03T21:26:27 | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1211 with parameters:
```
{'batch_size': 128, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 3,849 | [
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damnloveless/a2c-PandaPickAndPlace-v3 | 2023-11-03T21:40:05.000Z | [
"stable-baselines3",
"PandaPickAndPlace-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | damnloveless | null | null | damnloveless/a2c-PandaPickAndPlace-v3 | 0 | 2 | stable-baselines3 | 2023-11-03T21:34:55 | ---
library_name: stable-baselines3
tags:
- PandaPickAndPlace-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaPickAndPlace-v3
type: PandaPickAndPlace-v3
metrics:
- type: mean_reward
value: -50.00 +/- 0.00
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaPickAndPlace-v3**
This is a trained model of a **A2C** agent playing **PandaPickAndPlace-v3**
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
...
```
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crazypanther/Llama-2-7b-chat-finetune | 2023-11-03T21:43:15.000Z | [
"peft",
"region:us"
] | null | crazypanther | null | null | crazypanther/Llama-2-7b-chat-finetune | 0 | 2 | peft | 2023-11-03T21:42:16 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
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tranquocthanh/ppo-LunarLander-v2 | 2023-11-03T23:57:06.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | tranquocthanh | null | null | tranquocthanh/ppo-LunarLander-v2 | 1 | 2 | stable-baselines3 | 2023-11-03T23:56:46 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO-MlpPolicy
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 267.39 +/- 16.20
name: mean_reward
verified: false
---
# **PPO-MlpPolicy** Agent playing **LunarLander-v2**
This is a trained model of a **PPO-MlpPolicy** 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
...
```
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mtc/LeoLM-leo-mistral-hessianai-7b-all-labels-german-classification-with-explanation-50-qlora-4bit | 2023-11-04T01:04:24.000Z | [
"peft",
"region:us"
] | null | mtc | null | null | mtc/LeoLM-leo-mistral-hessianai-7b-all-labels-german-classification-with-explanation-50-qlora-4bit | 0 | 2 | peft | 2023-11-04T01:03:56 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
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tjkmitl/NegativeThaiNews_test1 | 2023-11-04T01:18:45.000Z | [
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | tjkmitl | null | null | tjkmitl/NegativeThaiNews_test1 | 0 | 2 | transformers | 2023-11-04T01:15:54 | ---
base_model: csebuetnlp/mT5_multilingual_XLSum
tags:
- generated_from_trainer
model-index:
- name: NegativeThaiNews_test1
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. -->
# NegativeThaiNews_test1
This model is a fine-tuned version of [csebuetnlp/mT5_multilingual_XLSum](https://huggingface.co/csebuetnlp/mT5_multilingual_XLSum) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4056
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.5843 | 2.5 | 500 | 3.4673 |
| 2.8438 | 5.0 | 1000 | 3.4056 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Jobiniah/a2c-PandaReachDense-v3 | 2023-11-04T01:23:02.000Z | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | Jobiniah | null | null | Jobiniah/a2c-PandaReachDense-v3 | 0 | 2 | stable-baselines3 | 2023-11-04T01:17:12 | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.37 +/- 0.32
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
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
...
```
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mtc/LeoLM-leo-mistral-hessianai-7b-all-labels-german-classification-with-explanation-100-qlora-4bit | 2023-11-04T01:19:02.000Z | [
"peft",
"region:us"
] | null | mtc | null | null | mtc/LeoLM-leo-mistral-hessianai-7b-all-labels-german-classification-with-explanation-100-qlora-4bit | 0 | 2 | peft | 2023-11-04T01:18:26 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
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mtc/LeoLM-leo-mistral-hessianai-7b-all-labels-german-classification-with-explanation-250-qlora-4bit | 2023-11-04T01:37:55.000Z | [
"peft",
"region:us"
] | null | mtc | null | null | mtc/LeoLM-leo-mistral-hessianai-7b-all-labels-german-classification-with-explanation-250-qlora-4bit | 0 | 2 | peft | 2023-11-04T01:37:19 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
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mattambs/ppo-LunarLander-v2 | 2023-11-04T03:29:05.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | mattambs | null | null | mattambs/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-04T03:28:44 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 247.69 +/- 34.24
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
...
```
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akshayvkt/detect-ai-text | 2023-11-04T06:27:07.000Z | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | akshayvkt | null | null | akshayvkt/detect-ai-text | 0 | 2 | transformers | 2023-11-04T05:07:16 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: detect-ai-text
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. -->
# detect-ai-text
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0067
- Accuracy: 0.9964
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 280 | 0.0059 | 0.9991 |
| 0.0319 | 2.0 | 560 | 0.0067 | 0.9964 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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moritz-hauptmann/ppo-LunarLander-v2 | 2023-11-04T06:43:51.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | moritz-hauptmann | null | null | moritz-hauptmann/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-04T06:43:29 | ---
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: 221.79 +/- 70.95
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
...
```
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] |
DrydenDev/autotrain-n64-cartridge-recognition-99270147297 | 2023-11-04T07:31:10.000Z | [
"transformers",
"pytorch",
"safetensors",
"swin",
"image-classification",
"autotrain",
"vision",
"dataset:DrydenDev/autotrain-data-n64-cartridge-recognition",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | DrydenDev | null | null | DrydenDev/autotrain-n64-cartridge-recognition-99270147297 | 0 | 2 | transformers | 2023-11-04T07:19:38 | ---
tags:
- autotrain
- vision
- image-classification
datasets:
- DrydenDev/autotrain-data-n64-cartridge-recognition
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
co2_eq_emissions:
emissions: 0.5176149010108697
---
# Disclaimer
- This is a Proof of Concept model, it hasn't been trained on enough n64 games to be considered reliable.
-
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 99270147297
- CO2 Emissions (in grams): 0.5176
## Validation Metrics
- Loss: 0.256
- Accuracy: 1.000
- Precision: 1.000
- Recall: 1.000
- AUC: 1.000
- F1: 1.000 | 868 | [
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phucnguyen150902/vit5-base_viquad-full-modified-norand_qg | 2023-11-04T07:51:18.000Z | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | phucnguyen150902 | null | null | phucnguyen150902/vit5-base_viquad-full-modified-norand_qg | 0 | 2 | transformers | 2023-11-04T07:50:35 | ---
license: mit
base_model: VietAI/vit5-base
tags:
- generated_from_trainer
model-index:
- name: vit5-base_viquad-full-modified-norand_qg_
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. -->
# vit5-base_viquad-full-modified-norand_qg_
This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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LaTarn/re-service-setfit-model | 2023-11-04T10:16:09.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | LaTarn | null | null | LaTarn/re-service-setfit-model | 0 | 2 | sentence-transformers | 2023-11-04T10:15:48 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/re-service-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("LaTarn/re-service-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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] |
team-lucid/hubert-xlarge-korean | 2023-11-06T15:50:49.000Z | [
"transformers",
"pytorch",
"jax",
"safetensors",
"hubert",
"feature-extraction",
"speech",
"audio",
"automatic-speech-recognition",
"custom_code",
"ko",
"arxiv:2106.07447",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | team-lucid | null | null | team-lucid/hubert-xlarge-korean | 1 | 2 | transformers | 2023-11-04T10:42:36 | ---
license: apache-2.0
language:
- ko
library_name: transformers
pipeline_tag: automatic-speech-recognition
tags:
- speech
- audio
---
# hubert-base-korean
## Model Details
HuBERT(Hidden-Unit BERT)는 Facebook에서 제안한 Speech Representation Learning 모델입니다.
HuBERT는 기존의 음성 인식 모델과 달리, 음성 신호를 raw waveform에서 바로 학습하는 self-supervised learning 방식을 사용합니다.
이 연구는 구글의 TPU Research Cloud(TRC)를 통해 지원받은 Cloud TPU로 학습되었습니다.
### Model Description
<table>
<tr>
<td colspan="2"></td>
<td>Base</td>
<td>Large</td>
</tr>
<tr>
<td rowspan="3">CNN Encoder</td>
<td>strides</td>
<td colspan="2">5, 2, 2, 2, 2, 2, 2</td>
</tr>
<tr>
<td>kernel width</td>
<td colspan="2">10, 3, 3, 3, 3, 2, 2</td>
</tr>
<tr>
<td>channel</td>
<td colspan="2">512</td>
</tr>
<tr>
<td rowspan="4">Transformer Encoder</td>
<td>Layer</td>
<td>12</td>
<td>24</td>
</tr>
<tr>
<td>embedding dim</td>
<td>768</td>
<td>1024</td>
</tr>
<tr>
<td>inner FFN dim</td>
<td>3072</td>
<td>4096</td>
</tr>
<tr>
<td>attention heads</td>
<td>8</td>
<td>16</td>
</tr>
<tr>
<td>Projection</td>
<td>dim</td>
<td>256</td>
<td>768</td>
</tr>
<tr>
<td colspan="2">Params</td>
<td>95M</td>
<td>317M </td>
</tr>
</table>
## How to Get Started with the Model
### Pytorch
```py
import torch
from transformers import HubertModel
model = HubertModel.from_pretrained("team-lucid/hubert-xlarge-korean")
wav = torch.ones(1, 16000)
outputs = model(wav)
print(f"Input: {wav.shape}") # [1, 16000]
print(f"Output: {outputs.last_hidden_state.shape}") # [1, 49, 768]
```
### JAX/Flax
```py
import jax.numpy as jnp
from transformers import FlaxAutoModel
model = FlaxAutoModel.from_pretrained("team-lucid/hubert-xlarge-korean", trust_remote_code=True)
wav = jnp.ones((1, 16000))
outputs = model(wav)
print(f"Input: {wav.shape}") # [1, 16000]
print(f"Output: {outputs.last_hidden_state.shape}") # [1, 49, 768]
```
## Training Details
### Training Data
해당 모델은 과학기술정보통신부의 재원으로 한국지능정보사회진흥원의 지원을 받아
구축된 [자유대화 음성(일반남여)](https://www.aihub.or.kr/aihubdata/data/view.do?dataSetSn=109), [다화자 음성합성 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?dataSetSn=542), [방송 콘텐츠 대화체 음성인식 데이터](https://www.aihub.or.kr/aihubdata/data/view.do?dataSetSn=463)
에서 약 4,000시간을 추출해 학습되었습니다.
### Training Procedure
[원 논문](https://arxiv.org/pdf/2106.07447.pdf)과 동일하게 MFCC 기반으로 Base 모델을 학습한 다음, 500 cluster로 k-means를 수행해 다시 Base와
Large 모델을 학습했습니다.
#### Training Hyperparameters
| Hyperparameter | Base | Large |
|:--------------------|---------|--------:|
| Warmup Steps | 32,000 | 32,000 |
| Learning Rates | 5e-4 | 1.5e-3 |
| Batch Size | 128 | 128 |
| Weight Decay | 0.01 | 0.01 |
| Max Steps | 400,000 | 400,000 |
| Learning Rate Decay | 0.1 | 0.1 |
| \\(Adam\beta_1\\) | 0.9 | 0.9 |
| \\(Adam\beta_2\\) | 0.99 | 0.99 | | 3,080 | [
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rishav30/my_awesome_qa_model | 2023-11-04T11:02:54.000Z | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | rishav30 | null | null | rishav30/my_awesome_qa_model | 0 | 2 | transformers | 2023-11-04T10:49:49 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: rishav30/my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# rishav30/my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.9239
- Validation Loss: 2.1812
- 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.6034 | 2.5369 | 0 |
| 2.1821 | 2.1812 | 1 |
| 1.9239 | 2.1812 | 2 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| 1,825 | [
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colmsjunk/PPO_LunarLander-v2 | 2023-11-04T10:50:38.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | colmsjunk | null | null | colmsjunk/PPO_LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-04T10:50:17 | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 247.25 +/- 16.58
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
...
```
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AgniVardhan/poca-SoccerTwos | 2023-11-04T11:24:18.000Z | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | AgniVardhan | null | null | AgniVardhan/poca-SoccerTwos | 0 | 2 | ml-agents | 2023-11-04T11:19:43 | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: AgniVardhan/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
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ramdhanfirdaus/falcon-7b-finetuned-aings-adapters-testing-2 | 2023-11-04T13:09:21.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | ramdhanfirdaus | null | null | ramdhanfirdaus/falcon-7b-finetuned-aings-adapters-testing-2 | 0 | 2 | peft | 2023-11-04T13:09:08 | ---
library_name: peft
base_model: tiiuae/falcon-7b
---
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## Training procedure
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## Training procedure
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- PEFT 0.7.0.dev0
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] |
ramdhanfirdaus/falcon-7b-finetuned-aings-adapters-testing-3 | 2023-11-04T13:11:03.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | ramdhanfirdaus | null | null | ramdhanfirdaus/falcon-7b-finetuned-aings-adapters-testing-3 | 0 | 2 | peft | 2023-11-04T13:10:50 | ---
library_name: peft
base_model: tiiuae/falcon-7b
---
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## Training procedure
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
### Framework versions
- PEFT 0.7.0.dev0
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ramdhanfirdaus/falcon-7b-finetuned-aings-adapters-testing | 2023-11-04T13:18:46.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | ramdhanfirdaus | null | null | ramdhanfirdaus/falcon-7b-finetuned-aings-adapters-testing | 0 | 2 | peft | 2023-11-04T13:18:36 | ---
library_name: peft
base_model: tiiuae/falcon-7b
---
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## Training procedure
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
### Framework versions
- PEFT 0.7.0.dev0
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minhnb/ssbc_model_spearman_rounded | 2023-11-04T17:35:20.000Z | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | minhnb | null | null | minhnb/ssbc_model_spearman_rounded | 0 | 2 | transformers | 2023-11-04T14:36:56 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: minhnb/ssbc_model_spearman_rounded
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. -->
# minhnb/ssbc_model_spearman_rounded
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: 0.4747
- Validation Loss: 0.7845
- Train Spearmanr: -0.0138
- Epoch: 4
## 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2170, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 Spearmanr | Epoch |
|:----------:|:---------------:|:---------------:|:-----:|
| 1.0340 | 0.8086 | -0.3120 | 0 |
| 0.7528 | 0.7463 | -0.2250 | 1 |
| 0.6238 | 0.7471 | -0.0633 | 2 |
| 0.5334 | 0.7504 | -0.0711 | 3 |
| 0.4747 | 0.7845 | -0.0138 | 4 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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kamalp99/ppo-LunarLander-v2 | 2023-11-04T15:02:51.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | kamalp99 | null | null | kamalp99/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-04T15:02:29 | ---
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: 261.75 +/- 22.52
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
...
```
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] |
LaTarn/ta-activity-setfit-model | 2023-11-04T16:08:54.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | LaTarn | null | null | LaTarn/ta-activity-setfit-model | 0 | 2 | sentence-transformers | 2023-11-04T16:08:34 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/ta-activity-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("LaTarn/ta-activity-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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qeternity/SynthIA-7B-v2.0-6bpw-exl2 | 2023-11-04T16:25:02.000Z | [
"transformers",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | qeternity | null | null | qeternity/SynthIA-7B-v2.0-6bpw-exl2 | 0 | 2 | transformers | 2023-11-04T16:16:39 | ---
license: apache-2.0
pipeline_tag: text-generation
language:
- en
library_name: transformers
---
Quantized using WizardLM Evol Instruct 70k
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LoneStriker/HelixNet-regenerator-8.0bpw-h8-exl2 | 2023-11-04T22:17:39.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/HelixNet-regenerator-8.0bpw-h8-exl2 | 0 | 2 | transformers | 2023-11-04T16:46:58 | ---
license: apache-2.0
---
# HelixNet exl2
- Model creator: [migtissera](https://huggingface.co/migtissera)
- Original model: [HelixNet](https://huggingface.co/migtissera/HelixNet)
# Sample HelixNet exl2 Code
```python
import time
import sys, os
import dataclasses
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from exllamav2 import(
ExLlamaV2,
ExLlamaV2Config,
ExLlamaV2Cache,
ExLlamaV2Tokenizer,
)
from exllamav2.generator import (
ExLlamaV2BaseGenerator,
ExLlamaV2Sampler
)
@dataclasses.dataclass
class ModelClass:
generator: ExLlamaV2BaseGenerator
tokenizer: ExLlamaV2Tokenizer
DEBUG = os.environ.get("DEBUG") and True or False
# Initialize model and cache
def load_model(model_directory, max_seq_len=8192):
"""
Loads a model from a directory and return the generator and tokenizer
"""
config = ExLlamaV2Config()
config.model_dir = model_directory
config.max_seq_len = max_seq_len
config.prepare()
model = ExLlamaV2(config)
print("Loading model: " + model_directory)
cache = ExLlamaV2Cache(model, lazy = True, max_seq_len=max_seq_len)
model.load_autosplit(cache)
tokenizer = ExLlamaV2Tokenizer(config)
generator = ExLlamaV2BaseGenerator(model, cache, tokenizer)
model = ModelClass(generator=generator, tokenizer=tokenizer)
generator.warmup()
return model
def generate_text(prompt, model, settings, max_new_tokens):
time_begin = time.time()
response = model.generator.generate_simple(prompt, settings, max_new_tokens)
response = response[len(prompt):]
time_end = time.time()
time_total = time_end - time_begin
tokens = model.tokenizer.encode(response)
count = tokens.shape[-1]
print(f"Response generated in {time_total:.2f} seconds, {count} tokens, {count / time_total:.2f} tokens/second, character len: {len(response)}")
return response
model_actor = load_model("/models/HelixNet-actor-6.0bpw-h6-exl2")
model_critic = load_model("/models/HelixNet-critic-6.0bpw-h6-exl2")
model_regenerator = load_model("/models/HelixNet-regenerator-6.0bpw-h6-exl2")
settings = ExLlamaV2Sampler.Settings()
settings.temperature = 0.75
settings.top_k = 50
settings.top_p = 1.0
max_new_tokens = 2000
system_prompt = "You are HelixNet. Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
while True:
user_input = input("You: ")
prompt_actor = f"SYSTEM: {system_prompt}\nUSER: {user_input}\nASSISTANT: "
if DEBUG: print(f"{prompt_actor}\n\n")
print("ACTOR:")
response_actor = generate_text(prompt_actor, model_actor, settings, max_new_tokens)
if DEBUG: print(f"{response_actor}\n\n")
print("="*132)
prompt_critic = f"SYSTEM: {system_prompt}\nUSER: {user_input}\nRESPONSE: {response_actor}\nCRITIQUE: "
if DEBUG: print(f"{prompt_critic}\n\n")
print("CRITIQUE:")
response_critic = generate_text(prompt_critic, model_critic, settings, max_new_tokens)
if DEBUG: print(f"{response_critic}\n\n")
print("="*132)
prompt_regenerator = f"SYSTEM: {system_prompt}\nUSER: {user_input}\nRESPONSE: {response_actor}\nCRITIQUE: {response_critic}\nREGENERATOR: "
if DEBUG: print(f"{prompt_regenerator}\n\n")
print("REGENERATION:")
response_regenerator = generate_text(prompt_regenerator, model_regenerator, settings, max_new_tokens)
print("="*132)
conversation = f"SYSTEM: {system_prompt}\nUSER: {user_input}\nASSISTANT: {response_regenerator}"
print(conversation)
```
# HelixNet
HelixNet is a Deep Learning architecture consisting of 3 x Mistral-7B LLMs. It has an `actor`, a `critic`, and a `regenerator`. The `actor` LLM produces an initial response to a given system-context and a question. The `critic` then takes in as input, a tuple of (system-context, question, response) and provides a critique based on the provided answer to the given system-context and the question. Its job is not to criticize, but to provide an intelligent critique so that the answer can be modified/regenerated to address the question better. Finally, the `regenerator` takes in a tuple of (system-context, question, response, critique) and regenerates the answer.
HelixNet is insprired from an actor-critic architecture most prominent in Reinforcement Learning algorithms. The name derives from Helix, referring to the spiral structure of a DNA molecule. It symbolizes the intertwined nature of the three networks, working in tandem, much like the strands of a DNA molecule.
HelixNet regenerates very pleasing and accurate responses, due to the entropy preservation of the regenerator. The regenerator was only trained on a dataset of 1000 samples, similar to Meta's LIMA. The actor network here was trained on about 250K very high-quality samples, and the critic network was trained on further 10K samples.
# Training Methodology
## Phase 1: Actor
The actor network was trained with Supervised Fine-Tuning, on 250K very high-quality samples. It has 75K of Open-Orca's Chain-of-Thought data, and a mixture of Dolphin (GPT-4), SynthIA's Tree-of-Thought data.
Here are the results for the Actor network on metrics used by [HuggingFaceH4 Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
||||
|:------:|:--------:|:-------:|
|**Task**|**Metric**|**Value**|
|*arc_challenge*|acc_norm|62.28|
|*hellaswag*|acc_norm|83.22|
|*mmlu*|acc_norm|63.10|
|*truthfulqa_mc*|mc2|50.10|
|**Total Average**|-|**0.64675**||
## Phase 2: Critic
To train the critic, the following process was followed:
- Use Actor, and send 10K system-context and question pairs. Generate responses
- Use the (system-context, question, response) tuples to generate critiques. Used OpenAI's GPT-4.
Using the above training dataset, a Mistral-7B was fine-tuned.
## Phase 3: Regenerator
- Use the (system-context, question, response, critique) tuples to regenerate the answers. Used OpenAI's GPT-4.
A thrid LLM was fine-tuned using the above data.
# Reusability of the critic and the regenerator
The `critic` and the `regenerator` was tested not only on the accopanying actor model, but 13B and 70B SynthIA models as well. They seem to be readily transferrable, as the function that it has learnt is to provide an intelligent critique and then a regeneration of the original response. Please feel free to try out other models as the `actor`. However, the architecture works best with all three as presented here in HelixNet.
# Sample Generations
# Prompt format:
```
SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: What is the relationship between Earth's atmosphere, magnetic field and gravity?
ASSISTANT:
```
# Example Usage
## Code example (Verbose):
The following is a code example on how to use HelixNet. No special system-context messages are needed for the `critic` and the `regenerator`.
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path_actor = "/home/ubuntu/llm/HelixNet/actor"
model_path_critic = "/home/ubuntu/llm/HelixNet/critic"
model_path_regenerator = "/home/ubuntu/llm/HelixNet/regenerator"
def load_model(model_path):
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="cuda",
load_in_4bit=False,
trust_remote_code=True,
)
return model
def load_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
return tokenizer
model_actor = load_model(model_path_actor)
model_critic = load_model(model_path_critic)
model_regenerator = load_model(model_path_regenerator)
tokenizer_actor = load_tokenizer(model_path_actor)
tokenizer_critic = load_tokenizer(model_path_critic)
tokenizer_regenerator = load_tokenizer(model_path_regenerator)
def generate_text(instruction, model, tokenizer):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.75,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
return f"{string}"
system_prompt = "You are HelixNet. Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
while True:
user_input = input("You: ")
prompt_actor = f"SYSTEM: {system_prompt} \nUSER: {user_input} \nASSISTANT: "
actor_response = generate_text(prompt_actor, model_actor, tokenizer_actor)
print(f"ACTOR: {actor_response}\n\n")
prompt_critic = f"SYSTEM: {system_prompt} \nUSER: {user_input} \nRESPONSE: {actor_response} \nCRITIQUE:"
critic_response = generate_text(prompt_critic, model_critic, tokenizer_critic)
print(f"CRITIQUE: {critic_response}\n\n")
prompt_regenerator = f"SYSTEM: {system_prompt} \nUSER: {user_input} \nRESPONSE: {actor_response} \nCRITIQUE: {critic_response} \nREGENERATOR:"
regenerator_response = generate_text(prompt_regenerator, model_regenerator, tokenizer_regenerator)
print(f"REGENERATION: {regenerator_response}")
```
## Code Example (Continuing a conversation)
To have a back-and-forth conversation, only carry forward the system-context, questions and regenerations as shown below.
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path_actor = "/home/ubuntu/llm/HelixNet/actor"
model_path_critic = "/home/ubuntu/llm/HelixNet/critic"
model_path_regenerator = "/home/ubuntu/llm/HelixNet/regenerator"
def load_model(model_path):
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="cuda",
load_in_4bit=False,
trust_remote_code=True,
)
return model
def load_tokenizer(model_path):
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
return tokenizer
model_actor = load_model(model_path_actor)
model_critic = load_model(model_path_critic)
model_regenerator = load_model(model_path_regenerator)
tokenizer_actor = load_tokenizer(model_path_actor)
tokenizer_critic = load_tokenizer(model_path_critic)
tokenizer_regenerator = load_tokenizer(model_path_regenerator)
def generate_text(instruction, model, tokenizer):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.75,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
return f"{string}"
system_prompt = "You are HelixNet. Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
conversation = f"SYSTEM:{system_prompt}"
while True:
user_input = input("You: ")
prompt_actor = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
actor_response = generate_text(prompt_actor, model_actor, tokenizer_actor)
print("Generated ACTOR RESPONSE")
prompt_critic = f"SYSTEM: {system_prompt} \nUSER: {user_input} \nRESPONSE: {actor_response} \nCRITIQUE:"
critic_response = generate_text(prompt_critic, model_critic, tokenizer_critic)
print("Generated CRITIQUE")
prompt_regenerator = f"SYSTEM: {system_prompt} \nUSER: {user_input} \nRESPONSE: {actor_response} \nCRITIQUE: {critic_response} \nREGENERATOR:"
regenerator_response = generate_text(prompt_regenerator, model_regenerator, tokenizer_regenerator)
print("Generated REGENERATION")
conversation = f"{conversation} \nUSER: {user_input} \nASSISTANT: {regenerator_response}"
print(conversation)
```
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shius/distilbert-base-uncased-finetuned-emotion | 2023-11-05T00:35:30.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | shius | null | null | shius/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-11-04T17:18:36 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9215
- name: F1
type: f1
value: 0.9212271569688067
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2255
- Accuracy: 0.9215
- F1: 0.9212
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8344 | 1.0 | 250 | 0.3271 | 0.9055 | 0.9044 |
| 0.2542 | 2.0 | 500 | 0.2255 | 0.9215 | 0.9212 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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LaTarn/ta-atmosphere-setfit-model | 2023-11-04T17:23:48.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | LaTarn | null | null | LaTarn/ta-atmosphere-setfit-model | 0 | 2 | sentence-transformers | 2023-11-04T17:23:30 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/ta-atmosphere-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("LaTarn/ta-atmosphere-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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tomashs/sdu_fine_tuning_beto_topics_ud | 2023-11-04T17:32:21.000Z | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | tomashs | null | null | tomashs/sdu_fine_tuning_beto_topics_ud | 0 | 2 | transformers | 2023-11-04T17:31:58 | ---
base_model: tomashs/acro_fine_tuning_beto_topics_ud
tags:
- generated_from_trainer
model-index:
- name: sdu_fine_tuning_beto_topics_ud
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. -->
# sdu_fine_tuning_beto_topics_ud
This model is a fine-tuned version of [tomashs/acro_fine_tuning_beto_topics_ud](https://huggingface.co/tomashs/acro_fine_tuning_beto_topics_ud) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 16
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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alosof/LunarLander-v2 | 2023-11-04T18:11:54.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | alosof | null | null | alosof/LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-04T18:11:36 | ---
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: 264.49 +/- 19.09
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
...
```
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mtc/LeoLM-leo-mistral-hessianai-7b-all-labels-german-classification-with-explanation-500-qlora-4bit | 2023-11-04T19:24:54.000Z | [
"peft",
"region:us"
] | null | mtc | null | null | mtc/LeoLM-leo-mistral-hessianai-7b-all-labels-german-classification-with-explanation-500-qlora-4bit | 0 | 2 | peft | 2023-11-04T19:24:21 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.5.0
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] |
perilli/ap_eyes | 2023-11-04T19:26:38.000Z | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | perilli | null | null | perilli/ap_eyes | 0 | 2 | transformers | 2023-11-04T19:26:32 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: ap_eyes
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.800000011920929
---
# ap_eyes
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### eye
#### eyes
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] |
njwrigh92/t-5-comedy | 2023-11-04T20:12:22.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:zachgitt/comedy-transcripts",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | njwrigh92 | null | null | njwrigh92/t-5-comedy | 0 | 2 | transformers | 2023-11-04T19:34:08 | ---
license: mit
datasets:
- zachgitt/comedy-transcripts
language:
- en
pipeline_tag: text2text-generation
library_name: transformers
---
# Stand-Up Comic Assistant Model
## Model Description
This model is designed as an assistant for stand-up comedians, providing suggestions, ideas, and content generation to support the creative process. It's trained on a diverse set of comedy transcripts, aiming to capture the essence of humor from various styles and contexts.
### How It Works
The model is based on `google/flan-t5-small`, a powerful and efficient transformer model optimized for language understanding and generation tasks. It has been fine-tuned on the `zachgitt/comedy-transcripts` dataset, which includes a wide range of stand-up comedy routines.
### Intended Use
- **Idea Generation**: Generate prompts or comedy concepts based on current trends, historical events, or user input.
- **Content Creation**: Assist in writing jokes, sketches, or full stand-up routines.
- **Interactive Comedy**: Engage with users by providing humorous responses in a conversational setting.
## Training
The model was trained using the `transformers` library on a dataset of stand-up comedy transcripts. The training process focused on understanding context, delivering punchlines, and preserving the comedic timing that's essential in stand-up comedy.
### Training Data
The dataset `zachgitt/comedy-transcripts` was used, which includes transcripts from various comedians across different eras of stand-up comedy.
## Limitations and Biases
- **Contextual Limitations**: While the model understands a range of comedic styles, it may not always align with the nuances of personal taste in humor.
- **Cultural Sensitivity**: The dataset includes historical content that may not be suitable or sensitive to current cultural contexts.
- **Language Biases**: The model may reflect biases present in the training data, which consists of primarily English-language comedy routines.
## Future Work
This model is a work in progress. Planned improvements include:
- Expanding the dataset with more diverse and contemporary sources.
- Implementing feedback loops to refine the model's sense of humor based on user interactions.
- Enhancing the model's understanding of different comedic devices like satire, irony, and slapstick.
## Acknowledgements
Thanks to all the contributors of the `zachgitt/comedy-transcripts` dataset and the teams behind `google/flan-t5-small` for providing the foundational models and tools that made this project possible.
---
**Disclaimer**: This model is intended for creative and entertainment purposes. It should be used responsibly, considering the potential for generating content that may be offensive or inappropriate in certain contexts.
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Buseak/spellcorrector_0411 | 2023-11-04T21:27:01.000Z | [
"transformers",
"pytorch",
"tensorboard",
"canine",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Buseak | null | null | Buseak/spellcorrector_0411 | 0 | 2 | transformers | 2023-11-04T19:41:01 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: spellcorrector_0411
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. -->
# spellcorrector_0411
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0830
- Precision: 0.9784
- Recall: 0.9815
- F1: 0.9799
- Accuracy: 0.9828
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2319 | 1.0 | 975 | 0.1268 | 0.9458 | 0.9834 | 0.9642 | 0.9741 |
| 0.1296 | 2.0 | 1950 | 0.1063 | 0.9530 | 0.9812 | 0.9669 | 0.9754 |
| 0.1095 | 3.0 | 2925 | 0.0883 | 0.9653 | 0.9788 | 0.9720 | 0.9786 |
| 0.0934 | 4.0 | 3900 | 0.0842 | 0.9692 | 0.9776 | 0.9734 | 0.9790 |
| 0.0829 | 5.0 | 4875 | 0.0794 | 0.9716 | 0.9797 | 0.9756 | 0.9809 |
| 0.0753 | 6.0 | 5850 | 0.0755 | 0.9729 | 0.9816 | 0.9773 | 0.9817 |
| 0.0695 | 7.0 | 6825 | 0.0739 | 0.9751 | 0.9789 | 0.9770 | 0.9815 |
| 0.0641 | 8.0 | 7800 | 0.0736 | 0.9767 | 0.9798 | 0.9782 | 0.9821 |
| 0.0591 | 9.0 | 8775 | 0.0744 | 0.9767 | 0.9805 | 0.9786 | 0.9822 |
| 0.0537 | 10.0 | 9750 | 0.0742 | 0.9777 | 0.9798 | 0.9787 | 0.9822 |
| 0.0502 | 11.0 | 10725 | 0.0753 | 0.9773 | 0.9806 | 0.9790 | 0.9825 |
| 0.0472 | 12.0 | 11700 | 0.0757 | 0.9780 | 0.9808 | 0.9794 | 0.9827 |
| 0.044 | 13.0 | 12675 | 0.0768 | 0.9772 | 0.9816 | 0.9794 | 0.9827 |
| 0.0407 | 14.0 | 13650 | 0.0784 | 0.9775 | 0.9815 | 0.9795 | 0.9827 |
| 0.039 | 15.0 | 14625 | 0.0790 | 0.9779 | 0.9816 | 0.9798 | 0.9828 |
| 0.0364 | 16.0 | 15600 | 0.0804 | 0.9778 | 0.9813 | 0.9795 | 0.9825 |
| 0.0343 | 17.0 | 16575 | 0.0811 | 0.9783 | 0.9811 | 0.9797 | 0.9828 |
| 0.0329 | 18.0 | 17550 | 0.0819 | 0.9785 | 0.9820 | 0.9803 | 0.9829 |
| 0.0314 | 19.0 | 18525 | 0.0822 | 0.9785 | 0.9808 | 0.9797 | 0.9826 |
| 0.0308 | 20.0 | 19500 | 0.0830 | 0.9784 | 0.9815 | 0.9799 | 0.9828 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
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jbochi/madlad400-7b-mt | 2023-11-06T16:49:07.000Z | [
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] | translation | jbochi | null | null | jbochi/madlad400-7b-mt | 2 | 2 | transformers | 2023-11-04T20:31:05 | ---
license: apache-2.0
language:
- en
- ru
- es
- fr
- de
- it
- pt
- pl
- nl
- vi
- tr
- sv
- id
- ro
- cs
- zh
- hu
- ja
- th
- fi
- fa
- uk
- da
- el
- "no"
- bg
- sk
- ko
- ar
- lt
- ca
- sl
- he
- et
- lv
- hi
- sq
- ms
- az
- sr
- ta
- hr
- kk
- is
- ml
- mr
- te
- af
- gl
- fil
- be
- mk
- eu
- bn
- ka
- mn
- bs
- uz
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- yue
- ne
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- ig
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- tet
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- bo
- om
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- ce
- cnh
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- hil
- udm
- os
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- ti
- vec
- ts
- tyv
- kbd
- ee
- iba
- av
- kha
- to
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- fj
- zza
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- ada
- otq
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- pam
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- min
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library_name: transformers
tags:
- text-generation-inference
datasets:
- allenai/MADLAD-400
pipeline_tag: translation
---
T5ForConditionalGeneration files for Google's [Madlad-400](https://github.com/google-research/google-research/tree/master/madlad_400) 7.2B parameter MT model.
Article: [MADLAD-400: A Multilingual And Document-Level Large Audited Dataset](https://arxiv.org/abs/2309.04662)
Abstract:
> We introduce MADLAD-400, a manually audited, general domain 3T token monolingual dataset based on CommonCrawl, spanning 419 languages. We discuss the limitations revealed by self-auditing MADLAD-400, and the role data auditing had in the dataset creation process. We then train and release a 10.7B-parameter multilingual machine translation model on 250 billion tokens covering over 450 languages using publicly available data, and find that it is competitive with models that are significantly larger, and report the results on different domains. In addition, we train a 8B-parameter language model, and assess the results on few-shot translation. We make the baseline models available to the research community.
```python
from transformers import T5ForConditionalGeneration, T5Tokenizer, GenerationConfig
model = T5ForConditionalGeneration.from_pretrained('jbochi/madlad400-7b-mt')
tokenizer = T5Tokenizer.from_pretrained('jbochi/madlad400-7b-mt')
text = "<2it> I love pizza!"
input_ids = tokenizer(text, return_tensors="pt").input_ids
outputs = model.generate(input_ids=input_ids)
tokenizer.decode(outputs[0], skip_special_tokens=True)
# Adoro la pizza!
```
Colab to generate these files is [here](https://colab.research.google.com/drive/1rZ2NRyl2zwmg0sQ2Wi-uZZF48iVYulTC#scrollTo=pVODoE6gA9sw). | 4,099 | [
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NatnichaYw/food_classifier | 2023-11-04T21:09:57.000Z | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | NatnichaYw | null | null | NatnichaYw/food_classifier | 0 | 2 | transformers | 2023-11-04T20:57:00 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: NatnichaYw/food_classifier
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. -->
# NatnichaYw/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.7988
- Validation Loss: 1.6494
- Train Accuracy: 0.837
- Epoch: 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7988 | 1.6494 | 0.837 | 0 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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ummagumm-a/output | 2023-11-05T04:18:13.000Z | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | ummagumm-a | null | null | ummagumm-a/output | 0 | 2 | transformers | 2023-11-04T21:07:32 | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: output
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8143
- Bleu: 22.3227
- Gen Len: 13.2906
## 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 | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 1.9835 | 1.0 | 14445 | 1.8351 | 22.1686 | 13.3106 |
| 1.9384 | 2.0 | 28890 | 1.8143 | 22.3227 | 13.2906 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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yjlee1011/ncodeR_data_setfit_multilabel_8_samples | 2023-11-04T21:46:12.000Z | [
"sentence-transformers",
"safetensors",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | yjlee1011 | null | null | yjlee1011/ncodeR_data_setfit_multilabel_8_samples | 0 | 2 | sentence-transformers | 2023-11-04T21:45:32 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# yjlee1011/ncodeR_data_setfit_multilabel_8_samples
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("yjlee1011/ncodeR_data_setfit_multilabel_8_samples")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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18winstonsmith97/Llama-Chat | 2023-11-04T22:33:20.000Z | [
"peft",
"region:us"
] | null | 18winstonsmith97 | null | null | 18winstonsmith97/Llama-Chat | 0 | 2 | peft | 2023-11-04T22:33:19 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
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adityaaswani1/ppo-LunarLander-v2 | 2023-11-05T00:56:16.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | adityaaswani1 | null | null | adityaaswani1/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-05T00:55:56 | ---
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: 267.67 +/- 17.31
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
...
```
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dharper40/ppo-LunarLander-v2 | 2023-11-05T01:13:44.000Z | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | dharper40 | null | null | dharper40/ppo-LunarLander-v2 | 0 | 2 | stable-baselines3 | 2023-11-05T01:13:30 | ---
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: 232.46 +/- 71.40
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
...
```
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brucejavaca/pw_encounter_note_cpt_code_prediction | 2023-11-05T03:07:58.000Z | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | brucejavaca | null | null | brucejavaca/pw_encounter_note_cpt_code_prediction | 0 | 2 | transformers | 2023-11-05T02:32:46 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: pw_encounter_note_cpt_code_prediction
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. -->
# pw_encounter_note_cpt_code_prediction
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: nan
- Validation Loss: nan
- Train Accuracy: 0.0
- Epoch: 4
## 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': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 24060, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 |
|:----------:|:---------------:|:--------------:|:-----:|
| nan | nan | 0.0 | 0 |
| nan | nan | 0.0 | 1 |
| nan | nan | 0.0 | 2 |
| nan | nan | 0.0 | 3 |
| nan | nan | 0.0 | 4 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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QuyenAnhDE/flant5base-medical | 2023-11-05T06:35:12.000Z | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | QuyenAnhDE | null | null | QuyenAnhDE/flant5base-medical | 0 | 2 | transformers | 2023-11-05T03:13:21 | ---
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: flant5base-medical
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. -->
# flant5base-medical
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base)
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 6
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
TheBloke/deepseek-coder-33B-base-GPTQ | 2023-11-05T14:25:23.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:other",
"text-generation-inference",
"region:us"
] | text-generation | TheBloke | null | null | TheBloke/deepseek-coder-33B-base-GPTQ | 1 | 2 | transformers | 2023-11-05T04:52:35 | ---
base_model: deepseek-ai/deepseek-coder-33b-base
inference: false
license: other
license_link: LICENSE
license_name: deepseek-license
model_creator: DeepSeek
model_name: Deepseek Coder 33B Base
model_type: deepseek
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Deepseek Coder 33B Base - GPTQ
- Model creator: [DeepSeek](https://huggingface.co/deepseek-ai)
- Original model: [Deepseek Coder 33B Base](https://huggingface.co/deepseek-ai/deepseek-coder-33b-base)
<!-- description start -->
## Description
This repo contains GPTQ model files for [DeepSeek's Deepseek Coder 33B Base](https://huggingface.co/deepseek-ai/deepseek-coder-33b-base).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/deepseek-coder-33B-base-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/deepseek-coder-33B-base-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/deepseek-coder-33B-base-GGUF)
* [DeepSeek's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/deepseek-ai/deepseek-coder-33b-base)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: None
```
{prompt}
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/deepseek-coder-33B-base-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 17.40 GB | Yes | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/deepseek-coder-33B-base-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 18.03 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/deepseek-coder-33B-base-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 19.96 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/deepseek-coder-33B-base-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 13.89 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/deepseek-coder-33B-base-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 33.84 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-3bit-32g-actorder_True](https://huggingface.co/TheBloke/deepseek-coder-33B-base-GPTQ/tree/gptq-3bit-32g-actorder_True) | 3 | 32 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 15.72 GB | No | 3-bit, with group size 64g and act-order. Highest quality 3-bit option. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/deepseek-coder-33B-base-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [Evol Instruct Code](https://huggingface.co/datasets/nickrosh/Evol-Instruct-Code-80k-v1) | 4096 | 34.60 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/deepseek-coder-33B-base-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/deepseek-coder-33B-base-GPTQ:gptq-4bit-128g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `deepseek-coder-33B-base-GPTQ`:
```shell
mkdir deepseek-coder-33B-base-GPTQ
huggingface-cli download TheBloke/deepseek-coder-33B-base-GPTQ --local-dir deepseek-coder-33B-base-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir deepseek-coder-33B-base-GPTQ
huggingface-cli download TheBloke/deepseek-coder-33B-base-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir deepseek-coder-33B-base-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir deepseek-coder-33B-base-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/deepseek-coder-33B-base-GPTQ --local-dir deepseek-coder-33B-base-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/deepseek-coder-33B-base-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/deepseek-coder-33B-base-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/deepseek-coder-33B-base-GPTQ:gptq-4bit-128g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `deepseek-coder-33B-base-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/deepseek-coder-33B-base-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires huggingface-hub 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## How to use this GPTQ model from Python code
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install transformers optimum
pip3 install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/ # Use cu117 if on CUDA 11.7
```
If you have problems installing AutoGPTQ using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.4.2
pip3 install .
```
### You can then use the following code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/deepseek-coder-33B-base-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, 阿明, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjäreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: DeepSeek's Deepseek Coder 33B Base
<p align="center">
<img width="1000px" alt="DeepSeek Coder" src="https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/pictures/logo.png?raw=true">
</p>
<p align="center"><a href="https://www.deepseek.com/">[🏠Homepage]</a> | <a href="https://coder.deepseek.com/">[🤖 Chat with DeepSeek Coder]</a> | <a href="https://discord.gg/Tc7c45Zzu5">[Discord]</a> | <a href="https://github.com/guoday/assert/blob/main/QR.png?raw=true">[Wechat(微信)]</a> </p>
<hr>
### 1. Introduction of Deepseek Coder
Deepseek Coder is composed of a series of code language models, each trained from scratch on 2T tokens, with a composition of 87% code and 13% natural language in both English and Chinese. We provide various sizes of the code model, ranging from 1B to 33B versions. Each model is pre-trained on project-level code corpus by employing a window size of 16K and a extra fill-in-the-blank task, to support project-level code completion and infilling. For coding capabilities, Deepseek Coder achieves state-of-the-art performance among open-source code models on multiple programming languages and various benchmarks.
- **Massive Training Data**: Trained from scratch on 2T tokens, including 87% code and 13% linguistic data in both English and Chinese languages.
- **Highly Flexible & Scalable**: Offered in model sizes of 1.3B, 5.7B, 6.7B, and 33B, enabling users to choose the setup most suitable for their requirements.
- **Superior Model Performance**: State-of-the-art performance among publicly available code models on HumanEval, MultiPL-E, MBPP, DS-1000, and APPS benchmarks.
- **Advanced Code Completion Capabilities**: A window size of 16K and a fill-in-the-blank task, supporting project-level code completion and infilling tasks.
### 2. Model Summary
deepseek-coder-33b-base is a 33B parameter model with Grouped-Query Attention trained on 2 trillion tokens.
- **Home Page:** [DeepSeek](https://deepseek.com/)
- **Repository:** [deepseek-ai/deepseek-coder](https://github.com/deepseek-ai/deepseek-coder)
- **Chat With DeepSeek Coder:** [DeepSeek-Coder](https://coder.deepseek.com/)
### 3. How to Use
Here give some examples of how to use our model.
#### 1）Code Completion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-33b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-33b-base", trust_remote_code=True).cuda()
input_text = "#write a quick sort algorithm"
inputs = tokenizer(input_text, return_tensors="pt").cuda()
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
#### 2）Code Insertion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-33b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-33b-base", trust_remote_code=True).cuda()
input_text = """<｜fim▁begin｜>def quick_sort(arr):
if len(arr) <= 1:
return arr
pivot = arr[0]
left = []
right = []
<｜fim▁hole｜>
if arr[i] < pivot:
left.append(arr[i])
else:
right.append(arr[i])
return quick_sort(left) + [pivot] + quick_sort(right)<｜fim▁end｜>"""
inputs = tokenizer(input_text, return_tensors="pt").cuda()
outputs = model.generate(**inputs, max_length=128)
print(tokenizer.decode(outputs[0], skip_special_tokens=True)[len(input_text):])
```
#### 3）Repository Level Code Completion
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/deepseek-coder-33b-base", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/deepseek-coder-33b-base", trust_remote_code=True).cuda()
input_text = """#utils.py
import torch
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score
def load_data():
iris = datasets.load_iris()
X = iris.data
y = iris.target
# Standardize the data
scaler = StandardScaler()
X = scaler.fit_transform(X)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
# Convert numpy data to PyTorch tensors
X_train = torch.tensor(X_train, dtype=torch.float32)
X_test = torch.tensor(X_test, dtype=torch.float32)
y_train = torch.tensor(y_train, dtype=torch.int64)
y_test = torch.tensor(y_test, dtype=torch.int64)
return X_train, X_test, y_train, y_test
def evaluate_predictions(y_test, y_pred):
return accuracy_score(y_test, y_pred)
#model.py
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import DataLoader, TensorDataset
class IrisClassifier(nn.Module):
def __init__(self):
super(IrisClassifier, self).__init__()
self.fc = nn.Sequential(
nn.Linear(4, 16),
nn.ReLU(),
nn.Linear(16, 3)
)
def forward(self, x):
return self.fc(x)
def train_model(self, X_train, y_train, epochs, lr, batch_size):
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(self.parameters(), lr=lr)
# Create DataLoader for batches
dataset = TensorDataset(X_train, y_train)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
for epoch in range(epochs):
for batch_X, batch_y in dataloader:
optimizer.zero_grad()
outputs = self(batch_X)
loss = criterion(outputs, batch_y)
loss.backward()
optimizer.step()
def predict(self, X_test):
with torch.no_grad():
outputs = self(X_test)
_, predicted = outputs.max(1)
return predicted.numpy()
#main.py
from utils import load_data, evaluate_predictions
from model import IrisClassifier as Classifier
def main():
# Model training and evaluation
"""
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=140)
print(tokenizer.decode(outputs[0]))
```
### 4. License
This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the Model License. DeepSeek Coder supports commercial use.
See the [LICENSE-MODEL](https://github.com/deepseek-ai/deepseek-coder/blob/main/LICENSE-MODEL) for more details.
### 5. Contact
If you have any questions, please raise an issue or contact us at [agi_code@deepseek.com](mailto:agi_code@deepseek.com).
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LaTarn/ta-experience-setfit-model | 2023-11-05T05:12:00.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | LaTarn | null | null | LaTarn/ta-experience-setfit-model | 0 | 2 | sentence-transformers | 2023-11-05T05:11:36 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/ta-experience-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("LaTarn/ta-experience-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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] |
longface/reasoning-task-model-llama-rev1 | 2023-11-05T05:28:43.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | longface | null | null | longface/reasoning-task-model-llama-rev1 | 0 | 2 | peft | 2023-11-05T05:28:41 | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-hf
---
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### Model Sources [optional]
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## Uses
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### Direct Use
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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### Testing Data, Factors & Metrics
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0
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bragovo/flux-mt5-base-multitask-model | 2023-11-05T09:41:54.000Z | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | bragovo | null | null | bragovo/flux-mt5-base-multitask-model | 0 | 2 | transformers | 2023-11-05T06:08:29 | ---
license: mit
base_model: cointegrated/rut5-base-multitask
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: flux-mt5-base-multitask-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. -->
# flux-mt5-base-multitask-model
This model is a fine-tuned version of [cointegrated/rut5-base-multitask](https://huggingface.co/cointegrated/rut5-base-multitask) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Rouge1: 0.0399
- Rouge2: 0.0123
- Rougel: 0.0358
- Rougelsum: 0.0358
- Gen Len: 11.5531
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.0 | 1.0 | 10877 | nan | 0.0399 | 0.0123 | 0.0358 | 0.0358 | 11.5531 |
| 0.0 | 2.0 | 21754 | nan | 0.0399 | 0.0123 | 0.0358 | 0.0358 | 11.5531 |
| 0.0 | 3.0 | 32631 | nan | 0.0399 | 0.0123 | 0.0358 | 0.0358 | 11.5531 |
| 0.0 | 4.0 | 43508 | nan | 0.0399 | 0.0123 | 0.0358 | 0.0358 | 11.5531 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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tomashs/acro_fine_tuning_beto_lda_ud | 2023-11-05T06:12:24.000Z | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | tomashs | null | null | tomashs/acro_fine_tuning_beto_lda_ud | 0 | 2 | transformers | 2023-11-05T06:12:03 | ---
base_model: dccuchile/bert-base-spanish-wwm-cased
tags:
- generated_from_trainer
model-index:
- name: acro_fine_tuning_beto_lda_ud
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. -->
# acro_fine_tuning_beto_lda_ud
This model is a fine-tuned version of [dccuchile/bert-base-spanish-wwm-cased](https://huggingface.co/dccuchile/bert-base-spanish-wwm-cased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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LaTarn/ta-facility-setfit-model | 2023-11-05T06:43:14.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | LaTarn | null | null | LaTarn/ta-facility-setfit-model | 0 | 2 | sentence-transformers | 2023-11-05T06:42:47 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/ta-facility-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("LaTarn/ta-facility-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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Jaeyunn/sw-bert-fine-tuned | 2023-11-05T16:27:18.000Z | [
"keras",
"text-classification",
"region:us"
] | text-classification | Jaeyunn | null | null | Jaeyunn/sw-bert-fine-tuned | 0 | 2 | keras | 2023-11-05T07:17:36 | ---
library_name: keras
tags:
- text-classification
---
## 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 | 4.999999873689376e-05 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-08 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> | 868 | [
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minhnb/ssbc_model_spearman_6_labels | 2023-11-05T16:00:18.000Z | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | minhnb | null | null | minhnb/ssbc_model_spearman_6_labels | 0 | 2 | transformers | 2023-11-05T07:38:59 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: minhnb/ssbc_model_spearman_6_labels
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. -->
# minhnb/ssbc_model_spearman_6_labels
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: 0.4926
- Validation Loss: 0.8276
- Train Spearmanr: 0.0017
- Epoch: 4
## 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2480, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 Spearmanr | Epoch |
|:----------:|:---------------:|:---------------:|:-----:|
| 1.0342 | 0.8235 | 0.1085 | 0 |
| 0.7680 | 0.8209 | -0.0218 | 1 |
| 0.6541 | 0.7958 | 0.1045 | 2 |
| 0.5602 | 0.8070 | -0.0070 | 3 |
| 0.4926 | 0.8276 | 0.0017 | 4 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
SujinHwang/criminal-sketch-lora-test | 2023-11-05T08:27:59.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | SujinHwang | null | null | SujinHwang/criminal-sketch-lora-test | 0 | 2 | diffusers | 2023-11-05T07:51:25 |
---
license: creativeml-openrail-m
base_model: Bingsu/my-korean-stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - SujinHwang/criminal-sketch-lora-test
These are LoRA adaption weights for Bingsu/my-korean-stable-diffusion-v1-5. The weights were fine-tuned on the SujinHwang/criminal-sketch-kr dataset. You can find some example images in the following.
| 570 | [
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LaTarn/ta-food-setfit-model | 2023-11-05T08:09:14.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | LaTarn | null | null | LaTarn/ta-food-setfit-model | 0 | 2 | sentence-transformers | 2023-11-05T08:08:49 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/ta-food-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("LaTarn/ta-food-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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AIYIYA/my_jieq1 | 2023-11-05T08:45:13.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | AIYIYA | null | null | AIYIYA/my_jieq1 | 0 | 2 | transformers | 2023-11-05T08:21:46 | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: AIYIYA/my_jieq1
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. -->
# AIYIYA/my_jieq1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.7378
- Validation Loss: 0.7088
- Train Accuracy: 0.8378
- 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': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 65, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_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 |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.7833 | 0.7313 | 0.8108 | 0 |
| 0.7214 | 0.7088 | 0.8378 | 1 |
| 0.7378 | 0.7088 | 0.8378 | 2 |
### Framework versions
- Transformers 4.35.0
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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LaTarn/ta-garage-setfit-model | 2023-11-05T09:59:50.000Z | [
"sentence-transformers",
"safetensors",
"bert",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | text-classification | LaTarn | null | null | LaTarn/ta-garage-setfit-model | 0 | 2 | sentence-transformers | 2023-11-05T09:59:28 | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# LaTarn/ta-garage-setfit-model
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("LaTarn/ta-garage-setfit-model")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
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] |
nsanghi/dqn-flappy-sb3 | 2023-11-05T10:27:00.000Z | [
"stable-baselines3",
"FlappyBird-v0",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | nsanghi | null | null | nsanghi/dqn-flappy-sb3 | 0 | 2 | stable-baselines3 | 2023-11-05T10:26:42 | ---
library_name: stable-baselines3
tags:
- FlappyBird-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FlappyBird-v0
type: FlappyBird-v0
metrics:
- type: mean_reward
value: 9.53 +/- 1.04
name: mean_reward
verified: false
---
# **DQN** Agent playing **FlappyBird-v0**
This is a trained model of a **DQN** agent playing **FlappyBird-v0**
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
...
```
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lucas-meyer/xls-r-asr_xh-run4 | 2023-11-05T13:08:14.000Z | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | lucas-meyer | null | null | lucas-meyer/xls-r-asr_xh-run4 | 0 | 2 | transformers | 2023-11-05T11:07:36 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: xls-r-asr_xh-run4
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. -->
# xls-r-asr_xh-run4
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4677
- Wer: 0.5332
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.6624 | 1.91 | 400 | 1.0878 | 0.9443 |
| 0.7107 | 3.83 | 800 | 0.4706 | 0.6537 |
| 0.3754 | 5.74 | 1200 | 0.4603 | 0.6259 |
| 0.241 | 7.66 | 1600 | 0.4685 | 0.5510 |
| 0.1815 | 9.57 | 2000 | 0.4677 | 0.5332 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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Baghdad99/speech-ha-small-dv | 2023-11-05T14:42:27.000Z | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ha",
"dataset:mozilla-foundation/common_voice_13_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | Baghdad99 | null | null | Baghdad99/speech-ha-small-dv | 0 | 2 | transformers | 2023-11-05T11:13:44 | ---
language:
- ha
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_13_0
metrics:
- wer
model-index:
- name: Hausa Whisper Small - Saad
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 13
type: mozilla-foundation/common_voice_13_0
config: ha
split: test
args: ha
metrics:
- name: Wer
type: wer
value: 44.41266209000763
---
<!-- 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. -->
# Hausa Whisper Small - Saad
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 13 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7524
- Wer Ortho: 47.7050
- Wer: 44.4127
## 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: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:-------:|
| 0.0104 | 3.18 | 500 | 0.7524 | 47.7050 | 44.4127 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
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