modelId
stringlengths 4
122
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
74.7M
| likes
int64 0
9.67k
| library_name
stringlengths 2
84
⌀ | tags
list | pipeline_tag
stringlengths 5
30
⌀ | createdAt
timestamp[us, tz=UTC] | card
stringlengths 1
901k
| embedding
list |
|---|---|---|---|---|---|---|---|---|---|---|
jat-project/jat-large
|
jat-project
| 2023-12-17T14:58:37Z | 0 | 0 |
transformers
|
[
"transformers",
"jat",
"custom_code",
"endpoints_compatible",
"region:us"
] | null | 2023-12-16T09:10:23Z |
Entry not found
|
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SarthakM320/q-FrozenLake-v1-4x4-noSlippery
|
SarthakM320
| 2023-12-16T09:11:56Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T09:11:53Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="SarthakM320/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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SarthakM320/q-FrozenLake-v1-8x8-noSlippery
|
SarthakM320
| 2023-12-16T09:32:33Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T09:15:24Z |
---
tags:
- FrozenLake-v1-8x8-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8-no_slippery
type: FrozenLake-v1-8x8-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="SarthakM320/q-FrozenLake-v1-8x8-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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EmmaGthn/runs_prefix_25000_moji
|
EmmaGthn
| 2023-12-16T17:28:24Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"region:us"
] | null | 2023-12-16T09:16:03Z |
Entry not found
|
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] |
Saabary/jihad
|
Saabary
| 2023-12-17T13:18:34Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T09:21:06Z |
---
license: apache-2.0
---
|
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] |
owanr/SBIC-roberta-base-inter-shuffle-model_annots_alpha0.5_whole_1e-05
|
owanr
| 2023-12-16T09:22:54Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-16T09:22:36Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-shuffle-model_annots_alpha0.5_whole_1e-05
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. -->
# SBIC-roberta-base-inter-shuffle-model_annots_alpha0.5_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1845
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.2515 | 1.0 | 12516 | 2.1440 |
| 2.1909 | 2.0 | 25032 | 2.1845 |
| 2.3142 | 3.0 | 37548 | 2.1845 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Raquelwhicks/TotalFitKetoACVGummies
|
Raquelwhicks
| 2023-12-16T09:23:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T09:22:43Z |
<h3 style="text-align: center;"><span style="background-color: #ffff00;">Official Website:-@>></span></h3>
<h3 style="text-align: center;"><br /><span style="background-color: #ffffff;"><a style="background-color: #ffffff;" href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/EdmYRnZQjnw">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/EdmYRnZQjnw</a></span></h3>
<p> </p>
<h2 style="text-align: center;"> <strong>➢ Product Name — </strong><span style="color: #ff0000;"><a style="color: #ff0000;" href="https://healthcare24hrs.com/totalfitketoacvgummiesbuy"><strong>Total Fit Keto ACV Gummies</strong></a></span></h2>
<h2 style="text-align: center;"> </h2>
<h2 style="text-align: center;"> </h2>
<p><span style="font-weight: 400;">The initial purpose of the nutritional supplement </span><span style="color: #008000;"><a style="color: #008000;" href="https://www.facebook.com/TotalFitKetoACVGummiesinUS/"><strong>Total Fit Keto ACV Gummies</strong></a></span><span style="font-weight: 400;"> was to support ketogenic diet users. The low-carb, high-fat focus of the ketogenic diet is well-known, as it aims to put the body into a state known as ketosis.</span><span style="font-weight: 400;">The ketogenic diet can result in weight loss and enhanced metabolic function since it uses fat as the main energy source rather than carbs.</span></p>
<p><span style="font-weight: 400;">They are thoughtfully crafted to offer a simple and pleasurable means of augmenting your ketogenic experience. Keto ACV Gummies are a more delectable supplement than standard ones, which can be hard to swallow or incorporate into your everyday routine. contains vital elements for the ketogenic diet. The goal of these candies is to assist your body in entering and staying in ketosis.</span></p>
<p><span style="font-weight: 400;">The ketogenic diet is gaining popularity as people search for better, healthier lifestyles. It has many advantages, including helping people regulate their weight and boosting their energy levels. People who care about their health are always searching for new approaches to streamline the medical system without sacrificing its efficacy.</span></p>
<p><span style="font-weight: 400;"> Thus, "<span style="color: #008000;"><a style="color: #008000;" href="https://www.facebook.com/TotalFitKetoACVGummiesinUS/"><strong>Total Fit Keto ACV Gummies</strong></a></span><span style="font-weight: 400;">" are the talk of the town this time around in the US, Canada, Australia, and New Zealand (NZ), and customers are searching for them through - The ideal blend of practicality and ketogenic support.</span></span></p>
<p><strong><span style="background-color: #ffcc00;">Official Facebook:-@>></span></strong></p>
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<p><span style="background-color: #ffffff;"><strong><a style="background-color: #ffffff;" href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/MRql-OX2Mlk">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/MRql-OX2Mlk</a> </strong></span></p>
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<p><span style="background-color: #00ff00;"><strong>Official Main Blogs @>></strong></span></p>
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<p><span style="background-color: #ff6600;"><strong>Revive CBD Gummies</strong></span></p>
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<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/TE7MA7w6K7o">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/TE7MA7w6K7o</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/zpT2OW4lc4c">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/zpT2OW4lc4c</a> </strong></p>
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<p><span style="background-color: #ccffff;"><strong>Vista Keto ACV Gummies</strong></span></p>
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<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/9yeYqcjC3ig">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/9yeYqcjC3ig</a> </strong></p>
<p> </p>
<p><span style="background-color: #ccffcc;"><strong>Good Keto BHB Gummies</strong></span></p>
<p><strong><a href="https://www.facebook.com/GoodKetoBHBGummiesinUS/">https://www.facebook.com/GoodKetoBHBGummiesinUS/</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/11OmoN231d0">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/11OmoN231d0</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/xRplG8qppak">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/xRplG8qppak</a> </strong></p>
<p><strong> </strong></p>
<p><span style="background-color: #ff00ff;"><strong>Pfizer Keto ACV Gummies</strong></span></p>
<p><strong><a href="https://www.facebook.com/people/Pfizer-Keto-ACV-Gummies/61553976665969/">https://www.facebook.com/people/Pfizer-Keto-ACV-Gummies/61553976665969/</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/3txZWnXaw9I">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/3txZWnXaw9I</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/IINf7TRpdI0">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/IINf7TRpdI0</a> </strong></p>
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Raquelwhicks/TotalFitKetoACVGummiesbuynow
|
Raquelwhicks
| 2023-12-16T09:24:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T09:23:41Z |
<h3 style="text-align: center;"><span style="background-color: #ffff00;">Official Website:-@>></span></h3>
<h3 style="text-align: center;"><br /><span style="background-color: #ffffff;"><a style="background-color: #ffffff;" href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/EdmYRnZQjnw">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/EdmYRnZQjnw</a></span></h3>
<p> </p>
<h2 style="text-align: center;"> <strong>➢ Product Name — </strong><span style="color: #ff0000;"><a style="color: #ff0000;" href="https://healthcare24hrs.com/totalfitketoacvgummiesbuy"><strong>Total Fit Keto ACV Gummies</strong></a></span></h2>
<h2 style="text-align: center;"> </h2>
<h2 style="text-align: center;"> </h2>
<p><span style="font-weight: 400;">The initial purpose of the nutritional supplement </span><span style="color: #008000;"><a style="color: #008000;" href="https://www.facebook.com/TotalFitKetoACVGummiesinUS/"><strong>Total Fit Keto ACV Gummies</strong></a></span><span style="font-weight: 400;"> was to support ketogenic diet users. The low-carb, high-fat focus of the ketogenic diet is well-known, as it aims to put the body into a state known as ketosis.</span><span style="font-weight: 400;">The ketogenic diet can result in weight loss and enhanced metabolic function since it uses fat as the main energy source rather than carbs.</span></p>
<p><span style="font-weight: 400;">They are thoughtfully crafted to offer a simple and pleasurable means of augmenting your ketogenic experience. Keto ACV Gummies are a more delectable supplement than standard ones, which can be hard to swallow or incorporate into your everyday routine. contains vital elements for the ketogenic diet. The goal of these candies is to assist your body in entering and staying in ketosis.</span></p>
<p><span style="font-weight: 400;">The ketogenic diet is gaining popularity as people search for better, healthier lifestyles. It has many advantages, including helping people regulate their weight and boosting their energy levels. People who care about their health are always searching for new approaches to streamline the medical system without sacrificing its efficacy.</span></p>
<p><span style="font-weight: 400;"> Thus, "<span style="color: #008000;"><a style="color: #008000;" href="https://www.facebook.com/TotalFitKetoACVGummiesinUS/"><strong>Total Fit Keto ACV Gummies</strong></a></span><span style="font-weight: 400;">" are the talk of the town this time around in the US, Canada, Australia, and New Zealand (NZ), and customers are searching for them through - The ideal blend of practicality and ketogenic support.</span></span></p>
<p><strong><span style="background-color: #ffcc00;">Official Facebook:-@>></span></strong></p>
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<p><span style="background-color: #ffffff;"><strong><a style="background-color: #ffffff;" href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/bOvKVmsmVUM">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/bOvKVmsmVUM</a> </strong></span></p>
<p><span style="background-color: #ffffff;"><strong><a style="background-color: #ffffff;" href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/MRql-OX2Mlk">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/MRql-OX2Mlk</a> </strong></span></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/WXtXvv-8E7s">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/WXtXvv-8E7s</a> </strong></p>
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<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/TE7MA7w6K7o">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/TE7MA7w6K7o</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/zpT2OW4lc4c">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/zpT2OW4lc4c</a> </strong></p>
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<p><span style="background-color: #ccffff;"><strong>Vista Keto ACV Gummies</strong></span></p>
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<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/9yeYqcjC3ig">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/9yeYqcjC3ig</a> </strong></p>
<p> </p>
<p><span style="background-color: #ccffcc;"><strong>Good Keto BHB Gummies</strong></span></p>
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<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/11OmoN231d0">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/11OmoN231d0</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/xRplG8qppak">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/xRplG8qppak</a> </strong></p>
<p><strong> </strong></p>
<p><span style="background-color: #ff00ff;"><strong>Pfizer Keto ACV Gummies</strong></span></p>
<p><strong><a href="https://www.facebook.com/people/Pfizer-Keto-ACV-Gummies/61553976665969/">https://www.facebook.com/people/Pfizer-Keto-ACV-Gummies/61553976665969/</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/3txZWnXaw9I">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/3txZWnXaw9I</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/IINf7TRpdI0">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/IINf7TRpdI0</a> </strong></p>
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mohammad78/text-to-image
|
mohammad78
| 2023-12-16T09:24:58Z | 0 | 0 | null |
[
"license:afl-3.0",
"region:us"
] | null | 2023-12-16T09:24:58Z |
---
license: afl-3.0
---
|
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Raquelwhicks/TotalFitKetoACVGummiespricenow
|
Raquelwhicks
| 2023-12-16T09:25:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T09:25:18Z |
<h3 style="text-align: center;"><span style="background-color: #ffff00;">Official Website:-@>></span></h3>
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<h2 style="text-align: center;"> </h2>
<h2 style="text-align: center;"> </h2>
<p><span style="font-weight: 400;">The initial purpose of the nutritional supplement </span><span style="color: #008000;"><a style="color: #008000;" href="https://www.facebook.com/TotalFitKetoACVGummiesinUS/"><strong>Total Fit Keto ACV Gummies</strong></a></span><span style="font-weight: 400;"> was to support ketogenic diet users. The low-carb, high-fat focus of the ketogenic diet is well-known, as it aims to put the body into a state known as ketosis.</span><span style="font-weight: 400;">The ketogenic diet can result in weight loss and enhanced metabolic function since it uses fat as the main energy source rather than carbs.</span></p>
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<p><span style="font-weight: 400;">The ketogenic diet is gaining popularity as people search for better, healthier lifestyles. It has many advantages, including helping people regulate their weight and boosting their energy levels. People who care about their health are always searching for new approaches to streamline the medical system without sacrificing its efficacy.</span></p>
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|
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] |
NabeelShar/essay_dec
|
NabeelShar
| 2023-12-16T11:08:27Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"license:other",
"region:us"
] |
text-generation
| 2023-12-16T09:25:44Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
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] |
Buseak/md_mt5_base_boun_split_second_v1_retrain_on_boun_1612
|
Buseak
| 2023-12-16T12:46:51Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:Buseak/md_mt5_base_boun_split_second_v1_retrain_on_second_imst",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-16T09:27:10Z |
---
license: apache-2.0
base_model: Buseak/md_mt5_base_boun_split_second_v1_retrain_on_second_imst
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: md_mt5_base_boun_split_second_v1_retrain_on_boun_1612
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. -->
# md_mt5_base_boun_split_second_v1_retrain_on_boun_1612
This model is a fine-tuned version of [Buseak/md_mt5_base_boun_split_second_v1_retrain_on_second_imst](https://huggingface.co/Buseak/md_mt5_base_boun_split_second_v1_retrain_on_second_imst) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1146
- Bleu: 0.7971
- Gen Len: 18.8069
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 0.3542 | 1.0 | 975 | 0.1679 | 0.7453 | 18.7959 |
| 0.3354 | 2.0 | 1950 | 0.1553 | 0.7492 | 18.8033 |
| 0.3183 | 3.0 | 2925 | 0.1500 | 0.7576 | 18.7972 |
| 0.3013 | 4.0 | 3900 | 0.1421 | 0.7618 | 18.7969 |
| 0.2912 | 5.0 | 4875 | 0.1364 | 0.7673 | 18.8005 |
| 0.2816 | 6.0 | 5850 | 0.1321 | 0.7732 | 18.8003 |
| 0.2753 | 7.0 | 6825 | 0.1287 | 0.7825 | 18.8067 |
| 0.2718 | 8.0 | 7800 | 0.1252 | 0.7866 | 18.8051 |
| 0.2631 | 9.0 | 8775 | 0.1222 | 0.7885 | 18.799 |
| 0.2576 | 10.0 | 9750 | 0.1197 | 0.793 | 18.8041 |
| 0.2541 | 11.0 | 10725 | 0.1182 | 0.7935 | 18.8056 |
| 0.2505 | 12.0 | 11700 | 0.1165 | 0.7956 | 18.8056 |
| 0.2492 | 13.0 | 12675 | 0.1156 | 0.7971 | 18.8056 |
| 0.2534 | 14.0 | 13650 | 0.1146 | 0.7964 | 18.8069 |
| 0.2455 | 15.0 | 14625 | 0.1146 | 0.7971 | 18.8069 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Raquelwhicks/TotalFitKetoACVGummiesreviews
|
Raquelwhicks
| 2023-12-16T09:28:04Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T09:27:40Z |
<h3 style="text-align: center;"><span style="background-color: #ffff00;">Official Website:-@>></span></h3>
<h3 style="text-align: center;"><br /><span style="background-color: #ffffff;"><a style="background-color: #ffffff;" href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/EdmYRnZQjnw">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/EdmYRnZQjnw</a></span></h3>
<p> </p>
<h2 style="text-align: center;"> <strong>➢ Product Name — </strong><span style="color: #ff0000;"><a style="color: #ff0000;" href="https://healthcare24hrs.com/totalfitketoacvgummiesbuy"><strong>Total Fit Keto ACV Gummies</strong></a></span></h2>
<h2 style="text-align: center;"> </h2>
<h2 style="text-align: center;"> </h2>
<p><span style="font-weight: 400;">The initial purpose of the nutritional supplement </span><span style="color: #008000;"><a style="color: #008000;" href="https://www.facebook.com/TotalFitKetoACVGummiesinUS/"><strong>Total Fit Keto ACV Gummies</strong></a></span><span style="font-weight: 400;"> was to support ketogenic diet users. The low-carb, high-fat focus of the ketogenic diet is well-known, as it aims to put the body into a state known as ketosis.</span><span style="font-weight: 400;">The ketogenic diet can result in weight loss and enhanced metabolic function since it uses fat as the main energy source rather than carbs.</span></p>
<p><span style="font-weight: 400;">They are thoughtfully crafted to offer a simple and pleasurable means of augmenting your ketogenic experience. Keto ACV Gummies are a more delectable supplement than standard ones, which can be hard to swallow or incorporate into your everyday routine. contains vital elements for the ketogenic diet. The goal of these candies is to assist your body in entering and staying in ketosis.</span></p>
<p><span style="font-weight: 400;">The ketogenic diet is gaining popularity as people search for better, healthier lifestyles. It has many advantages, including helping people regulate their weight and boosting their energy levels. People who care about their health are always searching for new approaches to streamline the medical system without sacrificing its efficacy.</span></p>
<p><span style="font-weight: 400;"> Thus, "<span style="color: #008000;"><a style="color: #008000;" href="https://www.facebook.com/TotalFitKetoACVGummiesinUS/"><strong>Total Fit Keto ACV Gummies</strong></a></span><span style="font-weight: 400;">" are the talk of the town this time around in the US, Canada, Australia, and New Zealand (NZ), and customers are searching for them through - The ideal blend of practicality and ketogenic support.</span></span></p>
<p><strong><span style="background-color: #ffcc00;">Official Facebook:-@>></span></strong></p>
<p><br /><strong><span style="background-color: #ffffff;"><a style="background-color: #ffffff;" href="https://www.facebook.com/TotalFitKetoACVgummiesinUS/">https://www.facebook.com/TotalFitKetoACVgummiesinUS/</a></span></strong></p>
<p> </p>
<p><span style="background-color: #ffff99;"><strong>Total Fit Keto ACV Gummies:-@>></strong></span></p>
<p><span style="background-color: #ffffff;"><strong><a style="background-color: #ffffff;" href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/bOvKVmsmVUM">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/bOvKVmsmVUM</a> </strong></span></p>
<p><span style="background-color: #ffffff;"><strong><a style="background-color: #ffffff;" href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/MRql-OX2Mlk">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/MRql-OX2Mlk</a> </strong></span></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/WXtXvv-8E7s">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/WXtXvv-8E7s</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/2yUBCiQ9Eag">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/2yUBCiQ9Eag</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/2yUBCiQ9Eag">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/2yUBCiQ9Eag</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/EdmYRnZQjnw">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/EdmYRnZQjnw</a> </strong></p>
<p><strong><a href="https://groups.google.com/g/mozilla.dev.platform/c/ArE_U-wMlHs">https://groups.google.com/g/mozilla.dev.platform/c/ArE_U-wMlHs</a> </strong></p>
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<p> </p>
<p><span style="background-color: #00ff00;"><strong>Official Main Blogs @>></strong></span></p>
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<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/TE7MA7w6K7o">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/TE7MA7w6K7o</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/zpT2OW4lc4c">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/zpT2OW4lc4c</a> </strong></p>
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<p><span style="background-color: #ccffff;"><strong>Vista Keto ACV Gummies</strong></span></p>
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<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/9yeYqcjC3ig">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/9yeYqcjC3ig</a> </strong></p>
<p> </p>
<p><span style="background-color: #ccffcc;"><strong>Good Keto BHB Gummies</strong></span></p>
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<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/11OmoN231d0">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/11OmoN231d0</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/xRplG8qppak">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/xRplG8qppak</a> </strong></p>
<p><strong> </strong></p>
<p><span style="background-color: #ff00ff;"><strong>Pfizer Keto ACV Gummies</strong></span></p>
<p><strong><a href="https://www.facebook.com/people/Pfizer-Keto-ACV-Gummies/61553976665969/">https://www.facebook.com/people/Pfizer-Keto-ACV-Gummies/61553976665969/</a> </strong></p>
<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/3txZWnXaw9I">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/3txZWnXaw9I</a> </strong></p>
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s-gladkykh/sky-diffusion-256-lr2-5
|
s-gladkykh
| 2023-12-18T01:29:35Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2023-12-16T09:29:36Z |
Entry not found
|
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Raquelwhicks/TotalFitKetoACVGummiesodernow
|
Raquelwhicks
| 2023-12-16T09:30:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T09:29:37Z |
<h3 style="text-align: center;"><span style="background-color: #ffff00;">Official Website:-@>></span></h3>
<h3 style="text-align: center;"><br /><span style="background-color: #ffffff;"><a style="background-color: #ffffff;" href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/EdmYRnZQjnw">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/EdmYRnZQjnw</a></span></h3>
<p> </p>
<h2 style="text-align: center;"> <strong>➢ Product Name — </strong><span style="color: #ff0000;"><a style="color: #ff0000;" href="https://healthcare24hrs.com/totalfitketoacvgummiesbuy"><strong>Total Fit Keto ACV Gummies</strong></a></span></h2>
<h2 style="text-align: center;"> </h2>
<h2 style="text-align: center;"> </h2>
<p><span style="font-weight: 400;">The initial purpose of the nutritional supplement </span><span style="color: #008000;"><a style="color: #008000;" href="https://www.facebook.com/TotalFitKetoACVGummiesinUS/"><strong>Total Fit Keto ACV Gummies</strong></a></span><span style="font-weight: 400;"> was to support ketogenic diet users. The low-carb, high-fat focus of the ketogenic diet is well-known, as it aims to put the body into a state known as ketosis.</span><span style="font-weight: 400;">The ketogenic diet can result in weight loss and enhanced metabolic function since it uses fat as the main energy source rather than carbs.</span></p>
<p><span style="font-weight: 400;">They are thoughtfully crafted to offer a simple and pleasurable means of augmenting your ketogenic experience. Keto ACV Gummies are a more delectable supplement than standard ones, which can be hard to swallow or incorporate into your everyday routine. contains vital elements for the ketogenic diet. The goal of these candies is to assist your body in entering and staying in ketosis.</span></p>
<p><span style="font-weight: 400;">The ketogenic diet is gaining popularity as people search for better, healthier lifestyles. It has many advantages, including helping people regulate their weight and boosting their energy levels. People who care about their health are always searching for new approaches to streamline the medical system without sacrificing its efficacy.</span></p>
<p><span style="font-weight: 400;"> Thus, "<span style="color: #008000;"><a style="color: #008000;" href="https://www.facebook.com/TotalFitKetoACVGummiesinUS/"><strong>Total Fit Keto ACV Gummies</strong></a></span><span style="font-weight: 400;">" are the talk of the town this time around in the US, Canada, Australia, and New Zealand (NZ), and customers are searching for them through - The ideal blend of practicality and ketogenic support.</span></span></p>
<p><strong><span style="background-color: #ffcc00;">Official Facebook:-@>></span></strong></p>
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<p><span style="background-color: #ccffff;"><strong>Vista Keto ACV Gummies</strong></span></p>
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<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/9yeYqcjC3ig">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/9yeYqcjC3ig</a> </strong></p>
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<p><span style="background-color: #ccffcc;"><strong>Good Keto BHB Gummies</strong></span></p>
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<p><strong><a href="https://groups.google.com/a/chromium.org/g/chromium-reviews/c/xRplG8qppak">https://groups.google.com/a/chromium.org/g/chromium-reviews/c/xRplG8qppak</a> </strong></p>
<p><strong> </strong></p>
<p><span style="background-color: #ff00ff;"><strong>Pfizer Keto ACV Gummies</strong></span></p>
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] |
Parth673/ML-Agents-SnowballTarget
|
Parth673
| 2023-12-16T09:32:31Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] |
reinforcement-learning
| 2023-12-16T09:31:43Z |
---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Eswann/ML-Agents-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
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DaniFoxyUltimate/ModelShit
|
DaniFoxyUltimate
| 2023-12-16T09:35:20Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T09:32:42Z |
Entry not found
|
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fdffe3/car
|
fdffe3
| 2023-12-16T09:36:38Z | 0 | 0 | null |
[
"ru",
"region:us"
] | null | 2023-12-16T09:35:14Z |
---
language:
- ru
---
|
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aaditya/whisper-big-hi
|
aaditya
| 2023-12-16T09:35:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T09:35:24Z |
Entry not found
|
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] |
ZhangFuXi/Zh-MT-LLM
|
ZhangFuXi
| 2023-12-17T20:17:59Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"chatglm",
"feature-extraction",
"custom_code",
"license:apache-2.0",
"region:us"
] |
feature-extraction
| 2023-12-16T09:38:03Z |
---
license: apache-2.0
---
|
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] |
vivekdugale/Llama-2-7b-chat-finetuned_e25_172
|
vivekdugale
| 2023-12-16T09:40:28Z | 0 | 0 | null |
[
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] |
kaengpil/OliviaRuby
|
kaengpil
| 2023-12-16T09:41:01Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T09:40:40Z |
---
license: openrail
---
|
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SkyR/Reinforce-cartpole
|
SkyR
| 2023-12-16T09:43:39Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T09:43:29Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-cartpole
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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] |
Shashashasha/glados-vits
|
Shashashasha
| 2023-12-16T09:48:14Z | 0 | 0 |
transformers
|
[
"transformers",
"license:openrail",
"endpoints_compatible",
"region:us"
] | null | 2023-12-16T09:45:01Z |
---
license: openrail
---
|
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] |
linqus/marian-finetuned-kde4-en-to-fr
|
linqus
| 2023-12-16T11:15:42Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-12-16T09:48:36Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- translation
- generated_from_trainer
datasets:
- kde4
model-index:
- name: marian-finetuned-kde4-en-to-fr
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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
owanr/SBIC-roberta-base-intra-shuffle-model_annots_alpha0.5_whole_1e-05
|
owanr
| 2023-12-16T09:48:54Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-16T09:48:36Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-intra-shuffle-model_annots_alpha0.5_whole_1e-05
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. -->
# SBIC-roberta-base-intra-shuffle-model_annots_alpha0.5_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0205
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.9908 | 1.0 | 12516 | 2.0312 |
| 2.093 | 2.0 | 25032 | 2.0205 |
| 1.9536 | 3.0 | 37548 | 2.0205 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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MrGordo/Scribe
|
MrGordo
| 2023-12-16T09:58:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T09:58:07Z |
Entry not found
|
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] |
hueser/hufaone
|
hueser
| 2023-12-16T09:59:36Z | 0 | 0 | null |
[
"license:cc-by-sa-3.0",
"region:us"
] | null | 2023-12-16T09:59:36Z |
---
license: cc-by-sa-3.0
---
|
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beautybeachketogummiesreview/beautybeachketogummiesreview
|
beautybeachketogummiesreview
| 2023-12-16T10:03:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T10:01:51Z |
---
license: openrail
language:
- en
library_name: espnet
tags:
Beauty Beach Keto Gummies
[Beauty Beach Keto Gummies](https://atozsupplement.com/beauty-beach-keto-gummies/) Keto chewy candies are a sort of dietary enhancement intended to help a ketogenic diet. The ketogenic diet is a low-starch, high-fat eating plan that expects to prompt a condition of ketosis in the body, where it consumes fat for fuel rather than sugars. Keto chewy candies are regularly made with fixings that are low in carbs and high in solid fats to assist people with keeping up with ketosis.
Click Here For More Details :-https://atozsupplement.com/beauty-beach-keto-gummies/
|
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] |
JugalOza/ReinforcePixelCopter
|
JugalOza
| 2023-12-16T12:21:36Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T10:04:19Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: ReinforcePixelCopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 21.20 +/- 19.53
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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bachngo/meta-Llama-2-7b-chat-vi-wikiv2
|
bachngo
| 2023-12-16T10:22:20Z | 0 | 0 | null |
[
"gguf",
"region:us"
] | null | 2023-12-16T10:05:31Z |
Entry not found
|
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shivangchopra11/butterfly_diffusion
|
shivangchopra11
| 2023-12-16T10:06:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T10:06:48Z |
Entry not found
|
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adelacvg/TTTS
|
adelacvg
| 2023-12-16T10:13:04Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-16T10:08:31Z |
---
license: mit
---
|
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hkivancoral/smids_5x_beit_base_rms_0001_fold1
|
hkivancoral
| 2023-12-16T11:30:08Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T10:10:00Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_rms_0001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9031719532554258
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_rms_0001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9099
- Accuracy: 0.9032
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2877 | 1.0 | 376 | 0.3734 | 0.8397 |
| 0.263 | 2.0 | 752 | 0.2897 | 0.8898 |
| 0.214 | 3.0 | 1128 | 0.4392 | 0.8815 |
| 0.1401 | 4.0 | 1504 | 0.3640 | 0.8865 |
| 0.0913 | 5.0 | 1880 | 0.4071 | 0.8982 |
| 0.0792 | 6.0 | 2256 | 0.5796 | 0.8765 |
| 0.0326 | 7.0 | 2632 | 0.5828 | 0.8781 |
| 0.0613 | 8.0 | 3008 | 0.5485 | 0.8965 |
| 0.0614 | 9.0 | 3384 | 0.5394 | 0.8815 |
| 0.0378 | 10.0 | 3760 | 0.5802 | 0.8932 |
| 0.016 | 11.0 | 4136 | 0.5517 | 0.8998 |
| 0.1114 | 12.0 | 4512 | 0.5851 | 0.8798 |
| 0.0304 | 13.0 | 4888 | 0.5301 | 0.8731 |
| 0.0236 | 14.0 | 5264 | 0.6243 | 0.8965 |
| 0.0147 | 15.0 | 5640 | 0.5697 | 0.8998 |
| 0.0009 | 16.0 | 6016 | 0.5289 | 0.9098 |
| 0.003 | 17.0 | 6392 | 0.6450 | 0.8932 |
| 0.045 | 18.0 | 6768 | 0.7662 | 0.8915 |
| 0.0003 | 19.0 | 7144 | 0.6709 | 0.8898 |
| 0.0083 | 20.0 | 7520 | 0.7941 | 0.8865 |
| 0.0011 | 21.0 | 7896 | 0.8204 | 0.8831 |
| 0.0265 | 22.0 | 8272 | 0.7663 | 0.8798 |
| 0.0065 | 23.0 | 8648 | 0.7543 | 0.8865 |
| 0.0005 | 24.0 | 9024 | 0.8605 | 0.8881 |
| 0.0223 | 25.0 | 9400 | 0.7879 | 0.8815 |
| 0.0093 | 26.0 | 9776 | 0.8444 | 0.8748 |
| 0.0004 | 27.0 | 10152 | 0.7708 | 0.8781 |
| 0.0001 | 28.0 | 10528 | 0.7477 | 0.8948 |
| 0.0051 | 29.0 | 10904 | 0.7865 | 0.8831 |
| 0.0131 | 30.0 | 11280 | 0.8049 | 0.9098 |
| 0.0 | 31.0 | 11656 | 0.8832 | 0.8915 |
| 0.0001 | 32.0 | 12032 | 0.8723 | 0.8965 |
| 0.0007 | 33.0 | 12408 | 1.0043 | 0.8865 |
| 0.0001 | 34.0 | 12784 | 0.9137 | 0.8881 |
| 0.0019 | 35.0 | 13160 | 0.7356 | 0.9048 |
| 0.0 | 36.0 | 13536 | 0.7048 | 0.9032 |
| 0.0 | 37.0 | 13912 | 0.8706 | 0.9015 |
| 0.0 | 38.0 | 14288 | 0.7699 | 0.9032 |
| 0.0 | 39.0 | 14664 | 0.8383 | 0.8982 |
| 0.0 | 40.0 | 15040 | 0.8533 | 0.9048 |
| 0.0008 | 41.0 | 15416 | 0.8710 | 0.9015 |
| 0.0001 | 42.0 | 15792 | 0.9271 | 0.8898 |
| 0.0 | 43.0 | 16168 | 0.9308 | 0.8982 |
| 0.0 | 44.0 | 16544 | 0.9577 | 0.8982 |
| 0.0 | 45.0 | 16920 | 0.9412 | 0.8898 |
| 0.0033 | 46.0 | 17296 | 0.9423 | 0.8998 |
| 0.0 | 47.0 | 17672 | 0.9136 | 0.9048 |
| 0.0 | 48.0 | 18048 | 0.9005 | 0.9065 |
| 0.0001 | 49.0 | 18424 | 0.9138 | 0.9048 |
| 0.0023 | 50.0 | 18800 | 0.9099 | 0.9032 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
lch34677/distilbert-base-uncased-finetuned-emotion
|
lch34677
| 2023-12-16T10:11:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-16T10:10:18Z |
Entry not found
|
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] |
hkivancoral/smids_5x_beit_base_rms_00001_fold1
|
hkivancoral
| 2023-12-16T11:30:47Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T10:10:30Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_rms_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8964941569282137
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_rms_00001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0277
- Accuracy: 0.8965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.169 | 1.0 | 376 | 0.2845 | 0.8848 |
| 0.1527 | 2.0 | 752 | 0.2709 | 0.9132 |
| 0.1446 | 3.0 | 1128 | 0.3421 | 0.8998 |
| 0.0485 | 4.0 | 1504 | 0.4474 | 0.9065 |
| 0.0159 | 5.0 | 1880 | 0.4847 | 0.8965 |
| 0.0162 | 6.0 | 2256 | 0.6046 | 0.8982 |
| 0.0753 | 7.0 | 2632 | 0.6419 | 0.8898 |
| 0.0455 | 8.0 | 3008 | 0.7218 | 0.8965 |
| 0.0437 | 9.0 | 3384 | 0.8405 | 0.8815 |
| 0.007 | 10.0 | 3760 | 0.7349 | 0.9015 |
| 0.0254 | 11.0 | 4136 | 0.8461 | 0.8915 |
| 0.0214 | 12.0 | 4512 | 0.7638 | 0.8898 |
| 0.0283 | 13.0 | 4888 | 0.8735 | 0.8948 |
| 0.0331 | 14.0 | 5264 | 0.8577 | 0.8932 |
| 0.0029 | 15.0 | 5640 | 0.9013 | 0.8982 |
| 0.0041 | 16.0 | 6016 | 0.9992 | 0.8698 |
| 0.0007 | 17.0 | 6392 | 0.9147 | 0.8865 |
| 0.0019 | 18.0 | 6768 | 0.9339 | 0.8915 |
| 0.0002 | 19.0 | 7144 | 0.8625 | 0.8982 |
| 0.0341 | 20.0 | 7520 | 0.9287 | 0.8815 |
| 0.0 | 21.0 | 7896 | 1.0011 | 0.8831 |
| 0.0 | 22.0 | 8272 | 0.8805 | 0.8948 |
| 0.0028 | 23.0 | 8648 | 0.9347 | 0.8965 |
| 0.0001 | 24.0 | 9024 | 0.9930 | 0.8965 |
| 0.001 | 25.0 | 9400 | 1.0054 | 0.8982 |
| 0.029 | 26.0 | 9776 | 0.8994 | 0.8932 |
| 0.0028 | 27.0 | 10152 | 0.9209 | 0.8865 |
| 0.0009 | 28.0 | 10528 | 0.9409 | 0.8998 |
| 0.0018 | 29.0 | 10904 | 1.0441 | 0.8848 |
| 0.0163 | 30.0 | 11280 | 0.9017 | 0.9032 |
| 0.0 | 31.0 | 11656 | 0.8554 | 0.9015 |
| 0.0 | 32.0 | 12032 | 0.8702 | 0.9048 |
| 0.0001 | 33.0 | 12408 | 0.9551 | 0.8965 |
| 0.0 | 34.0 | 12784 | 0.9265 | 0.8982 |
| 0.0004 | 35.0 | 13160 | 1.0253 | 0.8865 |
| 0.0044 | 36.0 | 13536 | 0.9098 | 0.8948 |
| 0.0003 | 37.0 | 13912 | 0.9290 | 0.9032 |
| 0.0 | 38.0 | 14288 | 1.0072 | 0.8948 |
| 0.0 | 39.0 | 14664 | 1.0677 | 0.8948 |
| 0.0 | 40.0 | 15040 | 1.0064 | 0.8982 |
| 0.0 | 41.0 | 15416 | 0.9891 | 0.8982 |
| 0.0 | 42.0 | 15792 | 1.0628 | 0.8948 |
| 0.0 | 43.0 | 16168 | 1.0396 | 0.8915 |
| 0.0 | 44.0 | 16544 | 1.0033 | 0.8982 |
| 0.0 | 45.0 | 16920 | 1.0214 | 0.8998 |
| 0.0033 | 46.0 | 17296 | 1.0498 | 0.8898 |
| 0.0 | 47.0 | 17672 | 1.0375 | 0.8932 |
| 0.0 | 48.0 | 18048 | 1.0305 | 0.8898 |
| 0.0 | 49.0 | 18424 | 1.0285 | 0.8948 |
| 0.0028 | 50.0 | 18800 | 1.0277 | 0.8965 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
hkivancoral/smids_5x_deit_tiny_rms_001_fold1
|
hkivancoral
| 2023-12-16T11:06:43Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T10:11:47Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_rms_001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.7562604340567612
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_tiny_rms_001_fold1
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6720
- Accuracy: 0.7563
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.9519 | 1.0 | 376 | 0.9699 | 0.4808 |
| 0.8617 | 2.0 | 752 | 0.8618 | 0.5392 |
| 0.8149 | 3.0 | 1128 | 0.8048 | 0.5893 |
| 0.8075 | 4.0 | 1504 | 0.7999 | 0.5609 |
| 0.9135 | 5.0 | 1880 | 0.7865 | 0.6160 |
| 0.783 | 6.0 | 2256 | 0.8586 | 0.5893 |
| 0.725 | 7.0 | 2632 | 0.8054 | 0.6227 |
| 0.6972 | 8.0 | 3008 | 0.7248 | 0.6444 |
| 0.72 | 9.0 | 3384 | 0.7167 | 0.6661 |
| 0.7292 | 10.0 | 3760 | 0.7657 | 0.6795 |
| 0.645 | 11.0 | 4136 | 0.6894 | 0.6861 |
| 0.7059 | 12.0 | 4512 | 0.7066 | 0.6928 |
| 0.7086 | 13.0 | 4888 | 0.7125 | 0.6995 |
| 0.6705 | 14.0 | 5264 | 0.6700 | 0.7078 |
| 0.6566 | 15.0 | 5640 | 0.6881 | 0.6861 |
| 0.5734 | 16.0 | 6016 | 0.7052 | 0.6694 |
| 0.5199 | 17.0 | 6392 | 0.7378 | 0.6628 |
| 0.659 | 18.0 | 6768 | 0.6486 | 0.7112 |
| 0.6288 | 19.0 | 7144 | 0.7161 | 0.6528 |
| 0.566 | 20.0 | 7520 | 0.6171 | 0.7212 |
| 0.6474 | 21.0 | 7896 | 0.6184 | 0.7262 |
| 0.5542 | 22.0 | 8272 | 0.6826 | 0.6861 |
| 0.5759 | 23.0 | 8648 | 0.6131 | 0.7229 |
| 0.6266 | 24.0 | 9024 | 0.6647 | 0.7112 |
| 0.6436 | 25.0 | 9400 | 0.6298 | 0.7078 |
| 0.5378 | 26.0 | 9776 | 0.6147 | 0.7229 |
| 0.534 | 27.0 | 10152 | 0.6258 | 0.7179 |
| 0.4794 | 28.0 | 10528 | 0.6515 | 0.7095 |
| 0.5282 | 29.0 | 10904 | 0.6735 | 0.6912 |
| 0.4828 | 30.0 | 11280 | 0.6279 | 0.7179 |
| 0.5597 | 31.0 | 11656 | 0.6003 | 0.7295 |
| 0.5931 | 32.0 | 12032 | 0.6323 | 0.7362 |
| 0.4604 | 33.0 | 12408 | 0.6185 | 0.7446 |
| 0.473 | 34.0 | 12784 | 0.6171 | 0.7396 |
| 0.5357 | 35.0 | 13160 | 0.6139 | 0.7279 |
| 0.5273 | 36.0 | 13536 | 0.6022 | 0.7379 |
| 0.446 | 37.0 | 13912 | 0.6164 | 0.7362 |
| 0.5051 | 38.0 | 14288 | 0.6160 | 0.7329 |
| 0.5127 | 39.0 | 14664 | 0.6147 | 0.7629 |
| 0.5424 | 40.0 | 15040 | 0.5988 | 0.7579 |
| 0.4672 | 41.0 | 15416 | 0.6152 | 0.7613 |
| 0.4259 | 42.0 | 15792 | 0.6298 | 0.7429 |
| 0.4313 | 43.0 | 16168 | 0.6086 | 0.7462 |
| 0.4716 | 44.0 | 16544 | 0.6307 | 0.7496 |
| 0.4303 | 45.0 | 16920 | 0.6176 | 0.7513 |
| 0.3889 | 46.0 | 17296 | 0.6198 | 0.7479 |
| 0.4191 | 47.0 | 17672 | 0.6340 | 0.7563 |
| 0.3752 | 48.0 | 18048 | 0.6420 | 0.7596 |
| 0.3744 | 49.0 | 18424 | 0.6614 | 0.7529 |
| 0.3137 | 50.0 | 18800 | 0.6720 | 0.7563 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
AIScholars/newrsai
|
AIScholars
| 2023-12-16T10:20:06Z | 0 | 0 |
bertopic
|
[
"bertopic",
"art",
"legal",
"question-answering",
"ar",
"dataset:wikimedia/wikipedia",
"dataset:nvidia/HelpSteer",
"dataset:fka/awesome-chatgpt-prompts",
"dataset:berkeley-nest/Nectar",
"license:apache-2.0",
"region:us"
] |
question-answering
| 2023-12-16T10:14:36Z |
---
license: apache-2.0
datasets:
- wikimedia/wikipedia
- nvidia/HelpSteer
- fka/awesome-chatgpt-prompts
- berkeley-nest/Nectar
language:
- ar
metrics:
- accuracy
- bleurt
library_name: bertopic
pipeline_tag: question-answering
tags:
- art
- legal
---
|
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owanr/SBIC-roberta-base-inter-frequency-human_annots_alpha0.5_whole_1e-05
|
owanr
| 2023-12-16T10:14:55Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-16T10:14:38Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-frequency-human_annots_alpha0.5_whole_1e-05
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. -->
# SBIC-roberta-base-inter-frequency-human_annots_alpha0.5_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1638
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0105 | 1.0 | 12516 | 1.1839 |
| 0.8312 | 2.0 | 25032 | 1.1638 |
| 0.8129 | 3.0 | 37548 | 1.1638 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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wisejiyoon/bert-finetuned-ner-nikl
|
wisejiyoon
| 2023-12-16T10:15:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T10:15:56Z |
Entry not found
|
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kanishka/smolm-autoreg-bpe-counterfactual-babylm-indef-naan-rerun-3e-4
|
kanishka
| 2023-12-16T23:33:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"generated_from_trainer",
"dataset:kanishka/counterfactual_babylm_aann_indef_naan",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-16T10:22:18Z |
---
tags:
- generated_from_trainer
datasets:
- kanishka/counterfactual_babylm_aann_indef_naan
metrics:
- accuracy
model-index:
- name: smolm-autoreg-bpe-counterfactual-babylm-indef-naan-rerun-3e-4
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/counterfactual_babylm_aann_indef_naan
type: kanishka/counterfactual_babylm_aann_indef_naan
metrics:
- name: Accuracy
type: accuracy
value: 0.41066454597322516
---
<!-- 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. -->
# smolm-autoreg-bpe-counterfactual-babylm-indef-naan-rerun-3e-4
This model was trained from scratch on the kanishka/counterfactual_babylm_aann_indef_naan dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3944
- Accuracy: 0.4107
## 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 3.7328 | 1.0 | 18595 | 3.8886 | 0.3463 |
| 3.4284 | 2.0 | 37190 | 3.5889 | 0.3765 |
| 3.2877 | 3.0 | 55785 | 3.5068 | 0.3882 |
| 3.2043 | 4.0 | 74380 | 3.4467 | 0.3957 |
| 3.1467 | 5.0 | 92975 | 3.3793 | 0.4010 |
| 3.0972 | 6.0 | 111570 | 3.3796 | 0.4030 |
| 3.058 | 7.0 | 130165 | 3.3637 | 0.4048 |
| 3.0303 | 8.0 | 148760 | 3.3449 | 0.4073 |
| 2.9995 | 9.0 | 167355 | 3.3597 | 0.4074 |
| 2.9726 | 10.0 | 185950 | 3.3591 | 0.4085 |
| 2.9449 | 11.0 | 204545 | 3.3621 | 0.4086 |
| 2.9249 | 12.0 | 223140 | 3.3672 | 0.4090 |
| 2.906 | 13.0 | 241735 | 3.3656 | 0.4094 |
| 2.8857 | 14.0 | 260330 | 3.3786 | 0.4093 |
| 2.8597 | 15.0 | 278925 | 3.3611 | 0.4110 |
| 2.8438 | 16.0 | 297520 | 3.3741 | 0.4103 |
| 2.8229 | 17.0 | 316115 | 3.3816 | 0.4099 |
| 2.8013 | 18.0 | 334710 | 3.3826 | 0.4106 |
| 2.7882 | 19.0 | 353305 | 3.3887 | 0.4108 |
| 2.7683 | 20.0 | 371900 | 3.3944 | 0.4107 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
xiaol/RWKV-v5-12B-one-state-base
|
xiaol
| 2023-12-17T05:15:00Z | 0 | 2 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T10:22:34Z |
---
license: apache-2.0
---
# Release date: December 28th
This is 12B SOTA model
(to be continued...)
|
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LiquescentRemedies/stolen
|
LiquescentRemedies
| 2023-12-16T10:26:18Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T10:26:18Z |
Entry not found
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] |
hueser/twoffc
|
hueser
| 2023-12-16T10:28:29Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2023-12-16T10:28:29Z |
---
license: bigscience-bloom-rail-1.0
---
|
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] |
Jesusbatrese/ToastyHeater
|
Jesusbatrese
| 2023-12-16T10:37:36Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T10:34:50Z |
---
license: apache-2.0
---
In the ever-evolving landscape of home appliances, one aspect that remains constant is the need for warmth and comfort during colder seasons. As temperatures drop, the quest for the perfect heating solution becomes paramount.
BLOGS:-
https://groups.google.com/a/chromium.org/g/chromium-reviews/c/Bc5X310MDaE
https://groups.google.com/a/chromium.org/g/chromium-reviews/c/YDUdfnEZ13I
https://groups.google.com/a/chromium.org/g/chromium-reviews/c/t7TfYmKDFaY
https://groups.google.com/a/chromium.org/g/chromium-reviews/c/tzoW5hlaH74
https://groups.google.com/a/chromium.org/g/chromium-reviews/c/VkJ_w2O7TFc
https://groups.google.com/a/chromium.org/g/chromium-reviews/c/8ZrRNq1lWW4
https://www.facebook.com/USToastyHeaterPrice/
https://www.facebook.com/GetToastyHeaterUS/
https://www.facebook.com/TryToastyHeater
https://sites.google.com/view/toasty-heater-reviews-buy/home
https://sites.google.com/view/toasty-heater-price-official/home
https://sites.google.com/view/is-toasty-heater-a-scam/home
https://sites.google.com/view/is-toasty-heater-legit/home
https://sites.google.com/view/toasty-heater-reviews-reddit/home
https://sites.google.com/view/toasty-heater-com/home
https://medium.com/@dorinejmitchell/toasty-heater-reviews-shocking-reviews-complaints-update-2023-scam-or-legit-089dfd9f29e4?postPublishedType=initial
https://medium.com/@dorinejmitchell/toasty-heater-price-is-it-legit-reviews-read-customer-reviews-before-buying-1122cef476ba
https://medium.com/@dorinejmitchell/is-toasty-heater-a-scam-urgent-customer-update-disturbing-side-effects-warning-f09c61886dfd
https://medium.com/@dorinejmitchell/is-toasty-heater-legit-top-rated-reviews-genuine-expense-51d94b633aae
https://medium.com/@dorinejmitchell/toasty-heater-reviews-reddit-is-fake-or-real-read-about-100-natural-product-d79dfe31f153?postPublishedType=initial
https://medium.com/@dorinejmitchell/toasty-heater-com-is-real-or-not-where-to-buy-original-product-75c8a2642156
https://toasty-heater-reviews-usa.webflow.io/
https://toasty-heater-price.webflow.io/
https://toasty-heater-review-reddit.webflow.io/
https://toasty-heater-com.webflow.io/
https://gamma.app/public/Toasty-Heater-Reviews-44txo65uqxw89uy
https://groups.google.com/g/toasty-heater-reviews-official/c/9Ehc2W7RCso
https://groups.google.com/g/toasty-heater-reviews-official/c/yRazYn72QRQ
https://groups.google.com/g/toasty-heater-reviews-official/c/bkYs9ECkJfA
https://groups.google.com/g/toasty-heater-reviews-official/c/75O1Ss2rT8A
https://groups.google.com/g/toasty-heater-reviews-official/c/EVN_0MO2usI
FACEBOOK:-
https://www.facebook.com/Renew.Calm.CBD.Gummies.Official/
https://www.facebook.com/RenewCalmCBDGummies/
https://www.facebook.com/TryGreenVibeCBDGummies/
https://www.facebook.com/GetGreenVibe.CBDGummies/
https://www.facebook.com/RebirthCBDGummiesCanada
https://www.facebook.com/RebirthCBDGummiesCA/
https://www.facebook.com/PureHarmonyCBDGummiesUS/
https://www.facebook.com/Pure.Harmony.CBD.Gummies.Official/
https://www.facebook.com/DrOzCBDGummies/
https://www.facebook.com/Dr.OZ.CBD.Gummies.Order/
https://www.facebook.com/DrOzCBDGummieReviews/
|
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] |
SarthakM320/q-FrozenLake-v1-8x8-slippery
|
SarthakM320
| 2023-12-16T10:53:46Z | 0 | 0 | null |
[
"FrozenLake-v1-8x8",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T10:38:22Z |
---
tags:
- FrozenLake-v1-8x8
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-8x8-slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-8x8
type: FrozenLake-v1-8x8
metrics:
- type: mean_reward
value: 0.32 +/- 0.47
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="SarthakM320/q-FrozenLake-v1-8x8-slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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] |
jin1125/llama2-dev
|
jin1125
| 2023-12-16T10:38:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T10:38:26Z |
---
license: apache-2.0
---
|
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] |
hueser/facethree
|
hueser
| 2023-12-16T10:39:07Z | 0 | 0 | null |
[
"license:bigcode-openrail-m",
"region:us"
] | null | 2023-12-16T10:39:07Z |
---
license: bigcode-openrail-m
---
|
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owanr/SBIC-roberta-base-inter-frequency-model_annots_alpha0.5_whole_1e-05
|
owanr
| 2023-12-16T10:41:00Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-16T10:40:40Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-frequency-model_annots_alpha0.5_whole_1e-05
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. -->
# SBIC-roberta-base-inter-frequency-model_annots_alpha0.5_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4181
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.5087 | 1.0 | 12516 | 2.3499 |
| 2.4599 | 2.0 | 25032 | 2.4181 |
| 2.5731 | 3.0 | 37548 | 2.4181 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
hueser/facefour
|
hueser
| 2023-12-16T10:44:57Z | 0 | 0 | null |
[
"license:afl-3.0",
"region:us"
] | null | 2023-12-16T10:44:57Z |
---
license: afl-3.0
---
|
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beepoop95/detr-resnet_cppe5
|
beepoop95
| 2023-12-16T10:47:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T10:47:10Z |
Entry not found
|
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] |
tndklab/hubert-rinna-jdrt_RTsplitModel-1216-1
|
tndklab
| 2023-12-16T11:28:46Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"hubert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:rinna/japanese-hubert-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-16T10:48:36Z |
---
license: apache-2.0
base_model: rinna/japanese-hubert-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: hubert-rinna-jdrt_RTsplitModel-1216-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# hubert-rinna-jdrt_RTsplitModel-1216-1
This model is a fine-tuned version of [rinna/japanese-hubert-base](https://huggingface.co/rinna/japanese-hubert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0159
- Wer: 0.0933
- Cer: 0.0526
## 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: 32
- eval_batch_size: 32
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 7.727 | 1.0 | 120 | 6.8098 | 1.0 | 1.0 |
| 4.4759 | 2.0 | 240 | 4.1318 | 1.0 | 1.0 |
| 3.0053 | 3.0 | 360 | 2.8843 | 1.0 | 1.0 |
| 1.4753 | 4.0 | 480 | 1.3657 | 1.0 | 0.5459 |
| 0.9939 | 5.0 | 600 | 0.9069 | 0.8628 | 0.4710 |
| 0.8505 | 6.0 | 720 | 0.7025 | 0.6998 | 0.4429 |
| 0.6935 | 7.0 | 840 | 0.6410 | 0.6448 | 0.4011 |
| 0.6237 | 8.0 | 960 | 0.5793 | 0.7235 | 0.4130 |
| 0.6579 | 9.0 | 1080 | 0.5095 | 0.6321 | 0.3145 |
| 0.5156 | 10.0 | 1200 | 0.4575 | 0.6425 | 0.3176 |
| 0.4632 | 11.0 | 1320 | 0.4390 | 0.6330 | 0.2942 |
| 0.4177 | 12.0 | 1440 | 0.3613 | 0.5441 | 0.2472 |
| 0.3788 | 13.0 | 1560 | 0.3085 | 0.3995 | 0.1480 |
| 0.3402 | 14.0 | 1680 | 0.2447 | 0.3659 | 0.1336 |
| 0.2651 | 15.0 | 1800 | 0.1952 | 0.3039 | 0.1158 |
| 0.257 | 16.0 | 1920 | 0.1816 | 0.2979 | 0.1283 |
| 0.2015 | 17.0 | 2040 | 0.1409 | 0.2389 | 0.0935 |
| 0.2552 | 18.0 | 2160 | 0.1270 | 0.2158 | 0.0810 |
| 0.1537 | 19.0 | 2280 | 0.0917 | 0.2027 | 0.0802 |
| 0.1171 | 20.0 | 2400 | 0.0685 | 0.1501 | 0.0592 |
| 0.1085 | 21.0 | 2520 | 0.0645 | 0.1676 | 0.0765 |
| 0.1092 | 22.0 | 2640 | 0.0456 | 0.1296 | 0.0605 |
| 0.0957 | 23.0 | 2760 | 0.0394 | 0.1372 | 0.0680 |
| 0.0658 | 24.0 | 2880 | 0.0321 | 0.1023 | 0.0492 |
| 0.0497 | 25.0 | 3000 | 0.0275 | 0.0961 | 0.0498 |
| 0.0532 | 26.0 | 3120 | 0.0217 | 0.1027 | 0.0593 |
| 0.0514 | 27.0 | 3240 | 0.0200 | 0.1019 | 0.0579 |
| 0.0515 | 28.0 | 3360 | 0.0186 | 0.1014 | 0.0580 |
| 0.0585 | 29.0 | 3480 | 0.0170 | 0.0984 | 0.0558 |
| 0.0484 | 30.0 | 3600 | 0.0159 | 0.0933 | 0.0526 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.0
|
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stealthwriter/your-model-name2
|
stealthwriter
| 2023-12-16T10:49:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T10:49:55Z |
Entry not found
|
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] |
hueser/facefive
|
hueser
| 2023-12-16T10:50:25Z | 0 | 0 | null |
[
"license:bsl-1.0",
"region:us"
] | null | 2023-12-16T10:50:25Z |
---
license: bsl-1.0
---
|
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] |
satyakiran/Email-SMS_Spam_Detector
|
satyakiran
| 2023-12-16T10:55:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T10:51:38Z |
---
license: apache-2.0
---
|
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artificialhug/lora-miley-SSD-1B
|
artificialhug
| 2023-12-16T10:53:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T10:53:37Z |
Entry not found
|
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bodam/1
|
bodam
| 2023-12-16T13:14:01Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2023-12-16T11:02:24Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
instance_prompt: A k2b3d chair
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - bodam/1
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were trained on A k2b3d chair using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
|
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] |
windigo/vit-base-patch16-224-in21k-finetuned-lora-food101
|
windigo
| 2023-12-16T11:30:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T11:02:39Z |
Entry not found
|
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] |
marziye-A/mms-1b-fa-with-val-without-halfspace-15
|
marziye-A
| 2023-12-18T00:46:35Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-16T11:03:54Z |
Entry not found
|
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Necraatall/shadowrunCharCreator
|
Necraatall
| 2023-12-16T11:05:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T11:05:58Z |
Entry not found
|
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] |
owanr/SBIC-roberta-base-intra-frequency-model_annots_alpha0.5_whole_1e-05
|
owanr
| 2023-12-16T11:07:04Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-16T11:06:47Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-intra-frequency-model_annots_alpha0.5_whole_1e-05
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. -->
# SBIC-roberta-base-intra-frequency-model_annots_alpha0.5_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9965
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.9074 | 1.0 | 12516 | 1.9966 |
| 2.0544 | 2.0 | 25032 | 1.9965 |
| 1.7844 | 3.0 | 37548 | 1.9965 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
hkivancoral/smids_5x_deit_tiny_rms_001_fold2
|
hkivancoral
| 2023-12-16T12:03:01Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/deit-tiny-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T11:07:47Z |
---
license: apache-2.0
base_model: facebook/deit-tiny-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_deit_tiny_rms_001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.848585690515807
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_deit_tiny_rms_001_fold2
This model is a fine-tuned version of [facebook/deit-tiny-patch16-224](https://huggingface.co/facebook/deit-tiny-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4964
- Accuracy: 0.8486
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.0328 | 1.0 | 375 | 0.9569 | 0.4459 |
| 0.8929 | 2.0 | 750 | 0.8978 | 0.5374 |
| 0.8224 | 3.0 | 1125 | 0.7888 | 0.5574 |
| 0.8327 | 4.0 | 1500 | 0.8571 | 0.5641 |
| 0.7266 | 5.0 | 1875 | 1.1729 | 0.5025 |
| 0.6507 | 6.0 | 2250 | 0.7875 | 0.6456 |
| 0.6983 | 7.0 | 2625 | 0.6489 | 0.6972 |
| 0.6312 | 8.0 | 3000 | 0.7326 | 0.6789 |
| 0.641 | 9.0 | 3375 | 0.5505 | 0.7488 |
| 0.6354 | 10.0 | 3750 | 0.5766 | 0.7354 |
| 0.5813 | 11.0 | 4125 | 0.4910 | 0.7920 |
| 0.6084 | 12.0 | 4500 | 0.5458 | 0.7720 |
| 0.4944 | 13.0 | 4875 | 0.4657 | 0.8020 |
| 0.5555 | 14.0 | 5250 | 0.5401 | 0.7621 |
| 0.526 | 15.0 | 5625 | 0.4958 | 0.7837 |
| 0.3751 | 16.0 | 6000 | 0.4911 | 0.8037 |
| 0.4264 | 17.0 | 6375 | 0.5204 | 0.7837 |
| 0.4312 | 18.0 | 6750 | 0.5011 | 0.7953 |
| 0.3686 | 19.0 | 7125 | 0.4979 | 0.7970 |
| 0.3954 | 20.0 | 7500 | 0.4812 | 0.8120 |
| 0.3782 | 21.0 | 7875 | 0.4706 | 0.8120 |
| 0.3544 | 22.0 | 8250 | 0.4461 | 0.8353 |
| 0.3759 | 23.0 | 8625 | 0.4516 | 0.8319 |
| 0.3473 | 24.0 | 9000 | 0.4332 | 0.8270 |
| 0.2572 | 25.0 | 9375 | 0.5951 | 0.8203 |
| 0.3628 | 26.0 | 9750 | 0.5630 | 0.7887 |
| 0.2737 | 27.0 | 10125 | 0.5304 | 0.8336 |
| 0.2272 | 28.0 | 10500 | 0.5597 | 0.8319 |
| 0.2226 | 29.0 | 10875 | 0.5680 | 0.8419 |
| 0.1778 | 30.0 | 11250 | 0.6295 | 0.8170 |
| 0.2382 | 31.0 | 11625 | 0.6223 | 0.8270 |
| 0.1721 | 32.0 | 12000 | 0.6049 | 0.8469 |
| 0.219 | 33.0 | 12375 | 0.5556 | 0.8569 |
| 0.0972 | 34.0 | 12750 | 0.6389 | 0.8502 |
| 0.1781 | 35.0 | 13125 | 0.7873 | 0.8253 |
| 0.1052 | 36.0 | 13500 | 0.8815 | 0.8236 |
| 0.1087 | 37.0 | 13875 | 0.7444 | 0.8453 |
| 0.09 | 38.0 | 14250 | 0.9779 | 0.8253 |
| 0.0859 | 39.0 | 14625 | 0.8817 | 0.8386 |
| 0.0521 | 40.0 | 15000 | 0.9849 | 0.8453 |
| 0.081 | 41.0 | 15375 | 1.0555 | 0.8203 |
| 0.0225 | 42.0 | 15750 | 1.1081 | 0.8303 |
| 0.0521 | 43.0 | 16125 | 1.2294 | 0.8253 |
| 0.0259 | 44.0 | 16500 | 1.3035 | 0.8336 |
| 0.0403 | 45.0 | 16875 | 1.3613 | 0.8253 |
| 0.0225 | 46.0 | 17250 | 1.4500 | 0.8103 |
| 0.0235 | 47.0 | 17625 | 1.5096 | 0.8270 |
| 0.0002 | 48.0 | 18000 | 1.5022 | 0.8469 |
| 0.0101 | 49.0 | 18375 | 1.4968 | 0.8469 |
| 0.0029 | 50.0 | 18750 | 1.4964 | 0.8486 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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TheBloke/agiin-13.6B-v0.1-AWQ
|
TheBloke
| 2023-12-16T11:42:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:Intel/orca_dpo_pairs",
"base_model:mncai/agiin-13.6B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-16T11:08:26Z |
---
base_model: mncai/agiin-13.6B-v0.1
datasets:
- Intel/orca_dpo_pairs
inference: false
language:
- en
license: apache-2.0
model_creator: MindsAndCompany
model_name: Agiin 13.6B v0.1
model_type: mistral
prompt_template: '<|user|>
{prompt}
<|assistant|>
'
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 -->
# Agiin 13.6B v0.1 - AWQ
- Model creator: [MindsAndCompany](https://huggingface.co/mncai)
- Original model: [Agiin 13.6B v0.1](https://huggingface.co/mncai/agiin-13.6B-v0.1)
<!-- description start -->
## Description
This repo contains AWQ model files for [MindsAndCompany's Agiin 13.6B v0.1](https://huggingface.co/mncai/agiin-13.6B-v0.1).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/agiin-13.6B-v0.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/agiin-13.6B-v0.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/agiin-13.6B-v0.1-GGUF)
* [MindsAndCompany's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mncai/agiin-13.6B-v0.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ToRA
```
<|user|>
{prompt}
<|assistant|>
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/agiin-13.6B-v0.1-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.55 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/agiin-13.6B-v0.1-AWQ`.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `agiin-13.6B-v0.1-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/agiin-13.6B-v0.1-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''<|user|>
{prompt}
<|assistant|>
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/agiin-13.6B-v0.1-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/agiin-13.6B-v0.1-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''<|user|>
{prompt}
<|assistant|>
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/agiin-13.6B-v0.1-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''<|user|>
{prompt}
<|assistant|>
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: MindsAndCompany's Agiin 13.6B v0.1
# Model Card for mncai/agiin-13.6B-v0.1
### Introduction of MindsAndCompany
https://mnc.ai/
We create various AI models and develop solutions that can be applied to businesses. And as for generative AI, we are developing products like Code Assistant, TOD Chatbot, LLMOps, and are in the process of developing Enterprise AGI (Artificial General Intelligence).
### Model Summary
This model was built based on the Mistral architecture. It was inspired by neural connection technology and rehabilitation therapy.
I have created a new model architecture that does not require pretraining, and training the model is sufficient with just one H100 for 7 hours.
### Data
Intel/orca_dpo_pairs (DPO)
### Surgery and Training
stack mistral 62 layers and DPO.
### How to Use
```python
message = [
{"role": "system", "content": "You are a helpful assistant chatbot."},
{"role": "user", "content": "두 개의 구가 각각 지름이 1, 2일때 두 구의 부피는 몇배지? 설명도 같이 해줘."}
]
tokenizer = AutoTokenizer.from_pretrained(hf_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
pipeline = transformers.pipeline(
"text-generation",
model=hf_model,
tokenizer=tokenizer
)
sequences = pipeline(
prompt,
do_sample=True,
temperature=0.7,
top_p=0.9,
num_return_sequences=1,
max_length=512,
)
print(sequences[0]['generated_text'])
```
### Contact
If you have any questions, please raise an issue or contact us at dwmyoung@mnc.ai
|
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TheBloke/agiin-13.6B-v0.1-GPTQ
|
TheBloke
| 2023-12-16T12:03:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:Intel/orca_dpo_pairs",
"base_model:mncai/agiin-13.6B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-16T11:08:26Z |
---
base_model: mncai/agiin-13.6B-v0.1
datasets:
- Intel/orca_dpo_pairs
inference: false
language:
- en
license: apache-2.0
model_creator: MindsAndCompany
model_name: Agiin 13.6B v0.1
model_type: mistral
prompt_template: '<|user|>
{prompt}
<|assistant|>
'
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 -->
# Agiin 13.6B v0.1 - GPTQ
- Model creator: [MindsAndCompany](https://huggingface.co/mncai)
- Original model: [Agiin 13.6B v0.1](https://huggingface.co/mncai/agiin-13.6B-v0.1)
<!-- description start -->
# Description
This repo contains GPTQ model files for [MindsAndCompany's Agiin 13.6B v0.1](https://huggingface.co/mncai/agiin-13.6B-v0.1).
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/agiin-13.6B-v0.1-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/agiin-13.6B-v0.1-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/agiin-13.6B-v0.1-GGUF)
* [MindsAndCompany's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mncai/agiin-13.6B-v0.1)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ToRA
```
<|user|>
{prompt}
<|assistant|>
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
GPTQ models are currently supported on Linux (NVidia/AMD) and Windows (NVidia only). macOS users: please use GGUF models.
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/agiin-13.6B-v0.1-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.57 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/agiin-13.6B-v0.1-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 8.36 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/agiin-13.6B-v0.1-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 14.07 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/agiin-13.6B-v0.1-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 14.38 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/agiin-13.6B-v0.1-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 15.33 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/agiin-13.6B-v0.1-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 7.83 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/agiin-13.6B-v0.1-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/agiin-13.6B-v0.1-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 `agiin-13.6B-v0.1-GPTQ`:
```shell
mkdir agiin-13.6B-v0.1-GPTQ
huggingface-cli download TheBloke/agiin-13.6B-v0.1-GPTQ --local-dir agiin-13.6B-v0.1-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir agiin-13.6B-v0.1-GPTQ
huggingface-cli download TheBloke/agiin-13.6B-v0.1-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir agiin-13.6B-v0.1-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 agiin-13.6B-v0.1-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/agiin-13.6B-v0.1-GPTQ --local-dir agiin-13.6B-v0.1-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/agiin-13.6B-v0.1-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/agiin-13.6B-v0.1-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/agiin-13.6B-v0.1-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: `agiin-13.6B-v0.1-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/agiin-13.6B-v0.1-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'''<|user|>
{prompt}
<|assistant|>
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: {response}")
```
<!-- README_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/agiin-13.6B-v0.1-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 = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''<|user|>
{prompt}
<|assistant|>
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama architecture models (including Mistral, Yi, DeepSeek, SOLAR, etc) 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**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: MindsAndCompany's Agiin 13.6B v0.1
# Model Card for mncai/agiin-13.6B-v0.1
### Introduction of MindsAndCompany
https://mnc.ai/
We create various AI models and develop solutions that can be applied to businesses. And as for generative AI, we are developing products like Code Assistant, TOD Chatbot, LLMOps, and are in the process of developing Enterprise AGI (Artificial General Intelligence).
### Model Summary
This model was built based on the Mistral architecture. It was inspired by neural connection technology and rehabilitation therapy.
I have created a new model architecture that does not require pretraining, and training the model is sufficient with just one H100 for 7 hours.
### Data
Intel/orca_dpo_pairs (DPO)
### Surgery and Training
stack mistral 62 layers and DPO.
### How to Use
```python
message = [
{"role": "system", "content": "You are a helpful assistant chatbot."},
{"role": "user", "content": "두 개의 구가 각각 지름이 1, 2일때 두 구의 부피는 몇배지? 설명도 같이 해줘."}
]
tokenizer = AutoTokenizer.from_pretrained(hf_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
pipeline = transformers.pipeline(
"text-generation",
model=hf_model,
tokenizer=tokenizer
)
sequences = pipeline(
prompt,
do_sample=True,
temperature=0.7,
top_p=0.9,
num_return_sequences=1,
max_length=512,
)
print(sequences[0]['generated_text'])
```
### Contact
If you have any questions, please raise an issue or contact us at dwmyoung@mnc.ai
|
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SarthakM320/Taxi_RL
|
SarthakM320
| 2023-12-16T11:09:30Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T11:09:28Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi_RL
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="SarthakM320/Taxi_RL", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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Ruscall/source
|
Ruscall
| 2023-12-16T11:10:21Z | 0 | 0 | null |
[
"license:llama2",
"region:us"
] | null | 2023-12-16T11:10:21Z |
---
license: llama2
---
|
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] |
hsc666/gomoku_rl_ppo_large
|
hsc666
| 2023-12-16T11:15:29Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2023-12-16T11:15:29Z |
---
license: mit
---
|
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GHArt/LEOSAM_AIArt_V1.0_xl_fp16
|
GHArt
| 2023-12-16T11:21:21Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2023-12-16T11:15:37Z |
Entry not found
|
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osmanfatihkilic/lora-trained-xl
|
osmanfatihkilic
| 2023-12-16T11:17:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T11:17:09Z |
Entry not found
|
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buy-beauty-beach-keto-gummies/beauty-beach-keto-gummies
|
buy-beauty-beach-keto-gummies
| 2023-12-16T11:19:05Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"Beauty Beach Keto Gummies",
"en",
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2023-12-16T11:18:05Z |
---
license: bigscience-bloom-rail-1.0
language:
- en
library_name: sample-factory
tags:
- Beauty Beach Keto Gummies
---
[Beauty Beach Keto Gummies](https://atozsupplement.com/beauty-beach-keto-gummies/) They are generally improved with sugar substitutes, for example, erythritol, stevia, or priest natural product separate, which insignificantly affect glucose levels.These gummies frequently contain fixings like gelatin or agar as the base, which gives the chewy surface. They may likewise be seasoned and shaded utilizing regular concentrates or food shading. Some keto gummies likewise incorporate extra fixings like nutrients, minerals, or collagen to offer extra healthful advantages.
VISIT HERE FOR OFFICIAL WEBSITE:-https://atozsupplement.com/beauty-beach-keto-gummies/
|
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sigma-bit-dot/rl_course_vizdoom_health_gathering_supreme
|
sigma-bit-dot
| 2023-12-16T11:21:28Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T11:21:09Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 5.54 +/- 1.44
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r sigma-bit-dot/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
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Prezily/DioloGPT-Large-DD
|
Prezily
| 2023-12-16T11:26:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T11:26:50Z |
Entry not found
|
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] |
hkivancoral/smids_5x_beit_base_rms_0001_fold2
|
hkivancoral
| 2023-12-16T12:51:28Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T11:30:58Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_rms_0001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9068219633943427
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_rms_0001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9869
- Accuracy: 0.9068
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3386 | 1.0 | 375 | 0.2631 | 0.8902 |
| 0.2769 | 2.0 | 750 | 0.2812 | 0.8852 |
| 0.1948 | 3.0 | 1125 | 0.4161 | 0.8686 |
| 0.1489 | 4.0 | 1500 | 0.3316 | 0.8852 |
| 0.1015 | 5.0 | 1875 | 0.3966 | 0.8835 |
| 0.0659 | 6.0 | 2250 | 0.5521 | 0.8686 |
| 0.0987 | 7.0 | 2625 | 0.4706 | 0.8852 |
| 0.0304 | 8.0 | 3000 | 0.6100 | 0.8835 |
| 0.0177 | 9.0 | 3375 | 0.5599 | 0.8835 |
| 0.0365 | 10.0 | 3750 | 0.5970 | 0.8902 |
| 0.07 | 11.0 | 4125 | 0.5587 | 0.8869 |
| 0.025 | 12.0 | 4500 | 0.6283 | 0.8885 |
| 0.013 | 13.0 | 4875 | 0.4540 | 0.9035 |
| 0.0155 | 14.0 | 5250 | 0.6593 | 0.8869 |
| 0.0612 | 15.0 | 5625 | 0.6571 | 0.8935 |
| 0.0058 | 16.0 | 6000 | 0.6333 | 0.8835 |
| 0.0564 | 17.0 | 6375 | 0.5490 | 0.8918 |
| 0.0204 | 18.0 | 6750 | 0.7225 | 0.8985 |
| 0.0128 | 19.0 | 7125 | 0.4844 | 0.9135 |
| 0.0241 | 20.0 | 7500 | 0.5085 | 0.9018 |
| 0.0042 | 21.0 | 7875 | 0.5500 | 0.9135 |
| 0.0209 | 22.0 | 8250 | 0.6987 | 0.8869 |
| 0.0277 | 23.0 | 8625 | 0.7227 | 0.8902 |
| 0.027 | 24.0 | 9000 | 0.8023 | 0.8769 |
| 0.0061 | 25.0 | 9375 | 0.7219 | 0.8985 |
| 0.0004 | 26.0 | 9750 | 0.7303 | 0.8935 |
| 0.0002 | 27.0 | 10125 | 0.6194 | 0.9118 |
| 0.0002 | 28.0 | 10500 | 0.7358 | 0.9085 |
| 0.0068 | 29.0 | 10875 | 0.7598 | 0.9002 |
| 0.0002 | 30.0 | 11250 | 0.7703 | 0.8935 |
| 0.0136 | 31.0 | 11625 | 0.7951 | 0.8902 |
| 0.0053 | 32.0 | 12000 | 0.8891 | 0.8918 |
| 0.0038 | 33.0 | 12375 | 0.7625 | 0.9018 |
| 0.0002 | 34.0 | 12750 | 0.8776 | 0.9052 |
| 0.0 | 35.0 | 13125 | 0.9210 | 0.9002 |
| 0.0195 | 36.0 | 13500 | 0.7510 | 0.9151 |
| 0.0008 | 37.0 | 13875 | 0.7794 | 0.9135 |
| 0.0007 | 38.0 | 14250 | 0.8315 | 0.9085 |
| 0.0005 | 39.0 | 14625 | 0.7854 | 0.9151 |
| 0.0033 | 40.0 | 15000 | 0.8459 | 0.9101 |
| 0.0001 | 41.0 | 15375 | 0.9023 | 0.9002 |
| 0.0027 | 42.0 | 15750 | 1.0108 | 0.9018 |
| 0.0026 | 43.0 | 16125 | 1.0264 | 0.8952 |
| 0.0026 | 44.0 | 16500 | 0.9790 | 0.9035 |
| 0.0027 | 45.0 | 16875 | 0.9445 | 0.9101 |
| 0.0 | 46.0 | 17250 | 0.9135 | 0.9185 |
| 0.0057 | 47.0 | 17625 | 0.9222 | 0.9085 |
| 0.0 | 48.0 | 18000 | 0.9390 | 0.9085 |
| 0.0052 | 49.0 | 18375 | 0.9876 | 0.9052 |
| 0.0025 | 50.0 | 18750 | 0.9869 | 0.9068 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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] |
PowerInfer/ReluLLaMA-7B-Predictor
|
PowerInfer
| 2023-12-16T11:49:40Z | 0 | 0 | null |
[
"PowerInfer",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T11:31:26Z |
---
license: apache-2.0
language:
- en
tags:
- PowerInfer
---
# ReluLLaMA-7B-Predictor
- Model creator: [PowerInfer](https://huggingface.co/PowerInfer)
This repository provides a group of sparsity predictors serving for [SparseLLM/ReluLLaMA-7B](https://huggingface.co/SparseLLM/ReluLLaMA-7B).
|
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] |
hkivancoral/smids_5x_beit_base_rms_00001_fold2
|
hkivancoral
| 2023-12-16T12:52:01Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T11:31:39Z |
---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_rms_00001_fold2
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9051580698835274
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# smids_5x_beit_base_rms_00001_fold2
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9950
- Accuracy: 0.9052
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2499 | 1.0 | 375 | 0.2304 | 0.9185 |
| 0.1775 | 2.0 | 750 | 0.2550 | 0.9018 |
| 0.1217 | 3.0 | 1125 | 0.4013 | 0.8869 |
| 0.0433 | 4.0 | 1500 | 0.5189 | 0.8819 |
| 0.0358 | 5.0 | 1875 | 0.4893 | 0.8985 |
| 0.0375 | 6.0 | 2250 | 0.5725 | 0.9052 |
| 0.0405 | 7.0 | 2625 | 0.5904 | 0.9101 |
| 0.012 | 8.0 | 3000 | 0.7002 | 0.9018 |
| 0.0053 | 9.0 | 3375 | 0.7065 | 0.9052 |
| 0.0037 | 10.0 | 3750 | 0.7485 | 0.8985 |
| 0.0126 | 11.0 | 4125 | 0.7919 | 0.8985 |
| 0.0005 | 12.0 | 4500 | 0.7919 | 0.8968 |
| 0.0159 | 13.0 | 4875 | 0.8564 | 0.8902 |
| 0.0001 | 14.0 | 5250 | 0.8426 | 0.8852 |
| 0.0002 | 15.0 | 5625 | 0.8433 | 0.8918 |
| 0.0148 | 16.0 | 6000 | 0.7634 | 0.9018 |
| 0.0208 | 17.0 | 6375 | 0.8403 | 0.8952 |
| 0.0001 | 18.0 | 6750 | 0.8471 | 0.9018 |
| 0.0491 | 19.0 | 7125 | 0.8371 | 0.9035 |
| 0.0 | 20.0 | 7500 | 0.7423 | 0.9052 |
| 0.0126 | 21.0 | 7875 | 0.8759 | 0.8935 |
| 0.0008 | 22.0 | 8250 | 0.8648 | 0.9002 |
| 0.0 | 23.0 | 8625 | 0.9554 | 0.9002 |
| 0.0005 | 24.0 | 9000 | 0.9755 | 0.8918 |
| 0.0184 | 25.0 | 9375 | 0.9160 | 0.8918 |
| 0.0 | 26.0 | 9750 | 0.9691 | 0.8918 |
| 0.0 | 27.0 | 10125 | 0.8701 | 0.8968 |
| 0.0002 | 28.0 | 10500 | 0.7677 | 0.9035 |
| 0.0001 | 29.0 | 10875 | 0.9258 | 0.9035 |
| 0.0033 | 30.0 | 11250 | 0.9080 | 0.9002 |
| 0.0045 | 31.0 | 11625 | 1.0210 | 0.8935 |
| 0.0045 | 32.0 | 12000 | 0.9883 | 0.8985 |
| 0.0017 | 33.0 | 12375 | 0.8984 | 0.9035 |
| 0.0 | 34.0 | 12750 | 0.8844 | 0.9101 |
| 0.0007 | 35.0 | 13125 | 0.9085 | 0.8918 |
| 0.0002 | 36.0 | 13500 | 0.9790 | 0.9035 |
| 0.0 | 37.0 | 13875 | 1.0705 | 0.8985 |
| 0.0 | 38.0 | 14250 | 1.0172 | 0.9035 |
| 0.0 | 39.0 | 14625 | 1.0259 | 0.9052 |
| 0.0032 | 40.0 | 15000 | 1.0712 | 0.9018 |
| 0.0 | 41.0 | 15375 | 1.0107 | 0.9002 |
| 0.0025 | 42.0 | 15750 | 1.0002 | 0.9068 |
| 0.0023 | 43.0 | 16125 | 1.0032 | 0.9035 |
| 0.003 | 44.0 | 16500 | 0.9837 | 0.9052 |
| 0.0018 | 45.0 | 16875 | 1.0127 | 0.9035 |
| 0.0 | 46.0 | 17250 | 0.9843 | 0.9068 |
| 0.0056 | 47.0 | 17625 | 1.0283 | 0.9002 |
| 0.0033 | 48.0 | 18000 | 1.0135 | 0.9052 |
| 0.0031 | 49.0 | 18375 | 0.9997 | 0.9052 |
| 0.0025 | 50.0 | 18750 | 0.9950 | 0.9052 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
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GHArt/kawaii_pencil_XL_V1.0.0_xl_fp16
|
GHArt
| 2023-12-16T11:39:45Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2023-12-16T11:31:52Z |
Entry not found
|
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owanr/SBIC-roberta-base-inter-data-frequency-human_annots_alpha0.5_whole_1e-05
|
owanr
| 2023-12-16T11:33:07Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-16T11:32:51Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-data-frequency-human_annots_alpha0.5_whole_1e-05
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. -->
# SBIC-roberta-base-inter-data-frequency-human_annots_alpha0.5_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8899
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.823 | 1.0 | 12516 | 0.9027 |
| 0.7233 | 2.0 | 25032 | 0.8899 |
| 0.6607 | 3.0 | 37548 | 0.8899 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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FullRevelation/rvc12b
|
FullRevelation
| 2023-12-16T12:10:32Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T11:33:22Z |
Entry not found
|
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] |
PowerInfer/ReluLLaMA-13B-Predictor
|
PowerInfer
| 2023-12-16T11:58:14Z | 0 | 0 | null |
[
"PowerInfer",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T11:33:41Z |
---
license: apache-2.0
language:
- en
tags:
- PowerInfer
---
# ReluLLaMA-13B-Predictor
- Model creator: [PowerInfer](https://huggingface.co/PowerInfer)
This repository provides a group of sparsity predictors serving for [SparseLLM/ReluLLaMA-13B](https://huggingface.co/SparseLLM/ReluLLaMA-13B).
|
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PowerInfer/ReluFalcon-40B-Predictor
|
PowerInfer
| 2023-12-16T12:01:01Z | 0 | 0 | null |
[
"PowerInfer",
"en",
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T11:35:06Z |
---
license: apache-2.0
language:
- en
tags:
- PowerInfer
---
# ReluFalcon-40B-Predictor
- Model creator: [PowerInfer](https://huggingface.co/PowerInfer)
This repository provides a group of sparsity predictors serving for [SparseLLM/ReluFalcon-40B](https://huggingface.co/SparseLLM/ReluFalcon-40B).
|
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avillia/azusa
|
avillia
| 2023-12-16T11:45:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T11:37:28Z |
Entry not found
|
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] |
SkyR/Reinforce-helicopter
|
SkyR
| 2023-12-16T11:39:52Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T11:39:03Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-helicopter
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 23.20 +/- 11.44
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
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] |
AIFahim/videomae-base-finetuned-ucf101-subset
|
AIFahim
| 2023-12-16T11:55:46Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-12-16T11:40:00Z |
Entry not found
|
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] |
Ttonio/xlm-roberta-base-finetuned-panx-de-fr
|
Ttonio
| 2023-12-16T11:52:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-16T11:40:20Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1613
- F1: 0.8606
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2938 | 1.0 | 715 | 0.1905 | 0.8057 |
| 0.1488 | 2.0 | 1430 | 0.1621 | 0.8502 |
| 0.0959 | 3.0 | 2145 | 0.1613 | 0.8606 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
phucd/whisper-small-ko-old
|
phucd
| 2023-12-16T14:41:26Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ko",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-16T11:45:26Z |
---
language:
- ko
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper - Korean 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. -->
# Whisper - Korean Model
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4784
- Wer: 0.9889
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0002 | 52.63 | 1000 | 0.4055 | 1.0755 |
| 0.0001 | 105.26 | 2000 | 0.4449 | 1.0222 |
| 0.0001 | 157.89 | 3000 | 0.4692 | 0.9315 |
| 0.0001 | 210.53 | 4000 | 0.4784 | 0.9889 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 1.12.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
phucd/whisper-small-vi-old
|
phucd
| 2023-12-16T15:28:08Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"vi",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-16T11:45:58Z |
---
language:
- vi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper - Vietnamese 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. -->
# Whisper - Vietnamese Model
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6612
- Wer: 1.2655
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0204 | 5.59 | 1000 | 0.5415 | 1.3689 |
| 0.0005 | 11.17 | 2000 | 0.6213 | 1.2094 |
| 0.0003 | 16.76 | 3000 | 0.6507 | 1.2515 |
| 0.0002 | 22.35 | 4000 | 0.6612 | 1.2655 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 1.12.1
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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GHArt/ZavyChromaXL_V3.0_xl_fp16
|
GHArt
| 2023-12-16T11:54:45Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2023-12-16T11:47:06Z |
Entry not found
|
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ByteDance-new/shot2story
|
ByteDance-new
| 2023-12-16T17:06:22Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-16T11:47:48Z |
---
license: apache-2.0
---
|
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lombardata/dino-large-2023_12_16-kornia_img-size256_batch-size32_epochs20
|
lombardata
| 2023-12-16T14:50:28Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"dinov2",
"generated_from_trainer",
"base_model:facebook/dinov2-large",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2023-12-16T11:50:27Z |
---
license: apache-2.0
base_model: facebook/dinov2-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: dino-large-2023_12_16-kornia_img-size256_batch-size32_epochs20
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. -->
# dino-large-2023_12_16-kornia_img-size256_batch-size32_epochs20
This model is a fine-tuned version of [facebook/dinov2-large](https://huggingface.co/facebook/dinov2-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9599
- F1 Micro: 0.4726
- F1 Macro: 0.4077
- Roc Auc: 0.6701
- Accuracy: 0.2079
- Learning Rate: 0.01
## 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: 0.01
- 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 Micro | F1 Macro | Roc Auc | Accuracy | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:-------:|:--------:|:----:|
| No log | 1.0 | 268 | 0.1864 | 0.6816 | 0.5702 | 0.7937 | 0.4030 | 0.01 |
| 0.222 | 2.0 | 536 | 0.1765 | 0.7430 | 0.6583 | 0.8554 | 0.4023 | 0.01 |
| 0.222 | 3.0 | 804 | 0.1629 | 0.7546 | 0.6939 | 0.8352 | 0.4423 | 0.01 |
| 0.1964 | 4.0 | 1072 | 0.1547 | 0.7635 | 0.6757 | 0.8499 | 0.4427 | 0.01 |
| 0.1964 | 5.0 | 1340 | 0.1843 | 0.7369 | 0.6523 | 0.8583 | 0.3676 | 0.01 |
| 0.195 | 6.0 | 1608 | 0.9599 | 0.4726 | 0.4077 | 0.6701 | 0.2079 | 0.01 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
|
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pinot/wav2vec2-conformer-cv-15
|
pinot
| 2023-12-16T11:50:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T11:50:30Z |
Entry not found
|
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bodam/2
|
bodam
| 2023-12-16T11:54:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-16T11:54:29Z |
Entry not found
|
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] |
Ttonio/xlm-roberta-base-finetuned-panx-fr
|
Ttonio
| 2023-12-16T12:00:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-16T11:56:41Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.fr
split: validation
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8446995273463875
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2731
- F1: 0.8447
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.5823 | 1.0 | 191 | 0.3000 | 0.7951 |
| 0.264 | 2.0 | 382 | 0.2782 | 0.8208 |
| 0.177 | 3.0 | 573 | 0.2731 | 0.8447 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Parth673/unit8-doom_health_gathering_supreme
|
Parth673
| 2023-12-16T11:59:42Z | 0 | 0 |
sample-factory
|
[
"sample-factory",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T11:57:56Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 12.63 +/- 5.43
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r akghxhs55/appo-doom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=appo-doom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=appo-doom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
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] |
Ratorizz/Markiplier
|
Ratorizz
| 2023-12-16T12:00:02Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-16T11:58:44Z |
---
license: openrail
---
|
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owanr/SBIC-roberta-base-inter-data-frequency-model_annots_alpha0.5_whole_1e-05
|
owanr
| 2023-12-16T11:59:08Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-16T11:58:52Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-data-frequency-model_annots_alpha0.5_whole_1e-05
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. -->
# SBIC-roberta-base-inter-data-frequency-model_annots_alpha0.5_whole_1e-05
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9772
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.9786 | 1.0 | 12516 | 1.8980 |
| 2.0002 | 2.0 | 25032 | 1.9772 |
| 2.1055 | 3.0 | 37548 | 1.9772 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Ttonio/xlm-roberta-base-finetuned-panx-it
|
Ttonio
| 2023-12-16T12:02:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-16T12:00:15Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.it
split: validation
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8311475409836065
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-it
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2504
- F1: 0.8311
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8203 | 1.0 | 70 | 0.3244 | 0.7448 |
| 0.2743 | 2.0 | 140 | 0.2462 | 0.8127 |
| 0.1856 | 3.0 | 210 | 0.2504 | 0.8311 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Eric01/opus_mt_zh_en_fine_fune
|
Eric01
| 2023-12-16T14:24:24Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-zh-en",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-12-16T12:00:47Z |
---
license: cc-by-4.0
base_model: Helsinki-NLP/opus-mt-zh-en
tags:
- translation
- generated_from_trainer
datasets:
- kde4
model-index:
- name: opus_mt_zh_en_fine_fune
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. -->
# opus_mt_zh_en_fine_fune
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-zh-en](https://huggingface.co/Helsinki-NLP/opus-mt-zh-en) on the kde4 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: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
aaa12963337/msi-vit-small-pretrain
|
aaa12963337
| 2023-12-16T14:49:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:WinKawaks/vit-small-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-16T12:01:22Z |
---
license: apache-2.0
base_model: WinKawaks/vit-small-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: msi-vit-small-pretrain
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6394150417827298
---
<!-- 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. -->
# msi-vit-small-pretrain
This model is a fine-tuned version of [WinKawaks/vit-small-patch16-224](https://huggingface.co/WinKawaks/vit-small-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4835
- Accuracy: 0.6394
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0897 | 1.0 | 781 | 1.7652 | 0.6574 |
| 0.0539 | 2.0 | 1562 | 2.5512 | 0.6017 |
| 0.0127 | 3.0 | 2343 | 2.4835 | 0.6394 |
### Framework versions
- Transformers 4.36.0
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Ttonio/xlm-roberta-base-finetuned-panx-en
|
Ttonio
| 2023-12-16T12:03:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2023-12-16T12:02:15Z |
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.en
split: validation
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6767337807606264
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-en
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4165
- F1: 0.6767
## 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: 24
- eval_batch_size: 24
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1731 | 1.0 | 50 | 0.6069 | 0.5575 |
| 0.5376 | 2.0 | 100 | 0.4192 | 0.6433 |
| 0.4105 | 3.0 | 150 | 0.4165 | 0.6767 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Parth673/LunarLander-v2
|
Parth673
| 2023-12-16T12:03:57Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-16T12:03:28Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 104.83 +/- 18.01
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
See the GitHub for full info and the journey on creating this on the surface not particularly exciting model: https://github.com/MattStammers/PPO_Lander_Implementation
It took me 8 attempts to get the score to nearly reach 0 using a cleanRL implementation and WandB metric tracking and then this version was trained after 10 attempts converging at about 3 million training steps
|
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