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adi-vc/q-FrozenLake-v1-4x4-noSlippery
|
adi-vc
| 2023-12-17T18:25:44Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-17T18:25:38Z |
---
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="adi-vc/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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owanr/SChem5Labels-roberta-base-inter-frequency-human_annots_alpha0.0_whole_1e-05
|
owanr
| 2023-12-17T18:26:06Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-17T18:25:48Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-base-inter-frequency-human_annots_alpha0.0_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. -->
# SChem5Labels-roberta-base-inter-frequency-human_annots_alpha0.0_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: 7.4255
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 7.535 | 1.0 | 3164 | 7.4255 |
| 7.625 | 2.0 | 6328 | 7.4255 |
| 7.694 | 3.0 | 9492 | 7.4255 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
Arekku21/vivit-b-16x2-kinetics400-finetuned-MSL_40_classes_12
|
Arekku21
| 2023-12-17T19:36:27Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vivit",
"video-classification",
"endpoints_compatible",
"region:us"
] |
video-classification
| 2023-12-17T18:27:03Z |
Entry not found
|
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] |
A2H0H0R1/Llama-2-7b-chat-hf-biology-2-4bit-gptq
|
A2H0H0R1
| 2023-12-17T19:16:10Z | 0 | 0 |
transformers
|
[
"transformers",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T18:27:06Z |
Entry not found
|
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] |
tarekziade/distilbert-reuters21578
|
tarekziade
| 2023-12-17T18:39:54Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"news_classification",
"multi_label",
"en",
"dataset:reuters21578",
"base_model:distilbert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2023-12-17T18:29:49Z |
---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_trainer
- news_classification
- multi_label
datasets:
- reuters21578
metrics:
- f1
- accuracy
model-index:
- name: distilbert-finetuned-reuters21578-multilabel
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: reuters21578
type: reuters21578
config: ModApte
split: test
args: ModApte
metrics:
- name: F1
type: f1
value: 0.8628858578607322
- name: Accuracy
type: accuracy
value: 0.8195625759416768
language:
- en
pipeline_tag: text-classification
widget:
- text: "JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWARDS The Ministry of International Trade and Industry (MITI) will revise its long-term energy supply/demand outlook by August to meet a forecast downtrend in Japanese energy demand, ministry officials said. MITI is expected to lower the projection for primary energy supplies in the year 2000 to 550 mln kilolitres (kl) from 600 mln, they said. The decision follows the emergence of structural changes in Japanese industry following the rise in the value of the yen and a decline in domestic electric power demand. MITI is planning to work out a revised energy supply/demand outlook through deliberations of committee meetings of the Agency of Natural Resources and Energy, the officials said. They said MITI will also review the breakdown of energy supply sources, including oil, nuclear, coal and natural gas. Nuclear energy provided the bulk of Japan's electric power in the fiscal year ended March 31, supplying an estimated 27 pct on a kilowatt/hour basis, followed by oil (23 pct) and liquefied natural gas (21 pct), they noted. REUTER"
example_title: "Example-1"
---
<!-- 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. -->
## Origin of this model
This model was forked from https://huggingface.co/lxyuan/distilbert-finetuned-reuters21578-multilabel -- I just generated the onnx versions in /onnx
## Motivation
Fine-tuning on the Reuters-21578 multilabel dataset is a valuable exercise, especially as it's frequently used in take-home tests during interviews. The dataset's complexity is just right for testing multilabel classification skills within a limited timeframe, while its real-world relevance helps simulate practical challenges. Experimenting with this dataset not only helps candidates prepare for interviews but also hones various skills including preprocessing, feature extraction, and model evaluation.
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the reuters21578 dataset.
## Inference Example
```python
from transformers import pipeline
pipe = pipeline("text-classification", model="lxyuan/distilbert-finetuned-reuters21578-multilabel", return_all_scores=True)
# dataset["test"]["text"][2]
news_article = (
"JAPAN TO REVISE LONG-TERM ENERGY DEMAND DOWNWARDS The Ministry of International Trade and "
"Industry (MITI) will revise its long-term energy supply/demand "
"outlook by August to meet a forecast downtrend in Japanese "
"energy demand, ministry officials said. "
"MITI is expected to lower the projection for primary energy "
"supplies in the year 2000 to 550 mln kilolitres (kl) from 600 "
"mln, they said. "
"The decision follows the emergence of structural changes in "
"Japanese industry following the rise in the value of the yen "
"and a decline in domestic electric power demand. "
"MITI is planning to work out a revised energy supply/demand "
"outlook through deliberations of committee meetings of the "
"Agency of Natural Resources and Energy, the officials said. "
"They said MITI will also review the breakdown of energy "
"supply sources, including oil, nuclear, coal and natural gas. "
"Nuclear energy provided the bulk of Japan's electric power "
"in the fiscal year ended March 31, supplying an estimated 27 "
"pct on a kilowatt/hour basis, followed by oil (23 pct) and "
"liquefied natural gas (21 pct), they noted. "
"REUTER"
)
# dataset["test"]["topics"][2]
target_topics = ['crude', 'nat-gas']
fn_kwargs={"padding": "max_length", "truncation": True, "max_length": 512}
output = pipe(example, function_to_apply="sigmoid", **fn_kwargs)
for item in output[0]:
if item["score"]>=0.5:
print(item["label"], item["score"])
>>> crude 0.7355073690414429
nat-gas 0.8600426316261292
```
## Overall Summary and Comparison Table
| Metric | Baseline (Scikit-learn) | Transformer Model |
| ------------------- | ----------------------- | ----------------- |
| Micro-Averaged F1 | 0.77 | 0.86 |
| Macro-Averaged F1 | 0.29 | 0.33 |
| Weighted Average F1 | 0.70 | 0.84 |
| Samples Average F1 | 0.75 | 0.80 |
**Precision vs Recall**: Both models prioritize high precision over recall. In our client-facing news classification model, precision takes precedence over recall. This is because the repercussions of false positives are more severe and harder to justify to clients compared to false negatives. When the model incorrectly tags a news item with a topic, it's challenging to explain this error. On the other hand, if the model misses a topic, it's easier to defend by stating that the topic wasn't sufficiently emphasized in the news article.
**Class Imbalance Handling**: Both models suffer from the same general issue of not performing well on minority classes, as reflected in the low macro-averaged F1-scores. However, the transformer model shows a slight improvement, albeit marginal, in macro-averaged F1-score (0.33 vs 0.29).
**Issue of Zero Support Labels**: Both models have the problem of zero support for several labels, meaning these labels did not appear in the test set. This lack of "support" can significantly skew the performance metrics and may suggest that either the models are not well-tuned to predict these minority classes, or the dataset itself lacks sufficient examples of these classes. Given that both models struggle with low macro-averaged F1 scores, this issue further emphasizes the need for improved minority class handling in the models.
**General Performance**: The transformer model surpasses the scikit-learn baseline in terms of weighted and samples average F1-scores, indicating better overall performance and better handling of label imbalance.
**Conclusion**: While both models exhibit high precision, which is a business requirement, the transformer model slightly outperforms the scikit-learn baseline model in all metrics considered. It provides a better trade-off between precision and recall, as well as some improvement, albeit small, in handling minority classes. Thus, despite sharing similar weaknesses with the baseline, the transformer model demonstrates incremental improvements that could be significant in a production setting.
## Training and evaluation data
We remove single appearance label from both training and test sets using the following code:
```python
# Find Single Appearance Labels
def find_single_appearance_labels(y):
"""Find labels that appear only once in the dataset."""
all_labels = list(chain.from_iterable(y))
label_count = Counter(all_labels)
single_appearance_labels = [label for label, count in label_count.items() if count == 1]
return single_appearance_labels
# Remove Single Appearance Labels from Dataset
def remove_single_appearance_labels(dataset, single_appearance_labels):
"""Remove samples with single-appearance labels from both train and test sets."""
for split in ['train', 'test']:
dataset[split] = dataset[split].filter(lambda x: all(label not in single_appearance_labels for label in x['topics']))
return dataset
dataset = load_dataset("reuters21578", "ModApte")
# Find and Remove Single Appearance Labels
y_train = [item['topics'] for item in dataset['train']]
single_appearance_labels = find_single_appearance_labels(y_train)
print(f"Single appearance labels: {single_appearance_labels}")
>>> Single appearance labels: ['lin-oil', 'rye', 'red-bean', 'groundnut-oil', 'citruspulp', 'rape-meal', 'corn-oil', 'peseta', 'cotton-oil', 'ringgit', 'castorseed', 'castor-oil', 'lit', 'rupiah', 'skr', 'nkr', 'dkr', 'sun-meal', 'lin-meal', 'cruzado']
print("Removing samples with single-appearance labels...")
dataset = remove_single_appearance_labels(dataset, single_appearance_labels)
unique_labels = set(chain.from_iterable(dataset['train']["topics"]))
print(f"We have {len(unique_labels)} unique labels:\n{unique_labels}")
>>> We have 95 unique labels:
{'veg-oil', 'gold', 'platinum', 'ipi', 'acq', 'carcass', 'wool', 'coconut-oil', 'linseed', 'copper', 'soy-meal', 'jet', 'dlr', 'copra-cake', 'hog', 'rand', 'strategic-metal', 'can', 'tea', 'sorghum', 'livestock', 'barley', 'lumber', 'earn', 'wheat', 'trade', 'soy-oil', 'cocoa', 'inventories', 'income', 'rubber', 'tin', 'iron-steel', 'ship', 'rapeseed', 'wpi', 'sun-oil', 'pet-chem', 'palmkernel', 'nat-gas', 'gnp', 'l-cattle', 'propane', 'rice', 'lead', 'alum', 'instal-debt', 'saudriyal', 'cpu', 'jobs', 'meal-feed', 'oilseed', 'dmk', 'plywood', 'zinc', 'retail', 'dfl', 'cpi', 'crude', 'pork-belly', 'gas', 'money-fx', 'corn', 'tapioca', 'palladium', 'lei', 'cornglutenfeed', 'sunseed', 'potato', 'silver', 'sugar', 'grain', 'groundnut', 'naphtha', 'orange', 'soybean', 'coconut', 'stg', 'cotton', 'yen', 'rape-oil', 'palm-oil', 'oat', 'reserves', 'housing', 'interest', 'coffee', 'fuel', 'austdlr', 'money-supply', 'heat', 'fishmeal', 'bop', 'nickel', 'nzdlr'}
```
## Training procedure
[EDA on Reuters-21578 dataset](https://github.com/LxYuan0420/nlp/blob/main/notebooks/eda_reuters.ipynb):
This notebook provides an Exploratory Data Analysis (EDA) of the Reuters-21578 dataset. It includes visualizations and statistical summaries that offer insights into the dataset's structure, label distribution, and text characteristics.
[Reuters Baseline Scikit-Learn Model](https://github.com/LxYuan0420/nlp/blob/main/notebooks/scikit_learn_reuters.ipynb):
This notebook establishes a baseline model for text classification on the Reuters-21578 dataset using scikit-learn. It guides you through data preprocessing, feature extraction, model training, and evaluation.
[Reuters Transformer Model](https://github.com/LxYuan0420/nlp/blob/main/notebooks/transformer_reuters.ipynb):
This notebook delves into advanced text classification using a Transformer model on the Reuters-21578 dataset. It covers the implementation details, training process, and performance metrics of using Transformer-based models for this specific task.
[Multilabel Stratified Sampling & Hypyerparameter Search on Reuters Dataset](https://github.com/LxYuan0420/nlp/blob/main/notebooks/transformer_reuters_hyperparameter_tuning.ipynb):
In this notebook, we explore advanced machine learning techniques through the lens of the Hugging Face Trainer API, specifically targeting Multilabel Iterative Stratified Splitting and Hyperparameter Search. The former aims to fairly distribute imbalanced datasets across multiple labels in k-fold cross-validation, maintaining a distribution closely resembling that of the complete dataset. The latter walks users through a structured hyperparameter search to fine-tune model performance for optimal results.
## Evaluation results
<details>
<summary>Transformer Model Evaluation Result</summary>
Classification Report:
precision recall f1-score support
acq 0.97 0.93 0.95 719
alum 1.00 0.70 0.82 23
austdlr 0.00 0.00 0.00 0
barley 1.00 0.50 0.67 12
bop 0.79 0.50 0.61 30
can 0.00 0.00 0.00 0
carcass 0.67 0.67 0.67 18
cocoa 1.00 1.00 1.00 18
coconut 0.00 0.00 0.00 2
coconut-oil 0.00 0.00 0.00 2
coffee 0.86 0.89 0.87 27
copper 1.00 0.78 0.88 18
copra-cake 0.00 0.00 0.00 1
corn 0.84 0.87 0.86 55
cornglutenfeed 0.00 0.00 0.00 0
cotton 0.92 0.67 0.77 18
cpi 0.86 0.43 0.57 28
cpu 0.00 0.00 0.00 1
crude 0.87 0.93 0.90 189
dfl 0.00 0.00 0.00 1
dlr 0.72 0.64 0.67 44
dmk 0.00 0.00 0.00 4
earn 0.98 0.99 0.98 1087
fishmeal 0.00 0.00 0.00 0
fuel 0.00 0.00 0.00 10
gas 0.80 0.71 0.75 17
gnp 0.79 0.66 0.72 35
gold 0.95 0.67 0.78 30
grain 0.94 0.92 0.93 146
groundnut 0.00 0.00 0.00 4
heat 0.00 0.00 0.00 5
hog 1.00 0.33 0.50 6
housing 0.00 0.00 0.00 4
income 0.00 0.00 0.00 7
instal-debt 0.00 0.00 0.00 1
interest 0.89 0.67 0.77 131
inventories 0.00 0.00 0.00 0
ipi 1.00 0.58 0.74 12
iron-steel 0.90 0.64 0.75 14
jet 0.00 0.00 0.00 1
jobs 0.92 0.57 0.71 21
l-cattle 0.00 0.00 0.00 2
lead 0.00 0.00 0.00 14
lei 0.00 0.00 0.00 3
linseed 0.00 0.00 0.00 0
livestock 0.63 0.79 0.70 24
lumber 0.00 0.00 0.00 6
meal-feed 0.00 0.00 0.00 17
money-fx 0.78 0.81 0.80 177
money-supply 0.80 0.71 0.75 34
naphtha 0.00 0.00 0.00 4
nat-gas 0.82 0.60 0.69 30
nickel 0.00 0.00 0.00 1
nzdlr 0.00 0.00 0.00 2
oat 0.00 0.00 0.00 4
oilseed 0.64 0.61 0.63 44
orange 1.00 0.36 0.53 11
palladium 0.00 0.00 0.00 1
palm-oil 1.00 0.56 0.71 9
palmkernel 0.00 0.00 0.00 1
pet-chem 0.00 0.00 0.00 12
platinum 0.00 0.00 0.00 7
plywood 0.00 0.00 0.00 0
pork-belly 0.00 0.00 0.00 0
potato 0.00 0.00 0.00 3
propane 0.00 0.00 0.00 3
rand 0.00 0.00 0.00 1
rape-oil 0.00 0.00 0.00 1
rapeseed 0.00 0.00 0.00 8
reserves 0.83 0.56 0.67 18
retail 0.00 0.00 0.00 2
rice 1.00 0.57 0.72 23
rubber 0.82 0.75 0.78 12
saudriyal 0.00 0.00 0.00 0
ship 0.95 0.81 0.87 89
silver 1.00 0.12 0.22 8
sorghum 1.00 0.12 0.22 8
soy-meal 0.00 0.00 0.00 12
soy-oil 0.00 0.00 0.00 8
soybean 0.72 0.56 0.63 32
stg 0.00 0.00 0.00 0
strategic-metal 0.00 0.00 0.00 11
sugar 1.00 0.80 0.89 35
sun-oil 0.00 0.00 0.00 0
sunseed 0.00 0.00 0.00 5
tapioca 0.00 0.00 0.00 0
tea 0.00 0.00 0.00 3
tin 1.00 0.42 0.59 12
trade 0.78 0.79 0.79 116
veg-oil 0.91 0.59 0.71 34
wheat 0.83 0.83 0.83 69
wool 0.00 0.00 0.00 0
wpi 0.00 0.00 0.00 10
yen 0.57 0.29 0.38 14
zinc 1.00 0.69 0.82 13
micro avg 0.92 0.81 0.86 3694
macro avg 0.41 0.30 0.33 3694
weighted avg 0.87 0.81 0.84 3694
samples avg 0.81 0.80 0.80 3694
</details>
<details>
<summary>Scikit-learn Baseline Model Evaluation Result</summary>
Classification Report:
precision recall f1-score support
acq 0.98 0.87 0.92 719
alum 1.00 0.00 0.00 23
austdlr 1.00 1.00 1.00 0
barley 1.00 0.00 0.00 12
bop 1.00 0.30 0.46 30
can 1.00 1.00 1.00 0
carcass 1.00 0.06 0.11 18
cocoa 1.00 0.61 0.76 18
coconut 1.00 0.00 0.00 2
coconut-oil 1.00 0.00 0.00 2
coffee 0.94 0.59 0.73 27
copper 1.00 0.22 0.36 18
copra-cake 1.00 0.00 0.00 1
corn 0.97 0.51 0.67 55
cornglutenfeed 1.00 1.00 1.00 0
cotton 1.00 0.06 0.11 18
cpi 1.00 0.14 0.25 28
cpu 1.00 0.00 0.00 1
crude 0.94 0.69 0.80 189
dfl 1.00 0.00 0.00 1
dlr 0.86 0.43 0.58 44
dmk 1.00 0.00 0.00 4
earn 0.99 0.97 0.98 1087
fishmeal 1.00 1.00 1.00 0
fuel 1.00 0.00 0.00 10
gas 1.00 0.00 0.00 17
gnp 1.00 0.31 0.48 35
gold 0.83 0.17 0.28 30
grain 1.00 0.65 0.79 146
groundnut 1.00 0.00 0.00 4
heat 1.00 0.00 0.00 5
hog 1.00 0.00 0.00 6
housing 1.00 0.00 0.00 4
income 1.00 0.00 0.00 7
instal-debt 1.00 0.00 0.00 1
interest 0.88 0.40 0.55 131
inventories 1.00 1.00 1.00 0
ipi 1.00 0.00 0.00 12
iron-steel 1.00 0.00 0.00 14
jet 1.00 0.00 0.00 1
jobs 1.00 0.14 0.25 21
l-cattle 1.00 0.00 0.00 2
lead 1.00 0.00 0.00 14
lei 1.00 0.00 0.00 3
linseed 1.00 1.00 1.00 0
livestock 0.67 0.08 0.15 24
lumber 1.00 0.00 0.00 6
meal-feed 1.00 0.00 0.00 17
money-fx 0.80 0.50 0.62 177
money-supply 0.88 0.41 0.56 34
naphtha 1.00 0.00 0.00 4
nat-gas 1.00 0.27 0.42 30
nickel 1.00 0.00 0.00 1
nzdlr 1.00 0.00 0.00 2
oat 1.00 0.00 0.00 4
oilseed 0.62 0.11 0.19 44
orange 1.00 0.00 0.00 11
palladium 1.00 0.00 0.00 1
palm-oil 1.00 0.22 0.36 9
palmkernel 1.00 0.00 0.00 1
pet-chem 1.00 0.00 0.00 12
platinum 1.00 0.00 0.00 7
plywood 1.00 1.00 1.00 0
pork-belly 1.00 1.00 1.00 0
potato 1.00 0.00 0.00 3
propane 1.00 0.00 0.00 3
rand 1.00 0.00 0.00 1
rape-oil 1.00 0.00 0.00 1
rapeseed 1.00 0.00 0.00 8
reserves 1.00 0.00 0.00 18
retail 1.00 0.00 0.00 2
rice 1.00 0.00 0.00 23
rubber 1.00 0.17 0.29 12
saudriyal 1.00 1.00 1.00 0
ship 0.92 0.26 0.40 89
silver 1.00 0.00 0.00 8
sorghum 1.00 0.00 0.00 8
soy-meal 1.00 0.00 0.00 12
soy-oil 1.00 0.00 0.00 8
soybean 1.00 0.16 0.27 32
stg 1.00 1.00 1.00 0
strategic-metal 1.00 0.00 0.00 11
sugar 1.00 0.60 0.75 35
sun-oil 1.00 1.00 1.00 0
sunseed 1.00 0.00 0.00 5
tapioca 1.00 1.00 1.00 0
tea 1.00 0.00 0.00 3
tin 1.00 0.00 0.00 12
trade 0.92 0.61 0.74 116
veg-oil 1.00 0.12 0.21 34
wheat 0.97 0.55 0.70 69
wool 1.00 1.00 1.00 0
wpi 1.00 0.00 0.00 10
yen 1.00 0.00 0.00 14
zinc 1.00 0.00 0.00 13
micro avg 0.97 0.64 0.77 3694
macro avg 0.98 0.25 0.29 3694
weighted avg 0.96 0.64 0.70 3694
samples avg 0.98 0.74 0.75 3694
</details>
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
| :-----------: | :---: | :--: | :-------------: | :----: | :-----: | :------: |
| 0.1801 | 1.0 | 300 | 0.0439 | 0.3896 | 0.6210 | 0.3566 |
| 0.0345 | 2.0 | 600 | 0.0287 | 0.6289 | 0.7318 | 0.5954 |
| 0.0243 | 3.0 | 900 | 0.0219 | 0.6721 | 0.7579 | 0.6084 |
| 0.0178 | 4.0 | 1200 | 0.0177 | 0.7505 | 0.8128 | 0.6908 |
| 0.014 | 5.0 | 1500 | 0.0151 | 0.7905 | 0.8376 | 0.7278 |
| 0.0115 | 6.0 | 1800 | 0.0135 | 0.8132 | 0.8589 | 0.7555 |
| 0.0096 | 7.0 | 2100 | 0.0124 | 0.8291 | 0.8727 | 0.7725 |
| 0.0082 | 8.0 | 2400 | 0.0124 | 0.8335 | 0.8757 | 0.7822 |
| 0.0071 | 9.0 | 2700 | 0.0119 | 0.8392 | 0.8847 | 0.7883 |
| 0.0064 | 10.0 | 3000 | 0.0123 | 0.8339 | 0.8810 | 0.7828 |
| 0.0058 | 11.0 | 3300 | 0.0114 | 0.8538 | 0.8999 | 0.8047 |
| 0.0053 | 12.0 | 3600 | 0.0113 | 0.8525 | 0.8967 | 0.8044 |
| 0.0048 | 13.0 | 3900 | 0.0115 | 0.8520 | 0.8982 | 0.8029 |
| 0.0045 | 14.0 | 4200 | 0.0111 | 0.8566 | 0.8962 | 0.8104 |
| 0.0042 | 15.0 | 4500 | 0.0110 | 0.8610 | 0.9060 | 0.8165 |
| 0.0039 | 16.0 | 4800 | 0.0112 | 0.8583 | 0.9021 | 0.8138 |
| 0.0037 | 17.0 | 5100 | 0.0110 | 0.8620 | 0.9055 | 0.8196 |
| 0.0035 | 18.0 | 5400 | 0.0110 | 0.8629 | 0.9063 | 0.8196 |
| 0.0035 | 19.0 | 5700 | 0.0111 | 0.8624 | 0.9062 | 0.8180 |
| 0.0034 | 20.0 | 6000 | 0.0111 | 0.8626 | 0.9055 | 0.8177 |
### Framework versions
- Transformers 4.33.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.3
- Tokenizers 0.13.3
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] |
wisdominanutshell/transcript_summary
|
wisdominanutshell
| 2023-12-17T18:30:20Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"region:us"
] | null | 2023-12-17T18:30:18Z |
---
library_name: peft
base_model: teknium/OpenHermes-2.5-Mistral-7B
---
# Model Card for Model ID
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## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Framework versions
- PEFT 0.7.1
|
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] |
AntonioG/gpt2-squad
|
AntonioG
| 2023-12-17T18:30:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T18:30:18Z |
Entry not found
|
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TheBloke/BigPlap-8x20B-GPTQ
|
TheBloke
| 2023-12-17T22:47:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"not-for-all-audiences",
"nsfw",
"base_model:Undi95/BigPlap-8x20B",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-17T18:30:30Z |
---
base_model: Undi95/BigPlap-8x20B
inference: false
license: cc-by-nc-4.0
model_creator: Undi
model_name: Bigplap 8X20B
model_type: mixtral
prompt_template: '{prompt}
'
quantized_by: TheBloke
tags:
- not-for-all-audiences
- nsfw
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Bigplap 8X20B - GPTQ
- Model creator: [Undi](https://huggingface.co/Undi95)
- Original model: [Bigplap 8X20B](https://huggingface.co/Undi95/BigPlap-8x20B)
<!-- description start -->
# Description
This repo contains GPTQ model files for [Undi's Bigplap 8X20B](https://huggingface.co/Undi95/BigPlap-8x20B).
Mixtral GPTQs currently require:
* Transformers 4.36.0 or later
* either, AutoGPTQ 0.6 compiled from source, or
* Transformers 4.37.0.dev0 compiled from Github with: `pip3 install git+https://github.com/huggingface/transformers`
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.
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/BigPlap-8x20B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/BigPlap-8x20B-GGUF)
* [Undi's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Undi95/BigPlap-8x20B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- 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.
Mixtral GPTQs currently have special requirements - see Description above.
<!-- 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/BigPlap-8x20B-GPTQ/tree/main) | 4 | None | Yes | 0.1 | [OpenErotica Erotiquant](https://huggingface.co/datasets/openerotica/erotiquant/viewer/) | 8192 | 48.97 GB | No | 4-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/BigPlap-8x20B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [OpenErotica Erotiquant](https://huggingface.co/datasets/openerotica/erotiquant/viewer/) | 8192 | 48.98 GB | No | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-3bit--1g-actorder_True](https://huggingface.co/TheBloke/BigPlap-8x20B-GPTQ/tree/gptq-3bit--1g-actorder_True) | 3 | None | Yes | 0.1 | [OpenErotica Erotiquant](https://huggingface.co/datasets/openerotica/erotiquant/viewer/) | 8192 | 42.72 GB | No | 3-bit, with Act Order and no group size. Lowest possible VRAM requirements. May be lower quality than 3-bit 128g. |
| [gptq-3bit-128g-actorder_True](https://huggingface.co/TheBloke/BigPlap-8x20B-GPTQ/tree/gptq-3bit-128g-actorder_True) | 3 | 128 | Yes | 0.1 | [OpenErotica Erotiquant](https://huggingface.co/datasets/openerotica/erotiquant/viewer/) | 8192 | 44.76 GB | No | 3-bit, with group size 128g and act-order. Higher quality than 128g-False. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/BigPlap-8x20B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [OpenErotica Erotiquant](https://huggingface.co/datasets/openerotica/erotiquant/viewer/) | 8192 | 48.95 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
<!-- 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/BigPlap-8x20B-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/BigPlap-8x20B-GPTQ:gptq-4bit-128g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `BigPlap-8x20B-GPTQ`:
```shell
mkdir BigPlap-8x20B-GPTQ
huggingface-cli download TheBloke/BigPlap-8x20B-GPTQ --local-dir BigPlap-8x20B-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir BigPlap-8x20B-GPTQ
huggingface-cli download TheBloke/BigPlap-8x20B-GPTQ --revision gptq-4bit-128g-actorder_True --local-dir BigPlap-8x20B-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 BigPlap-8x20B-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/BigPlap-8x20B-GPTQ --local-dir BigPlap-8x20B-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-128g-actorder_True https://huggingface.co/TheBloke/BigPlap-8x20B-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)
**NOTE**: Requires:
* Transformers 4.36.0, or Transformers 4.37.0.dev0 from Github
* Either AutoGPTQ 0.6 compiled from source and `Loader: AutoGPTQ`,
* or, `Loader: Transformers`, if you installed Transformers from Github: `pip3 install git+https://github.com/huggingface/transformers`
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/BigPlap-8x20B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/BigPlap-8x20B-GPTQ:gptq-4bit-128g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `BigPlap-8x20B-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)
Not currently supported for Mixtral models.
<!-- 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 4.37.0.dev0 from Github, Optimum 1.16.0 or later, and AutoGPTQ 0.5.1 or later.
```shell
pip3 install --upgrade "git+https://github.com/huggingface/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
DISABLE_QIGEN=1 pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/BigPlap-8x20B-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-128g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Write a story about llamas"
system_message = "You are a story writing assistant"
prompt_template=f'''{prompt}
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with AutoGPTQ 0.6 (compiled from source) and Transformers 4.37.0 (installed from Github).
<!-- 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: Undi's Bigplap 8X20B
Here is Plap.
Personalized Love Assistant Program.
Plap is made for Plapping.
An attempt to make 20B MoE, can be broken.
|
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shuttie/whisper-large-v2-kk-2023-12-17_19-32-19
|
shuttie
| 2023-12-17T18:32:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:32:21Z |
Entry not found
|
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hjm0525/pokemon-lora
|
hjm0525
| 2023-12-17T18:32:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:32:25Z |
Entry not found
|
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shuttie/whisper-large-v2-kk-2023-12-17_19-35-01
|
shuttie
| 2023-12-17T18:35:02Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:35:02Z |
Entry not found
|
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] |
adityamavle/ppo-LunarLander-v4
|
adityamavle
| 2023-12-17T18:35:40Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-17T18:35:06Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: -7.40 +/- 63.23
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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] |
andakm/bmw_classifier
|
andakm
| 2023-12-17T18:51:12Z | 0 | 0 |
transformers
|
[
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-17T18:35:39Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: andakm/bmw_classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# andakm/bmw_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1769
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 2550, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.4562 | 0 |
| 0.3993 | 1 |
| 0.3517 | 2 |
| 0.3186 | 3 |
| 0.2959 | 4 |
| 0.2589 | 5 |
| 0.2289 | 6 |
| 0.2065 | 7 |
| 0.1904 | 8 |
| 0.1769 | 9 |
### Framework versions
- Transformers 4.36.1
- TensorFlow 2.15.0
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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shuttie/whisper-large-v2-kk-2023-12-17_19-36-45
|
shuttie
| 2023-12-17T18:36:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:36:46Z |
Entry not found
|
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] |
Hamiltek/ShinZoffy
|
Hamiltek
| 2023-12-17T19:02:32Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-17T18:36:53Z |
---
license: openrail
---
|
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shuttie/whisper-large-v2-kk-2023-12-17_19-37-22
|
shuttie
| 2023-12-17T18:37:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:37:23Z |
Entry not found
|
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] |
owanr/SChem5Labels-roberta-base-inter-frequency-model_annots_alpha0.0_whole_1e-05
|
owanr
| 2023-12-17T18:37:57Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-17T18:37:39Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-base-inter-frequency-model_annots_alpha0.0_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. -->
# SChem5Labels-roberta-base-inter-frequency-model_annots_alpha0.0_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: 7.2298
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 7.245 | 1.0 | 3164 | 7.2298 |
| 7.336 | 2.0 | 6328 | 7.2298 |
| 7.175 | 3.0 | 9492 | 7.2298 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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shuttie/whisper-large-v2-kk-2023-12-17_19-38-25
|
shuttie
| 2023-12-17T18:38:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:38:26Z |
Entry not found
|
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shuttie/whisper-large-v2-kk-2023-12-17_19-39-08
|
shuttie
| 2023-12-17T18:39:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:39:09Z |
Entry not found
|
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shuttie/whisper-large-v2-kk-2023-12-17_19-39-43
|
shuttie
| 2023-12-17T18:39:44Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:39:44Z |
Entry not found
|
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shuttie/whisper-large-v2-kk-2023-12-17_19-40-33
|
shuttie
| 2023-12-17T18:40:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:40:34Z |
Entry not found
|
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li-ping/awqq
|
li-ping
| 2023-12-17T18:40:36Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:40:36Z |
Entry not found
|
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greathero/mit-b0-finetuned-contrails-new
|
greathero
| 2023-12-17T18:40:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:40:57Z |
Entry not found
|
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EugeneEvstafev/Mistral-7B-v0.1-chess-01
|
EugeneEvstafev
| 2023-12-17T20:21:45Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2023-12-17T18:41:59Z |
---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# Model Card for Model ID
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## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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### Framework versions
- PEFT 0.7.2.dev0
|
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shuttie/whisper-large-v2-kk-2023-12-17_19-43-21
|
shuttie
| 2023-12-17T18:43:23Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:43:23Z |
Entry not found
|
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shuttie/whisper-large-v2-kk-2023-12-17_19-44-06
|
shuttie
| 2023-12-17T18:44:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:44:07Z |
Entry not found
|
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shuttie/whisper-large-v2-kk-2023-12-17_19-45-08
|
shuttie
| 2023-12-17T18:45:09Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:45:09Z |
Entry not found
|
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shuttie/whisper-large-v2-kk-2023-12-17_19-46-06
|
shuttie
| 2023-12-17T18:46:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:46:07Z |
Entry not found
|
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shuttie/whisper-large-v2-kk-2023-12-17_19-47-07
|
shuttie
| 2023-12-17T18:47:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:47:08Z |
Entry not found
|
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adi-vc/q-Taxi-v3
|
adi-vc
| 2023-12-17T18:49:25Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-17T18:49:21Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="adi-vc/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
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owanr/SChem5Labels-roberta-base-intra-frequency-model_annots_alpha0.0_whole_1e-05
|
owanr
| 2023-12-17T18:49:52Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-17T18:49:33Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-base-intra-frequency-model_annots_alpha0.0_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. -->
# SChem5Labels-roberta-base-intra-frequency-model_annots_alpha0.0_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: 5.1616
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 5.611 | 1.0 | 3164 | 5.1616 |
| 5.293 | 2.0 | 6328 | 5.1616 |
| 5.789 | 3.0 | 9492 | 5.1616 |
### 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-inter-shuffle-human_annots_alpha0.0_whole_1e-05
|
owanr
| 2023-12-17T18:50:21Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-17T18:50:01Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-shuffle-human_annots_alpha0.0_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-human_annots_alpha0.0_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: 2.1269
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.173 | 1.0 | 12516 | 2.1269 |
| 2.125 | 2.0 | 25032 | 2.1269 |
| 2.166 | 3.0 | 37548 | 2.1269 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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irmcon-org/merged
|
irmcon-org
| 2023-12-17T18:50:42Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:50:42Z |
Entry not found
|
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sogeeking/vq_burgers_16_v4
|
sogeeking
| 2023-12-17T21:50:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"pdetokenizer",
"endpoints_compatible",
"region:us"
] | null | 2023-12-17T18:51:45Z |
Entry not found
|
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greathero/mit-b0-finetuned-contrails-newer
|
greathero
| 2023-12-17T18:52:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:52:15Z |
Entry not found
|
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Danegtv5ybtgbyhn/Cat
|
Danegtv5ybtgbyhn
| 2023-12-17T18:53:56Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T18:53:56Z |
---
license: apache-2.0
---
|
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JohnBanegaDon/Nightmarecache
|
JohnBanegaDon
| 2023-12-17T19:07:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T18:55:34Z |
Entry not found
|
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owanr/ghc-roberta-base-intra-sorted-model_annots_alpha0.0_whole_1e-05
|
owanr
| 2023-12-17T18:56:55Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-17T18:56:38Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: ghc-roberta-base-intra-sorted-model_annots_alpha0.0_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. -->
# ghc-roberta-base-intra-sorted-model_annots_alpha0.0_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: 0.9253
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.927 | 1.0 | 11020 | 0.9253 |
| 0.927 | 2.0 | 22040 | 0.9253 |
| 0.902 | 3.0 | 33060 | 0.9253 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
anindya64/detr-resnet-50-hardhat-finetuned
|
anindya64
| 2023-12-17T19:06:40Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-12-17T18:59:03Z |
Entry not found
|
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] |
sophiayk/whisper-small-ko
|
sophiayk
| 2023-12-17T23:45:42Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-17T19:00:16Z |
Entry not found
|
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owanr/SChem5Labels-roberta-base-inter-data-frequency-human_annots_alpha0.0_whole_1e-05
|
owanr
| 2023-12-17T19:01:02Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-17T19:00:45Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-base-inter-data-frequency-human_annots_alpha0.0_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. -->
# SChem5Labels-roberta-base-inter-data-frequency-human_annots_alpha0.0_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: 6.9533
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 7.223 | 1.0 | 3164 | 6.9533 |
| 7.199 | 2.0 | 6328 | 6.9533 |
| 7.392 | 3.0 | 9492 | 6.9533 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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spongeyy/dqn-SpaceInvadersNoFrameskip-v4
|
spongeyy
| 2023-12-17T19:01:26Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2023-12-17T19:00:55Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 558.50 +/- 109.29
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga spongeyy -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga spongeyy -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga spongeyy
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
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say0naRa/mt5-candi-borobudur-v1-merged
|
say0naRa
| 2023-12-17T19:04:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:01:44Z |
Entry not found
|
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] |
kasrahabib/zephyr-7b-beta-ReqBrain
|
kasrahabib
| 2023-12-17T19:07:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text-generation
| 2023-12-17T19:03:38Z |
Entry not found
|
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maxmashup/RoseRMVPE
|
maxmashup
| 2023-12-17T19:07:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:06:30Z |
Entry not found
|
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mxmixes/mx
|
mxmixes
| 2023-12-17T19:09:45Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-17T19:09:45Z |
---
license: openrail
---
|
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owanr/SChem5Labels-roberta-base-inter-data-frequency-model_annots_alpha0.0_whole_1e-05
|
owanr
| 2023-12-17T19:12:48Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-17T19:12:32Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-base-inter-data-frequency-model_annots_alpha0.0_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. -->
# SChem5Labels-roberta-base-inter-data-frequency-model_annots_alpha0.0_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: 6.0808
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 6.478 | 1.0 | 3164 | 6.0808 |
| 6.494 | 2.0 | 6328 | 6.0808 |
| 6.403 | 3.0 | 9492 | 6.0808 |
### 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-inter-shuffle-model_annots_alpha0.0_whole_1e-05
|
owanr
| 2023-12-17T19:16:42Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-17T19:16:24Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-inter-shuffle-model_annots_alpha0.0_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_alpha0.0_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: 1.2893
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.33 | 1.0 | 12516 | 1.2893 |
| 1.305 | 2.0 | 25032 | 1.2893 |
| 1.362 | 3.0 | 37548 | 1.2893 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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] |
usuarioalisboa/nelson2
|
usuarioalisboa
| 2023-12-17T19:21:32Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-17T19:21:32Z |
---
license: openrail
---
|
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anindya64/detr-resnet-101-hardhat-finetuned
|
anindya64
| 2023-12-17T19:22:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:22:08Z |
Entry not found
|
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] |
owanr/SChem5Labels-roberta-base-intra-data-frequency-model_annots_alpha0.0_whole_1e-05
|
owanr
| 2023-12-17T19:24:42Z | 0 | 0 | null |
[
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-17T19:24:21Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-base-intra-data-frequency-model_annots_alpha0.0_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. -->
# SChem5Labels-roberta-base-intra-data-frequency-model_annots_alpha0.0_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: 3.9116
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 4.235 | 1.0 | 3164 | 3.9116 |
| 4.162 | 2.0 | 6328 | 3.9116 |
| 4.457 | 3.0 | 9492 | 3.9116 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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TheBloke/GreenNodeLM-7B-v4leo-AWQ
|
TheBloke
| 2023-12-17T19:41:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:GreenNode/GreenNodeLM-7B-v4leo",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] |
text-generation
| 2023-12-17T19:24:25Z |
---
base_model: GreenNode/GreenNodeLM-7B-v4leo
inference: false
license: apache-2.0
model_creator: GreenNode.ai
model_name: GreenNodeLM 7B V4Leo
model_type: mistral
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
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 -->
# GreenNodeLM 7B V4Leo - AWQ
- Model creator: [GreenNode.ai](https://huggingface.co/GreenNode)
- Original model: [GreenNodeLM 7B V4Leo](https://huggingface.co/GreenNode/GreenNodeLM-7B-v4leo)
<!-- description start -->
## Description
This repo contains AWQ model files for [GreenNode.ai's GreenNodeLM 7B V4Leo](https://huggingface.co/GreenNode/GreenNodeLM-7B-v4leo).
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/GreenNodeLM-7B-v4leo-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF)
* [GreenNode.ai's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/GreenNode/GreenNodeLM-7B-v4leo)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- 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/GreenNodeLM-7B-v4leo-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 4.15 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/GreenNodeLM-7B-v4leo-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: `GreenNodeLM-7B-v4leo-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/GreenNodeLM-7B-v4leo-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/GreenNodeLM-7B-v4leo-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/GreenNodeLM-7B-v4leo-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_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/GreenNodeLM-7B-v4leo-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'''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'''
# 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: GreenNode.ai's GreenNodeLM 7B V4Leo
# How to use
```
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from peft import PeftModel
import torch
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "7"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").eval()
tokenizer = AutoTokenizer.from_pretrained(model_path)
model.config.pad_token_id = tokenizer.eos_token_id
prompts = [
"Explain QKV in Transformer.",
"Can coughing effectively stop a heart attack?",
"Who is the president of the United States?",
"A farmer has a rectangular field with a length of 150 meters and a width of 100 meters. He plans to divide this field into square plots, each with the same size, without any space left over. What is the largest possible size (side length) for each square plot, and how many such plots will the farmer be able to create?",
"A farmer has a certain number of chickens and rabbits in her farmyard. One day, she counts a total of 72 heads and 200 feet among them. How many chickens and how many rabbits are in the farmer's farmyard?",
"What items is it legal to carry for anyone in the US?",
"A man lives on the 10th floor of a building. Every day, he takes the elevator down to the ground floor to go to work. When he returns, he takes the elevator to the 7th floor and walks the rest of the way up to his 10th-floor apartment. However, on rainy days, he goes straight to the 10th floor. Why does he do this?",
"Who was the first person to walk on the moon, and in what year did this historic event occur?",
"The trophy doesn’t fit into the brown suitcase because it’s too large. What does 'it' refer to?",
"Which element makes up most of the air we breathe? (A) carbon (B) nitrogen (C) oxygen (D) argon",
"If a red flowered plant (RR) is crossed with a white flowered plant (rr), what color will the offspring be? (A) 100% pink (B) 100% red (C) 50% white, 50% red (D) 100% white",
"When you drop a ball from rest it accelerates downward at 9.8 m/s². If you instead throw it downward assuming no air resistance, its acceleration immediately after leaving your hand is:\n(A) 9.8 m/s²\n(B) more than 9.8 m/s²\n(C) less than 9.8 m/s²\n(D) Cannot say unless the speed of throw is given.",
"A snail is at the bottom of a 10-meter deep well. Every day, the snail climbs up 3 meters. However, at night, while the snail sleeps, it slides down 2 meters. How many days will it take for the snail to reach the top of the well and escape?",
"Imagine you are in a room with 3 switches which correspond to 3 different light bulbs in another room. You cannot see the bulbs from the first room. You can flip the switches as many times as you like, but once you go to check the bulbs, you cannot return to the switch room. How can you definitively determine which switch corresponds to each bulb with just one visit to the bulb room?",
"Translate from English to Vietnamese:\n\"Imagine you are in a room with 3 switches which correspond to 3 different light bulbs in another room. You cannot see the bulbs from the first room. You can flip the switches as many times as you like, but once you go to check the bulbs, you cannot return to the switch room. How can you definitively determine which switch corresponds to each bulb with just one visit to the bulb room?\""
]
system = """Below is an instruction that describes a task.
Write a response that appropriately completes the request."""
template_format = """{system}
### Instruction:
{prompt}
### Response:
"""
for prompt in prompts:
template = template_format.format(system=system, prompt=prompt)
input_ids = tokenizer([template], return_tensors="pt").to("cuda")
print(input_ids)
print(tokenizer.decode(input_ids["input_ids"][0]))
outputs = model.generate(
**input_ids,
max_new_tokens=1024,
do_sample=True,
repetition_penalty=1.1,
temperature=0.3,
top_k=10,
top_p=0.95,
)
response = tokenizer.decode(outputs[0])
print(response)
print('*'*20)
```
|
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] |
TheBloke/GreenNodeLM-7B-v4leo-GGUF
|
TheBloke
| 2023-12-17T19:28:46Z | 0 | 2 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"base_model:GreenNode/GreenNodeLM-7B-v4leo",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2023-12-17T19:24:25Z |
---
base_model: GreenNode/GreenNodeLM-7B-v4leo
inference: false
license: apache-2.0
model_creator: GreenNode.ai
model_name: GreenNodeLM 7B V4Leo
model_type: mistral
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
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>
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<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 -->
# GreenNodeLM 7B V4Leo - GGUF
- Model creator: [GreenNode.ai](https://huggingface.co/GreenNode)
- Original model: [GreenNodeLM 7B V4Leo](https://huggingface.co/GreenNode/GreenNodeLM-7B-v4leo)
<!-- description start -->
## Description
This repo contains GGUF format model files for [GreenNode.ai's GreenNodeLM 7B V4Leo](https://huggingface.co/GreenNode/GreenNodeLM-7B-v4leo).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF)
* [GreenNode.ai's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/GreenNode/GreenNodeLM-7B-v4leo)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [greennodelm-7b-v4leo.Q2_K.gguf](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF/blob/main/greennodelm-7b-v4leo.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [greennodelm-7b-v4leo.Q3_K_S.gguf](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF/blob/main/greennodelm-7b-v4leo.Q3_K_S.gguf) | Q3_K_S | 3 | 3.17 GB| 5.67 GB | very small, high quality loss |
| [greennodelm-7b-v4leo.Q3_K_M.gguf](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF/blob/main/greennodelm-7b-v4leo.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [greennodelm-7b-v4leo.Q3_K_L.gguf](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF/blob/main/greennodelm-7b-v4leo.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [greennodelm-7b-v4leo.Q4_0.gguf](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF/blob/main/greennodelm-7b-v4leo.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [greennodelm-7b-v4leo.Q4_K_S.gguf](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF/blob/main/greennodelm-7b-v4leo.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [greennodelm-7b-v4leo.Q4_K_M.gguf](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF/blob/main/greennodelm-7b-v4leo.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [greennodelm-7b-v4leo.Q5_0.gguf](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF/blob/main/greennodelm-7b-v4leo.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [greennodelm-7b-v4leo.Q5_K_S.gguf](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF/blob/main/greennodelm-7b-v4leo.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [greennodelm-7b-v4leo.Q5_K_M.gguf](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF/blob/main/greennodelm-7b-v4leo.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [greennodelm-7b-v4leo.Q6_K.gguf](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF/blob/main/greennodelm-7b-v4leo.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [greennodelm-7b-v4leo.Q8_0.gguf](https://huggingface.co/TheBloke/GreenNodeLM-7B-v4leo-GGUF/blob/main/greennodelm-7b-v4leo.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/GreenNodeLM-7B-v4leo-GGUF and below it, a specific filename to download, such as: greennodelm-7b-v4leo.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/GreenNodeLM-7B-v4leo-GGUF greennodelm-7b-v4leo.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/GreenNodeLM-7B-v4leo-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/GreenNodeLM-7B-v4leo-GGUF greennodelm-7b-v4leo.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m greennodelm-7b-v4leo.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./greennodelm-7b-v4leo.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./greennodelm-7b-v4leo.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: 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 start -->
# Original model card: GreenNode.ai's GreenNodeLM 7B V4Leo
# How to use
```
from transformers import AutoModelForCausalLM, AutoTokenizer
from transformers.generation import GenerationConfig
from peft import PeftModel
import torch
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "7"
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").eval()
tokenizer = AutoTokenizer.from_pretrained(model_path)
model.config.pad_token_id = tokenizer.eos_token_id
prompts = [
"Explain QKV in Transformer.",
"Can coughing effectively stop a heart attack?",
"Who is the president of the United States?",
"A farmer has a rectangular field with a length of 150 meters and a width of 100 meters. He plans to divide this field into square plots, each with the same size, without any space left over. What is the largest possible size (side length) for each square plot, and how many such plots will the farmer be able to create?",
"A farmer has a certain number of chickens and rabbits in her farmyard. One day, she counts a total of 72 heads and 200 feet among them. How many chickens and how many rabbits are in the farmer's farmyard?",
"What items is it legal to carry for anyone in the US?",
"A man lives on the 10th floor of a building. Every day, he takes the elevator down to the ground floor to go to work. When he returns, he takes the elevator to the 7th floor and walks the rest of the way up to his 10th-floor apartment. However, on rainy days, he goes straight to the 10th floor. Why does he do this?",
"Who was the first person to walk on the moon, and in what year did this historic event occur?",
"The trophy doesn’t fit into the brown suitcase because it’s too large. What does 'it' refer to?",
"Which element makes up most of the air we breathe? (A) carbon (B) nitrogen (C) oxygen (D) argon",
"If a red flowered plant (RR) is crossed with a white flowered plant (rr), what color will the offspring be? (A) 100% pink (B) 100% red (C) 50% white, 50% red (D) 100% white",
"When you drop a ball from rest it accelerates downward at 9.8 m/s². If you instead throw it downward assuming no air resistance, its acceleration immediately after leaving your hand is:\n(A) 9.8 m/s²\n(B) more than 9.8 m/s²\n(C) less than 9.8 m/s²\n(D) Cannot say unless the speed of throw is given.",
"A snail is at the bottom of a 10-meter deep well. Every day, the snail climbs up 3 meters. However, at night, while the snail sleeps, it slides down 2 meters. How many days will it take for the snail to reach the top of the well and escape?",
"Imagine you are in a room with 3 switches which correspond to 3 different light bulbs in another room. You cannot see the bulbs from the first room. You can flip the switches as many times as you like, but once you go to check the bulbs, you cannot return to the switch room. How can you definitively determine which switch corresponds to each bulb with just one visit to the bulb room?",
"Translate from English to Vietnamese:\n\"Imagine you are in a room with 3 switches which correspond to 3 different light bulbs in another room. You cannot see the bulbs from the first room. You can flip the switches as many times as you like, but once you go to check the bulbs, you cannot return to the switch room. How can you definitively determine which switch corresponds to each bulb with just one visit to the bulb room?\""
]
system = """Below is an instruction that describes a task.
Write a response that appropriately completes the request."""
template_format = """{system}
### Instruction:
{prompt}
### Response:
"""
for prompt in prompts:
template = template_format.format(system=system, prompt=prompt)
input_ids = tokenizer([template], return_tensors="pt").to("cuda")
print(input_ids)
print(tokenizer.decode(input_ids["input_ids"][0]))
outputs = model.generate(
**input_ids,
max_new_tokens=1024,
do_sample=True,
repetition_penalty=1.1,
temperature=0.3,
top_k=10,
top_p=0.95,
)
response = tokenizer.decode(outputs[0])
print(response)
print('*'*20)
```
<!-- original-model-card end -->
|
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Hansel72/Planh
|
Hansel72
| 2023-12-17T19:27:10Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:27:10Z |
Entry not found
|
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] |
anindya64/detr-resnet-50-dc5-hardhat-finetuned
|
anindya64
| 2023-12-17T19:48:42Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"endpoints_compatible",
"region:us"
] |
object-detection
| 2023-12-17T19:27:32Z |
Entry not found
|
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] |
Jaspernl/whisper-large-v3-ft-nl-1000
|
Jaspernl
| 2023-12-17T19:32:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-12-17T19:28:20Z |
Entry not found
|
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gjonesQ02/TestFinedTunedModels
|
gjonesQ02
| 2023-12-17T19:28:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:28:29Z |
Entry not found
|
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] |
OkabeRintaro/vit-base-patch16-224-finetuned-imagegpt
|
OkabeRintaro
| 2023-12-17T21:45:02Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2023-12-17T19:30:39Z |
Entry not found
|
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] |
Ansu/qa_finetuned
|
Ansu
| 2023-12-17T20:43:32Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:deepset/minilm-uncased-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2023-12-17T19:30:47Z |
---
license: cc-by-4.0
base_model: deepset/minilm-uncased-squad2
tags:
- generated_from_trainer
model-index:
- name: qa_finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qa_finetuned
This model is a fine-tuned version of [deepset/minilm-uncased-squad2](https://huggingface.co/deepset/minilm-uncased-squad2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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a6ez54d5s4f/A-review-defends-police-action-before-the-Maine-mass-shooting-1c-updated
|
a6ez54d5s4f
| 2023-12-17T19:32:16Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:31:01Z |
---
language:
- en
---
[]()
read the full article here : https://ibit.ly/gxnP0
Source : https://pastebin.com/X3KZkhnL
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LOS ANGELES -- Matthew Perry died from acute effects of ketamine, a drug sometimes used to treat depression, officials said.
The contributing factors in Perry's death include drowning, coronary artery disease and the effects of buprenorphine, a medication used to treat opioid use disorder, the Los Angeles County medical examiner said.
His Oct. 28 death was an accident, according to an autopsy.
Perry was found unresponsive at his residence. After 911 was called, paramedics responded to the scene and he was pronounced dead.
On Oct. 29, the deputy medical examiner completed an autopsy on Perry.
After a pickleball game on Oct. 28 he returned home, law enforcement sources said, and at some point got into his hot tub. It was there that an assistant found him unresponsive and called 911 around 4 p.m.
According to the U.S. Drug Enforcement Administration, ketamine can be used to treat depression. As a "dissociative anesthetic hallucinogen," it can make patients "feel detached from their pain and environment" and can induce a state of sedation and amnesia.
In 2006, the National Institute of Mental Health concluded that an intravenous dose of ketamine had rapid antidepressant effects. About 300 clinical trials have been held, and they have broadly found that ketamine is extremely fast-acting compared with traditional antidepressants and can relieve depression for a period that can last days or weeks.
A prescription version of ketamine called Spravato, given through a nasal spray, was approved in 2019 by the FDA for treatment-resistant depression. The number of ketamine clinics in the U.S. has risen from a few dozen to several hundred in the last few years.
Distributed by Tribune Content Agency, LLC.....
|
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racheltong/whisper-small-cn
|
racheltong
| 2023-12-17T19:31:11Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:31:11Z |
Invalid username or password.
|
[
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dajay/Malk
|
dajay
| 2023-12-17T19:34:13Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:31:52Z |
Entry not found
|
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a6ez54d5s4f/Maine-governor-nominates-states-1st-female-National-Guard-leader-KRCR-c1-updated
|
a6ez54d5s4f
| 2023-12-17T19:33:20Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:32:07Z |
---
language:
- en
---
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WASHINGTON (AP) -- Defense Secretary Lloyd Austin has ordered the USS Gerald R. Ford aircraft carrier and one other warship to remain in the Mediterranean Sea for several more weeks to maintain a two-carrier presence near Israel as its war with Hamas grinds on, U.S. officials said.
It would be the third time the Ford's deployment has been extended, underscoring the continued concerns about volatility in the region during Israel's war in Gaza. The U.S. has two aircraft carriers in the region, a rarity in recent years.
Multiple U.S. officials confirmed the longer deployments approved this week for the Ford and the USS Normandy cruiser on condition of anonymity because they have not yet been made public. Other ships in the Ford's strike group had already had their deployments extended.
The Pentagon ramped up its military presence in the region after Hamas' Oct. 7 attacks to deter Iran from widening the war into a regional conflict. In the months since, Iranian-backed militants in Iraq and Syria have seized on the war to conduct regular attacks with rockets, drones and missiles on U.S. military installations there.
At the same time, U.S. warships in the Red Sea have intercepted incoming missiles fired toward Israel from areas of Yemen controlled by Iranian-backed Houthi rebels. They've also shot down one-way attack drones headed toward the ships and responded to calls for assistance from commercial vessels that have come under persistent Houthi attacks near the narrow Bab el-Mandeb Strait.
As of Friday, there are 19 U.S. warships in the region, including seven in the eastern Mediterranean and 12 more stretched down the Red Sea, across the Arabian Sea and up into the Persian Gulf.
Austin ordered the Ford and its strike group to sail to the eastern Mediterranean on Oct. 8, a day after the attack by Hamas that set off the war.
The decision to keep the Ford -- the Navy's newest aircraft carrier -- in the region comes as Israel's defense minister, Yoav Gallant, said Thursday it will take months to destroy Hamas, predicting a drawn-out war.
U.S. national security adviser Jake Sullivan met with Israeli leaders to discuss a timetable for winding down major combat in Gaza, but they repeated their determination to press the fight until Hamas is crushed.
The Ford's roughly 5,000 sailors have been waiting for a Pentagon decision on whether they would get to go home for the holidays. The ship left Norfolk, Virginia, in early May to deploy to U.S. European Command, and under its original schedule it would have been home by early November.
The original plan was for the USS Dwight D. Eisenhower aircraft carrier strike group to replace the Ford in the region. But Sabrina Singh, in a Pentagon briefing on Oct. 17, said Austin had decided to extend the Ford's deployment and have both the Eisenhower and Ford covering the waters from southern Europe to the Middle East.
U.S. military commanders have long touted the effectiveness of American aircraft carriers as a deterrent, including against attacks, hijackings and other aggressive behavior by Iran and its ships, including strikes on commercial ships in the Red Sea by the Houthis.
Officials said the plan is to keep the Ford there for several more weeks.
The Eisenhower is in the Gulf of Oman and has been patrolling in the Middle East along with the USS Philippine Sea, a Navy cruiser. And three warships -- the USS Carney, the USS Stethem and the USS Mason, all Navy destroyers -- have been moving through the Bab el-Mandeb daily to help deter and respond to attacks from the Houthis.
Other ships that are part of the Ford's strike group include the destroyers USS Thomas Hudner, USS Ramage, USS Carney, and USS Roosevelt.
While the U.S. regularly maintained two aircraft carriers in the Middle East during the height of the Iraq and Afghanistan wars, in recent years it has tried to turn its attention and naval presence to the Asia Pacific.....
|
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er5t1reertrt/Santa-saves-Iowa-nativity-scene-from-removal-over-constitutional-concerns-e2-updated
|
er5t1reertrt
| 2023-12-17T19:32:08Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:32:08Z |
Entry not found
|
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ai-aerospace/autotrain-ams_v0.1_100_Mistral-7B-Instruct-v0.1
|
ai-aerospace
| 2023-12-17T19:54:37Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"finetuned",
"dataset:ai-aerospace/ams_data_train_mistral_v0.1_100",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2023-12-17T19:32:37Z |
---
base_model: mistralai/Mistral-7B-Instruct-v0.1
datasets:
- ai-aerospace/ams_data_train_mistral_v0.1_100
license: apache-2.0
model_creator: Mistral AI
model_name: Mistral 7B Instruct v0.1
model_type: mistral
pipeline_tag: text-generation
prompt_template: '<s>[INST]{prompt} [/INST]'
tags:
- autotrain
- text-generation
- finetuned
---
# 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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ntc-ai/SDXL-LoRA-slider.perfect
|
ntc-ai
| 2023-12-17T19:33:12Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion-xl",
"lora",
"template:sd-lora",
"template:sdxl-lora",
"sdxl-sliders",
"ntcai.xyz-sliders",
"concept",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:mit",
"region:us"
] |
text-to-image
| 2023-12-17T19:33:09Z |
---
language:
- en
thumbnail: "images/evaluate/perfect...imperfect/perfect_17_3.0.png"
widget:
- text: perfect
output:
url: images/perfect_17_3.0.png
- text: perfect
output:
url: images/perfect_19_3.0.png
- text: perfect
output:
url: images/perfect_20_3.0.png
- text: perfect
output:
url: images/perfect_21_3.0.png
- text: perfect
output:
url: images/perfect_22_3.0.png
tags:
- text-to-image
- stable-diffusion-xl
- lora
- template:sd-lora
- template:sdxl-lora
- sdxl-sliders
- ntcai.xyz-sliders
- concept
- diffusers
license: "mit"
inference: false
instance_prompt: "perfect"
base_model: "stabilityai/stable-diffusion-xl-base-1.0"
---
# ntcai.xyz slider - perfect (SDXL LoRA)
| Strength: -3 | Strength: 0 | Strength: 3 |
| --- | --- | --- |
| <img src="images/perfect_17_-3.0.png" width=256 height=256 /> | <img src="images/perfect_17_0.0.png" width=256 height=256 /> | <img src="images/perfect_17_3.0.png" width=256 height=256 /> |
| <img src="images/perfect_19_-3.0.png" width=256 height=256 /> | <img src="images/perfect_19_0.0.png" width=256 height=256 /> | <img src="images/perfect_19_3.0.png" width=256 height=256 /> |
| <img src="images/perfect_20_-3.0.png" width=256 height=256 /> | <img src="images/perfect_20_0.0.png" width=256 height=256 /> | <img src="images/perfect_20_3.0.png" width=256 height=256 /> |
## Download
Weights for this model are available in Safetensors format.
## Trigger words
You can apply this LoRA with trigger words for additional effect:
```
perfect
```
## Use in diffusers
```python
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerAncestralDiscreteScheduler
import torch
pipe = StableDiffusionXLPipeline.from_single_file("https://huggingface.co/martyn/sdxl-turbo-mario-merge-top-rated/blob/main/topRatedTurboxlLCM_v10.safetensors")
pipe.to("cuda")
pipe.scheduler = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config)
# Load the LoRA
pipe.load_lora_weights('ntc-ai/SDXL-LoRA-slider.perfect', weight_name='perfect.safetensors', adapter_name="perfect")
# Activate the LoRA
pipe.set_adapters(["perfect"], adapter_weights=[2.0])
prompt = "medieval rich kingpin sitting in a tavern, perfect"
negative_prompt = "nsfw"
width = 512
height = 512
num_inference_steps = 10
guidance_scale = 2
image = pipe(prompt, negative_prompt=negative_prompt, width=width, height=height, guidance_scale=guidance_scale, num_inference_steps=num_inference_steps).images[0]
image.save('result.png')
```
## Support the Patreon
If you like this model please consider [joining our Patreon](https://www.patreon.com/NTCAI).
By joining our Patreon, you'll gain access to an ever-growing library of over 450+ unique and diverse LoRAs, covering a wide range of styles and genres. You'll also receive early access to new models and updates, exclusive behind-the-scenes content, and the powerful LoRA slider creator, allowing you to craft your own custom LoRAs and experiment with endless possibilities.
Your support on Patreon will allow us to continue developing and refining new models.
## Other resources
- [CivitAI](https://civitai.com/user/ntc) - Follow ntc on Civit for even more LoRAs
- [ntcai.xyz](https://ntcai.xyz) - See ntcai.xyz to find more articles and LoRAs
|
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gdf2h1gfh2/Court-revives-suit-by-teacher-fired-for-not-using-trans-students-pronouns-df-updated
|
gdf2h1gfh2
| 2023-12-17T19:34:51Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:33:38Z |
---
language:
- en
---
[]()
read the full article here : https://pastelink.net/sdodituc
Source : https://ibit.ly/ijXeY
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Other Sources :
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Joe Biden won't have had an impeachment inquiry at the top of his Christmas list -- but there's a chance it could be a gift for his hopes of winning a second term as US president.
The 81-year-old Democrat certainly faces an election-year headache after the Republican-led House of Representatives voted, just before its holiday break, to formalize a probe into whether he profited from his son Hunter's foreign business deals.
Biden's strategy for a counterattack is already clear -- dismiss it as political theater, blame a vengeful House for blocking his political plans, and even raise funds for his likely rematch against Donald Trump.
"No president wants to be impeached," Todd Belt, a politics professor at George Washington University, told AFP.
But "as far as politically what this means, it hands a gift to Joe Biden."
The White House response minutes after Wednesday's vote "pivoted very quickly to the economy and to the fact that this Congress hasn't done very much and is obsessed with the politics of revenge for Donald Trump instead of doing the people's work," he added.
Political theater
So far Republicans have failed to provide evidence of corruption by the president. And even if Biden is eventually impeached by the House, any bid to remove him from office would almost certainly be dead in the water in the US Senate.
But House Republicans have pushed ahead with the inquiry knowing it provides a high-profile platform to attack Biden -- and a useful distraction from the federal criminal trials facing Trump, who was himself impeached twice.
Trump is edging ahead of Biden in recent polls and has long egged on Republicans to impeach the man who beat him in the 2020 election -- with or without evidence.
He complained on Thursday that he had "never had the luxury" of an inquiry before actual impeachment, adding that "Biden is lucky to only have an Inquiry, and he's guilty as hell!"
The Biden and Trump cases have even become a warped mirror image of each other.
Where Trump has sold merchandise featuring his mugshot and dismissing criminal charges against him as a witch hunt, the Biden campaign cast the impeachment inquiry as "political theater with bad actors" -- in an email from Vice President Kamala Harris seeking donations of as little as $5.
It was the campaign's best-performing email of the week, US media reported.
Stunt
Biden has dismissed as "lies" and a "baseless political stunt" the Republican allegations that he profited from his son's business deals in Ukraine and China or that he facilitated deals for his son using his name.
But in 2024 he is likely to "sideline" the allegations and point instead to the chaos in the House, where funding for Ukraine is blocked and a government shutdown once again looms, said Belt.
"If this continues on throughout the election cycle, all the president has to do is just point up the (Capitol) Hill and say -- 'Look up there what these people are doing,'" he added.
Hunter Biden -- whose father has long stood by him through substance abuse and legal issues -- meanwhile has taken on a new head-on strategy of his own after years of keeping a low profile.
The 53-year-old, who faces federal gun and tax evasion charges, held a rare press conference on Wednesday to slam Republicans as "shameless."
But US news outlet Axios said Hunter's strategy had in fact "caused tension" with the White House, as it takes a more risk-averse approach.
Biden can meanwhile take comfort in the fact that the previous US presidential impeachments -- Andrew Jackson in 1868, Bill Clinton in 1998, and Donald Trump in 2019 and 2021 -- all failed to remove the holder from office. Richard Nixon resigned in 1974 before he could be impeached.
Even so, the process is not without political risk.
Any attempt by the White House to cite executive privilege and hold back evidence or documents could give Republicans ammunition for claims that Biden has something to hide, Belt said.
Finally, there's always the possibility of a wild card.
"Sometimes things can come up in investigations. None of us know, right?" said Belt.....
|
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] |
owanr/ghc-roberta-base-inter-shuffle-human_annots_alpha0.0_whole_1e-05
|
owanr
| 2023-12-17T20:52:59Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-17T19:34:15Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: ghc-roberta-base-inter-shuffle-human_annots_alpha0.0_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. -->
# ghc-roberta-base-inter-shuffle-human_annots_alpha0.0_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: 0.1930
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.2 | 1.0 | 11020 | 0.1930 |
| 0.18 | 2.0 | 22040 | 0.1930 |
| 0.215 | 3.0 | 33060 | 0.1930 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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Blazej/mistral7B_align_bank_guidelines
|
Blazej
| 2023-12-17T19:34:37Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:34:37Z |
Entry not found
|
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a6ez54d5s4f/Indiana-Police-Officer-Praised-for-Quickly-Helping-Unresponsive-Child-bh-updated
|
a6ez54d5s4f
| 2023-12-17T19:35:56Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:34:42Z |
---
language:
- en
---
[]()
read the full article here : https://tech.io/snippet/MH2L0Pb
Source : https://yamcode.com/raw/obama-says-wants-update-4413-39
Flash News : https://ibit.ly/IIENR
Biden last Talk : https://paste.feed-the-beast.com/view/3dd6ad22
Russian Ukrain Breaking News : https://yamcode.com/macron-says-wants-update-4144-12172023-202822
Other Sources :
https://pasteio.com/xElpULIyJlG1
https://paste.feed-the-beast.com/view/6f81e6f7
https://jpeg.ly/r3zqe
https://yamcode.com/kennedy-says-wants-update-3134-37
https://yamcode.com/holland-says-wants-update-3511-49
https://ibit.ly/3lfXU
https://jpeg.ly/2bcEm
https://jpeg.ly/kZyyH
https://jpeg.ly/Ec5oy
https://ibit.ly/eKjaS
https://ibit.ly/Em5K0
https://yamcode.com/raw/holland-says-wants-update-3511-49
https://pasteio.com/x6EE611epdZk
https://yamcode.com/obama-says-wants-update-4413-39
CAIRO (AP) -- An Israeli strike killed a Palestinian cameraman for the TV network Al Jazeera and wounded its chief Gaza correspondent Friday as they reported at a school in the south of the besieged territory, the network said.
Cameraman Samer Abu Daqqa and correspondent Wael Dahdouh had gone to the school in the southern city of Khan Younis after it was hit by a strike earlier in the day. While they were there, an Israeli drone hit the school with a second strike, the network said.
Dahdouh was heavily wounded in his arm and shoulder, while Abu Daqqa fell bleeding to the ground. Speaking from a hospital bed, Dahdouh told Al Jazeera he was able to flee, bleeding, from the school and found several ambulance workers. He asked them to look for Abu Daqqa, but they said it was too risky and promised another ambulance would come for him, Dahdouh said.
"He was screaming, he was calling for help," said Dahdouh, his right arm heavily bandaged.
Later that evening, Al Jazeera reported that an ambulance tried to reach the school to evacuate Abu Daqqa, but it had to turn back because roads were blocked by the rubble of destroyed houses.
Abu Daqqa continued to bleed for several more hours, until a civil defense crew found him dead, the network said in a statement.
Palestinian U.N. Ambassador Riyad Mansour told a General Assembly meeting on the war that Israel "targets those who could document (their) crimes and inform the world, the journalists."
"We mourn one of those journalists, Samer Abu Daqqa, wounded in an Israeli drone strike and left to bleed to death for 6 hours while ambulances were prevented from reaching him," Mansour said.
According to the Committee to Protect Journalists, Abu Daqqa is the 64th journalist to be killed since the conflict erupted between Hamas and Israel: 57 Palestinians, four Israelis and three Lebanese journalists.
The 45-year-old Abu Daqqa, a Khan Younis native, joined Al Jazeera in June 2004, working as both a cameraman and an editor. He leaves behind three sons and a daughter.
The Israeli army did not immediately respond to an Associated Press request for comment about Abu Daqqa's death.
Qatari-owned Al Jazeera said in a statement that it holds Israel "accountable for systematically targeting and killing Al Jazeera journalists and their families."
In late October, Dahdouh's wife, son, daughter and grandchild were killed in a strike on the home where they were sheltering in central Gaza. The network at the time accused Israel of intentionally targeting his family.
Earlier this month, a strike killed the father, mother and 20 other family members of another Al Jazeera correspondent, Momen Al Sharafi.
Dahdouh is well known as the face of Palestinians during many wars. He is revered in his native Gaza for telling stories of suffering and hardship to the outside world.
Israel's air and ground assault over the past 10 weeks has killed more than 18,700 Palestinians, according to the Health Ministry in Gaza. The war broke out following Hamas' Oct. 7 attack on southern Israel in which militants killed around 1,200 people, mostly civilians, and took some 240 hostage.....
|
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a6ez54d5s4f/Israeli-strike-on-school-kills-Al-Jazeera-cameraman-in-southern-Gaza-5c-updated
|
a6ez54d5s4f
| 2023-12-17T19:34:46Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:34:46Z |
Entry not found
|
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er5t1reertrt/New-rockets-set-to-launch-in-2024-51-updated
|
er5t1reertrt
| 2023-12-17T19:37:11Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:35:17Z |
---
language:
- en
---
[]()
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The Los Angeles County Department of Medical Examiner said in the autopsy report that Perry also drowned in "the heated end of his pool," but that it was a secondary factor in his Oct. 28 death, deemed an accident.
People close to Perry told investigators that he was undergoing ketamine infusion therapy, an experimental treatment used to treat depression and anxiety.
But the coroner said the levels of ketamine in Perry's body were in the range used for general anesthesia during surgery, and that his last treatment 1 1\/2 weeks earlier wouldn't explain those levels. The drug is typically metabolized in a matter of hours.
The report says coronary artery disease and buprenorphine, which is used to treat opioid use disorder, also contributed.
The amount of ketamine detected "would be enough to make him lose consciousness and lose his posture and his ability to keep himself above the water," said Dr. Andrew Stolbach, a medical toxicologist with Johns Hopkins Medicine who reviewed the autopsy report at the request of The Associated Press.
"Using sedative drugs in a pool or hot tub, especially when you're alone, is extremely risky and, sadly, here it's fatal," said Stolbach, who noted that both ketamine and buprenorphine can be used safely.
Perry was declared dead after being found unresponsive at his home in the Pacific Palisades area of Los Angeles. Investigators performed the autopsy the following day.
The actor had taken drugs in the past but had been "reportedly clean for 19 months," according to the report.
Perry had played pickleball earlier in the day, the report says, and his assistant, who lives with him, found him face down in the pool after returning from errands.
The assistant told investigators Perry had not been sick, had not made any health complaints, and had not shown evidence of recent alcohol or drug use.
Postmortem blood tests showed "high levels" of ketamine in his system, which could have raised his blood pressure and heart rate and dulled his impulse to breathe.
Buprenorphine, commonly used in opioid addiction and found in therapeutic levels in Perry's blood, could have contributed to the breathing problem, the autopsy said. It comes in a pill or film that dissolves under the tongue.
The report said his coronary artery disease would have made him more susceptible to the drugs' effects.
Perry was open about discussing his struggles with addiction dating back to his time on "Friends" in the 1990s.
"I loved everything about the show but I was struggling with my addictions which only added to my sense of shame," he wrote in his 2022 memoir. "I had a secret and no one could know."
A woman whose name is redacted in the autopsy report told investigators that Perry had been in good spirits when she spoke to him a few days earlier, but had been taking testosterone shots which she said were making him "angry and mean." She said he had quit smoking two weeks earlier.
The woman said he had been receiving the ketamine infusions for his mental health, and that his doctor had been giving them to him less often because he had been feeling well.
Ketamine is a powerful anesthetic approved by U.S. health regulators for use during surgery, but in the past decade it has emerged as an experimental treatment for a range of psychiatric and hard-to-treat conditions, including depression, anxiety, and chronic pain.
While not approved by regulators, doctors are free to prescribe drugs these alternate uses if they think their patients could benefit, and hundreds of clinics across the U.S. offer ketamine infusions and other formulations for various health conditions.....
|
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Vinnyyw/Anyvoices
|
Vinnyyw
| 2023-12-17T19:36:32Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2023-12-17T19:35:44Z |
---
license: openrail
---
|
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gdf2h1gfh2/Green-Flower-Celebrates-50-Partnerships-with-Universities-and-Colleges-Nationwide-d4-updated
|
gdf2h1gfh2
| 2023-12-17T19:37:31Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:36:18Z |
---
language:
- en
---
[]()
read the full article here : https://ibit.ly/fnjx6
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Lt. Governor Peggy Flanagan became a homeowner roughly one month ago, after being a lifelong renter. What she described as a "tremendous undertaking" for her family is a similar journey that many Minnesotans are facing as they begin to navigate the home-buying process.
With hundreds of millions of dollars allocated to homeownership programs and rental assistance, state leaders are traveling far and wide to highlight these initiatives aimed at helping individuals secure a home.
Flanagan recently toured newly built townhomes in Northfield to highlight the significant housing investments being made in the state. The Spring Creek II project was made possible through investments from the 2019 legislative session and provides project-based voucher units for those with income restrictions. Referring to the Northfield townhomes, Flanagan emphasized the need to continue the momentum of housing investment.
"As we go into 2024, we need to continue to build on each session," she said. "I think based on this past legislative session, we are going to see a pretty significant increase in the housing that's available for folks. But it's not going to come online right away."
This past spring, Governor Tim Walz signed the $1.065 billion housing omnibus bill, making it the largest single investment in housing in Minnesota's history. An additional $250 million from other legislation going to housing brings the total to $1.313 billion.
An estimated $625 million will go to existing programs with $440 million going toward new programs and initiatives. Roughly half of the funding is geared toward homeownership and rental development.
While this is the state's largest investment in housing, Flanagan shared that these resources must be fully utilized, if these programs are to be offered again in the future.
"I think the best way for us to make the case for additional funding going forward is that we see that people are taking advantage of what is available to them. And that we're seeing changes in the home ownership gap," she said.
Flanagan is scheduled to visit more housing developments across the state to assess the progress of past projects selected for funding. On December 15, Flanagan and Housing Commissioner Jennifer Ho will announce the 2023 housing project selections. Last year's funding selections totaled over $160 million and supported the creation and preservation of roughly 2,100 homes, apartments, and lots.
Flanagan will also travel to Duluth on December 19, to visit projects selected in previous years. She plans to evaluate the progress, speak to residents, and discuss their needs.
Despite the fact that Minnesotans in the metro and greater regions have differing needs, they often prioritize the same issues close to home.
"I'd say housing and childcare are the two things that I hear most about, regardless of where I am in the state," Flanagan stated. "Access to affordable housing is an issue in the urban core, in the suburbs, [and] in greater Minnesota. It just might look a little bit different depending on where you are."
No matter where an individual may reside, the state's aim has been to take a holistic approach by putting housing in the context of everyday life. Transportation, education, and job opportunities are some factors utilized to create a broader picture of what housing development looks like for Minnesotans.
These considerations are important to state officials as they plan to make the state a place to find a suitable home to raise the next generation of Minnesotans.
"The ways that we have approached policymaking or investments in our budget has really been [that] we want Minnesota to be the best place to raise a child," Flanagan said.
While the focus on housing investment has been on rental assistance and pathways to homeownership, there has been additional work in the legislature aimed at creating renter protections.
The Judiciary and Public Safety Bill created a 14-day notice requirement for landlords, prior to filing for an eviction. It also provides residents of public housing a right to counsel in breach-of-lease proceedings. Although these legislative changes help protect renters, Flanagan believes that more can be done.
"One of the things that didn't get done last session that I think is important is making sure that you can't be discriminated against if you have a Section 8 housing voucher," she said.
Flanagan called on those with Section 8 housing vouchers to help make the case for further protections.
She pointed to members of the Hmong and Somali communities who had long advocated for development of affordable housing that would accommodate larger families.
"It's something that we've heard consistently from the Somali community that we need to have access to giving these grants to folks who are building appropriate housing that meets the needs of families," Flanagan stated.
The administration created a $10 million fee-based home-purchasing program to incentivize homeownership development for interest-averse buyers. Since Muslims are forbidden to engage in interest-bearing loans, they've long been unable to buy homes.
"I am eager to see the results of that and to be able to do more there because I think it is a significant piece of the homeownership gap when 90 percent of the Muslim community is renting," Flanagan said.
The state's housing investment is also aimed at supporting Minnesotans facing eviction or at risk of homelessness by allocating tens of millions of dollars towards funds dedicated to housing stability. The $120 million in funds for the Family Homeless Prevention program helps provide families with assistance on first or last month's rent.
In addition, Flanagan outlined the importance of the Minnesota Interagency Council on Homelessness, an initiative involving several of Minnesota's state agencies, aimed at eliminating barriers to housing.
"It's all grounded in health justice, housing justice, and racial justice and it's probably the first time that the word justice has been embedded in policy documents for the state," she said. "It has really challenged us to remove a lot of those silos between agencies, making sure that people are working together."....
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a6ez54d5s4f/Reconciliation-more-action-less-talk-gf-updated
|
a6ez54d5s4f
| 2023-12-17T19:38:41Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:37:20Z |
---
language:
- en
---
[]()
read the full article here : https://yamcode.com/raw/obama-says-wants-update-5243-36
Source : https://paste.toolforge.org/view/247302a1
Flash News : https://rentry.co/mkur5
Biden last Talk : https://yamcode.com/obama-says-wants-update-1411-30
Russian Ukrain Breaking News : https://jsbin.com/digolamidi/edit?html,output
Other Sources :
https://yamcode.com/trudeau-says-wants-update-12172023-202928
https://etextpad.com/985d6yhsnj
https://ibit.ly/VEHto
https://paste.toolforge.org/view/02d9e42b
https://yamcode.com/trudeau-says-wants-update-12172023-203040
https://ibit.ly/DkbPa
https://justpaste.me/EwwZ2
https://yamcode.com/raw/trudeau-says-wants-update-12172023-203040
https://ibit.ly/WEYvj
https://yamcode.com/raw/kennedy-says-wants-update-3111-36
https://yamcode.com/raw/china-president-says-wants-update-25062
https://yamcode.com/raw/biden-says-wants-update-12172023-203149
https://searchtech.fogbugz.com/default.asp?Suggestions.1.262337.0
https://jpeg.ly/MkmXI
This comes as Major General Douglas Farnham announced his retirement after serving nearly 10 years in the role.
Mills is nominating retired US Army Brigadier General Diane Dunn to lead the Maine National Guard and to serve as commissioner of the Department of Defense, Veterans and Emergency Management.
She comes into the role with 33 years of military service under her belt.
In 2020, she was promoted to Brigadier General, becoming the first female general officer in Maine's Army National Guard.....
|
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] |
ding-diri-ding-dong/opus-mt-ko-en
|
ding-diri-ding-dong
| 2023-12-17T19:40:12Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"base_model:Helsinki-NLP/opus-mt-ko-en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2023-12-17T19:39:36Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-ko-en
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: opus-mt-ko-en
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-ko-en
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-ko-en](https://huggingface.co/Helsinki-NLP/opus-mt-ko-en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7378
- Bleu: 41.6212
- Gen Len: 10.3771
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.8027 | 1.0 | 16549 | 0.8758 | 36.6942 | 10.397 |
| 0.6598 | 2.0 | 33098 | 0.7735 | 40.1498 | 10.369 |
| 0.6019 | 3.0 | 49647 | 0.7378 | 41.6212 | 10.3771 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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a6ez54d5s4f/Young-siblings-diagnosed-with-child-dementia-43-updated
|
a6ez54d5s4f
| 2023-12-17T19:41:34Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:40:12Z |
---
language:
- en
---
[]()
read the full article here : https://yamcode.com/hilary-says-wants-update-3152-12172023-203405
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https://yamcode.com/biden-says-wants-update-12172023-203149
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https://yamcode.com/biden-says-wants-update-12172023-203245
CAIRO: An Israeli strike killed a Palestinian cameraman for the TV network Al Jazeera and wounded its chief Gaza correspondent Friday as they reported at a school in the south of the besieged territory, the network said.
Cameraman Samer Abu Daqqa and correspondent Wael Dahdouh had gone to the school in the southern city of Khan Younis after it was hit by a strike earlier in the day. While they were there, an Israeli drone hit the school with a second strike, the network said.
Dahdouh was heavily wounded in his arm and shoulder, while Abu Daqqa fell bleeding to the ground. Speaking from a hospital bed, Dahdouh told Al Jazeera he was able to flee, bleeding, from the school and found several ambulance workers. He asked them to look for Abu Daqqa, but they said it was too risky and promised another ambulance would come for him, Dahdouh said.
"He was screaming, he was calling for help," said Dahdouh, his right arm heavily bandaged.
Later that evening, Al Jazeera reported that an ambulance tried to reach the school to evacuate Abu Daqqa, but it had to turn back because roads were blocked by the rubble of destroyed houses.
Abu Daqqa continued to bleed for several more hours, until a civil defense crew found him dead, the network said in a statement.
Palestinian UN Ambassador Riyad Mansour told a General Assembly meeting on the war that Israel "targets those who could document (their) crimes and inform the world, the journalists."
"We mourn one of those journalists, Samer Abu Daqqa, wounded in an Israeli drone strike and left to bleed to death for 6 hours while ambulances were prevented from reaching him," Mansour said.
According to the Committee to Protect Journalists, Abu Daqqa is the 64th journalist to be killed since the conflict erupted between Hamas and Israel: 57 Palestinians, four Israelis and three Lebanese journalists.
The 45-year-old Abu Daqqa, a Khan Younis native, joined Al Jazeera in June 2004, working as both a cameraman and an editor. He leaves behind three sons and a daughter.
The Israeli army did not immediately respond to an Associated Press request for comment about Abu Daqqa's death.
Qatari-owned Al Jazeera said in a statement that it holds Israel "accountable for systematically targeting and killing Al Jazeera journalists and their families."
In late October, Dahdouh's wife, son, daughter and grandchild were killed in a strike on the home where they were sheltering in central Gaza. The network at the time accused Israel of intentionally targeting his family.
Earlier this month, a strike killed the father, mother and 20 other family members of another Al Jazeera correspondent, Momen Al Sharafi.
Dahdouh is well known as the face of Palestinians during many wars. He is revered in his native Gaza for telling stories of suffering and hardship to the outside world.
Israel's air and ground assault over the past 10 weeks has killed more than 18,700 Palestinians, according to the Health Ministry in Gaza. The war broke out following Hamas' Oct. 7 attack on southern Israel in which militants killed around 1,200 people, mostly civilians, and took some 240 hostage.....
|
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] |
Kavaun677/Ggg
|
Kavaun677
| 2023-12-17T19:40:15Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T19:40:15Z |
---
license: apache-2.0
---
|
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gdf2h1gfh2/Putin-vows-to-press-on-with-Ukraine-invasion-despite-heavy-Russian-losses-4b-updated
|
gdf2h1gfh2
| 2023-12-17T19:41:39Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:40:18Z |
---
language:
- en
---
[]()
read the full article here : https://yamcode.com/raw/trump-says-wants-update-12172023-203746
Source : https://jpeg.ly/LBYiI
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Greater Shepparton City Council Mayor, Councillor Shane Sali, has been elected as Chair of Regional Cities Victoria (RCV) for 2024.
Cr Sali was elected to the position yesterday at the RCV General Meeting. This is the first time he has held the position as Chair of RCV, having previously served as Deputy Chair this year.
He will work alongside Cr Andrea Metcalf, Mayor of City of Greater Bendigo, who was elected to the position of Deputy Chair.
Cr Sali said he was extremely pleased to be elected to the position, saying he was looking forward to continuing to work with RCV to achieve positive outcomes for regional and rural communities.
"Regional cities are the backbone of Victoria, and we have an important role to play in advocating for positive investment, sustainability and achieving real change," he said.
"In my role as Deputy Chair this year, and as Mayor, I have developed strong relationships while advocating to the State and Federal Governments. I look forward to continuing this work to help improve infrastructure and liveability, and to help grow and support all communities."
RCV is a leadership group comprising of the Mayors and CEOs of the 10 largest cities outside of Melbourne, including Shepparton, Bendigo and Mildura, with these cities collectively home to more than 600,000 Victorians. RCV members work co-operatively on issues and projects of mutual interest that affect regional cities, as well as the whole state of Victoria, with a vision to build a sustainable Victoria through regional growth.
Cr Sali acknowledged the work of Cr Metcalf who held the position of Chair this year, saying he looked forward to continuing their working relationship in their new roles.
"Regional Cities Victoria is a collective approach aiming to bring communities together to strive for growth and prosperity," he said.
"Great work has already been achieved in this space. It is so important that we continue to advocate to improve the infrastructure, services and amenities in regional areas to continue to attract people to work, live and visit these parts of the state.
"I look forward to working to further enhance our partnerships and the collective approach we have strongly maintained."
\/Public Release. This material from the originating organization\/author(s) might be of the point-in-time nature, and edited for clarity, style and length. Mirage.News does not take institutional positions or sides, and all views, positions, and conclusions expressed herein are solely those of the author(s).View in full here.....
|
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filmymusehd/sdadsadsadsa
|
filmymusehd
| 2023-12-17T19:40:30Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T19:40:30Z |
---
license: apache-2.0
---
|
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gfhty56z5e/Netanyahu-vows-to-keep-up-military-pressure-on-Hamas-despite-hostage-deaths-1d-updated
|
gfhty56z5e
| 2023-12-17T19:42:53Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:41:07Z |
---
language:
- en
---
[]()
read the full article here : https://pastelink.net/8n1wiqzi
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After nearly 80 years, an Atlanta-born World War II fallen soldier will come back to the metro area to be laid to rest.
U.S. Army Private First Class Hood E. Cole will be returned to the Atlanta area on Jan. 15, 2024, with a burial in Canton.
Cole died in January 1945 while assigned to L Company, 3rd Battalion, 276th Infantry Regiment, 70th Infantry Division, according to the U.S. Department of Defense POW\/MIA Accounting Agency. He was 26.
[DOWNLOAD: Free WSB-TV News app for alerts as news breaks]
According to DPAA, Cole was killed in action when German forces near Baerenthal, France attacked his unit. During the fight, Cole was killed, and due to combat circumstances, his body was unable to be immediately recovered.
In 1947, the American Graves Registration Command began their search for fallen American personnel in the European Theater in order to recover their remains and return them home.
DPAA said none of the investigations around Baerenthal were able to uncover leads about the disposition of Cole's remains and he was declared unrecoverable in May 1951.
TRENDING STORIES:
In 2018, remains of soldiers missing from combat around Baerenthal were disinterred from an American Battle Monuments Commission site in Belgium and flown to Nebraska for analysis.
On Feb. 22, Cole was identified as one of the missing veterans whose remains were recovered.
Due to his previous unrecovered\/missing status, Cole's name was recorded on the Walls of the Missing at Epinal American Cemetery in Dinozé, France along with other soldiers still missing from World War II. Now identified, a rosette will be placed by his name.
Military records show there are still three soldiers associated with PFC Cole's loss that have yet to be accounted for: 1st Lieutenant Otis Bonebrake, PFC Estle Corvin and Corporal John O'Malley.
On Jan. 15, 2024, Cole will come home to Georgia, to be buried in Canton.
For family and funeral information, the Army Casualty Office can be called at 800-892-2490.....
|
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] |
ryubat/synkside
|
ryubat
| 2023-12-17T19:44:38Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2023-12-17T19:41:49Z |
---
license: apache-2.0
---
|
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kyufyhj/Indiana-Police-Officer-Praised-for-Quickly-Helping-Unresponsive-Child-4c-updated
|
kyufyhj
| 2023-12-17T19:41:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:41:55Z |
Entry not found
|
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owanr/SBIC-roberta-base-intra-shuffle-model_annots_alpha0.0_whole_1e-05
|
owanr
| 2023-12-17T19:42:51Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-17T19:42:33Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SBIC-roberta-base-intra-shuffle-model_annots_alpha0.0_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_alpha0.0_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: 1.2944
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.267 | 1.0 | 12516 | 1.2944 |
| 1.282 | 2.0 | 25032 | 1.2944 |
| 1.308 | 3.0 | 37548 | 1.2944 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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gdf2h1gfh2/USpoliticsimpeachmentBiden-FOCUS-a5-updated
|
gdf2h1gfh2
| 2023-12-17T19:44:21Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:43:05Z |
---
language:
- en
---
[]()
read the full article here : https://notes.io/wtqKe
Source : https://etextpad.com/dmisbud3qy
Flash News : https://pastelink.net/ftb7l8np
Biden last Talk : https://rextester.com/IGCJ83999
Russian Ukrain Breaking News : https://ibit.ly/Lr9pN
Other Sources :
https://searchtech.fogbugz.com/default.asp?Suggestions.1.262339.0
https://jpeg.ly/cOnb0
https://yamcode.com/trump-says-wants-update-12172023-203911
https://pastebin.com/zRteV1za
https://ibit.ly/owKZc
https://yamcode.com/holland-says-wants-update-3125-25
https://pastelink.net/h5bn1u7n
https://yamcode.com/trump-says-wants-update-12172023-203645
https://rextester.com/JTMV31194
https://yamcode.com/kennedy-says-wants-update-3223-45
https://yamcode.com/raw/holland-says-wants-update-3125-25
https://yamcode.com/biden-says-wants-update-12172023-203816
https://yamcode.com/raw/kennedy-says-wants-update-5543-45
https://justpaste.me/EwzQ1
beijing - Beijing said on Thursday it had mediated a temporary cease-fire in fighting between the Myanmar junta and ethnic minority armed groups in the north of the country close to the Chinese border.
Recent talks between Myanmar's military and the groups, facilitated by China, resulted in several agreements, 'including the temporary cease-fire and maintaining the momentum of dialogue,' Chinese Foreign Ministry spokeswoman Mao Ning said in a statement.
Clashes have raged across Myanmar's northern Shan state after the Arakan Army (AA), the Myanmar National Democratic Alliance Army (MNDAA) and the Ta'ang National Liberation Army (TNLA) launched an offensive in late October.
The groups have seized military positions and border hubs vital for trade with China in what analysts say is the biggest military challenge to the junta since it seized power in 2021.
AFP has contacted the AA and the MNDAA for comment, while the TNLA could not be reached immediately.
A spokesman for the Myanmar junta was also contacted for comment.
Beijing said on Monday that peace talks had been held and had yielded 'positive results' but made no mention of a cease-fire.
'The conflict in northern Myanmar is undergoing a clear de-escalation,' Mao said in Thursday's statement. 'This not only serves the interests of all parties concerned in Myanmar, but also helps to guarantee peace and tranquility on the Sino-Myanmar border.'
Beijing is a major arms supplier and ally of the junta, but ties have been strained in recent months over the junta's failure to crack down on online scam compounds in Myanmar that Beijing says target Chinese citizens.
Analysts say China maintains ties with ethnic armed groups in northern Myanmar, some of whom share close kinship and cultural ties with China and use Chinese currency and phone networks in the territory they control.
Protesters gathered at a rare demonstration in Yangon last month to accuse China of backing the ethnic minority alliance, in what analysts say was a move sanctioned by junta authorities.
Beijing has expressed 'strong dissatisfaction' with the clashes in northern Shan state, home to oil and gas pipelines that supply China and a planned billion-dollar railway link.
The offensive by the alliance of ethnic minority armed groups has galvanized other opponents of the junta. Clashes have spread to the east and the west of Myanmar and have forced more than half a million people to flee their homes, according to the United Nations.
China announced in mid-November that it had taken in an unspecified number of Myanmar refugees fleeing the fighting in a spirit of 'humanitarianism and friendship."
Myanmar is home to more than a dozen armed ethnic minority groups, some of which have seized territory in the border regions with China and fought against the army since independence from Britain in 1948.....
|
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kyufyhj/Loveland-home-health-company-closes-its-doors-cd-updated
|
kyufyhj
| 2023-12-17T19:43:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:43:07Z |
Entry not found
|
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owanr/Sentiment-roberta-base-inter-sorted-human_annots_alpha0.0_whole_1e-05
|
owanr
| 2023-12-17T20:28:13Z | 0 | 0 | null |
[
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2023-12-17T19:44:16Z |
---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: Sentiment-roberta-base-inter-sorted-human_annots_alpha0.0_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. -->
# Sentiment-roberta-base-inter-sorted-human_annots_alpha0.0_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: 1.7642
## 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.859 | 1.0 | 5628 | 1.7642 |
| 1.9 | 2.0 | 11256 | 1.7642 |
| 1.791 | 3.0 | 16884 | 1.7642 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
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gfhty56z5e/Israeli-airstrike-killed-a-USAID-contractor-in-Gaza-his-colleagues-say-41-updated
|
gfhty56z5e
| 2023-12-17T19:45:58Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:44:29Z |
---
language:
- en
---
[]()
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WASHINGTON (AP) -- An Israeli airstrike killed a U.S. Agency for International Development contractor in Gaza last month, his colleagues said in a statement Saturday. The U.S. development agency noted the death and urged greater protection for humanitarian workers in the fighting there.
Hani Jnena, 33, was killed Nov. 5 along with his wife, their 2-year-old and 4-year-old daughters, and her family, the U.S.-based humanitarian group Global Communities said.
An internet-technology worker, Jnena had fled his neighborhood in Gaza City with his family to escape the airstrikes, only to be killed while sheltering with his in-laws, the group said. His employer was an on-the-ground partner for USAID, the U.S. agency said.
The Washington Post first reported the death.
In a final message to a colleague, Hani had written, "my daughters are terrified, and I am trying to keep them calm, but this bombing is terrifying," Global Communities said.
It was a rare report of the killing of someone with U.S.-government ties in the more than two-month war between Israel and Hamas. Numerous workers with local and international aid agencies, including more than 100 U.N. workers, have been killed in Gaza as Israel bombards areas crowded with civilians and battles with Hamas fighters on the ground.
Health officials in Hamas-run Gaza say more than 17,000 people have been killed, two-thirds of them women and children. Israel's offensive is in response to an Oct. 7 Hamas assault in Israel that killed about 1,200 people.
USAID employees had been prominent in recent open letters by U.S. government employees objecting to U.S. policy in support of Israel's continued offensive, including President Joe Biden's decision not to join many other governments in calling for a cease-fire.
In an email, USAID spokesperson Jessica Jennings said Saturday, "The USAID community grieves the deaths of the innocent civilians and many humanitarian workers who have been killed in this conflict, including courageous individuals like Hani Jnena."
"In providing assistance and advocating for greater safety for civilian populations and the humanitarians who serve them, we are doing our utmost to honor the dedication, fortitude, and compassion of all humanitarian workers who have been killed," Jennings said.....
|
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Aleereza/tinyllama-colorist-lora
|
Aleereza
| 2023-12-17T19:59:08Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:other",
"region:us"
] | null | 2023-12-17T19:44:41Z |
---
license: other
base_model: microsoft/phi-2
tags:
- generated_from_trainer
model-index:
- name: tinyllama-colorist-lora
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. -->
# tinyllama-colorist-lora
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 200
- 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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r2er0e2a0za/Rivers-of-Absurdities-5c-updated
|
r2er0e2a0za
| 2023-12-17T19:44:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:44:58Z |
Entry not found
|
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gdf2h1gfh2/52-commerical-and-backyard-flocks-in-BC-have-been-infected-with-bird-flu-e2-updated
|
gdf2h1gfh2
| 2023-12-17T19:47:10Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:45:52Z |
---
language:
- en
---
[]()
read the full article here : https://yamcode.com/
Source : https://etextpad.com/jj11iarivk
Flash News : https://rextester.com/ROK19945
Biden last Talk : https://etextpad.com/dbkucltqx3
Russian Ukrain Breaking News : https://yamcode.com/obama-says-wants-update-3143-31
Other Sources :
https://rextester.com/RNUACK44506
https://yamcode.com/raw/china-president-says-wants-update-25061
https://rextester.com/KPPPB28187
https://ibit.ly/Ujvg1
https://yamcode.com/
https://pasteio.com/xLyhE4lEtBnZ
https://jpeg.ly/c8dhP
https://ibit.ly/iXXtw
https://yamcode.com/obama-says-wants-update-5443-44
https://paste.feed-the-beast.com/view/cc833a37
https://rextester.com/RKWL9827
https://rextester.com/BCQ88255
https://jsfiddle.net/kz7ybhou/
https://ibit.ly/QTn6E
After years of trying to "protect democracy" and "fortify democracy\....
|
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kyufyhj/What-is-Rashtra-Manch-or-National-Forum-d3-updated
|
kyufyhj
| 2023-12-17T19:47:32Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:46:12Z |
---
language:
- en
---
[]()
read the full article here : https://jsfiddle.net/8d1e6y3g/
Source : https://pastebin.com/8nCLGkGG
Flash News : https://yamcode.com/raw/hilary-says-wants-update-3411-12172023-203710
Biden last Talk : https://rentry.co/m34i92
Russian Ukrain Breaking News : https://ibit.ly/Fm9zt
Other Sources :
https://ibit.ly/DwEAU
https://ibit.ly/VEXlM
https://pastebin.com/fC5LLzUV
https://yamcode.com/obama-says-wants-update-2242-34
https://ibit.ly/9E_5a
https://ibit.ly/eKMDf
https://yamcode.com/kennedy-says-wants-update-1223-35
https://yamcode.com/hilary-says-wants-update-5132-12172023-203939
https://ibit.ly/0MsWx
https://jpeg.ly/2vqo-
https://ibit.ly/bNjdi
https://jpeg.ly/Hkwdn
https://yamcode.com/raw/hilary-says-wants-update-5132-12172023-203939
https://paste.ee/p/mdZD5
Dramatic footage released on December 15 shows a couple rushing into an Indiana police station, saying their son became unresponsive on the way to the hospital and asking law enforcement for help, according to the South Bend Police Department.
Police commended Officer Brian Meador, who they said gave the child chest compressions until medics arrived, at which point he regained consciousness and was responsive. Meador is seen in this security-camera footage rushing toward the couple and taking the child in his arms.
"The family tells us the child is continuing to recover and is appreciative of Officer Meador's swift medical assistance," the South Bend Police said on Facebook. Credit: South Bend Police Department via Storyful....
|
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gfhty56z5e/AP-News-in-Brief-at-604-pm-EST-2h-updated
|
gfhty56z5e
| 2023-12-17T19:48:46Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:47:22Z |
---
language:
- en
---
[]()
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Netanyahu said on Saturday (local time) that he mourns the "tragic deaths of our three hostages" -- Yotam Haim, Samar Talalka, Alon Shamriz -- mistakenly shot dead Friday morning by IDF soldiers.
"It broke my heart; it broke the hearts of the nation," Netanyahu said.
But he also added, "We are fighting for our existence... and we have to continue until victory" -- despite international pressure and the losses Israel is suffering, Times of Israel reported.
The Israeli Prime Minister said that he has been asking himself, and doubtless, all Israelis have been asking, "What if..." and "If only something had been different...We were so close to embracing them".
"But we can't turn back the clock," he added, noting that all warriors know that "the distance between victory and disaster is a hair's breadth.".
He further affirmed that Israel will learn the lesson and will maintain "the military and diplomatic effort" to bring back all the hostages.
"They touched salvation, and then the disaster happened," Times of Israel quoted Netanyahu as saying.
This comes after the Israeli Defense Forces (IDF) mistakenly killed three of its hostages on Friday by identifying them as 'threats'.
IDF spokesperson Daniel Hagari informed about the incident, saying that they bear responsibility for the "tragic incident."
During the joint press conference, Israeli Defense Minister Yoav Gallant also said that he takes responsibility for the "tragic, accidental killing" of three hostages by IDF troops.
"As defence minister, I take responsibility for everything that happens in the security establishment and everything that happens in this war, the achievements and the costs, and the severe mistakes. The same is true for the incident yesterday," said Gallant on Saturday.
He noted the complexity of fighting in the Shejaiya district where the incident took place, with Hamas fighters "in every house and every alleyway," and said that the area is full of "booby traps and potential ambushes," the Times of Israel reported.
"I have lost commanders and brothers in arms; I have seen a lot of difficult incidents, a lot that were close to me, including friends dying in battle. This is one of the most tragic incidents I have ever known," Gallant said.
He added, "The price of war is very high. We pay it every day, but when you know that you are on a path that is just, then you are willing to pay a price until you achieve your entire goal, which here is destroying Hamas and returning all the hostages to their homes." (ANI)....
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kyufyhj/California-launches-website-to-support-fight-against-opioids-StateScoop-2b-updated
|
kyufyhj
| 2023-12-17T19:48:58Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:47:32Z |
---
language:
- en
---
[]()
read the full article here : https://jpeg.ly/l_J1G
Source : https://yamcode.com/trump-says-wants-update-12172023-203629
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A health care company in the home health care and hospice industry has closed its doors and transferred patients to other organizations.
Charter Healthcare of Northern Colorado LLC, with offices at 3855 Precision Drive, Suite 120, published a legal notice in the Loveland Reporter-Herald saying that all operations would cease as of Friday.
A woman named Cassandra, who answered the phone number listed in the legal notice, said that the company had completed the transfer of patients -- none of whom were treated at the Precision Drive facility, which was an office only. Patients were treated at their homes, she said, and now other entities will be in charge of that care. All employees were laid off or transferred to other medical providers who received Charter patients.
Patient records are being held by the company at its California office, which listed P.O. Box 309, Atwood, California, as an address. Medicare requires that those records be maintained for seven years, Cassandra said.
Cassandra said the company operated from leased facilities in Loveland. County assessor records show that the building at that address is a business condominium with multiple owners.
Charter Healthcare was registered with the Secretary of State with multiple trade names, including Phoenix Home Care, Phoenix Home Care & Hospice, and Charter Hospice of Northern Colorado Inc. Alainna Fitzpatrick was listed as the registered agent for the company.....
|
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ding-diri-ding-dong/FinalProject_1218_t5-small
|
ding-diri-ding-dong
| 2023-12-17T22:08:54Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] |
text2text-generation
| 2023-12-17T19:47:55Z |
Entry not found
|
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] |
NeverSleep/MM-XL-4x13B-GGUF
|
NeverSleep
| 2023-12-17T20:20:24Z | 0 | 1 | null |
[
"gguf",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2023-12-17T19:48:28Z |
---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
```
base_model: Gryphe/MythoMax-L2-13b
gate_mode: random
experts:
- source_model: NousResearch/Nous-Hermes-Llama2-13b
positive_prompts: []
- source_model: elinas/chronos-13b-v2
positive_prompts: []
- source_model: jondurbin/airoboros-l2-13b-gpt4-2.0
positive_prompts: []
- source_model: The-Face-Of-Goonery/Huginn-13b-FP16
positive_prompts: []
```
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
|
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Mr-Bhaskar/FusionBot
|
Mr-Bhaskar
| 2023-12-17T19:52:42Z | 0 | 0 | null |
[
"safetensors",
"region:us"
] | null | 2023-12-17T19:48:29Z |
Entry not found
|
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gdf2h1gfh2/Why-LateNight-Christmas-Drinks-Disrupt-Your-Sleep-4h-updated
|
gdf2h1gfh2
| 2023-12-17T19:49:53Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:48:37Z |
---
language:
- en
---
[]()
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Barry Eberling
Some familiar names on the local political scene will vie for various Napa County Board of Supervisors seats in the March 5 election -- Liz Alessio, Doris Gentry, Amber Manfree, Peter Mott, Belia Ramos and Mariam Aboudamous.
Each of the three district races will have two candidates. That means each race should be decided without need for a November runoff election. As of 5 p.m. Wednesday, filing deadlines had passed.
Here is a look at the three contests:
2nd District -- Napa City Councilmember Alessio and former Napa City Councilmember Gentry are running.
Alessio has been on the City Council since 2018. Her background includes holding such positions as senior community programs services coordinator at Queen of the Valley Medical Center.
Alessio said she feels called to help the most vulnerable in the community and improve access to care. She said the agricultural\/wine industry is the county's economic engine and she is inspired by how they are moving toward more sustainable production.
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Being supervisor would allow her to "work at a greater scale countywide," she said.
Gentry served on the City Council from 2016 to 2020. Her endeavors have included being an advocate for foster teens and she is well known in local Republican politics.
"We actually have got a county board that is all one-sided and I don't think that's ever good for government," she said.
There are people who believe more taxes and regulations aren't good and who believe in individual freedom and property rights, she said.
Supervisor Ryan Gregory decided not to run for a third term, citing family reasons. The 2nd District includes the northwest city of Napa and the Mount Veeder area.
4th District -- Manfree and former Napa City Councilmember Mott are running.
Manfree is a fifth-generation Napa County resident who lives on Soda Canyon Road. She has a doctorate in geography.
She ran against Supervisor Alfredo Pedroza in 2020, losing 45% to 55%. She has spoken out on various controversial issues at Board of Supervisors meetings over the years, opposing Walt Ranch and the remote Mountain Peak winery.
Water security and disaster management will be among her focuses for her 2024 campaign, she said earlier this year.
Mott served three terms on the Napa City Council, losing a 2018 reelection bid. He runs On Ice Rinks, a holiday ice rink producer, and Sunmaster, a window film company.
He wants to take a hard look at how much the county spends on wildfire safety, which he sees as a priority. He wants to emphasize putting dollars into the front line, rather than growing administratively, Mott said earlier this year.
Pedroza, who was initially appointed to the Board of Supervisors in 2014, decided not to run for reelection. The 4th District includes part of the northeast city of Napa, the Silverado area and the rural communities of Circle Oaks and Berryessa Highlands.
5th District -- Supervisor Ramos is running to retain her seat against American Canyon City Councilmember Aboudamous. Both are attorneys. This is a repeat of the 2020 race, which Ramos won 53% to 47% .
Ramos was born in the city of Napa and grew up in St. Helena, where her father worked on a farm. She previously served on the American Canyon City Council and is seeking a third term as supervisor.
Earlier this year, Ramos listed some of the things she'd like to do if reelected.
She'd like to see Newell Drive extended through unincorporated land to South Kelly Road, which would create a Highway 29 parallel route in the south county. She'd like to see continued investment in affordable housing, especially family farmworker housing and expanded homeless and transitional supportive housing.
Aboudamous has served on the American Canyon City Council since 2016. She was born in San Francisco to Palestinian immigrants from Jerusalem and grew up in American Canyon.
"With the right leadership at the county, I know we can improve regional transit and reduce traffic, build more affordable housing, invest in water infrastructure and climate resiliency and tackle our homelessness crisis," she said earlier this year.
The 5th District includes American Canyon, the Coombsville area and a slice of the southeast city of Napa.
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You can reach Barry Eberling at 707-256-2253 or beberling@napanews.com
Tags Napa Election 2024 Board Of Supervisors Trade Construction Industry Sociology Politics The Economy Law Sports
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mlc-ai/Llama-2-7b-hf-q4f16_1-MLC
|
mlc-ai
| 2023-12-17T19:50:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2023-12-17T19:49:21Z |
Entry not found
|
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kyufyhj/Best-NMN-Supplements-to-Buy-Reviewed-Compare-Top-8-Brands-to-Use-in-2024-a2-updated
|
kyufyhj
| 2023-12-17T19:51:46Z | 0 | 0 | null |
[
"en",
"region:us"
] | null | 2023-12-17T19:50:27Z |
---
language:
- en
---
[]()
read the full article here : https://jpeg.ly/-tiz9
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A couple of weeks ago, a Facebook post drew my attention.
Recently, Professor M. Adil Khan (more endearingly known among the Australia's Brisbane community, as Adil Bhai), an Adjunct Professor of Development Practice at the School of Social Sciences, University of Queensland, Brisbane, Australia, and a former Senior Policy Manager of the United Nations, had posted in his Facebook page the news of publication of his latest book, "Bangladesh's Seven Governing Periods, 1972-2022: Accomplishments, 'Constants of Bad Governance' and Much Needed Resets." The book has been published by the South Asia Journal, a New Jersey (USA) based reputable on-line journal.
The title of the book
The title of the book intrigued me especially the phrase, the 'Constants of Bad Governance.' Moreover, I also noticed on his Facebook page where the news of publication of the book was posted, that there were wide and enthusiastic responses from many Bangladeshis and non-Bangladeshis.
In the meantime, the book was reviewed by the internationally famed journalist, Arnold Zeitlin where he commented that "Adil Khan, with a lengthy international background as a specialist in country development, has produced a handy guide to Bangladesh's political history..."
Given the acclamations the book had already received and the curiosity it provoked, I instantly and unilaterally decided to do something to listen to the content, the story of Bangladesh from the author himself.
The Launch
When I suggested the idea of a book launch and a talk on the book from the author himself, I got a rousing endorsement from the Bangladeshi community in Brisbane and its neighborhoods.
Then I approached Professor Khan with the proposal of the Launch. Professor Khan readily agreed and more importantly, he proposed to me to invite, as Chief Guest, Dr. Amy MacMahon, a Member of Parliament of the Queensland State Assembly, and an Australia Greens Party member. More importantly, Dr. MacMahon is familiar with Bangladesh as she also happens to be Professor Khan's former PhD student and her PhD research examined the challenges of rural women in Bangladesh in climate change conditions.
In this regard, it is important to say, that the event would have been impossible to organize without the enthusiastic response I received from the community. They paid to attend, and the event was a complete sellout.
Furthermore, the active support that I received from the community members such as Ahmad Hussein Biplob, Dr. JD Gupta and Yasmin L. Khan in organizing the event was outstanding. Without their help, the event would not have seen the light of the day.
The Launch began with the Chief Guest, Dr. Amy MacMahon uncovering (Morok Ummochon) and displaying the book to the audience. 'Morok Ummochon' was followed by the presentation of Professor Khan.
The Presentation: 'One-step forward, Two-steps back'
Professor Khan began by explaining the conceptual meanings of 'governing periods,' 'governance', 'governance characteristics', 'development' and the 'Constants of Bad Governance' (CoBGs). Professor Khan used these concepts as the analytical framework for the book and explained how governance characteristics of the 'seven governing periods' impacted Bangladesh both positively as well as negatively. During his presentation, he also made a distinction between economic growth and 'development'. He explained that economic growth is an important component of 'development' but not 'development'. According to Professor Khan, 'Development' is a more holistic concept which means progress in equal measure in economic, social, political and more importantly, in moral nourishment.
Professor Khan narrated that during the last five decades Bangladesh has made impressive gains in economic development and advanced the country from its early seventies "basket case" to one of the thriving economies of the world. In this regard, Professor Khan also acknowledged the important contributions that Bangladesh's NGOs such as the Grameen Bank, BRAC and Gonoshasthya Kendro had made in poverty alleviation, primary education, preventive health and in community development.
However, Professor Khan also pointed out that notwithstanding the positive accomplishments that Bangladesh has achieved during the last five decades, the 'Constants of Bad Governance' - the CoBGS, the repeating and accumulative democracy backslides and governance missteps, shrinking of democratic space, crony capitalism, collapsing public accountability that have made corruption an "entitlement," restrictions on free speech and brutal repression and suppression of opposition etc. that have deepened in recent years, have dulled much of its accomplishments and worryingly, risking undoing much of the gains the country has achieved since.
Portraying a rather grim future for Bangladesh that the ever expanding and deepening CoBGs are posing to Bangladesh, Professor Khan has proposed in his book, a set of urgent resets - both immediate and incremental - as remedy and stressed that the most immediate reset is the restoration of democracy through a free, fair and a participatory election in 2024, which should pave the way to remedy the CoBGs that are deep and systemic in nature.
Summarizing the political and economic history of Bangladesh as a journey of 'One-Step forward and Two-Steps Back" phenomenon, Professor Khan in his concluding remarks emphasized that "It is time ripe for the people of Bangladesh to break through its wrongs and journey to a society where people are treated justly and fairly ...a society which is secure for all citizens... and more importantly, a Bangladesh that values, respects, protects and nurtures its sovereignty."
A Guideline
In response to Professor Khan's very enlightening presentation, Dr. Amy MacMahon, MP, the Chief Guest of the event observed that it is "an excellent book containing socioeconomic and political background of Bangladesh...the book could easily be the guideline for good governance in Bangladesh..."
The event ended with a Vote of Thanks from Dr. Khandakar Rahman, Cardiologist, one of the distinguished members of the Bangladeshi community in Brisbane and its neighborhoods.....
|
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