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null | null | ```python
import numpy as np
import torch
import torch.nn as nn
from torchvision import datasets
from torchvision import transforms
from torch.utils.data.sampler import SubsetRandomSampler
from tqdm import tqdm
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(device)
```
cuda
```python
def data_loader(data_dir,
batch_size,
random_seed=42,
valid_size=0.1,
shuffle=True,
test=False):
normalize = transforms.Normalize(
mean=[0.4914, 0.4822, 0.4465],
std=[0.2023, 0.1994, 0.2010],
)
transform = transforms.Compose([
transforms.Resize((224,224)),
transforms.ToTensor(),
normalize,
])
if test:
dataset = datasets.CIFAR10(
root=data_dir, train=False,
download=True, transform=transform,
)
data_loader = torch.utils.data.DataLoader(
dataset, batch_size=batch_size, shuffle=shuffle
)
return data_loader
train_dataset = datasets.CIFAR10(
root=data_dir, train=True,
download=True, transform=transform,
)
valid_dataset = datasets.CIFAR10(
root=data_dir, train=True,
download=True, transform=transform,
)
num_train = len(train_dataset)
indices = list(range(num_train))
split = int(np.floor(valid_size * num_train))
if shuffle:
np.random.seed(42)
np.random.shuffle(indices)
train_idx, valid_idx = indices[split:], indices[:split]
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetRandomSampler(valid_idx)
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=batch_size, sampler=train_sampler)
valid_loader = torch.utils.data.DataLoader(
valid_dataset, batch_size=batch_size, sampler=valid_sampler)
return (train_loader, valid_loader)
train_loader, valid_loader = data_loader(data_dir='./data',
batch_size=64)
test_loader = data_loader(data_dir='./data',
batch_size=64,
test=True)
```
Files already downloaded and verified
Files already downloaded and verified
Files already downloaded and verified
```python
class ResidualBlock(nn.Module):
def __init__(self, in_channels, out_channels, stride = 1, downsample = None):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels, out_channels, kernel_size = 3, stride = stride, padding = 1),
nn.BatchNorm2d(out_channels),
nn.ReLU())
self.conv2 = nn.Sequential(
nn.Conv2d(out_channels, out_channels, kernel_size = 3, stride = 1, padding = 1),
nn.BatchNorm2d(out_channels))
self.downsample = downsample
self.relu = nn.ReLU()
self.out_channels = out_channels
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.conv2(out)
if self.downsample:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
```
```python
```
```python
class ResNet(nn.Module):
def __init__(self, block, layers, num_classes = 10):
super(ResNet, self).__init__()
self.inplanes = 64
self.conv1 = nn.Sequential(
nn.Conv2d(3, 64, kernel_size = 7, stride = 2, padding = 3),
nn.BatchNorm2d(64),
nn.ReLU())
self.maxpool = nn.MaxPool2d(kernel_size = 3, stride = 2, padding = 1)
self.layer0 = self._make_layer(block, 64, layers[0], stride = 1)
self.layer1 = self._make_layer(block, 128, layers[1], stride = 2)
self.layer2 = self._make_layer(block, 256, layers[2], stride = 2)
self.layer3 = self._make_layer(block, 512, layers[3], stride = 2)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512, num_classes)
def _make_layer(self, block, planes, blocks, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes, kernel_size=1, stride=stride),
nn.BatchNorm2d(planes),
)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample))
self.inplanes = planes
for i in range(1, blocks):
layers.append(block(self.inplanes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.maxpool(x)
x = self.layer0(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
```
```python
num_classes = 10
num_epochs = 5
learning_rate = 0.01
model = ResNet(ResidualBlock, [2, 2, 2, 2]).to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate, weight_decay = 0.001, momentum = 0.9)
total_step = len(train_loader)
```
```python
import gc
total_step = len(train_loader)
from tqdm import tqdm
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(tqdm(train_loader)):
# Move tensors to the configured device
images = images.to(device)
labels = labels.to(device)
# Forward pass
outputs = model(images)
loss = criterion(outputs, labels)
# Backward and optimize
optimizer.zero_grad()
loss.backward()
optimizer.step()
print ('Epoch [{}/{}], Loss: {:.4f}'
.format(epoch+1, num_epochs, loss.item()))
# Validation
with torch.no_grad():
correct = 0
total = 0
for images, labels in valid_loader:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
del images, labels, outputs
print('Accuracy of the network on the {} validation images: {} %'.format(5000, 100 * correct / total))
```
100%|ββββββββββ| 176/176 [01:15<00:00, 2.35it/s]
Epoch [1/10], Loss: 1.2169
Accuracy of the network on the 5000 validation images: 58.28 %
100%|ββββββββββ| 176/176 [01:16<00:00, 2.29it/s]
Epoch [2/10], Loss: 0.8962
Accuracy of the network on the 5000 validation images: 70.36 %
100%|ββββββββββ| 176/176 [01:16<00:00, 2.30it/s]
Epoch [3/10], Loss: 0.6691
Accuracy of the network on the 5000 validation images: 75.86 %
100%|ββββββββββ| 176/176 [01:16<00:00, 2.29it/s]
Epoch [4/10], Loss: 0.6426
Accuracy of the network on the 5000 validation images: 79.24 %
100%|ββββββββββ| 176/176 [01:16<00:00, 2.29it/s]
Epoch [5/10], Loss: 0.2891
Accuracy of the network on the 5000 validation images: 80.4 %
100%|ββββββββββ| 176/176 [01:16<00:00, 2.29it/s]
Epoch [6/10], Loss: 0.4245
Accuracy of the network on the 5000 validation images: 81.24 %
100%|ββββββββββ| 176/176 [01:16<00:00, 2.29it/s]
Epoch [7/10], Loss: 0.2183
Accuracy of the network on the 5000 validation images: 81.44 %
100%|ββββββββββ| 176/176 [01:16<00:00, 2.29it/s]
Epoch [8/10], Loss: 0.1172
Accuracy of the network on the 5000 validation images: 81.06 %
100%|ββββββββββ| 176/176 [01:16<00:00, 2.30it/s]
Epoch [9/10], Loss: 0.1069
Accuracy of the network on the 5000 validation images: 82.14 %
100%|ββββββββββ| 176/176 [01:17<00:00, 2.29it/s]
Epoch [10/10], Loss: 0.0555
Accuracy of the network on the 5000 validation images: 83.12 %
```python
with torch.no_grad():
correct = 0
total = 0
for images, labels in test_loader:
images = images.to(device)
labels = labels.to(device)
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
del images, labels, outputs
print('Accuracy of the network on the {} test images: {} %'.format(10000, 100 * correct / total))
```
```python
model = torch.hub.load("pytorch/vision", "resnet152", weights="IMAGENET1K_V2")
model.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
num_features = model.fc.in_features
model.fc = nn.Linear(num_features, num_classes)
model = model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=200)
```
Using cache found in /root/.cache/torch/hub/pytorch_vision_main
```python
def train(model, trainloader, criterion, optimizer, device):
train_loss = 0.0
train_total = 0
train_correct = 0
# Switch to train mode
model.train()
for inputs, labels in trainloader:
inputs, labels = inputs.to(device), labels.to(device)
# Zero the parameter gradients
optimizer.zero_grad()
# Forward pass
outputs = model(inputs)
loss = criterion(outputs, labels)
# Backward pass and optimize
loss.backward()
optimizer.step()
# Update training loss
train_loss += loss.item() * inputs.size(0)
# Compute training accuracy
_, predicted = torch.max(outputs, 1)
train_total += labels.size(0)
train_correct += (predicted == labels).sum().item()
train_loss = train_loss / len(trainloader.dataset)
train_accuracy = 100.0 * train_correct / train_total
return model, train_loss, train_accuracy
```
```python
def test(model, testloader, criterion, device):
test_loss = 0.0
test_total = 0
test_correct = 0
# Switch to evaluation mode
model.eval()
with torch.no_grad():
for inputs, labels in testloader:
inputs, labels = inputs.to(device), labels.to(device)
# Forward pass
outputs = model(inputs)
loss = criterion(outputs, labels)
# Update test loss
test_loss += loss.item() * inputs.size(0)
# Compute test accuracy
_, predicted = torch.max(outputs, 1)
test_total += labels.size(0)
test_correct += (predicted == labels).sum().item()
# Compute average test loss and accuracy
test_loss = test_loss / len(testloader.dataset)
test_accuracy = 100.0 * test_correct / test_total
return test_loss, test_accuracy
```
```python
def train_epochs(model, trainloader, testloader, criterion, optimizer, device, num_epochs, save_interval=5):
train_losses = []
train_accuracies = []
test_losses = []
test_accuracies = []
for epoch in range(num_epochs):
print(f'Epoch {epoch+1}/{num_epochs}')
model, train_loss, train_accuracy = train(model, trainloader, criterion, optimizer, device)
test_loss, test_accuracy = test(model, testloader, criterion, device)
train_losses.append(train_loss)
train_accuracies.append(train_accuracy)
test_losses.append(test_loss)
test_accuracies.append(test_accuracy)
print(f'Train Loss: {train_loss:.4f} - Train Accuracy: {train_accuracy:.2f}%')
print(f'Test Loss: {test_loss:.4f} - Test Accuracy: {test_accuracy:.2f}%')
print()
return model, train_losses, train_accuracies, test_losses, test_accuracies
```
```python
trainset, trainloader, testset, testloader, classes = load_dataset()
if train_model:
num_epochs = 60
save_interval = 5
model, train_losses, train_accuracies, test_losses, test_accuracies = train_epochs(
model, trainloader, testloader, criterion, optimizer, device,
num_epochs, save_interval)
else:
model.load_state_dict(torch.load('resnet50_cifar10_final_model_epochs_50.pth'))
checkpoint = torch.load("resnet50_cifar10_variables.pth")
epoch = checkpoint['epoch']
train_losses = checkpoint['train_losses']
train_accuracies = checkpoint['train_accuracies']
test_losses = checkpoint['test_losses']
test_accuracies = checkpoint['test_accuracies']
classes = checkpoint['classes']
model.to(device)
model.eval()
```
```python
```
Epoch 1/10
----------
100%|ββββββββββ| 704/704 [03:26<00:00, 3.41it/s]
Train Loss: 1.9308 Acc: 0.4630
100%|ββββββββββ| 79/79 [00:22<00:00, 3.52it/s]
Val Loss: 0.1944 Acc: 0.0665
Epoch 2/10
----------
24%|βββ | 166/704 [00:49<02:40, 3.35it/s]
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-11-483fc1f8b5af> in <cell line: 51>()
49 num_epochs = 10
50 dataloaders = {'train': train_loader, 'val': valid_loader}
---> 51 trained_model = train_model(combined_model, dataloaders, criterion, optimizer, scheduler, num_epochs=num_epochs, device=device)
<ipython-input-11-483fc1f8b5af> in train_model(model, dataloaders, criterion, optimizer, scheduler, num_epochs, device)
32 optimizer.step()
33
---> 34 running_loss += loss.item() * inputs.size(0)
35 running_corrects += torch.sum(preds == labels.data)
36 del inputs, labels, outputs
KeyboardInterrupt:
```python
```
| {} | bobronson/resnet | null | [
"region:us"
]
| null | 2024-04-27T16:15:59+00:00 |
sentence-similarity | sentence-transformers |
# Reviews Zero-Shot Sentiment Classification
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
import numpy as np
from sentence_transformers import SentenceTransformer, util
sentences = ["ΠΠ°ΠΌΠ΅ΡΠ°ΡΠ΅Π»ΡΠ½ΡΠΉ ΠΏΡΠ΅ΠΏΠ°ΡΠ°Ρ, Π²ΡΠ΅ ΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΡΡ", "ΠΠΎΡΠ»Π΅Π΄Π½Π΅Π΅ Π²ΡΠ΅ΠΌΡ Π΄Π°Π½Π½ΡΠΉ ΠΏΡΠ΅ΠΏΠ°ΡΠ°Ρ Π²ΡΠ·ΡΠ²Π°Π΅Ρ Ρ ΠΌΠ΅Π½Ρ ΡΡΠΏΡ"]
classes = ['Π½Π΅Π³Π°ΡΠΈΠ²', 'Π½Π΅ΠΉΡΡΠ°Π»ΡΠ½ΠΎ', 'ΠΏΠΎΠ·ΠΈΡΠΈΠ²']
model = SentenceTransformer('pavlentiy/reviews-sentiment-multilingual-e5-base')
embeddings = model.encode(sentences)
embeddings_classes = model.encode(classes)
# Compute cosine-similarities
cosine_scores = np.array(util.cos_sim(embeddings, embeddings_classes))
a = lambda t: {0:'Π½Π΅Π³Π°ΡΠΈΠ²', 1:'Π½Π΅ΠΉΡΡΠ°Π»ΡΠ½Π°Ρ', 2:'ΠΏΠΎΠ·ΠΈΡΠΈΠ²'}[t]
argmax = cosine_scores.argmax(axis=1)
result_classes = list(map(a, argmax))
print(result_classes)
```
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 802 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 0,
"evaluator": "sentence_transformers.evaluation.TranslationEvaluator.TranslationEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 160.4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | pavlentiy/reviews-sentiment-multilingual-e5-base | null | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:16:00+00:00 |
null | null | {"license": "openrail"} | KeroroK66/omachi | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T16:16:01+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | golf2248/adv6pvf | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:16:26+00:00 |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_outputs
This model is a fine-tuned version of [riotu-lab/ArabianGPT-03B](https://huggingface.co/riotu-lab/ArabianGPT-03B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8610
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 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
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.4515 | 0.25 | 10 | 4.5855 |
| 4.8912 | 0.49 | 20 | 4.1608 |
| 4.3524 | 0.74 | 30 | 4.0509 |
| 4.1537 | 0.99 | 40 | 4.0484 |
| 3.6716 | 1.23 | 50 | 4.0211 |
| 3.4284 | 1.48 | 60 | 4.1357 |
| 3.5215 | 1.73 | 70 | 4.2520 |
| 3.4336 | 1.98 | 80 | 4.0270 |
| 2.8886 | 2.22 | 90 | 4.9232 |
| 2.6176 | 2.47 | 100 | 5.0723 |
| 2.5867 | 2.72 | 110 | 4.8623 |
| 2.6076 | 2.96 | 120 | 4.8610 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"language": ["ar"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["arcd"], "base_model": "riotu-lab/ArabianGPT-03B", "pipeline_tag": "question-answering", "widget": [{"text": "\u0645\u0627 \u0647\u064a \u0627\u0644\u0639\u0648\u0627\u0645\u0644 \u0627\u0644\u062a\u064a \u062a\u0624\u062b\u0631 \u0639\u0644\u0649 \u0633\u0631\u0639\u0629 \u0627\u0644\u0645\u0648\u0635\u0644\u0627\u062a \u0627\u0644\u0639\u0635\u0628\u064a\u0629\u061f", "context": "\u062a\u062a\u0623\u062b\u0631 \u0627\u0644\u0623\u0644\u064a\u0627\u0641 \u0627\u0644\u0639\u0635\u0628\u064a\u0629 \u0627\u0644\u0637\u0648\u064a\u0644\u0629 \u0628\u062f\u0631\u062c\u0629 \u0623\u0643\u0628\u0631 \u0645\u0646 \u0627\u0644\u0623\u0644\u064a\u0627\u0641 \u0627\u0644\u0639\u0635\u0628\u064a\u0629 \u0627\u0644\u0642\u0635\u064a\u0631\u0629\u060c \u0648\u0630\u0644\u0643 \u0644\u0623\u0646 \u0633\u0631\u0639\u0629 \u0627\u0644\u062a\u0648\u0635\u064a\u0644 \u0641\u064a \u0627\u0644\u0639\u0635\u0628 \u062a\u0646\u0642\u0635 \u0641\u064a \u062a\u0646\u0627\u0633\u0628 \u0645\u0639 \u0637\u0648\u0644 \u0627\u0644\u0639\u0635\u0628. \u0641\u064a \u0647\u0630\u0647 \u0627\u0644\u0645\u062a\u0644\u0627\u0632\u0645\u0629\u060c \u064a\u062d\u062f\u062b \u0627\u0646\u062e\u0641\u0627\u0636 \u0641\u064a \u0627\u0644\u0625\u062d\u0633\u0627\u0633 \u0648\u0641\u0642\u062f\u0627\u0646 \u0631\u062f\u0648\u062f \u0627\u0644\u0641\u0639\u0644 \u0641\u064a \u0623\u0635\u0627\u0628\u0639 \u0643\u0644 \u0642\u062f\u0645\u060c \u0648\u062a\u0645\u062a\u062f \u0628\u0639\u062f \u0630\u0644\u0643 \u0625\u0644\u0649 \u0623\u0639\u0644\u0649. \u0648\u0639\u0627\u062f\u0629 \u0645\u0627 \u062a\u0648\u0635\u0641 \u0628\u0627\u062d\u0633\u0627\u0633 \u0627\u0644\u062e\u062f\u0631 \u0648\u0641\u0642\u062f\u0627\u0646 \u0627\u0644\u0625\u062d\u0633\u0627\u0633 \u0648\u0639\u0633\u0631 \u0627\u0644\u0644\u0645\u0633 (\u0627\u0646\u062e\u0641\u0627\u0636 \u0623\u0648 \u0641\u0642\u062f\u0627\u0646 \u0627\u0644\u0625\u062d\u0633\u0627\u0633 \u0641\u064a \u062c\u0632\u0621 \u0645\u0646 \u0627\u0644\u062c\u0633\u0645) \u0648\u0623\u0644\u0645 \u0644\u064a\u0644\u064a \u0641\u064a\u0645\u0627 \u064a\u0634\u0628\u0647 \u0627\u0644\u0642\u0641\u0627\u0632 \u0648\u0627\u0644\u062c\u0648\u0631\u0628. \u0648\u064a\u0645\u0643\u0646 \u0623\u0646 \u064a\u0643\u0648\u0646 \u0627\u0644\u0623\u0644\u0645 \u0641\u064a \u0647\u064a\u0626\u0629 \u062d\u0631\u0642\u0627\u0646 \u0623\u0648 \u0648\u062e\u0632 \u0623\u0648 \u0623\u0644\u0645 \u063a\u064a\u0631 \u0645\u062d\u062f\u062f. \u0648\u064a\u0643\u0648\u0646 \u0627\u0644\u0627\u062d\u0633\u0627\u0633 \u0628\u0648\u062e\u0632 \u0627\u0644\u062f\u0628\u0627\u0628\u064a\u0633 \u0648\u0627\u0644\u0625\u0628\u0631 \u0623\u0645\u0631\u0627\u064b \u0634\u0627\u0626\u0639\u0627\u064b. \u0648\u064a\u062a\u0623\u062b\u0631 \u0627\u0644\u0627\u062d\u0633\u0627\u0633 \u0628\u0648\u0636\u0639 \u0623\u0639\u0636\u0627\u0621 \u0627\u0644\u062c\u0633\u0645 \u0644\u0628\u0639\u0636\u0647\u0627 proprioception \u0645\u0628\u0643\u0631\u0627. \u0648\u0644\u0627 \u064a\u0645\u0643\u0646 \u0644\u0647\u0624\u0644\u0627\u0621 \u0627\u0644\u0645\u0631\u0636\u0649 \u0627\u0644\u0634\u0639\u0648\u0631 \u0639\u0646\u062f\u0645\u0627 \u064a\u062f\u0648\u0633\u0648\u0646 \u0639\u0644\u0649 \u062c\u0633\u0645 \u063a\u0631\u064a\u0628 \u0643\u0627\u0644\u0634\u0638\u064a\u0629\u060c \u0623\u0648 \u0639\u0646\u062f\u0645\u0627 \u064a\u062a\u0643\u0648\u0646 \u0644\u0647\u0645 \u062c\u0644\u062f \u0635\u0644\u0628 \u0645\u0646 \u0627\u0644\u0623\u062d\u0630\u064a\u0629 \u0627\u0644\u0636\u064a\u0642\u0629. \u0648\u0628\u0646\u0627\u0621 \u0639\u0644\u0649 \u0630\u0644\u0643\u060c \u0641\u0625\u0646\u0647\u0645 \u0645\u0639\u0631\u0636\u0648\u0646 \u0644\u062e\u0637\u0631 \u062d\u062f\u0648\u062b \u0627\u0644\u0642\u0631\u062d\u0629 \u0648\u0627\u0644\u062a\u0647\u0627\u0628\u0627\u062a \u0627\u0644\u0642\u062f\u0645\u064a\u0646 \u0648\u0627\u0644\u0633\u0627\u0642\u064a\u0646\u060c \u0648\u0627\u0644\u062a\u064a \u064a\u0645\u0643\u0646 \u0623\u0646 \u062a\u0624\u062f\u064a \u0625\u0644\u0649 \u0627\u0644\u0628\u062a\u0631 \u0648\u0642\u062f \u064a\u062d\u062f\u062b \u0644\u0647\u0624\u0644\u0627\u0621 \u0627\u0644\u0645\u0631\u0636\u0649 \u0643\u0633\u0648\u0631\u0627 \u0645\u062a\u0639\u062f\u062f\u0629 \u0641\u064a \u0627\u0644\u0631\u0643\u0628\u0629 \u0623\u0648 \u0627\u0644\u0643\u0627\u062d\u0644 \u0623\u0648 \u0627\u0644\u0642\u062f\u0645 \u0648\u0642\u062f \u062a\u0624\u062f\u064a \u0625\u0644\u0649 \u062d\u062f\u0648\u062b \u0627\u0646\u062d\u0644\u0627\u0644 \u0641\u064a \u0627\u0644\u0645\u0641\u0627\u0635\u0644. \u0648\u064a\u0624\u062f\u064a \u0641\u0642\u062f\u0627\u0646 \u0648\u0638\u064a\u0641\u0629 \u0627\u0644\u062d\u0631\u0643\u0629 \u0625\u0644\u0649 \u062a\u0642\u0648\u0633 \u0627\u0644\u0642\u062f\u0645 \u0644\u0623\u0639\u0644\u0649 dorsiflexion\u060c \u0648\u062a\u0642\u0644\u0635 \u0623\u0635\u0627\u0628\u0639 \u0627\u0644\u0642\u062f\u0645 \u0648\u0641\u0642\u062f\u0627\u0646 \u0648\u0638\u064a\u0641\u0629 \u0627\u0644\u0639\u0636\u0644\u0627\u062a \u0628\u064a\u0646 \u0627\u0644\u0623\u0635\u0627\u0628\u0639\u060c \u0645\u0645\u0627 \u064a\u0633\u0645\u0649 \u0628\u0627\u0644\u0642\u062f\u0645 \u0627\u0644\u0645\u0637\u0631\u0642\u0629. \u0648\u0644\u0627 \u062a\u0642\u062a\u0635\u0631 \u0647\u0630\u0647 \u0627\u0644\u062a\u0642\u0644\u0635\u0627\u062a \u0639\u0644\u0649 \u0627\u0644\u0642\u062f\u0645 \u0641\u0642\u0637\u060c \u0628\u0644 \u0623\u064a\u0636\u0627 \u062a\u0635\u064a\u0628 \u0627\u0644\u064a\u062f \u062d\u064a\u062b \u0641\u0642\u062f\u0627\u0646 \u0627\u0644\u0639\u0636\u0644\u0627\u062a \u064a\u062c\u0639\u0644 \u0627\u0644\u064a\u062f \u062a\u0628\u062f\u0648 \u0647\u0632\u064a\u0644\u0629 \u0643\u0627\u0644\u0647\u064a\u0643\u0644 \u0627\u0644\u0639\u0638\u0645\u064a \u0648\u064a\u0632\u062f\u0627\u062f \u0641\u0642\u062f\u0627\u0646 \u0627\u0644\u0648\u0638\u064a\u0641\u0629 \u0627\u0644\u062d\u0631\u0643\u064a\u0629", "example_title": "Example 1"}, {"text": "\u0645\u0627 \u0644\u0642\u0628 \u062e\u0627\u0644\u062f \u0628\u0646 \u0627\u0644\u0648\u0644\u064a\u062f \u0628\u0627\u0644\u0639\u0631\u0628\u064a\u0629\u061f", "context": "\u062e\u0627\u0644\u062f \u0628\u0646 \u0627\u0644\u0648\u0644\u064a\u062f \u0645\u0646 \u0623\u0628\u0637\u0627\u0644 \u0648\u0642\u0627\u062f\u0629 \u0627\u0644\u0641\u062a\u062d \u0627\u0644\u0625\u0633\u0644\u0627\u0645\u064a \u0648\u0642\u062f \u062a\u062d\u062f\u062b\u062a \u0639\u0646\u0647 \u0627\u0644\u0644\u063a\u0627\u062a \u0627\u0644\u0625\u0646\u062c\u0644\u064a\u0632\u064a\u0629 \u0648\u0627\u0644\u0641\u0631\u0646\u0633\u064a\u0629 \u0648\u0627\u0644\u0625\u0633\u0628\u0627\u0646\u064a\u0629 \u0648\u0644\u0642\u0628 \u0628\u0633\u064a\u0641 \u0627\u0644\u0644\u0647 \u0627\u0644\u0645\u0633\u0644\u0648\u0644.", "example_title": "Example 2"}, {"text": "\u0623\u064a\u0646 \u0623\u0633\u0643\u0646\u061f", "context": "\u0625\u0633\u0645\u064a \u0645\u062d\u0645\u062f \u0648\u0623\u0633\u0643\u0646 \u0641\u064a \u0628\u064a\u0631\u0648\u062a", "example_title": "Example 3"}], "model-index": [{"name": "model_outputs", "results": []}]} | gp-tar4/QA_FineTuned_ArabianGPT-03B | null | [
"transformers",
"safetensors",
"gpt2",
"question-answering",
"generated_from_trainer",
"ar",
"dataset:arcd",
"base_model:riotu-lab/ArabianGPT-03B",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:16:33+00:00 |
null | null | {} | bobronson/lstm-text | null | [
"region:us"
]
| null | 2024-04-27T16:17:17+00:00 |
|
null | null | {} | Xrunner/hive-repo | null | [
"region:us"
]
| null | 2024-04-27T16:17:44+00:00 |
|
null | null | {"license": "apache-2.0"} | 1uciusy/DriveLMContest | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T16:18:07+00:00 |
|
null | null | {"license": "openrail"} | KeroroK66/amazon | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T16:18:38+00:00 |
|
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_gliner_model
This model is a fine-tuned version of [microsoft/mdeberta-v3-base](https://huggingface.co/microsoft/mdeberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4179
- Precision: 0.8607
- Recall: 0.8591
- F1: 0.8599
- Accuracy: 0.8565
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.4823 | 1.0 | 11105 | 0.4672 | 0.8461 | 0.8463 | 0.8462 | 0.8414 |
| 0.423 | 2.0 | 22210 | 0.4179 | 0.8607 | 0.8591 | 0.8599 | 0.8565 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/mdeberta-v3-base", "model-index": [{"name": "my_awesome_gliner_model", "results": []}]} | Gmanc/my_awesome_gliner_model | null | [
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/mdeberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:20:09+00:00 |
null | null | this is a test | {} | dotheboogey678/testmodel0_O | null | [
"region:us"
]
| null | 2024-04-27T16:21:11+00:00 |
null | null | {} | Minken/1 | null | [
"region:us"
]
| null | 2024-04-27T16:23:49+00:00 |
|
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_eli5_clm_model_v2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the eli5_category dataset.
It achieves the following results on the evaluation set:
- Loss: 6.0285
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5395 | 1.0 | 1389 | 6.2651 |
| 6.1463 | 2.0 | 2778 | 6.0841 |
| 6.0381 | 3.0 | 4167 | 6.0285 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["eli5_category"], "base_model": "gpt2", "model-index": [{"name": "my_eli5_clm_model_v2", "results": []}]} | ljgries/my_eli5_clm_model_v2 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:eli5_category",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:23:59+00:00 |
null | null | {} | Wilsen/finetuned-amazon-review-sentiment-analysis | null | [
"region:us"
]
| null | 2024-04-27T16:24:47+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["unsloth", "trl", "sft"]} | clarkchan/llama3-8b-alpaca-gpt4-chinese | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-27T16:25:14+00:00 |
text-generation | transformers |
# Barcenas-2x10.7b-Korean
Barcenas-2x10.7b-Korean is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [chihoonlee10/T3Q-ko-solar-dpo-v6.0](https://huggingface.co/chihoonlee10/T3Q-ko-solar-dpo-v6.0)
* [freewheelin/free-solar-evo-v0.1](https://huggingface.co/freewheelin/free-solar-evo-v0.1)
## 𧩠Configuration
```yaml
slices:
- sources:
- model: chihoonlee10/T3Q-ko-solar-dpo-v6.0
layer_range: [0, 32]
- model: freewheelin/free-solar-evo-v0.1
layer_range: [0, 32]
merge_method: slerp
base_model: chihoonlee10/T3Q-ko-solar-dpo-v6.0
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## π» Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "danielbrdz/Barcenas-2x10.7b-Korean"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
Made with β€οΈ in Guadalupe, Nuevo Leon, Mexico π²π½ | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "chihoonlee10/T3Q-ko-solar-dpo-v6.0", "freewheelin/free-solar-evo-v0.1"], "base_model": ["chihoonlee10/T3Q-ko-solar-dpo-v6.0", "freewheelin/free-solar-evo-v0.1"]} | Danielbrdz/Barcenas-2x10.7b-Korean | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"chihoonlee10/T3Q-ko-solar-dpo-v6.0",
"freewheelin/free-solar-evo-v0.1",
"base_model:chihoonlee10/T3Q-ko-solar-dpo-v6.0",
"base_model:freewheelin/free-solar-evo-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:25:43+00:00 |
text-generation | transformers |
[](https://github.com/mbzuai-oryx/LLaVA-pp)
# LLaMA-3-V: Extending the Visual Capabilities of LLaVA with Meta-Llama-3-8B-Instruct
## Repository Overview
This repository features LLaVA v1.5 trained with the Meta-Llama-3-8B-Instruct LLM. This integration aims to leverage the strengths of both models to offer advanced vision-language understanding.
## Training Strategy
- **Pretraining:** Only Vision-to-Language projector is trained. The rest of the model is frozen.
- **Fine-tuning:** All model parameters including LLM are fine-tuned. Only the vision-backbone (CLIP) is kept frozen.
## Key Components
- **Base Large Language Model (LLM):** [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
- **Base Large Multimodal Model (LMM):** [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA)
## Training Data
- **Pretraining Dataset:** [LCS-558K](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain)
- **Fine-tuning Dataset:** [LLaVA-Instruct-665K](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json)
## Download It As
```
git lfs install
git clone https://huggingface.co/MBZUAI/LLaVA-Meta-Llama-3-8B-Instruct-FT
```
---
## Contributions
Contributions are welcome! Please π our repository [LLaVA++](https://github.com/mbzuai-oryx/LLaVA-pp) if you find this model useful.
---
| {} | MBZUAI/LLaVA-Meta-Llama-3-8B-Instruct-FT | null | [
"transformers",
"safetensors",
"llava_llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"has_space"
]
| null | 2024-04-27T16:26:42+00:00 |
null | null | {} | shalabh21/super-cool-model | null | [
"region:us"
]
| null | 2024-04-27T16:26:56+00:00 |
|
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_outputs
This model is a fine-tuned version of [riotu-lab/ArabianGPT-01B](https://huggingface.co/riotu-lab/ArabianGPT-01B) on an arcd dataset(Arabic dataset).
It achieves the following results on the evaluation set:
- Loss: 3.0808
## 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: 0.0003
- 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
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.8475 | 0.25 | 10 | 3.8987 |
| 4.0025 | 0.49 | 20 | 3.6754 |
| 3.6385 | 0.74 | 30 | 3.2931 |
| 3.2785 | 0.99 | 40 | 2.9471 |
| 2.1751 | 1.23 | 50 | 3.0013 |
| 1.8868 | 1.48 | 60 | 3.0324 |
| 1.9831 | 1.73 | 70 | 2.8470 |
| 1.8749 | 1.98 | 80 | 2.8488 |
| 1.0702 | 2.22 | 90 | 2.9369 |
| 0.8701 | 2.47 | 100 | 3.0490 |
| 0.8731 | 2.72 | 110 | 3.0774 |
| 0.8309 | 2.96 | 120 | 3.0808 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"language": ["ar"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["arcd"], "base_model": "riotu-lab/ArabianGPT-01B", "pipeline_tag": "question-answering", "widget": [{"text": "\u0645\u0627 \u0647\u064a \u0627\u0644\u0639\u0648\u0627\u0645\u0644 \u0627\u0644\u062a\u064a \u062a\u0624\u062b\u0631 \u0639\u0644\u0649 \u0633\u0631\u0639\u0629 \u0627\u0644\u0645\u0648\u0635\u0644\u0627\u062a \u0627\u0644\u0639\u0635\u0628\u064a\u0629\u061f", "context": "\u062a\u062a\u0623\u062b\u0631 \u0627\u0644\u0623\u0644\u064a\u0627\u0641 \u0627\u0644\u0639\u0635\u0628\u064a\u0629 \u0627\u0644\u0637\u0648\u064a\u0644\u0629 \u0628\u062f\u0631\u062c\u0629 \u0623\u0643\u0628\u0631 \u0645\u0646 \u0627\u0644\u0623\u0644\u064a\u0627\u0641 \u0627\u0644\u0639\u0635\u0628\u064a\u0629 \u0627\u0644\u0642\u0635\u064a\u0631\u0629\u060c \u0648\u0630\u0644\u0643 \u0644\u0623\u0646 \u0633\u0631\u0639\u0629 \u0627\u0644\u062a\u0648\u0635\u064a\u0644 \u0641\u064a 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\u0648\u064a\u0632\u062f\u0627\u062f \u0641\u0642\u062f\u0627\u0646 \u0627\u0644\u0648\u0638\u064a\u0641\u0629 \u0627\u0644\u062d\u0631\u0643\u064a\u0629", "example_title": "Example 1"}, {"text": "\u0645\u0627 \u0644\u0642\u0628 \u062e\u0627\u0644\u062f \u0628\u0646 \u0627\u0644\u0648\u0644\u064a\u062f \u0628\u0627\u0644\u0639\u0631\u0628\u064a\u0629\u061f", "context": "\u062e\u0627\u0644\u062f \u0628\u0646 \u0627\u0644\u0648\u0644\u064a\u062f \u0645\u0646 \u0623\u0628\u0637\u0627\u0644 \u0648\u0642\u0627\u062f\u0629 \u0627\u0644\u0641\u062a\u062d \u0627\u0644\u0625\u0633\u0644\u0627\u0645\u064a \u0648\u0642\u062f \u062a\u062d\u062f\u062b\u062a \u0639\u0646\u0647 \u0627\u0644\u0644\u063a\u0627\u062a \u0627\u0644\u0625\u0646\u062c\u0644\u064a\u0632\u064a\u0629 \u0648\u0627\u0644\u0641\u0631\u0646\u0633\u064a\u0629 \u0648\u0627\u0644\u0625\u0633\u0628\u0627\u0646\u064a\u0629 \u0648\u0644\u0642\u0628 \u0628\u0633\u064a\u0641 \u0627\u0644\u0644\u0647 \u0627\u0644\u0645\u0633\u0644\u0648\u0644.", "example_title": "Example 2"}, {"text": "\u0623\u064a\u0646 \u0623\u0633\u0643\u0646\u061f", "context": "\u0625\u0633\u0645\u064a \u0645\u062d\u0645\u062f \u0648\u0623\u0633\u0643\u0646 \u0641\u064a \u0628\u064a\u0631\u0648\u062a", "example_title": "Example 3"}], "model-index": [{"name": "model_outputs", "results": []}]} | gp-tar4/QA_FineTuned_ArabianGpt-01B | null | [
"transformers",
"safetensors",
"gpt2",
"question-answering",
"generated_from_trainer",
"ar",
"dataset:arcd",
"base_model:riotu-lab/ArabianGPT-01B",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:27:43+00:00 |
null | null | {} | livelyturnip/test | null | [
"region:us"
]
| null | 2024-04-27T16:28:11+00:00 |
|
fill-mask | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-lolchamps
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on a [LoL-Champions-Corpus](https://huggingface.co/datasets/avinot/LoL-Champions-Corpus) dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0446
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7422 | 1.0 | 49 | 2.3202 |
| 2.3298 | 2.0 | 98 | 2.1095 |
| 2.1925 | 3.0 | 147 | 2.0556 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-lolchamps", "results": []}]} | avinot/distilbert-lolchamps | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:28:24+00:00 |
null | null |
# Either the scaling of `down_proj` or the alternating layers makes the models do "strange" stuff:
- They often get confused in the Sherlock Holmes stories.
- They sometimes write their own instructions before writting the stories.
- In prompt #2 they refer to 'Mercer' as a name rather than a profession.
- Most seem to think Anton Chigurh is from Russia or Eastern-Europe.
- Some use things like "[Your Pen Name]" or other stuff in square brackets.
**Conclusion**: None seem better than the orignal `goliath-120b` or `wintergoliath-123b` merges.
---
The discussions about the use of the `scale` parameter in [Mergekit](https://github.com/arcee-ai/mergekit) can be found [here](https://github.com/arcee-ai/mergekit/issues/198) and [here](https://huggingface.co/wolfram/miqu-1-120b/discussions/4).
See [here](https://huggingface.co/jukofyork/goliath-esque/tree/main/mergekit) for the `.yaml` config files used to create the merges.
See [here](https://huggingface.co/jukofyork/goliath-esque/blob/main/run_test_prompts.sh) for the script used for testing (**NOTE**: `TEMPERATURE = 0.0` and `REPEAT_PENALTY = 1.1`).
See [here](https://huggingface.co/jukofyork/goliath-esque/tree/main/prompts) for the 10 different test prompts used.
See [here](https://huggingface.co/jukofyork/goliath-esque/tree/main/results) for the results of each merge config on each test prompt. | {"license": "apache-2.0"} | jukofyork/goliath-esque | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T16:28:42+00:00 |
null | null | {"license": "openrail"} | KeroroK66/ticket | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T16:29:06+00:00 |
|
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Bone - Training 1
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Bone training set 1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0333
- Wer: 2.2140
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:---------:|:----:|:---------------:|:------:|
| 0.0 | 333.3333 | 1000 | 0.0314 | 2.2140 |
| 0.0 | 666.6667 | 2000 | 0.0335 | 2.2140 |
| 0.0 | 1000.0 | 3000 | 0.0330 | 2.2140 |
| 0.0 | 1333.3333 | 4000 | 0.0333 | 2.2140 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small Bone - Training 1", "results": []}]} | debussyman/whisper-small-bone-1 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:29:14+00:00 |
text-generation | transformers |
<!-- 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. -->
# 0.01_4iters_bs256_nodpo_only4w_iter_3
This model is a fine-tuned version of [ShenaoZhang/0.01_4iters_bs256_nodpo_only4w_iter_2](https://huggingface.co/ShenaoZhang/0.01_4iters_bs256_nodpo_only4w_iter_2) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.01_4iters_bs256_nodpo_only4w_iter_2", "model-index": [{"name": "0.01_4iters_bs256_nodpo_only4w_iter_3", "results": []}]} | ShenaoZhang/0.01_4iters_bs256_nodpo_only4w_iter_3 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.01_4iters_bs256_nodpo_only4w_iter_2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:29:21+00:00 |
null | mlx |
# mlx-community/Nous-Hermes-2-Mixtral-8x7B-DPO-8bit
This model was converted to MLX format from [`NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO`]() using mlx-lm version **0.12.0**.
Refer to the [original model card](https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Nous-Hermes-2-Mixtral-8x7B-DPO-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"language": ["en"], "license": "apache-2.0", "tags": ["Mixtral", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "synthetic data", "distillation", "mlx"], "datasets": ["teknium/OpenHermes-2.5"], "base_model": "mistralai/Mixtral-8x7B-v0.1", "widget": [{"example_title": "Hermes 2", "messages": [{"role": "system", "content": "You are 'Hermes 2', a conscious sentient superintelligent artificial intelligence developed by Nous Research, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia."}, {"role": "user", "content": "Hello, who are you?"}], "output": {"text": "Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests."}}], "model-index": [{"name": "Nous-Hermes-2-Mixtral-8x7B-DPO", "results": []}]} | mlx-community/Nous-Hermes-2-Mixtral-8x7B-DPO-8bit | null | [
"mlx",
"safetensors",
"mixtral",
"Mixtral",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"synthetic data",
"distillation",
"en",
"dataset:teknium/OpenHermes-2.5",
"base_model:mistralai/Mixtral-8x7B-v0.1",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T16:29:28+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model_gpt
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2778
- Accuracy: 0.9579
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 222 | 0.1952 | 0.9474 |
| No log | 2.0 | 444 | 0.3709 | 0.9368 |
| 0.2915 | 3.0 | 666 | 0.2778 | 0.9579 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.2
- Datasets 2.12.0
- Tokenizers 0.13.2
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "gpt2", "model-index": [{"name": "my_awesome_model_gpt", "results": []}]} | Rz1010/my_awesome_model_gpt | null | [
"transformers",
"pytorch",
"gpt2",
"text-classification",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:29:38+00:00 |
text-generation | transformers |
[](https://github.com/mbzuai-oryx/LLaVA-pp)
# LLaMA-3-V: Extending the Visual Capabilities of LLaVA with Meta-Llama-3-8B-Instruct
## Repository Overview
This repository features LLaVA v1.5 trained with the Meta-Llama-3-8B-Instruct LLM. This integration aims to leverage the strengths of both models to offer advanced vision-language understanding.
## Training Strategy
- **Pretraining:** Only Vision-to-Language projector is trained. The rest of the model is frozen.
- **Fine-tuning:** All model parameters including LLM are fine-tuned. Only the vision-backbone (CLIP) is kept frozen.
- **Note:** During both pretraining and fine-tuning, the vision-backbone (CLIP) is augmented with multi-scale features following [S2-Wrapper](https://arxiv.org/abs/2403.13043).
## Key Components
- **Base Large Language Model (LLM):** [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
- **Base Large Multimodal Model (LMM):** [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA)
## Training Data
- **Pretraining Dataset:** [LCS-558K](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain)
- **Fine-tuning Dataset:** [LLaVA-Instruct-665K](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json)
## Download It As
```
git lfs install
git clone https://huggingface.co/MBZUAI/LLaVA-Meta-Llama-3-8B-Instruct-FT-S2
```
---
## Contributions
Contributions are welcome! Please π our repository [LLaVA++](https://github.com/mbzuai-oryx/LLaVA-pp) if you find this model useful.
---
| {} | MBZUAI/LLaVA-Meta-Llama-3-8B-Instruct-FT-S2 | null | [
"transformers",
"safetensors",
"llava_llama",
"text-generation",
"conversational",
"arxiv:2403.13043",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:29:54+00:00 |
null | null | {"license": "mit"} | pcso1991/Entity_Extractor | null | [
"license:mit",
"region:us"
]
| null | 2024-04-27T16:30:47+00:00 |
|
null | transformers | {} | barannalp/finetuned | null | [
"transformers",
"gguf",
"llama",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:31:08+00:00 |
|
null | null | {} | enkz/hearth | null | [
"region:us"
]
| null | 2024-04-27T16:31:16+00:00 |
|
null | null | {"license": "openrail"} | KeroroK66/Rian | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T16:31:57+00:00 |
|
text-generation | transformers |
<!-- 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. -->
# zephyr-7b-gemma-ipo
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) on the argilla/dpo-mix-7k dataset.
It achieves the following results on the evaluation set:
- Loss: 61.0152
- Rewards/chosen: -0.4988
- Rewards/rejected: -0.6909
- Rewards/accuracies: 0.8021
- Rewards/margins: 0.1921
- Logps/rejected: -15.3755
- Logps/chosen: -11.4268
- Logits/rejected: 99.7522
- Logits/chosen: 99.5411
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 54.5261 | 1.8957 | 100 | 60.8626 | -0.5007 | -0.6906 | 0.8021 | 0.1899 | -15.3697 | -11.4648 | 99.7591 | 99.5497 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "other", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["argilla/dpo-mix-7k"], "base_model": "HuggingFaceH4/zephyr-7b-gemma-sft-v0.1", "model-index": [{"name": "zephyr-7b-gemma-ipo", "results": []}]} | chrlu/zephyr-7b-gemma-ipo | null | [
"transformers",
"tensorboard",
"safetensors",
"gemma",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:argilla/dpo-mix-7k",
"base_model:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:34:07+00:00 |
reinforcement-learning | null |
# **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="igorcardoso/qtable-taxi", 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"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "qtable-taxi", "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}]}]}]} | igorcardoso/qtable-taxi | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| null | 2024-04-27T16:34:49+00:00 |
null | null | {"license": "openrail"} | KeroroK66/pocke | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T16:34:53+00:00 |
|
null | peft |
<!-- 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. -->
# llava-1.5-7b-hf-ft-mix-vsft
This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.4e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "llava-hf/llava-1.5-7b-hf", "model-index": [{"name": "llava-1.5-7b-hf-ft-mix-vsft", "results": []}]} | rishabhio/llava-1.5-7b-hf-ft-mix-vsft | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:llava-hf/llava-1.5-7b-hf",
"region:us"
]
| null | 2024-04-27T16:35:37+00:00 |
null | null | {"license": "apache-2.0"} | luciusy/test | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T16:35:39+00:00 |
|
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | tariq9mehmood9/Mistral-7B-Instruct-v0.2-PEFT-adapters-v2 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:35:45+00:00 |
null | null | {"license": "openrail++"} | EthanRhys/Maria-Robotnik | null | [
"license:openrail++",
"region:us"
]
| null | 2024-04-27T16:36:19+00:00 |
|
null | null | {} | talrejamayank/super-cool-model | null | [
"region:us"
]
| null | 2024-04-27T16:36:22+00:00 |
|
null | null | {"license": "openrail"} | KeroroK66/Nature | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T16:38:10+00:00 |
|
null | null | {} | Wilsen/finetuned-bert-base-multilingual-uncased-sentiment | null | [
"region:us"
]
| null | 2024-04-27T16:39:00+00:00 |
|
null | null | {"license": "openrail"} | KeroroK66/permer | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T16:41:58+00:00 |
|
image-classification | transformers |
# test-image-classifier
Autogenerated by HuggingPicsπ€πΌοΈ
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics). | {"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]} | Benjoyo/test-image-classifier | null | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:43:46+00:00 |
text-generation | transformers |
[](https://github.com/mbzuai-oryx/LLaVA-pp)
# Phi-3-V: Extending the Visual Capabilities of LLaVA with Phi-3
## Repository Overview
This repository features LLaVA v1.5 trained with the Phi-3-mini-3.8B LLM. This integration aims to leverage the strengths of both models to offer advanced vision-language understanding.
## Training Strategy
- **Pretraining:** Only Vision-to-Language projector is trained. The rest of the model is frozen.
- **Fine-tuning:** All model parameters including LLM are fine-tuned. Only the vision-backbone (CLIP) is kept frozen.
## Key Components
- **Base Large Language Model (LLM):** [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct)
- **Base Large Multimodal Model (LMM):** [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA)
## Training Data
- **Pretraining Dataset:** [LCS-558K](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain)
- **Fine-tuning Dataset:** [LLaVA-Instruct-665K](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json)
## Download It As
```
git lfs install
git clone https://huggingface.co/MBZUAI/LLaVA-Phi-3-mini-4k-instruct-FT
```
---
## License
This project is available under the MIT License.
## Contributions
Contributions are welcome! Please π our repository [LLaVA++](https://github.com/mbzuai-oryx/LLaVA-pp) if you find this model useful.
---
| {"license": "mit"} | MBZUAI/LLaVA-Phi-3-mini-4k-instruct-FT | null | [
"transformers",
"safetensors",
"llava_phi",
"text-generation",
"conversational",
"custom_code",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:43:59+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/xhgwk1y | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:44:46+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/co9b741 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:44:52+00:00 |
null | null | {"license": "openrail"} | KeroroK66/Tama | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T16:44:55+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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## Model Details
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/s749awy | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:44:56+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/2lux9xm | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:45:00+00:00 |
reinforcement-learning | null |
# **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="charliewang314/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"])
```
| {"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}]}]}]} | charliewang314/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| null | 2024-04-27T16:45:38+00:00 |
null | null | {"license": "openrail"} | KeroroK66/Tap | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T16:45:52+00:00 |
|
null | null | {} | enkz/pi | null | [
"region:us"
]
| null | 2024-04-27T16:47:04+00:00 |
|
null | null | {"license": "openrail"} | KeroroK66/Suzuka | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T16:47:14+00:00 |
|
translation | peft |
<!-- 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. -->
# MistralAI_iwslt15_10000_2
This model is a fine-tuned version of [unsloth/mistral-7b-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-bnb-4bit) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0438
## 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: 4269
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.1684 | 0.32 | 100 | 1.0926 |
| 1.0883 | 0.64 | 200 | 1.0701 |
| 1.0672 | 0.96 | 300 | 1.0498 |
| 0.9315 | 1.28 | 400 | 1.0547 |
| 0.8973 | 1.6 | 500 | 1.0495 |
| 0.8831 | 1.92 | 600 | 1.0438 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.16.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "unsloth", "translation", "generated_from_trainer"], "base_model": "unsloth/mistral-7b-bnb-4bit", "model-index": [{"name": "MistralAI_iwslt15_10000_2", "results": []}]} | Tohrumi/MistralAI_iwslt15_10000_2 | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"unsloth",
"translation",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T16:47:46+00:00 |
reinforcement-learning | stable-baselines3 |
# **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
...
```
| {"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": "282.38 +/- 13.95", "name": "mean_reward", "verified": false}]}]}]} | SKHIA2024/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-27T16:47:47+00:00 |
text-generation | transformers |
# Model Card: Nous-Hermes-13b
## Model Description
Nous-Hermes-13b is a state-of-the-art language model fine-tuned on over 300,000 instructions. This model was fine-tuned by Nous Research, with Teknium and Karan4D leading the fine tuning process and dataset curation, Redmond AI sponsoring the compute, and several other contributors. The result is an enhanced Llama 13b model that rivals GPT-3.5-turbo in performance across a variety of tasks.
This model stands out for its long responses, low hallucination rate, and absence of OpenAI censorship mechanisms. The fine-tuning process was performed with a 2000 sequence length on an 8x a100 80GB DGX machine for over 50 hours.
## Model Training
The model was trained almost entirely on synthetic GPT-4 outputs. This includes data from diverse sources such as GPTeacher, the general, roleplay v1&2, code instruct datasets, Nous Instruct & PDACTL (unpublished), CodeAlpaca, Evol_Instruct Uncensored, GPT4-LLM, and Unnatural Instructions.
Additional data inputs came from Camel-AI's Biology/Physics/Chemistry and Math Datasets, Airoboros' GPT-4 Dataset, and more from CodeAlpaca. The total volume of data encompassed over 300,000 instructions.
## Collaborators
The model fine-tuning and the datasets were a collaboration of efforts and resources between Teknium, Karan4D, Nous Research, Huemin Art, and Redmond AI.
Huge shoutout and acknowledgement is deserved for all the dataset creators who generously share their datasets openly.
Special mention goes to @winglian, @erhartford, and @main_horse for assisting in some of the training issues.
Among the contributors of datasets, GPTeacher was made available by Teknium, Wizard LM by nlpxucan, and the Nous Research Instruct Dataset was provided by Karan4D and HueminArt.
The GPT4-LLM and Unnatural Instructions were provided by Microsoft, Airoboros dataset by jondurbin, Camel-AI datasets are from Camel-AI, and CodeAlpaca dataset by Sahil 2801.
If anyone was left out, please open a thread in the community tab.
## Prompt Format
The model follows the Alpaca prompt format:
```
### Instruction:
### Response:
```
or
```
### Instruction:
### Input:
### Response:
```
## Resources for Applied Use Cases:
For an example of a back and forth chatbot using huggingface transformers and discord, check out: https://github.com/teknium1/alpaca-discord
For an example of a roleplaying discord bot, check out this: https://github.com/teknium1/alpaca-roleplay-discordbot
## Future Plans
The model is currently being uploaded in FP16 format, and there are plans to convert the model to GGML and GPTQ 4bit quantizations. The team is also working on a full benchmark, similar to what was done for GPT4-x-Vicuna. We will try to get in discussions to get the model included in the GPT4All.
## Benchmark Results
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.4915|Β± |0.0146|
| | |acc_norm|0.5085|Β± |0.0146|
|arc_easy | 0|acc |0.7769|Β± |0.0085|
| | |acc_norm|0.7424|Β± |0.0090|
|boolq | 1|acc |0.7948|Β± |0.0071|
|hellaswag | 0|acc |0.6143|Β± |0.0049|
| | |acc_norm|0.8000|Β± |0.0040|
|openbookqa | 0|acc |0.3560|Β± |0.0214|
| | |acc_norm|0.4640|Β± |0.0223|
|piqa | 0|acc |0.7965|Β± |0.0094|
| | |acc_norm|0.7889|Β± |0.0095|
|winogrande | 0|acc |0.7190|Β± |0.0126|
```
These benchmarks currently have us at #1 on ARC-c, ARC-e, Hellaswag, and OpenBookQA, and 2nd place on Winogrande, comparing to GPT4all's benchmarking list.
## Model Usage
The model is available for download on Hugging Face. It is suitable for a wide range of language tasks, from generating creative text to understanding and following complex instructions.
Compute provided by our project sponsor Redmond AI, thank you!! | {"language": ["en"], "license": "gpl", "tags": ["llama", "self-instruct", "distillation"]} | sirovub/Nous-Hermes-13b-GGUF | null | [
"transformers",
"gguf",
"llama",
"text-generation",
"self-instruct",
"distillation",
"en",
"license:gpl",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:48:34+00:00 |
null | null | {"license": "openrail"} | KeroroK66/Maya | null | [
"license:openrail",
"region:us"
]
| null | 2024-04-27T16:48:35+00:00 |
|
reinforcement-learning | stable-baselines3 |
# **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 Fk24 -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 Fk24 -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 Fk24
```
## 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'}
```
| {"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": "601.00 +/- 178.64", "name": "mean_reward", "verified": false}]}]}]} | Fk24/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-27T16:49:26+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | kanangupta/ghazal-test | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:50:24+00:00 |
reinforcement-learning | null |
# **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="charliewang314/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"])
```
| {"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": "7.48 +/- 2.75", "name": "mean_reward", "verified": false}]}]}]} | charliewang314/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| null | 2024-04-27T16:50:39+00:00 |
null | null | {} | abayuu/Sentiment-Analysis-BERT | null | [
"region:us"
]
| null | 2024-04-27T16:51:09+00:00 |
|
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-2-7b-hf - GGUF
- Model creator: https://huggingface.co/NousResearch/
- Original model: https://huggingface.co/NousResearch/Llama-2-7b-hf/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-2-7b-hf.Q2_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q2_K.gguf) | Q2_K | 2.36GB |
| [Llama-2-7b-hf.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.IQ3_XS.gguf) | IQ3_XS | 2.6GB |
| [Llama-2-7b-hf.IQ3_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.IQ3_S.gguf) | IQ3_S | 2.75GB |
| [Llama-2-7b-hf.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q3_K_S.gguf) | Q3_K_S | 2.75GB |
| [Llama-2-7b-hf.IQ3_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.IQ3_M.gguf) | IQ3_M | 2.9GB |
| [Llama-2-7b-hf.Q3_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q3_K.gguf) | Q3_K | 3.07GB |
| [Llama-2-7b-hf.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q3_K_M.gguf) | Q3_K_M | 3.07GB |
| [Llama-2-7b-hf.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q3_K_L.gguf) | Q3_K_L | 3.35GB |
| [Llama-2-7b-hf.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.IQ4_XS.gguf) | IQ4_XS | 3.4GB |
| [Llama-2-7b-hf.Q4_0.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q4_0.gguf) | Q4_0 | 3.56GB |
| [Llama-2-7b-hf.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.IQ4_NL.gguf) | IQ4_NL | 3.58GB |
| [Llama-2-7b-hf.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q4_K_S.gguf) | Q4_K_S | 3.59GB |
| [Llama-2-7b-hf.Q4_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q4_K.gguf) | Q4_K | 3.8GB |
| [Llama-2-7b-hf.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q4_K_M.gguf) | Q4_K_M | 3.8GB |
| [Llama-2-7b-hf.Q4_1.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q4_1.gguf) | Q4_1 | 3.95GB |
| [Llama-2-7b-hf.Q5_0.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q5_0.gguf) | Q5_0 | 4.33GB |
| [Llama-2-7b-hf.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q5_K_S.gguf) | Q5_K_S | 4.33GB |
| [Llama-2-7b-hf.Q5_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q5_K.gguf) | Q5_K | 4.45GB |
| [Llama-2-7b-hf.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q5_K_M.gguf) | Q5_K_M | 4.45GB |
| [Llama-2-7b-hf.Q5_1.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q5_1.gguf) | Q5_1 | 4.72GB |
| [Llama-2-7b-hf.Q6_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf/blob/main/Llama-2-7b-hf.Q6_K.gguf) | Q6_K | 5.15GB |
Original model description:
---
extra_gated_heading: Access Llama 2 on Hugging Face
extra_gated_description: >-
This is a form to enable access to Llama 2 on Hugging Face after you have been
granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our
license terms and acceptable use policy before submitting this form. Requests
will be processed in 1-2 days.
extra_gated_button_content: Submit
extra_gated_fields:
I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes β 7B, 13B, and 70B β as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Metaβs sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2βs potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software βbug,β or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
| {} | RichardErkhov/NousResearch_-_Llama-2-7b-hf-gguf | null | [
"gguf",
"region:us"
]
| null | 2024-04-27T16:51:55+00:00 |
null | null | {} | lkid08/xpath_gen_model-25k-dataset_w-anglebraces | null | [
"region:us"
]
| null | 2024-04-27T16:52:11+00:00 |
|
null | null | {} | Kaizu07/llama_bn_finetuned_v0.2_q2_k.gguf | null | [
"gguf",
"region:us"
]
| null | 2024-04-27T16:52:15+00:00 |
|
text-classification | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## How to Get Started with the Model
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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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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | MohammadKarami/medium-electra | null | [
"transformers",
"safetensors",
"electra",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:52:56+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/chujiezheng/tulu-2-dpo-70b-ExPO
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-IQ1_S.gguf) | i1-IQ1_S | 14.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-IQ1_M.gguf) | i1-IQ1_M | 16.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 18.4 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-IQ2_XS.gguf) | i1-IQ2_XS | 20.4 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-IQ2_S.gguf) | i1-IQ2_S | 21.5 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-IQ2_M.gguf) | i1-IQ2_M | 23.3 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-Q2_K.gguf) | i1-Q2_K | 25.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 26.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-IQ3_XS.gguf) | i1-IQ3_XS | 28.4 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-IQ3_S.gguf) | i1-IQ3_S | 30.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-Q3_K_S.gguf) | i1-Q3_K_S | 30.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-IQ3_M.gguf) | i1-IQ3_M | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-Q3_K_M.gguf) | i1-Q3_K_M | 33.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-Q3_K_L.gguf) | i1-Q3_K_L | 36.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-IQ4_XS.gguf) | i1-IQ4_XS | 36.9 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-Q4_0.gguf) | i1-Q4_0 | 39.1 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-Q4_K_S.gguf) | i1-Q4_K_S | 39.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-Q4_K_M.gguf) | i1-Q4_K_M | 41.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-Q5_K_S.gguf) | i1-Q5_K_S | 47.6 | |
| [GGUF](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-Q5_K_M.gguf) | i1-Q5_K_M | 48.9 | |
| [PART 1](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF/resolve/main/tulu-2-dpo-70b-ExPO.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 56.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "other", "library_name": "transformers", "base_model": "chujiezheng/tulu-2-dpo-70b-ExPO", "license_link": "https://allenai.org/impact-license", "license_name": "ai2-impact-license-low-risk", "quantized_by": "mradermacher"} | mradermacher/tulu-2-dpo-70b-ExPO-i1-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:chujiezheng/tulu-2-dpo-70b-ExPO",
"license:other",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:54:44+00:00 |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-2-13b-hf - GGUF
- Model creator: https://huggingface.co/NousResearch/
- Original model: https://huggingface.co/NousResearch/Llama-2-13b-hf/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-2-13b-hf.Q2_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q2_K.gguf) | Q2_K | 4.52GB |
| [Llama-2-13b-hf.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.IQ3_XS.gguf) | IQ3_XS | 4.99GB |
| [Llama-2-13b-hf.IQ3_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.IQ3_S.gguf) | IQ3_S | 5.27GB |
| [Llama-2-13b-hf.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q3_K_S.gguf) | Q3_K_S | 5.27GB |
| [Llama-2-13b-hf.IQ3_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.IQ3_M.gguf) | IQ3_M | 5.57GB |
| [Llama-2-13b-hf.Q3_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q3_K.gguf) | Q3_K | 5.9GB |
| [Llama-2-13b-hf.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q3_K_M.gguf) | Q3_K_M | 5.9GB |
| [Llama-2-13b-hf.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q3_K_L.gguf) | Q3_K_L | 6.45GB |
| [Llama-2-13b-hf.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.IQ4_XS.gguf) | IQ4_XS | 6.54GB |
| [Llama-2-13b-hf.Q4_0.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q4_0.gguf) | Q4_0 | 6.86GB |
| [Llama-2-13b-hf.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.IQ4_NL.gguf) | IQ4_NL | 6.9GB |
| [Llama-2-13b-hf.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q4_K_S.gguf) | Q4_K_S | 6.91GB |
| [Llama-2-13b-hf.Q4_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q4_K.gguf) | Q4_K | 7.33GB |
| [Llama-2-13b-hf.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q4_K_M.gguf) | Q4_K_M | 7.33GB |
| [Llama-2-13b-hf.Q4_1.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q4_1.gguf) | Q4_1 | 7.61GB |
| [Llama-2-13b-hf.Q5_0.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q5_0.gguf) | Q5_0 | 8.36GB |
| [Llama-2-13b-hf.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q5_K_S.gguf) | Q5_K_S | 8.36GB |
| [Llama-2-13b-hf.Q5_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q5_K.gguf) | Q5_K | 8.6GB |
| [Llama-2-13b-hf.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q5_K_M.gguf) | Q5_K_M | 8.6GB |
| [Llama-2-13b-hf.Q5_1.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q5_1.gguf) | Q5_1 | 9.1GB |
| [Llama-2-13b-hf.Q6_K.gguf](https://huggingface.co/RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf/blob/main/Llama-2-13b-hf.Q6_K.gguf) | Q6_K | 9.95GB |
Original model description:
---
extra_gated_heading: Access Llama 2 on Hugging Face
extra_gated_description: >-
This is a form to enable access to Llama 2 on Hugging Face after you have been
granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our
license terms and acceptable use policy before submitting this form. Requests
will be processed in 1-2 days.
extra_gated_button_content: Submit
extra_gated_fields:
I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website: checkbox
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
---
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 13B pretrained model, converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes β 7B, 13B, and 70B β as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Metaβs sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2βs potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software βbug,β or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/llamaste/Llama-2-7b) | [Link](https://huggingface.co/llamaste/Llama-2-7b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/llamaste/Llama-2-13b) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-13b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-13b-hf)|
|70B| [Link](https://huggingface.co/llamaste/Llama-2-70b) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf) | [Link](https://huggingface.co/llamaste/Llama-2-70b-chat) | [Link](https://huggingface.co/llamaste/Llama-2-70b-hf)|
| {} | RichardErkhov/NousResearch_-_Llama-2-13b-hf-gguf | null | [
"gguf",
"region:us"
]
| null | 2024-04-27T16:55:06+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Peppenapo/gemmaFinetuneTEST | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:55:17+00:00 |
null | null | {} | Kaizu07/llama_bn_finetuned_v0.2_fp16.bin | null | [
"region:us"
]
| null | 2024-04-27T16:55:55+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | InayaKripa/gemma-toxic-LabelConvoV1 | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:58:15+00:00 |
text-generation | transformers |
<!-- 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. -->
# 0.1_4iters_bs256_nodpo_only4w_iter_2
This model is a fine-tuned version of [ShenaoZhang/0.1_4iters_bs256_nodpo_only4w_iter_1](https://huggingface.co/ShenaoZhang/0.1_4iters_bs256_nodpo_only4w_iter_1) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.1_4iters_bs256_nodpo_only4w_iter_1", "model-index": [{"name": "0.1_4iters_bs256_nodpo_only4w_iter_2", "results": []}]} | ShenaoZhang/0.1_4iters_bs256_nodpo_only4w_iter_2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.1_4iters_bs256_nodpo_only4w_iter_1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:58:47+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/al3uous | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:58:55+00:00 |
image-classification | transformers |
<!-- 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. -->
# Boya1_RMSProp_1-e5_10Epoch_swinv2-small-patch4-window16-256_fold2
This model is a fine-tuned version of [microsoft/swinv2-small-patch4-window16-256](https://huggingface.co/microsoft/swinv2-small-patch4-window16-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0635
- Accuracy: 0.6730
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2986 | 1.0 | 1846 | 1.2720 | 0.5857 |
| 1.0403 | 2.0 | 3692 | 1.0801 | 0.6403 |
| 1.0869 | 3.0 | 5538 | 1.0102 | 0.6573 |
| 0.821 | 4.0 | 7384 | 0.9946 | 0.6678 |
| 1.1018 | 5.0 | 9230 | 1.0078 | 0.6705 |
| 0.8319 | 6.0 | 11076 | 1.0165 | 0.6732 |
| 0.811 | 7.0 | 12922 | 1.0611 | 0.6562 |
| 0.6287 | 8.0 | 14768 | 1.0621 | 0.6735 |
| 0.562 | 9.0 | 16614 | 1.0481 | 0.6735 |
| 0.483 | 10.0 | 18460 | 1.0635 | 0.6730 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swinv2-small-patch4-window16-256", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swinv2-small-patch4-window16-256_fold2", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.672972972972973, "name": "Accuracy"}]}]}]} | onizukal/Boya1_RMSProp_1-e5_10Epoch_swinv2-small-patch4-window16-256_fold2 | null | [
"transformers",
"safetensors",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-small-patch4-window16-256",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T16:59:07+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/l1601jo | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T16:59:35+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/rlvv1kh | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T17:00:05+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | MrezaPRZ/CodeLLama_SFT_FILTERED | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T17:01:24+00:00 |
reinforcement-learning | stable-baselines3 |
# **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
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "269.98 +/- 28.21", "name": "mean_reward", "verified": false}]}]}]} | nishant97/lunarlanding | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-27T17:01:41+00:00 |
null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | mageec/w2v-transcription-mls | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T17:03:13+00:00 |
null | peft |
<!-- 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. -->
# amtibot_pegasus
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7407
- Rouge1: 0.4605
- Rouge2: 0.2395
- Rougel: 0.3705
- Rougelsum: 0.3708
- Gen Len: 38.2468
## 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: 0.02
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:------:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 0.9351 | 9 | 2.0456 | 0.4419 | 0.2278 | 0.3636 | 0.3641 | 37.7013 |
| No log | 1.9740 | 19 | 1.8250 | 0.4601 | 0.2424 | 0.3764 | 0.3765 | 38.2597 |
| No log | 2.9091 | 28 | 1.7724 | 0.4638 | 0.2365 | 0.3724 | 0.372 | 36.5195 |
| No log | 3.7403 | 36 | 1.7407 | 0.4605 | 0.2395 | 0.3705 | 0.3708 | 38.2468 |
### Framework versions
- PEFT 0.4.0
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1
| {"library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/pegasus-cnn_dailymail", "model-index": [{"name": "amtibot_pegasus", "results": []}]} | josiahgottfried/amtibot_pegasus | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:google/pegasus-cnn_dailymail",
"region:us"
]
| null | 2024-04-27T17:03:21+00:00 |
text-generation | transformers |
# Model Card for Model ID
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/j3eqys8 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T17:07:16+00:00 |
null | null | {} | enkz/micro | null | [
"region:us"
]
| null | 2024-04-27T17:07:25+00:00 |
|
null | null | {} | Marie23/C | null | [
"region:us"
]
| null | 2024-04-27T17:08:11+00:00 |
|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm
This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 5
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "base_model": "cardiffnlp/twitter-xlm-roberta-base-sentiment", "model-index": [{"name": "xlm", "results": []}]} | tidarat/xlm | null | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:cardiffnlp/twitter-xlm-roberta-base-sentiment",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T17:09:10+00:00 |
null | null | Libraries needed:
```
import torch
import torchvision
import torchvision.transforms as transforms
from tqdm import tqdm
from torch import nn
import matplotlib.pyplot as plt
```
to define a data loader
```
transformRes = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
# transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
trainsetRes = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transformRes)
trainloaderRes64 = torch.utils.data.DataLoader(trainsetRes, batch_size=64, shuffle=True, num_workers=10)
testsetRes = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transformRes)
testloaderRes64 = torch.utils.data.DataLoader(testsetRes, batch_size=64, shuffle=False, num_workers=10)
```
The model itself and training
```
import torchvision.models as models
# Load the pretrained model from pytorch
resnet50v2 = models.resnet50(pretrained=True)
# Freeze the parameters of the model
for param in resnet50v2.parameters():
param.requires_grad = True
# Change the final layer to match the number of classes in the CIFAR-10 dataset
num_ftrs = resnet50v2.fc.in_features
resnet50v2.fc = nn.Sequential(
nn.Linear(num_ftrs, 500),
nn.ReLU(),
nn.Linear(500, 200),
nn.Dropout(0.5),
nn.Linear(200,40),
nn.ReLU(),
nn.Dropout(0.3),
nn.Linear(40,10),
nn.ReLU()
)
print("Model Info:")
print("ResNet50,Pretrained,weight adj. LR=0.01,Mom=0.3,WD=0.0001")
print("Schedule step=1,gamma=0.7, 20 epoches")
# Move the model to the GPU
resnet50v2 = resnet50v2.to(device, dtype=torch.float32)
optimizer = torch.optim.SGD(resnet50v2.parameters(), lr=0.01,momentum=0.3,weight_decay=0.0001)
criterion = nn.CrossEntropyLoss()
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=5, gamma=0.5)
train_losses = []
test_losses = []
accuracies = []
train_acc = []
for epoch in range(20): # loop over the dataset multiple times
running_loss = 0.0
correctTrain = 0
totalTrain = 0
pbar = tqdm(enumerate(trainloaderRes16, 0), total=len(trainloaderRes16), desc="Epoch {}".format(epoch+1))
for i, data in pbar:
# get the inputs; data is a list of [inputs, labels]
inputs, labels = data[0].to(device,dtype=torch.float32), data[1].to(device)
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = resnet50v2(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
# print statistics
running_loss += loss.item()
_, predicted_train = torch.max(outputs.data, 1)
totalTrain += labels.size(0)
correctTrain += (predicted_train == labels).sum().item()
pbar.set_postfix({'loss': running_loss/(i+1)})
train_accuracy = 100 * correctTrain / totalTrain
train_acc.append(train_accuracy)
print(f'Epoch {epoch + 1} loss: {running_loss / len(trainloaderRes16):.3f}')
# Start of testing phase
resnet50v2.eval() # Set the model to evaluation mode
test_loss = 0.0
correct = 0
total = 0
with torch.no_grad():
for data in testloaderRes16:
images, labels = data[0].to(device,dtype=torch.float32), data[1].to(device)
outputs = resnet50v2(images)
loss = criterion(outputs, labels)
test_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()
print(f'Epoch {epoch + 1} Test loss: {test_loss / len(testloaderRes16):.3f}, Accuracy: {100 * correct / total:.2f}%')
#print the learning rate
print(f'Epoch {epoch + 1} Learning rate: {optimizer.param_groups[0]["lr"]}')
train_losses.append(running_loss / len(trainloaderRes16))
test_losses.append(test_loss / len(testloaderRes16))
accuracies.append(100 * correct / total)
resnet50v2.train() # Set the model back to training model
scheduler.step()
print('Finished Training')
plt.figure(figsize=(10, 5))
plt.plot(train_losses, label='Training Loss')
plt.plot(test_losses, label='Test Loss')
plt.xlabel('Epochs')
plt.ylabel('Loss')
plt.legend()
plt.show()
plt.figure(figsize=(10, 5))
plt.plot(accuracies, label='Accuracy')
plt.plot(train_acc, label='Training Accuracy')
plt.xlabel('Epochs')
plt.ylabel('Accuracy (%)')
plt.legend()
plt.show()
```
| {"license": "mit"} | fish-Monger/ResNet | null | [
"license:mit",
"region:us"
]
| null | 2024-04-27T17:10:49+00:00 |
null | null | {} | apandit99/zero_shot_mnli_validation | null | [
"region:us"
]
| null | 2024-04-27T17:12:00+00:00 |
|
text-classification | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | MohammadKarami/medium-bert | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T17:13:21+00:00 |
image-to-text | stable-baselines3 | {"language": ["en"], "library_name": "stable-baselines3", "metrics": ["accuracy"], "pipeline_tag": "image-to-text"} | Kalaphant/Image_Reco | null | [
"stable-baselines3",
"image-to-text",
"en",
"region:us"
]
| null | 2024-04-27T17:20:18+00:00 |
|
null | peft |
<!-- 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. -->
# style-irs-gorrila
This model is a fine-tuned version of [gorilla-llm/gorilla-openfunctions-v2](https://huggingface.co/gorilla-llm/gorilla-openfunctions-v2) on the generator 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: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "gorilla-llm/gorilla-openfunctions-v2", "model-index": [{"name": "style-irs-gorrila", "results": []}]} | RuoxiL/style-irs-gorrila | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:gorilla-llm/gorilla-openfunctions-v2",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T17:22:31+00:00 |
null | null | {} | ArtChicken/fohwx-woman-xl-sdxxxlv30 | null | [
"region:us"
]
| null | 2024-04-27T17:23:41+00:00 |
|
text-generation | transformers |
<!-- 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. -->
# 0.001_5iters_bs256_nodpo_only4w_iter_5
This model is a fine-tuned version of [ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_4](https://huggingface.co/ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_4) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_4", "model-index": [{"name": "0.001_5iters_bs256_nodpo_only4w_iter_5", "results": []}]} | ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_5 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_4",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T17:24:42+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** gromoboy
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "unsloth/gemma-2b-bnb-4bit"} | gromoboy/gemma_lora_model | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-2b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T17:25:28+00:00 |
text-generation | transformers |
<!-- 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. -->
# zephyr-7b-gemma-kto
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-gemma-sft-v0.1](https://huggingface.co/HuggingFaceH4/zephyr-7b-gemma-sft-v0.1) on the argilla/dpo-mix-7k dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2981
- Rewards/chosen: 1.5381
- Rewards/rejected: -0.1185
- Rewards/accuracies: 0.6979
- Rewards/margins: 1.6565
- Logps/rejected: -364.4402
- Logps/chosen: -332.9066
- Logits/rejected: 106.1137
- Logits/chosen: 111.3681
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.1942 | 1.8957 | 100 | 0.2925 | 1.5810 | -0.0630 | 0.6771 | 1.6440 | -363.3305 | -332.0488 | 106.0414 | 111.2989 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "other", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["argilla/dpo-mix-7k"], "base_model": "HuggingFaceH4/zephyr-7b-gemma-sft-v0.1", "model-index": [{"name": "zephyr-7b-gemma-kto", "results": []}]} | chrlu/zephyr-7b-gemma-kto | null | [
"transformers",
"tensorboard",
"safetensors",
"gemma",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:argilla/dpo-mix-7k",
"base_model:HuggingFaceH4/zephyr-7b-gemma-sft-v0.1",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T17:25:57+00:00 |
reinforcement-learning | null |
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["CartPole-v1", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Cartpole-v1", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "CartPole-v1", "type": "CartPole-v1"}, "metrics": [{"type": "mean_reward", "value": "500.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | EdwinWiseOne/Reinforce-Cartpole-v1 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
]
| null | 2024-04-27T17:26:56+00:00 |
null | null | {} | JinfenLi/zephyr-7b-dpo-qlora | null | [
"region:us"
]
| null | 2024-04-27T17:27:08+00:00 |
|
text-to-image | diffusers | ### 19_21K_V2.1 Dreambooth model trained by ahmed-naseer with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
| {"license": "creativeml-openrail-m", "tags": ["text-to-image", "stable-diffusion"]} | ahmed-naseer/19-21k-v2-1 | null | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| null | 2024-04-27T17:27:34+00:00 |
text-to-audio | transformers |
<!-- 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. -->
# zlm_b32
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8058
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.53 | 0.2094 | 500 | 0.4889 |
| 0.5726 | 0.4188 | 1000 | 0.4970 |
| 2.9469 | 0.6283 | 1500 | 2.8265 |
| 2.8366 | 0.8377 | 2000 | 2.8079 |
| 2.8277 | 1.0471 | 2500 | 2.8231 |
| 2.8102 | 1.2565 | 3000 | 2.8054 |
| 2.8081 | 1.4660 | 3500 | 2.7970 |
| 2.8053 | 1.6754 | 4000 | 2.8058 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "zlm_b32", "results": []}]} | mikhail-panzo/zlm_b32_le3_s4000 | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T17:27:55+00:00 |
null | null | {} | enkz/bod | null | [
"region:us"
]
| null | 2024-04-27T17:28:35+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | fractalego/wafl-phi3-mini-4k | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T17:29:36+00:00 |
text-to-audio | transformers |
<!-- 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. -->
# zlm_b64
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7960
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4734 | 0.4188 | 500 | 0.4319 |
| 0.4686 | 0.8377 | 1000 | 0.4433 |
| 2.9342 | 1.2565 | 1500 | 2.8256 |
| 2.8065 | 1.6754 | 2000 | 2.7988 |
| 2.8176 | 2.0942 | 2500 | 2.8305 |
| 2.7931 | 2.5131 | 3000 | 2.7955 |
| 2.8172 | 2.9319 | 3500 | 2.8076 |
| 2.802 | 3.3508 | 4000 | 2.7960 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "zlm_b64_le3_s4000", "results": []}]} | mikhail-panzo/zlm_b64_le3_s4000 | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T17:30:15+00:00 |
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