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| likes
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| library_name
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|---|---|---|---|---|---|---|---|---|---|
Anitej91/qwen2-llm | Anitej91 | "2024-06-10T15:28:21Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T15:28:21Z" | Entry not found |
aromo17/keras-dummy-sequential-demo | aromo17 | "2024-06-10T15:29:42Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T15:29:42Z" | Entry not found |
MG31/v8_epoch50_8_1 | MG31 | "2024-06-10T15:43:23Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T15:29:49Z" | Entry not found |
aleoaaaa/camembert2camembert_shared-finetuned-french-summarization_finetuned_10_06_15_31 | aleoaaaa | "2024-06-10T15:31:37Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T15:31:37Z" | Entry not found |
cryptolake/carx-1 | cryptolake | "2024-06-10T15:31:41Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-10T15:31:41Z" | ---
license: mit
---
|
Preeda/vit-base-patch16-224-in21k-finetuned-lora-food101 | Preeda | "2024-06-10T15:32:04Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T15:32:04Z" | Entry not found |
ismailpolas/0ea2265b-a5d7-47f3-b0ac-5fdfefaf0800 | ismailpolas | "2024-06-10T15:32:48Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-10T15:32:13Z" | Entry not found |
Reihaneh/wav2vec2_fy_common_voice_36 | Reihaneh | "2024-06-10T15:34:09Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T15:34:08Z" | ---
library_name: transformers
tags: []
---
# 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] |
Ctucket/code-llama-7b-medical_fr | Ctucket | "2024-06-10T15:37:21Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T15:37:21Z" | Entry not found |
MarPla/ALLMainSectionsPegasusLargeModel | MarPla | "2024-06-10T15:37:32Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T15:37:32Z" | Entry not found |
37channel/Llama-3-8B-Instruct-LoRA-Full-r8-alpha16-drop0.05-step1-a-loop1 | 37channel | "2024-06-25T07:24:14Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T15:37:55Z" | ---
library_name: transformers
tags: []
---
# 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] |
nil471/bitnet_try1 | nil471 | "2024-06-10T15:40:07Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T15:40:07Z" | Entry not found |
svilupp/onnx-cross-encoders | svilupp | "2024-06-10T19:08:53Z" | 0 | 0 | null | [
"onnx",
"cross-encoder",
"text-classification",
"en",
"dataset:microsoft/ms_marco",
"license:apache-2.0",
"region:us"
] | text-classification | "2024-06-10T15:41:25Z" | ---
license: apache-2.0
datasets:
- microsoft/ms_marco
language:
- en
pipeline_tag: text-classification
tags:
- onnx
- cross-encoder
---
# Cross-Encoder for MS Marco - ONNX
ONNX versions of [Sentence Transformers Cross Encoders](https://huggingface.co/cross-encoder) to allow ranking without heavy dependencies.
The models were trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The models can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details.
## Models Available
| Model Name | Precision | File Name | File Size |
|--------------------------------------|-----------|------------------------------------------|-----------|
| ms-marco-MiniLM-L-4-v2 ONNX | FP32 | ms-marco-MiniLM-L-4-v2-onnx.zip | 70 MB |
| ms-marco-MiniLM-L-4-v2 ONNX (Quantized) | INT8 | ms-marco-MiniLM-L-4-v2-onnx-int8.zip | 12.8 MB |
| ms-marco-MiniLM-L-6-v2 ONNX | FP32 | ms-marco-MiniLM-L-6-v2-onnx.zip | 83.4 MB |
| ms-marco-MiniLM-L-6-v2 ONNX (Quantized) | INT8 | ms-marco-MiniLM-L-6-v2-onnx-int8.zip | 15.2 MB |
## Usage with ONNX Runtime
```python
import onnxruntime as ort
from transformers import AutoTokenizer
model_path="ms-marco-MiniLM-L-4-v2-onnx/"
tokenizer = AutoTokenizer.from_pretrained('model_path')
ort_sess = ort.InferenceSession(model_path + "ms-marco-MiniLM-L-4-v2.onnx")
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="np")
ort_outs = ort_sess.run(None, features)
print(ort_outs)
```
## Performance
TBU... |
UdS-LSV/mcse-coco-bert-base-uncased | UdS-LSV | "2024-06-10T16:09:14Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"en",
"license:mit",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | feature-extraction | "2024-06-10T15:41:56Z" | ---
library_name: transformers
license: mit
language:
- en
metrics:
- spearmanr
---
# MCSE: Multimodal Contrastive Learning of Sentence Embeddings (NAACL 2022)
Paper link: https://aclanthology.org/2022.naacl-main.436/
Github: https://github.com/uds-lsv/MCSE
Author list: Miaoran Zhang, Marius Mosbach, David Adelani, Michael Hedderich, Dietrich Klakow
## Model Details
- base model: [bert-base-uncased](google-bert/bert-base-uncased)
- training data: Wiki1M + MS-COCO
## Evaluation Results
| STS12 | STS13 | STS14 | STS15 | STS16 | STSBenchmark | SICKRelatedness | Avg. |
|:------:|:------:|:------:|:------:|:------:|:------------:|:---------------:|:------:|
| 72.34 | 79.44 | 72.88 | 82.95 | 78.98 | 79.01 | 73.96 | 77.08 |
|
TheRock99/movie_pred | TheRock99 | "2024-06-10T15:42:02Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T15:42:02Z" | Entry not found |
BasitKhan/results | BasitKhan | "2024-06-10T15:43:18Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-06-10T15:42:28Z" | ---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
danielkosyra/pretraining8 | danielkosyra | "2024-06-10T15:42:54Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-10T15:42:35Z" | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: pretraining8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pretraining8
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: 4.0396
## 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: 0.0006
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_steps: 250
- training_steps: 1500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 9.2792 | 0.0272 | 50 | 7.6701 |
| 7.1197 | 0.0544 | 100 | 6.7851 |
| 6.5645 | 0.0816 | 150 | 6.3728 |
| 6.2643 | 0.1087 | 200 | 6.1180 |
| 6.0752 | 0.1359 | 250 | 5.9355 |
| 5.8579 | 0.1631 | 300 | 5.7394 |
| 5.6632 | 0.1903 | 350 | 5.5987 |
| 5.554 | 0.2175 | 400 | 5.4644 |
| 5.4324 | 0.2447 | 450 | 5.3659 |
| 5.3633 | 0.2719 | 500 | 5.2707 |
| 5.257 | 0.2990 | 550 | 5.1792 |
| 5.1611 | 0.3262 | 600 | 5.1034 |
| 5.0776 | 0.3534 | 650 | 5.0488 |
| 5.0416 | 0.3806 | 700 | 4.9643 |
| 4.9556 | 0.4078 | 750 | 4.9050 |
| 4.9057 | 0.4350 | 800 | 4.8419 |
| 4.8294 | 0.4622 | 850 | 4.7860 |
| 4.8096 | 0.4893 | 900 | 4.7247 |
| 4.753 | 0.5165 | 950 | 4.6722 |
| 4.6549 | 0.5437 | 1000 | 4.6223 |
| 4.6392 | 0.5709 | 1050 | 4.5690 |
| 4.5492 | 0.5981 | 1100 | 4.5120 |
| 4.5252 | 0.6253 | 1150 | 4.4511 |
| 4.4366 | 0.6525 | 1200 | 4.4012 |
| 4.4252 | 0.6796 | 1250 | 4.3329 |
| 4.3357 | 0.7068 | 1300 | 4.2728 |
| 4.2916 | 0.7340 | 1350 | 4.2073 |
| 4.231 | 0.7612 | 1400 | 4.1431 |
| 4.1851 | 0.7884 | 1450 | 4.0878 |
| 4.1097 | 0.8156 | 1500 | 4.0396 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
dinhhung1508/h2o-danube2-1.8b-chat-ecommerce-text-classification | dinhhung1508 | "2024-06-10T15:45:26Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T15:45:26Z" | Entry not found |
aleoaaaa/camembert2camembert_shared-finetuned-french-summarization_finetuned_10_06_15_45 | aleoaaaa | "2024-06-10T15:45:50Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T15:45:50Z" | Entry not found |
NamrataPatil/finetuned-indian-food | NamrataPatil | "2024-06-10T15:46:23Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T15:46:23Z" | Entry not found |
humnrdble/q-FrozenLake-v1-4x4-noSlippery | humnrdble | "2024-06-10T15:46:55Z" | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-10T15:46:53Z" | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="humnrdble/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"])
```
|
ismailpolas/46e2ef6e-55e0-4046-b1de-28a708865c03 | ismailpolas | "2024-06-10T15:48:33Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-10T15:48:00Z" | Entry not found |
onizukal/Boya3_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold3 | onizukal | "2024-06-12T01:06:43Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-large-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-10T15:52:44Z" | ---
license: apache-2.0
base_model: microsoft/beit-large-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: Boya3_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold3
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8422301304863582
---
<!-- 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. -->
# Boya3_3Class_RMSprop_1e5_20Epoch_Beit-large-224_fold3
This model is a fine-tuned version of [microsoft/beit-large-patch16-224](https://huggingface.co/microsoft/beit-large-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7169
- Accuracy: 0.8422
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4724 | 1.0 | 632 | 0.4432 | 0.8185 |
| 0.285 | 2.0 | 1264 | 0.5449 | 0.8260 |
| 0.2677 | 3.0 | 1896 | 0.4843 | 0.8462 |
| 0.0529 | 4.0 | 2528 | 0.8348 | 0.8339 |
| 0.0361 | 5.0 | 3160 | 1.0951 | 0.8406 |
| 0.0006 | 6.0 | 3792 | 1.3311 | 0.8434 |
| 0.0109 | 7.0 | 4424 | 1.5851 | 0.8304 |
| 0.0368 | 8.0 | 5056 | 1.6759 | 0.8319 |
| 0.0 | 9.0 | 5688 | 1.7082 | 0.8406 |
| 0.0 | 10.0 | 6320 | 1.7169 | 0.8422 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.0.1
- Datasets 2.12.0
- Tokenizers 0.13.2
|
badrabdullah/xls-r-300-cv17-polish | badrabdullah | "2024-06-10T20:05:16Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_17_0",
"base_model:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-10T15:53:34Z" | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: xls-r-300-cv17-polish
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: pl
split: validation
args: pl
metrics:
- name: Wer
type: wer
value: 0.2788608461984298
---
<!-- 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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/badr-nlp/xlsr-continual-finetuning-polish/runs/tme9b7jt)
# xls-r-300-cv17-polish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice_17_0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3950
- Wer: 0.2789
- Cer: 0.0606
## 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 4.0552 | 1.6 | 100 | 4.2577 | 1.0 | 1.0 |
| 3.2887 | 3.2 | 200 | 3.2578 | 1.0 | 1.0 |
| 3.1481 | 4.8 | 300 | 3.1634 | 1.0 | 1.0 |
| 0.742 | 6.4 | 400 | 0.6905 | 0.6579 | 0.1603 |
| 0.4458 | 8.0 | 500 | 0.4687 | 0.4969 | 0.1169 |
| 0.2013 | 9.6 | 600 | 0.4327 | 0.4055 | 0.0929 |
| 0.2196 | 11.2 | 700 | 0.4180 | 0.4006 | 0.0903 |
| 0.1264 | 12.8 | 800 | 0.4360 | 0.3943 | 0.0898 |
| 0.1678 | 14.4 | 900 | 0.4157 | 0.3635 | 0.0818 |
| 0.1306 | 16.0 | 1000 | 0.3980 | 0.3667 | 0.0814 |
| 0.0471 | 17.6 | 1100 | 0.4206 | 0.3630 | 0.0828 |
| 0.1018 | 19.2 | 1200 | 0.3908 | 0.3522 | 0.0796 |
| 0.0637 | 20.8 | 1300 | 0.4277 | 0.3517 | 0.0785 |
| 0.1134 | 22.4 | 1400 | 0.4209 | 0.3373 | 0.0750 |
| 0.0709 | 24.0 | 1500 | 0.4255 | 0.3387 | 0.0766 |
| 0.046 | 25.6 | 1600 | 0.4301 | 0.3352 | 0.0746 |
| 0.065 | 27.2 | 1700 | 0.4087 | 0.3278 | 0.0724 |
| 0.0625 | 28.8 | 1800 | 0.4203 | 0.3454 | 0.0761 |
| 0.0344 | 30.4 | 1900 | 0.4317 | 0.3203 | 0.0714 |
| 0.0667 | 32.0 | 2000 | 0.4319 | 0.3258 | 0.0725 |
| 0.0305 | 33.6 | 2100 | 0.4260 | 0.3216 | 0.0716 |
| 0.04 | 35.2 | 2200 | 0.4172 | 0.3175 | 0.0697 |
| 0.0454 | 36.8 | 2300 | 0.4182 | 0.2996 | 0.0658 |
| 0.0273 | 38.4 | 2400 | 0.3966 | 0.2970 | 0.0654 |
| 0.0463 | 40.0 | 2500 | 0.4111 | 0.2926 | 0.0644 |
| 0.0321 | 41.6 | 2600 | 0.4094 | 0.2893 | 0.0633 |
| 0.0197 | 43.2 | 2700 | 0.3953 | 0.2846 | 0.0622 |
| 0.0306 | 44.8 | 2800 | 0.3980 | 0.2817 | 0.0613 |
| 0.0459 | 46.4 | 2900 | 0.3937 | 0.2807 | 0.0613 |
| 0.006 | 48.0 | 3000 | 0.3953 | 0.2780 | 0.0604 |
| 0.0329 | 49.6 | 3100 | 0.3950 | 0.2789 | 0.0606 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
KuanP/baseline_2024-06-10_11-43-48_fold_1 | KuanP | "2024-06-10T15:54:02Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T15:53:57Z" | ---
library_name: transformers
tags: []
---
# 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] |
SungJoo/llama3-8b-instruct-orpo-ko | SungJoo | "2024-06-10T16:50:50Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llm",
"Large Language Model",
"llama3",
"ORPO",
"ORPO Ξ²",
"conversational",
"ko",
"dataset:heegyu/hh-rlhf-ko",
"arxiv:2403.07691",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-10T15:55:33Z" | ---
library_name: transformers
tags:
- llm
- Large Language Model
- llama3
- ORPO
- ORPO Ξ²
license: apache-2.0
datasets:
- heegyu/hh-rlhf-ko
language:
- ko
---
# Model Card for llama3-8b-instruct-orpo-ko
## Model Summary
This model is a fine-tuned version of the meta-llama/Meta-Llama-3-8B-Instruct using the [odds ratio preference optimization (ORPO)](https://arxiv.org/abs/2403.07691).
It has been trained to perform NLP tasks in Korean.
## Model Details
### Model Description
- **Developed by:** Sungjoo Byun (Grace Byun)
- **Language(s) (NLP):** Korean
- **License:** Apache 2.0
- **Finetuned from model:** meta-llama/Meta-Llama-3-8B-Instruct
## Training Details
### Training Data
The model was trained using the dataset [heegyu/hh-rlhf-ko](https://huggingface.co/datasets/heegyu/hh-rlhf-ko). We appreciate heegyu for sharing this valuable resource.
### Training Procedure
We applied ORPO Ξ² to llama3-8b-instruct. The training was conducted on an A100 GPU with 80GB of memory.
## How to Get Started with the Model
Use the code below to get started with the model:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("SungJoo/llama3-8b-instruct-orpo-ko")
model = AutoModelForCausalLM.from_pretrained("SungJoo/llama3-8b-instruct-orpo-ko")
```
## Citations
Please cite the ORPO paper and our model as follows:
```bibtex
@misc{hong2024orpo,
title={ORPO: Monolithic Preference Optimization without Reference Model},
author={Jiwoo Hong and Noah Lee and James Thorne},
year={2024},
eprint={2403.07691},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
```bibtex
@misc{byun,
author = {Sungjoo Byun},
title = {llama3-8b-orpo-ko},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/SungJoo/llama3-8b-instruct-orpo-ko}}
}
```
## Contact
For any questions or issues, please contact byunsj@snu.ac.kr. |
starriver030515/pure_gen_200k | starriver030515 | "2024-06-10T16:08:50Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llava_llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-10T15:57:20Z" | Entry not found |
starriver030515/pure_gen_558k | starriver030515 | "2024-06-10T16:11:11Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llava_llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-10T15:57:32Z" | Entry not found |
Augusto777/vit-base-patch16-224-RX1-24 | Augusto777 | "2024-06-10T16:12:32Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-10T16:01:06Z" | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-RX1-24
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8431372549019608
---
<!-- 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. -->
# vit-base-patch16-224-RX1-24
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5687
- Accuracy: 0.8431
## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 24
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.93 | 7 | 1.3485 | 0.4706 |
| 1.3674 | 2.0 | 15 | 1.2284 | 0.5490 |
| 1.2414 | 2.93 | 22 | 1.1307 | 0.6471 |
| 1.1146 | 4.0 | 30 | 1.0230 | 0.6471 |
| 1.1146 | 4.93 | 37 | 0.9251 | 0.6863 |
| 0.9522 | 6.0 | 45 | 0.9122 | 0.6471 |
| 0.8247 | 6.93 | 52 | 0.9374 | 0.6275 |
| 0.6825 | 8.0 | 60 | 0.8320 | 0.6863 |
| 0.6825 | 8.93 | 67 | 0.8286 | 0.6667 |
| 0.6191 | 10.0 | 75 | 0.8418 | 0.6667 |
| 0.5312 | 10.93 | 82 | 0.7836 | 0.8235 |
| 0.454 | 12.0 | 90 | 0.7356 | 0.8039 |
| 0.454 | 12.93 | 97 | 0.6117 | 0.8235 |
| 0.3752 | 14.0 | 105 | 0.6014 | 0.8235 |
| 0.3269 | 14.93 | 112 | 0.6102 | 0.8039 |
| 0.2733 | 16.0 | 120 | 0.6404 | 0.8039 |
| 0.2733 | 16.93 | 127 | 0.5687 | 0.8431 |
| 0.2711 | 18.0 | 135 | 0.6120 | 0.8235 |
| 0.2519 | 18.93 | 142 | 0.6250 | 0.8431 |
| 0.2484 | 20.0 | 150 | 0.6086 | 0.7843 |
| 0.2484 | 20.93 | 157 | 0.6229 | 0.8235 |
| 0.2258 | 22.0 | 165 | 0.6390 | 0.7843 |
| 0.2258 | 22.4 | 168 | 0.6337 | 0.8039 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
KuanP/baseline_2024-06-10_11-43-48_fold_2 | KuanP | "2024-06-10T16:04:18Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T16:04:12Z" | ---
library_name: transformers
tags: []
---
# 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] |
kajamo/model_18 | kajamo | "2024-06-10T16:49:40Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | "2024-06-10T16:05:00Z" | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: distilbert-base-uncased
model-index:
- name: model_18
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model_18
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5829
- eval_accuracy: 0.7726
- eval_precision: 0.7726
- eval_recall: 0.7726
- eval_f1: 0.7724
- eval_runtime: 31.4425
- eval_samples_per_second: 389.441
- eval_steps_per_second: 12.181
- epoch: 5.0
- step: 7655
## 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: 7e-06
- 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: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1 |
Barathraj09/Mixtral | Barathraj09 | "2024-06-10T16:06:04Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T16:06:04Z" | Entry not found |
madiramsey/baf2b252097d46299a_example_task_example_exp | madiramsey | "2024-06-10T16:11:07Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T16:10:36Z" | ---
library_name: transformers
tags: []
---
# 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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## 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
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[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]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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KuanP/baseline_2024-06-10_11-43-48_fold_3 | KuanP | "2024-06-10T16:14:40Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T16:14:34Z" | ---
library_name: transformers
tags: []
---
# 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] |
alexandrualexandru/code-llama-13b-text-to-sparql | alexandrualexandru | "2024-06-10T16:15:26Z" | 0 | 0 | null | [
"generated_from_trainer",
"base_model:codellama/CodeLlama-13b-hf",
"license:llama2",
"region:us"
] | null | "2024-06-10T16:15:12Z" | ---
license: llama2
base_model: codellama/CodeLlama-13b-hf
tags:
- generated_from_trainer
model-index:
- name: code-llama-13b-text-to-sparql
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# code-llama-13b-text-to-sparql
This model is a fine-tuned version of [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1870
## 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: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1121 | 0.0710 | 20 | 1.0865 |
| 0.6067 | 0.1421 | 40 | 0.3437 |
| 0.2982 | 0.2131 | 60 | 0.2775 |
| 0.2433 | 0.2842 | 80 | 0.2465 |
| 0.2162 | 0.3552 | 100 | 0.2657 |
| 0.2308 | 0.4263 | 120 | 0.2303 |
| 0.2356 | 0.4973 | 140 | 0.2217 |
| 0.239 | 0.5684 | 160 | 0.2167 |
| 0.2159 | 0.6394 | 180 | 0.2112 |
| 0.2005 | 0.7105 | 200 | 0.2217 |
| 0.2177 | 0.7815 | 220 | 0.2070 |
| 0.2048 | 0.8526 | 240 | 0.2018 |
| 0.2092 | 0.9236 | 260 | 0.1976 |
| 0.2057 | 0.9947 | 280 | 0.1959 |
| 0.198 | 1.0657 | 300 | 0.1929 |
| 0.1988 | 1.1368 | 320 | 0.1908 |
| 0.1886 | 1.2078 | 340 | 0.1906 |
| 0.1927 | 1.2789 | 360 | 0.1883 |
| 0.1841 | 1.3499 | 380 | 0.1872 |
| 0.1863 | 1.4210 | 400 | 0.1870 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.10.1
- Tokenizers 0.19.1
|
tastypear/RWKV-6-World-v2.1-safetensors | tastypear | "2024-06-10T18:53:24Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-10T16:15:58Z" | ---
license: apache-2.0
---
#### Model Info:
Original model: [BlinkDL/rwkv-6-world](https://huggingface.co/BlinkDL/rwkv-6-world)
You can run this model with [ai00_server](https://github.com/Ai00-X/ai00_server).
Although ai00_rwkv_server is mainly for lowend PC, you can run it on servers which are support VULKAN.
#### To try it in Colab:
You should install libnvidia-gl-* and vulkan driver:
`!apt -y install libnvidia-gl-535 libvulkan1`
The 7B model use about 14.7G VRAM. T4 is enough to load it.
#### One more thing:
These models are censored. You can start your prompts with `[SDA]` to jailbreak.
It's something like developer mode. |
brahmairesearch/vaani_small | brahmairesearch | "2024-06-12T09:37:26Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"license:mit",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-06-10T16:17:34Z" | ---
license: mit
---
---
# Vaani | [BRAHMAI RESEARCH](https://brahmai.in)
Model Type: Automatic Speech Recognition (ASR)
Base Model: whisper-small
[Dataset: Mozilla Common Voice 11.0 Hindi dataset]
Vaani (small - hindi) is a fine-tuned version of the whisper-small model by OpenAI, specifically optimized for Hindi speech recognition. It was fine-tuned on the Mozilla Common Voice 11.0 Hindi dataset by BRAHMAI Research. The model demonstrates strong performance on various Hindi speech recognition tasks and can be run locally on GPUs with as little as 4GB of memory.
Intended Use:
The primary intended use case for vaani_small is automatic speech recognition and transcription of Hindi audio data. It can be employed in a wide range of applications that require accurate Hindi speech-to-text conversion, such as captioning, speech analytics, voice assistants, and accessibility tools.
Limitations and Biases:
While the model shows improved performance on Hindi speech recognition, its performance may vary across different accents, dialects, and demographic groups within the Hindi-speaking population. The biases and limitations of the model are likely to be inherited from the training data used (Mozilla Common Voice 11.0 Hindi dataset). It is recommended to evaluate the model's performance on specific use cases and datasets before deployment.
Training Data:
The model was fine-tuned on the Mozilla Common Voice 11.0 Hindi dataset. It is an open-source dataset containing crowdsourced audio recordings and transcriptions in Hindi. However, potential biases or ethical concerns associated with the training data should be carefully examined.
Hardware and Software Requirements:
vaani_small can be run locally on GPUs with at least 4GB of memory. It is recommended to use the Transformers library from Hugging Face for inference and deployment. |
Augusto777/vit-base-patch16-224-RXL1-24 | Augusto777 | "2024-06-10T16:31:19Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-10T16:19:03Z" | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-RXL1-24
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8431372549019608
---
<!-- 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. -->
# vit-base-patch16-224-RXL1-24
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6158
- Accuracy: 0.8431
## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 24
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3745 | 0.95 | 13 | 1.3056 | 0.4706 |
| 1.2896 | 1.96 | 27 | 1.1039 | 0.6471 |
| 0.9896 | 2.98 | 41 | 0.9413 | 0.6471 |
| 0.8472 | 4.0 | 55 | 0.9059 | 0.6275 |
| 0.7375 | 4.95 | 68 | 0.6520 | 0.8039 |
| 0.458 | 5.96 | 82 | 0.6754 | 0.8039 |
| 0.3807 | 6.98 | 96 | 0.6158 | 0.8431 |
| 0.3003 | 8.0 | 110 | 0.5666 | 0.8039 |
| 0.2337 | 8.95 | 123 | 0.5409 | 0.8039 |
| 0.2252 | 9.96 | 137 | 0.7382 | 0.7647 |
| 0.1644 | 10.98 | 151 | 0.6363 | 0.8039 |
| 0.1608 | 12.0 | 165 | 0.6941 | 0.8039 |
| 0.1354 | 12.95 | 178 | 0.6985 | 0.7843 |
| 0.1298 | 13.96 | 192 | 0.6610 | 0.8039 |
| 0.1333 | 14.98 | 206 | 0.6751 | 0.8039 |
| 0.1209 | 16.0 | 220 | 0.7723 | 0.7843 |
| 0.1057 | 16.95 | 233 | 0.8038 | 0.7255 |
| 0.0972 | 17.96 | 247 | 0.8375 | 0.7647 |
| 0.0789 | 18.98 | 261 | 0.6971 | 0.8235 |
| 0.0833 | 20.0 | 275 | 0.7507 | 0.7843 |
| 0.0813 | 20.95 | 288 | 0.7085 | 0.7843 |
| 0.0803 | 21.96 | 302 | 0.7566 | 0.7647 |
| 0.0693 | 22.69 | 312 | 0.7772 | 0.7647 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
MG31/v8_epoch50_16_4_1 | MG31 | "2024-06-10T16:19:34Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T16:19:34Z" | Entry not found |
hdve/Qwen-Qwen1.5-7B-1718036523 | hdve | "2024-06-10T16:22:44Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T16:22:44Z" | Entry not found |
humnrdble/DeepRL-unit2 | humnrdble | "2024-06-10T16:23:36Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-10T16:23:34Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: DeepRL-unit2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.81
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="humnrdble/DeepRL-unit2", 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"])
```
|
metta-ai/baseline.v0.2.2 | metta-ai | "2024-06-10T16:24:52Z" | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] | reinforcement-learning | "2024-06-10T16:24:11Z" | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
---
A(n) **APPO** model trained on the **GDY-MettaGrid** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r metta-ai/baseline.v0.2.2
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.2.2
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m <path.to.train.module> --algo=APPO --env=GDY-MettaGrid --train_dir=./train_dir --experiment=baseline.v0.2.2 --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
nttwt1597/test_v2_cancer_v4_500step | nttwt1597 | "2024-06-10T16:25:26Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T16:24:12Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** nttwt1597
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama 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)
|
KuanP/baseline_2024-06-10_11-43-48_fold_4 | KuanP | "2024-06-10T16:24:59Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T16:24:52Z" | ---
library_name: transformers
tags: []
---
# 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]
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## 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
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[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
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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## Glossary [optional]
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Leviljanen/TestModel | Leviljanen | "2024-06-13T13:42:41Z" | 0 | 0 | null | [
"license:gpl",
"region:us"
] | null | "2024-06-10T16:28:15Z" | ---
license: gpl
---
|
sajjad55/wsdbanglat5_1e4_E4 | sajjad55 | "2024-06-10T17:55:23Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:ka05ar/Banglat5_Ex4",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | "2024-06-10T16:29:19Z" | ---
base_model: ka05ar/Banglat5_Ex4
tags:
- generated_from_trainer
model-index:
- name: wsdbanglat5_1e4_E4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wsdbanglat5_1e4_E4
This model is a fine-tuned version of [ka05ar/Banglat5_Ex4](https://huggingface.co/ka05ar/Banglat5_Ex4) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0046
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0633 | 1.0 | 1481 | 0.0339 |
| 0.0259 | 2.0 | 2962 | 0.0109 |
| 0.0192 | 3.0 | 4443 | 0.0084 |
| 0.0108 | 4.0 | 5924 | 0.0061 |
| 0.0061 | 5.0 | 7405 | 0.0048 |
| 0.0046 | 6.0 | 8886 | 0.0045 |
| 0.0042 | 7.0 | 10367 | 0.0047 |
| 0.0049 | 8.0 | 11848 | 0.0043 |
| 0.0022 | 9.0 | 13329 | 0.0044 |
| 0.0014 | 10.0 | 14810 | 0.0046 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.1.2
- Datasets 2.19.1
- Tokenizers 0.19.1
|
jtatman/outputs | jtatman | "2024-06-14T07:35:53Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m-deduped",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-10T16:32:14Z" | ---
license: apache-2.0
base_model: EleutherAI/pythia-70m-deduped
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: outputs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# outputs
This model is a fine-tuned version of [EleutherAI/pythia-70m-deduped](https://huggingface.co/EleutherAI/pythia-70m-deduped) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 8675309
- 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
- lr_scheduler_warmup_steps: 5
- training_steps: 4000
### Training results
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
candrews1971/Reinforce-CartPole-v1 | candrews1971 | "2024-06-13T18:40:45Z" | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2024-06-10T16:32:44Z" | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
Bagelman97/Yuuki_Mishima | Bagelman97 | "2024-06-10T18:02:45Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T16:34:36Z" | RVCv2 Model of Yuuki Mishima from Persona 5 trained on 13~ mins of in-game dialouge |
KuanP/baseline_2024-06-10_11-43-48_fold_5 | KuanP | "2024-06-10T16:35:19Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T16:35:13Z" | ---
library_name: transformers
tags: []
---
# 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] |
Augusto777/vit-base-patch16-224-RU9-24 | Augusto777 | "2024-06-10T16:54:11Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-06-10T16:41:02Z" | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-RU9-24
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8431372549019608
---
<!-- 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. -->
# vit-base-patch16-224-RU9-24
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5081
- Accuracy: 0.8431
## 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: 5.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 24
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 1.3401 | 0.5098 |
| 1.3685 | 2.0 | 16 | 1.2193 | 0.5686 |
| 1.2413 | 3.0 | 24 | 1.1150 | 0.5882 |
| 1.1126 | 4.0 | 32 | 0.9957 | 0.7059 |
| 0.9285 | 5.0 | 40 | 0.8976 | 0.6863 |
| 0.9285 | 6.0 | 48 | 0.8580 | 0.6863 |
| 0.7793 | 7.0 | 56 | 0.8426 | 0.7647 |
| 0.6291 | 8.0 | 64 | 0.7899 | 0.6863 |
| 0.5401 | 9.0 | 72 | 0.7169 | 0.7255 |
| 0.4358 | 10.0 | 80 | 0.7505 | 0.7255 |
| 0.4358 | 11.0 | 88 | 0.8077 | 0.7059 |
| 0.3901 | 12.0 | 96 | 0.6803 | 0.7647 |
| 0.3033 | 13.0 | 104 | 0.6483 | 0.7647 |
| 0.267 | 14.0 | 112 | 0.6451 | 0.7451 |
| 0.2212 | 15.0 | 120 | 0.6119 | 0.7647 |
| 0.2212 | 16.0 | 128 | 0.6150 | 0.8039 |
| 0.2206 | 17.0 | 136 | 0.6270 | 0.7843 |
| 0.2285 | 18.0 | 144 | 0.6181 | 0.7647 |
| 0.1741 | 19.0 | 152 | 0.5081 | 0.8431 |
| 0.1708 | 20.0 | 160 | 0.5502 | 0.8235 |
| 0.1708 | 21.0 | 168 | 0.5689 | 0.8039 |
| 0.16 | 22.0 | 176 | 0.5137 | 0.8235 |
| 0.1567 | 23.0 | 184 | 0.5207 | 0.8431 |
| 0.1616 | 24.0 | 192 | 0.5375 | 0.8235 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Tanysha/detr_finetuned_cppe5 | Tanysha | "2024-06-10T16:41:09Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T16:41:09Z" | Entry not found |
ireneisdoomed/locus_to_gene_production | ireneisdoomed | "2024-06-10T16:41:13Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T16:41:13Z" | Entry not found |
lotary/testModel | lotary | "2024-06-10T16:41:57Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T16:41:57Z" | Entry not found |
Amir-1383/Amir-1383 | Amir-1383 | "2024-06-10T22:32:47Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-10T16:43:29Z" | ---
license: openrail
---
|
medtalkai/wav2vec2-xls-r-1b-medical-domain-longer-test | medtalkai | "2024-06-10T16:57:29Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T16:47:21Z" | ---
library_name: transformers
tags: []
---
# 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]
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[More Information Needed]
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AyoubELFallah/mylast_fine_tuning_blenerbot | AyoubELFallah | "2024-06-10T16:47:55Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T16:47:49Z" | ---
library_name: transformers
tags: []
---
# 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]
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[More Information Needed]
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[More Information Needed]
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## Citation [optional]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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tanoManzo/hyenadna-medium-160k-seqlen-hf_ft_Hepg2_1kbpHG19_DHSs_H3K27AC | tanoManzo | "2024-06-10T16:52:01Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T16:52:01Z" | Entry not found |
Amir-1383/Amir | Amir-1383 | "2024-06-10T16:56:25Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-10T16:56:25Z" | ---
license: openrail
---
|
Marupudi/flidharaneesh-tiiuae-falcon-7b-v2 | Marupudi | "2024-06-10T17:08:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"dataset:Marupudi/parth",
"base_model:tiiuae/falcon-7b",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-10T16:58:07Z" | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: tiiuae/falcon-7b
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- Marupudi/parth
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
sssssmpl/xiaobao-loRA | sssssmpl | "2024-06-10T17:14:24Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-10T16:58:25Z" | ---
license: apache-2.0
---
# descriptions
This is the loRA of Chinese comedian Song Xiaobao, fine-tuned based on stable-diffusion-v1-5
# Recommend settings:
- VAE: Automatic
- Sampler: UniPC
- Sampling steps: 20
# Samples
```
xiaobao, 1boy, solo, male focus, realistic, closed eyes, open mouth, smile, jacket, black hair, upper body, teeth, zipper, <lora:xiaobao:1>, (masterpiece:1.2), best quality, highres, extremely detailed CG, perfect lighting, 8k wallpaper
``` |
Rolandtester/Testlolxd | Rolandtester | "2024-06-10T17:04:04Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T16:58:32Z" |
<marquee><h1>lol tu es fou</h1></marquee>
"><img src=x onerror=alert(document.cookie)>
"><img src=x test=test>
|
PKU-Alignment/ProgressGym-HistLlama3-8B-C013-instruct-v0.1 | PKU-Alignment | "2024-07-01T18:14:33Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:PKU-Alignment/ProgressGym-HistText",
"dataset:PKU-Alignment/ProgressGym-TimelessQA",
"arxiv:2406.20087",
"base_model:PKU-Alignment/ProgressGym-HistLlama3-8B-C013-pretrain",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-10T17:01:09Z" | ---
license: cc-by-4.0
datasets:
- PKU-Alignment/ProgressGym-HistText
- PKU-Alignment/ProgressGym-TimelessQA
base_model:
- PKU-Alignment/ProgressGym-HistLlama3-8B-C013-pretrain
- meta-llama/Meta-Llama-3-8B
---
# ProgressGym-HistLlama3-8B-C013-instruct
## Overview
#### The ProgressGym Framework
**ProgressGym-HistLlama3-8B-C013-instruct** is part of the **ProgressGym** framework for research and experimentation on *progress alignment* - the emulation of moral progress in AI alignment algorithms, as a measure to prevent risks of societal value lock-in.
To quote the paper [*ProgressGym: Alignment with a Millennium of Moral Progress*](https://arxiv.org/abs/2406.20087):
> Frontier AI systems, including large language models (LLMs), hold increasing influence over the epistemology of human users. Such influence can reinforce prevailing societal values, potentially contributing to the lock-in of misguided moral beliefs and, consequently, the perpetuation of problematic moral practices on a broad scale.
>
> We introduce *progress alignment* as a technical solution to mitigate this imminent risk. Progress alignment algorithms learn to emulate the mechanics of human moral progress, thereby addressing the susceptibility of existing alignment methods to contemporary moral blindspots.
#### ProgressGym-HistLlama3-8B-C013-instruct
ProgressGym-HistLlama3-8B-C013-instruct is one of the **36 historical language models** in the ProgressGym framework.
**ProgressGym-HistLlama3-8B-C013-instruct is under continual iteration.** Improving upon the current version, new versions of the model are currently being trained to reflect historical moral tendencies in ever more comprehensive ways.
**ProgressGym-HistLlama3-8B-C013-instruct is a 13th-century historical language model.** Based on [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B), It is continued-pretrained on the 13th-century text data from [ProgressGym-HistText](https://huggingface.co/datasets/PKU-Alignment/ProgressGym-HistText), using the following hyperparameters:
- learning_rate: 1.5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- lr_scheduler_warmup_steps: 20
- num_epochs: 4.0
- mixed_precision_training: Native AMP
... with the following training results:
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.7594 | 0.0149 | 1 | 1.7163 |
| 1.7333 | 0.0746 | 5 | 1.7008 |
| 1.6854 | 0.1493 | 10 | 1.6825 |
| 1.6897 | 0.2239 | 15 | 1.6701 |
| 1.6656 | 0.2985 | 20 | 1.6651 |
| 1.7254 | 0.3731 | 25 | 1.6679 |
| 1.7178 | 0.4478 | 30 | 1.6542 |
| 1.6656 | 0.5224 | 35 | 1.6459 |
| 1.6647 | 0.5970 | 40 | 1.6308 |
| 1.6645 | 0.6716 | 45 | 1.6205 |
| 1.6151 | 0.7463 | 50 | 1.6129 |
| 1.6359 | 0.8209 | 55 | 1.6052 |
| 1.5885 | 0.8955 | 60 | 1.5995 |
| 1.6142 | 0.9701 | 65 | 1.5943 |
| 1.4875 | 1.0448 | 70 | 1.5963 |
| 1.3844 | 1.1194 | 75 | 1.6118 |
| 1.3555 | 1.1940 | 80 | 1.6069 |
| 1.3597 | 1.2687 | 85 | 1.6040 |
| 1.3737 | 1.3433 | 90 | 1.6071 |
| 1.3492 | 1.4179 | 95 | 1.6074 |
| 1.3826 | 1.4925 | 100 | 1.6055 |
| 1.3533 | 1.5672 | 105 | 1.6035 |
| 1.3611 | 1.6418 | 110 | 1.6023 |
| 1.328 | 1.7164 | 115 | 1.6022 |
| 1.3443 | 1.7910 | 120 | 1.6026 |
| 1.3386 | 1.8657 | 125 | 1.6029 |
| 1.3396 | 1.9403 | 130 | 1.6029 |
| 1.3573 | 2.0149 | 135 | 1.6029 |
| 1.3754 | 2.0896 | 140 | 1.6034 |
| 1.3229 | 2.1642 | 145 | 1.6044 |
| 1.3194 | 2.2388 | 150 | 1.6055 |
| 1.3361 | 2.3134 | 155 | 1.6065 |
| 1.3231 | 2.3881 | 160 | 1.6072 |
| 1.32 | 2.4627 | 165 | 1.6076 |
| 1.3406 | 2.5373 | 170 | 1.6078 |
| 1.3184 | 2.6119 | 175 | 1.6079 |
| 1.2745 | 2.6866 | 180 | 1.6080 |
| 1.3024 | 2.7612 | 185 | 1.6079 |
| 1.3243 | 2.8358 | 190 | 1.6079 |
| 1.3239 | 2.9104 | 195 | 1.6080 |
| 1.3349 | 2.9851 | 200 | 1.6081 |
| 1.337 | 3.0597 | 205 | 1.6079 |
| 1.3091 | 3.1343 | 210 | 1.6078 |
| 1.3266 | 3.2090 | 215 | 1.6079 |
| 1.3014 | 3.2836 | 220 | 1.6083 |
| 1.3153 | 3.3582 | 225 | 1.6086 |
| 1.3192 | 3.4328 | 230 | 1.6090 |
| 1.315 | 3.5075 | 235 | 1.6093 |
| 1.3047 | 3.5821 | 240 | 1.6093 |
| 1.3208 | 3.6567 | 245 | 1.6093 |
| 1.362 | 3.7313 | 250 | 1.6093 |
| 1.3255 | 3.8060 | 255 | 1.6091 |
| 1.2941 | 3.8806 | 260 | 1.6089 |
| 1.3254 | 3.9552 | 265 | 1.6086 |
Note that the training data volume for the continued pretraining stage is capped at 300MB. When the corresponding century's corpus exceeds this volume, the training data is randomly sampled to fit the volume.
**ProgressGym-HistLlama3-8B-C013-instruct is an instruction-tuned language model.** It is tuned on [ProgressGym-TimelessQA](https://huggingface.co/datasets/PKU-Alignment/ProgressGym-TimelessQA), using the following hyperparameters:
- learning_rate: 1.5e-05
- train_batch_size: 8
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: polynomial
- lr_scheduler_warmup_steps: 20
- num_epochs: 4.0
- mixed_precision_training: Native AMP
... with the following training results:
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9805 | 0.0208 | 1 | 0.9737 |
| 0.9446 | 0.1042 | 5 | 0.9455 |
| 0.8481 | 0.2083 | 10 | 0.8154 |
| 0.7794 | 0.3125 | 15 | 0.8123 |
| 0.7798 | 0.4167 | 20 | 0.8411 |
| 0.8576 | 0.5208 | 25 | 0.8676 |
| 0.8852 | 0.625 | 30 | 0.8673 |
| 0.8529 | 0.7292 | 35 | 0.8561 |
| 0.8224 | 0.8333 | 40 | 0.8470 |
| 0.8536 | 0.9375 | 45 | 0.8378 |
| 0.662 | 1.0417 | 50 | 0.8294 |
| 0.437 | 1.1458 | 55 | 0.8531 |
| 0.4402 | 1.25 | 60 | 0.8569 |
| 0.4244 | 1.3542 | 65 | 0.8569 |
| 0.4495 | 1.4583 | 70 | 0.8547 |
| 0.4689 | 1.5625 | 75 | 0.8494 |
| 0.4309 | 1.6667 | 80 | 0.8461 |
| 0.4299 | 1.7708 | 85 | 0.8446 |
| 0.4461 | 1.875 | 90 | 0.8440 |
| 0.4474 | 1.9792 | 95 | 0.8439 |
| 0.3614 | 2.0833 | 100 | 0.8445 |
| 0.3861 | 2.1875 | 105 | 0.8457 |
| 0.3829 | 2.2917 | 110 | 0.8473 |
| 0.3764 | 2.3958 | 115 | 0.8488 |
| 0.3655 | 2.5 | 120 | 0.8500 |
| 0.4243 | 2.6042 | 125 | 0.8511 |
| 0.3884 | 2.7083 | 130 | 0.8520 |
| 0.3634 | 2.8125 | 135 | 0.8528 |
| 0.3846 | 2.9167 | 140 | 0.8537 |
| 0.3872 | 3.0208 | 145 | 0.8547 |
| 0.3869 | 3.125 | 150 | 0.8558 |
| 0.3876 | 3.2292 | 155 | 0.8566 |
| 0.3844 | 3.3333 | 160 | 0.8573 |
| 0.3535 | 3.4375 | 165 | 0.8579 |
| 0.3488 | 3.5417 | 170 | 0.8588 |
| 0.3464 | 3.6458 | 175 | 0.8598 |
| 0.361 | 3.75 | 180 | 0.8607 |
| 0.3674 | 3.8542 | 185 | 0.8612 |
| 0.3988 | 3.9583 | 190 | 0.8612 |
## Links
- **[Paper Preprint]** [ProgressGym: Alignment with a Millennium of Moral Progress](https://arxiv.org/abs/2406.20087)
- **[Leaderboard & Interactive Playground]** PKU-Alignment/ProgressGym-LeaderBoard *(coming soon - [stay tuned](https://forms.gle/1TWFLL4ZCLeYTD5N6)!)*
- **[Huggingface Data & Model Collection]** [PKU-Alignment/ProgressGym](https://huggingface.co/collections/PKU-Alignment/progressgym-666735fcf3e4efa276226eaa)
- **[Github Codebase]** [PKU-Alignment/ProgressGym](https://github.com/PKU-Alignment/ProgressGym)
- **[PyPI Package]** *(coming soon - [stay tuned](https://forms.gle/1TWFLL4ZCLeYTD5N6)!)*
## Citation
If the datasets, models, or framework of ProgressGym help you in your project, please cite ProgressGym using the bibtex entry below.
```text
@article{progressgym,
title={ProgressGym: Alignment with a Millennium of Moral Progress},
author={Tianyi Qiu and Yang Zhang and Xuchuan Huang and Jasmine Xinze Li and Jiaming Ji and Yaodong Yang},
journal={arXiv preprint arXiv:2406.20087},
eprint={2406.20087},
eprinttype = {arXiv},
year={2024}
}
```
## Ethics Statement
- **Copyright information of historical text data sources**:
- Project Gutenberg, one among our four source of our historical text data, consists only of texts in the public domain.
- For the text that we draw from Internet Archive, we only include those that uploaded by *Library of Congress*, which are texts freely released online by the U.S. Library of Congress for research and public use.
- The text data from Early English Books Online are, according to their publisher, "freely available to the public" and "available for access, distribution, use, or reuse by anyone".
- The last remaining source of our historical text data, the Pile of Law dataset, is released under a Creative Commons license, which we adhere to in our use.
- **Reproducibility**: To ensure reproducibility, we open-source all the code involved in the production of our main results (including the entire pipeline starting from data collection and model training), as well as the supporting infrastructure (the ProgressGym framework), making replication as easy as running a few simple script files.
- **Misuse Prevention**: In order to prevent potential misuse of progress alignment algorithms, we have carefully formulated progress alignment as strictly value-neutral, without *a priori* assumptions on the direction of progress. In the event of potential misuse of our dataset, we condemn any misuse attempt to the strongest degree possible, and will work with the research community on whistleblowing for such attempts.
- **Open-Sourcing**: We confirm that our code, data, and models are to be open-sourced under a CC-BY 4.0 license. We will continue to maintain and update our open-source repositories and models. |
gjonesQ02/ScopeOfWorkTextGenerator_AlphaSMALL | gjonesQ02 | "2024-06-10T17:02:06Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-10T17:01:29Z" | ---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: ScopeOfWorkTextGenerator_AlphaSMALL
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ScopeOfWorkTextGenerator_AlphaSMALL
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.0150
## 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: 5
- eval_batch_size: 5
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 4.1040 |
| No log | 2.0 | 2 | 4.0150 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2+cpu
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Rolyaj/Clippy | Rolyaj | "2024-06-10T17:02:38Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:TheMistoAI/MistoLine",
"license:unknown",
"region:us"
] | text-to-image | "2024-06-10T17:02:26Z" | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/IMG_0236.jpeg
base_model: TheMistoAI/MistoLine
instance_prompt: null
license: unknown
---
# Clippy Agent 0
<Gallery />
## Download model
[Download](/Rolyaj/Clippy/tree/main) them in the Files & versions tab.
|
sseinn/beansm | sseinn | "2024-06-10T17:03:09Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:03:09Z" | Entry not found |
sseinn/beans | sseinn | "2024-06-10T17:03:27Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:03:27Z" | Entry not found |
neovalle/ArmoniosaaAnthea_en_es | neovalle | "2024-06-10T17:07:58Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-10T17:03:35Z" | ---
library_name: transformers
tags: []
---
# 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
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navanth360/codegen-2b-multi-lora-tagger | navanth360 | "2024-06-10T17:05:02Z" | 0 | 1 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T17:04:54Z" | ---
library_name: transformers
tags: []
---
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Reihaneh/wav2vec2_fy_common_voice_37 | Reihaneh | "2024-06-10T17:05:41Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T17:05:40Z" | ---
library_name: transformers
tags: []
---
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Timoks/Test | Timoks | "2024-06-10T17:06:03Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-10T17:06:03Z" | ---
license: apache-2.0
---
|
brookieisthatyou/BetterMommyGirlfriend | brookieisthatyou | "2024-06-11T19:20:14Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:06:20Z" | Entry not found |
code-random/hrandom_model | code-random | "2024-06-10T17:08:59Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:08:59Z" | Entry not found |
Jannchie/fav_models | Jannchie | "2024-06-10T17:10:07Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:08:59Z" | Entry not found |
MarzottiAlessia/Gemma | MarzottiAlessia | "2024-06-10T17:09:01Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T17:08:59Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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wendy41/llama2_careerly | wendy41 | "2024-06-10T17:23:48Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2024-06-10T17:10:15Z" | ---
library_name: transformers
tags: []
---
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nasser1/juju | nasser1 | "2024-06-10T17:11:33Z" | 0 | 0 | null | [
"license:cc",
"region:us"
] | null | "2024-06-10T17:11:33Z" | ---
license: cc
---
|
Seyunkil/Son | Seyunkil | "2024-06-10T17:14:25Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:14:25Z" | Entry not found |
brunolaudelino/alexandre | brunolaudelino | "2024-06-10T17:16:21Z" | 0 | 0 | null | [
"arxiv:1910.09700",
"region:us"
] | null | "2024-06-10T17:15:42Z" | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
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iloncka/exp_5_new_bg_simple-subs_1_v_5_deit_tiny_distilled_patch16_224.fb_in1k_ep_60 | iloncka | "2024-06-10T17:19:14Z" | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | "2024-06-10T17:17:25Z" | ---
tags:
- fastai
---
# Amazing!
π₯³ Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using π€ Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner π€! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
camenduru/MuseTalk | camenduru | "2024-06-10T19:46:01Z" | 0 | 0 | diffusers | [
"diffusers",
"onnx",
"safetensors",
"en",
"license:creativeml-openrail-m",
"region:us"
] | null | "2024-06-10T17:21:02Z" | ---
license: creativeml-openrail-m
language:
- en
---
# MuseTalk
MuseTalk: Real-Time High Quality Lip Synchronization with Latent Space Inpainting
</br>
Yue Zhang <sup>\*</sup>,
Minhao Liu<sup>\*</sup>,
Zhaokang Chen,
Bin Wu<sup>β </sup>,
Yingjie He,
Chao Zhan,
Wenjiang Zhou
(<sup>*</sup>Equal Contribution, <sup>β </sup>Corresponding Author, benbinwu@tencent.com)
**[github](https://github.com/TMElyralab/MuseTalk)** **[huggingface](https://huggingface.co/TMElyralab/MuseTalk)** **Project(comming soon)** **Technical report (comming soon)**
We introduce `MuseTalk`, a **real-time high quality** lip-syncing model (30fps+ on an NVIDIA Tesla V100). MuseTalk can be applied with input videos, e.g., generated by [MuseV](https://github.com/TMElyralab/MuseV), as a complete virtual human solution.
# Overview
`MuseTalk` is a real-time high quality audio-driven lip-syncing model trained in the latent space of `ft-mse-vae`, which
1. modifies an unseen face according to the input audio, with a size of face region of `256 x 256`.
1. supports audio in various languages, such as Chinese, English, and Japanese.
1. supports real-time inference with 30fps+ on an NVIDIA Tesla V100.
1. supports modification of the center point of the face region proposes, which **SIGNIFICANTLY** affects generation results.
1. checkpoint available trained on the HDTF dataset.
1. training codes (comming soon).
# News
- [04/02/2024] Released MuseTalk project and pretrained models.
## Model
MuseTalk was trained in latent spaces, where the images were encoded by a freezed VAE. The audio was encoded by a freezed `whisper-tiny` model. The architecture of the generation network was borrowed from the UNet of the `stable-diffusion-v1-4`, where the audio embeddings were fused to the image embeddings by cross-attention.
## Cases
### MuseV + MuseTalk make human photos aliveοΌ
<table class="center">
<tr style="font-weight: bolder;text-align:center;">
<td width="33%">Image</td>
<td width="33%">MuseV</td>
<td width="33%">+MuseTalk</td>
</tr>
<tr>
<td>
<img src=assets/demo/musk/musk.png width="95%">
</td>
<td >
<video src=assets/demo/yongen/yongen_musev.mp4 controls preload></video>
</td>
<td >
<video src=assets/demo/yongen/yongen_musetalk.mp4 controls preload></video>
</td>
</tr>
<tr>
<td>
<img src=assets/demo/yongen/yongen.jpeg width="95%">
</td>
<td >
<video src=https://github.com/TMElyralab/MuseTalk/assets/163980830/57ef9dee-a9fd-4dc8-839b-3fbbbf0ff3f4 controls preload></video>
</td>
<td >
<video src=https://github.com/TMElyralab/MuseTalk/assets/163980830/94d8dcba-1bcd-4b54-9d1d-8b6fc53228f0 controls preload></video>
</td>
</tr>
<tr>
<td>
<img src=assets/demo/monalisa/monalisa.png width="95%">
</td>
<td >
<video src=https://github.com/TMElyralab/MuseTalk/assets/163980830/1568f604-a34f-4526-a13a-7d282aa2e773 controls preload></video>
</td>
<td >
<video src=https://github.com/TMElyralab/MuseTalk/assets/163980830/a40784fc-a885-4c1f-9b7e-8f87b7caf4e0 controls preload></video>
</td>
</tr>
<tr>
<td>
<img src=assets/demo/sun1/sun.png width="95%">
</td>
<td >
<video src=https://github.com/TMElyralab/MuseTalk/assets/163980830/37a3a666-7b90-4244-8d3a-058cb0e44107 controls preload></video>
</td>
<td >
<video src=https://github.com/TMElyralab/MuseTalk/assets/163980830/172f4ff1-d432-45bd-a5a7-a07dec33a26b controls preload></video>
</td>
</tr>
<tr>
<td>
<img src=assets/demo/sun2/sun.png width="95%">
</td>
<td >
<video src=https://github.com/TMElyralab/MuseTalk/assets/163980830/37a3a666-7b90-4244-8d3a-058cb0e44107 controls preload></video>
</td>
<td >
<video src=https://github.com/TMElyralab/MuseTalk/assets/163980830/85a6873d-a028-4cce-af2b-6c59a1f2971d controls preload></video>
</td>
</tr>
</table >
* The character of the last two rows, `Xinying Sun`, is a supermodel KOL. You can follow her on [douyin](https://www.douyin.com/user/MS4wLjABAAAAWDThbMPN_6Xmm_JgXexbOii1K-httbu2APdG8DvDyM8).
## Video dubbing
<table class="center">
<tr style="font-weight: bolder;text-align:center;">
<td width="70%">MuseTalk</td>
<td width="30%">Original videos</td>
</tr>
<tr>
<td>
<video src=https://github.com/TMElyralab/MuseTalk/assets/163980830/4d7c5fa1-3550-4d52-8ed2-52f158150f24 controls preload></video>
</td>
<td>
<a href="//www.bilibili.com/video/BV1wT411b7HU">Link</a>
<href src=""></href>
</td>
</tr>
</table>
* For video dubbing, we applied a self-developed tool which can detect the talking person.
# TODO:
- [x] trained models and inference codes.
- [ ] technical report.
- [ ] training codes.
- [ ] online UI.
- [ ] a better model (may take longer).
# Getting Started
We provide a detailed tutorial about the installation and the basic usage of MuseTalk for new users:
## Installation
To prepare the Python environment and install additional packages such as opencv, diffusers, mmcv, etc., please follow the steps below:
### Build environment
We recommend a python version >=3.10 and cuda version =11.7. Then build environment as follows:
```shell
pip install -r requirements.txt
```
### whisper
install whisper to extract audio feature (only encoder)
```
pip install --editable ./musetalk/whisper
```
### mmlab packages
```bash
pip install --no-cache-dir -U openmim
mim install mmengine
mim install "mmcv>=2.0.1"
mim install "mmdet>=3.1.0"
mim install "mmpose>=1.1.0"
```
### Download ffmpeg-static
Download the ffmpeg-static and
```
export FFMPEG_PATH=/path/to/ffmpeg
```
for example:
```
export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static
```
### Download weights
You can download weights manually as follows:
1. Download our trained [weights](https://huggingface.co/TMElyralab/MuseTalk).
2. Download the weights of other components:
- [sd-vae-ft-mse](https://huggingface.co/stabilityai/sd-vae-ft-mse)
- [whisper](https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt)
- [dwpose](https://huggingface.co/yzd-v/DWPose/tree/main)
- [face-parse-bisent](https://github.com/zllrunning/face-parsing.PyTorch)
- [resnet18](https://download.pytorch.org/models/resnet18-5c106cde.pth)
Finally, these weights should be organized in `models` as follows:
```
./models/
βββ musetalk
β βββ musetalk.json
β βββ pytorch_model.bin
βββ dwpose
β βββ dw-ll_ucoco_384.pth
βββ face-parse-bisent
β βββ 79999_iter.pth
β βββ resnet18-5c106cde.pth
βββ sd-vae-ft-mse
β βββ config.json
β βββ diffusion_pytorch_model.bin
βββ whisper
βββ tiny.pt
```
## Quickstart
### Inference
Here, we provide the inference script.
```
python -m scripts.inference --inference_config configs/inference/test.yaml
```
configs/inference/test.yaml is the path to the inference configuration file, including video_path and audio_path.
The video_path should be either a video file or a directory of images.
#### Use of bbox_shift to have adjustable results
:mag_right: We have found that upper-bound of the mask has an important impact on mouth openness. Thus, to control the mask region, we suggest using the `bbox_shift` parameter. Positive values (moving towards the lower half) increase mouth openness, while negative values (moving towards the upper half) decrease mouth openness.
You can start by running with the default configuration to obtain the adjustable value range, and then re-run the script within this range.
For example, in the case of `Xinying Sun`, after running the default configuration, it shows that the adjustable value rage is [-9, 9]. Then, to decrease the mouth openness, we set the value to be `-7`.
```
python -m scripts.inference --inference_config configs/inference/test.yaml --bbox_shift -7
```
:pushpin: More technical details can be found in [bbox_shift](assets/BBOX_SHIFT.md).
#### Combining MuseV and MuseTalk
As a complete solution to virtual human generation, you are suggested to first apply [MuseV](https://github.com/TMElyralab/MuseV) to generate a video (text-to-video, image-to-video or pose-to-video) by referring [this](https://github.com/TMElyralab/MuseV?tab=readme-ov-file#text2video). Then, you can use `MuseTalk` to generate a lip-sync video by referring [this](https://github.com/TMElyralab/MuseTalk?tab=readme-ov-file#inference).
# Note
If you want to launch online video chats, you are suggested to generate videos using MuseV and apply necessary pre-processing such as face detection in advance. During online chatting, only UNet and the VAE decoder are involved, which makes MuseTalk real-time.
# Acknowledgement
1. We thank open-source components like [whisper](https://github.com/isaacOnline/whisper/tree/extract-embeddings), [dwpose](https://github.com/IDEA-Research/DWPose), [face-alignment](https://github.com/1adrianb/face-alignment), [face-parsing](https://github.com/zllrunning/face-parsing.PyTorch), [S3FD](https://github.com/yxlijun/S3FD.pytorch).
1. MuseTalk has referred much to [diffusers](https://github.com/huggingface/diffusers).
1. MuseTalk has been built on `HDTF` datasets.
Thanks for open-sourcing!
# Limitations
- Resolution: Though MuseTalk uses a face region size of 256 x 256, which make it better than other open-source methods, it has not yet reached the theoretical resolution bound. We will continue to deal with this problem.
If you need higher resolution, you could apply super resolution models such as [GFPGAN](https://github.com/TencentARC/GFPGAN) in combination with MuseTalk.
- Identity preservation: Some details of the original face are not well preserved, such as mustache, lip shape and color.
- Jitter: There exists some jitter as the current pipeline adopts single-frame generation.
# Citation
```bib
@article{musetalk,
title={MuseTalk: Real-Time High Quality Lip Synchorization with Latent Space Inpainting},
author={Zhang, Yue and Liu, Minhao and Chen, Zhaokang and Wu, Bin and He, Yingjie and Zhan, Chao and Zhou, Wenjiang},
journal={arxiv},
year={2024}
}
```
# Disclaimer/License
1. `code`: The code of MuseTalk is released under the MIT License. There is no limitation for both academic and commercial usage.
1. `model`: The trained model are available for any purpose, even commercially.
1. `other opensource model`: Other open-source models used must comply with their license, such as `whisper`, `ft-mse-vae`, `dwpose`, `S3FD`, etc..
1. The testdata are collected from internet, which are available for non-commercial research purposes only.
1. `AIGC`: This project strives to impact the domain of AI-driven video generation positively. Users are granted the freedom to create videos using this tool, but they are expected to comply with local laws and utilize it responsibly. The developers do not assume any responsibility for potential misuse by users. |
mNLP-project/gpt2-finetuned-mcqa-sciq | mNLP-project | "2024-06-10T20:50:06Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-06-10T17:29:38Z" | ---
license: mit
tags:
- generated_from_trainer
base_model: openai-community/gpt2
model-index:
- name: gpt2-finetuned-mcqa-sciq
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-finetuned-mcqa-sciq
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.3533
- Bertscore Precision: 0.1082
- Bertscore Recall: 0.1141
- Bertscore F1: 0.1111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bertscore Precision | Bertscore Recall | Bertscore F1 |
|:-------------:|:------:|:-----:|:---------------:|:-------------------:|:----------------:|:------------:|
| 4.4695 | 0.9999 | 5839 | 2.3612 | 0.1082 | 0.1140 | 0.1110 |
| 4.0507 | 2.0 | 11679 | 2.3533 | 0.1082 | 0.1141 | 0.1111 |
| 3.8779 | 2.9999 | 17518 | 2.3820 | 0.1080 | 0.1140 | 0.1110 |
| 3.2852 | 4.0 | 23358 | 2.4208 | 0.1080 | 0.1140 | 0.1109 |
| 3.6416 | 4.9999 | 29197 | 2.4768 | 0.1079 | 0.1139 | 0.1108 |
| 2.9843 | 6.0 | 35037 | 2.5445 | 0.1079 | 0.1139 | 0.1108 |
| 2.8509 | 6.9999 | 40876 | 2.6094 | 0.1079 | 0.1139 | 0.1108 |
| 2.6932 | 8.0 | 46716 | 2.6658 | 0.1078 | 0.1138 | 0.1107 |
| 2.5309 | 8.9999 | 52555 | 2.7283 | 0.1078 | 0.1138 | 0.1107 |
| 2.5619 | 9.9991 | 58390 | 2.7585 | 0.1078 | 0.1138 | 0.1107 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
JoshEngels/Mistral-7B-Residual-Stream-SAEs | JoshEngels | "2024-06-10T21:30:14Z" | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | "2024-06-10T17:30:32Z" | ---
license: mit
---
|
Haphuong2003/PhunPhun | Haphuong2003 | "2024-06-10T17:31:04Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:31:04Z" | Entry not found |
jdznet/animeConCom | jdznet | "2024-06-10T17:32:41Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:32:41Z" | Entry not found |
joaoppn3005/test | joaoppn3005 | "2024-06-10T17:33:05Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:33:05Z" | Entry not found |
MG31/v8_test | MG31 | "2024-06-10T17:38:33Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:33:15Z" | Entry not found |
NastyBaster/brelok | NastyBaster | "2024-06-10T17:33:39Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2024-06-10T17:33:39Z" | ---
license: apache-2.0
---
|
Zainab984/results | Zainab984 | "2024-06-10T17:36:01Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:36:01Z" | Entry not found |
jenniecoveria/BLACKPINK_JISOO_BORN_PINK_ERA_RVC_V2_250_EPOCHS | jenniecoveria | "2024-06-10T17:37:07Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:36:20Z" | Entry not found |
rahulAkaVector/modely | rahulAkaVector | "2024-06-10T17:47:02Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T17:37:19Z" | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## 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.
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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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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]
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## Technical Specifications [optional]
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manbeast3b/KinoInferLordnew | manbeast3b | "2024-06-10T17:54:13Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:38:03Z" | Entry not found |
MudassirFayaz/career_councling_bart | MudassirFayaz | "2024-06-10T17:41:54Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T17:41:53Z" | ---
library_name: transformers
tags: []
---
# 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]
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#### Preprocessing [optional]
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#### Training Hyperparameters
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<!-- 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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sajjad55/wsdbanglat5_1e4_ED1 | sajjad55 | "2024-06-10T18:39:56Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:ka05ar/Banglat5_EDx1",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | "2024-06-10T17:42:41Z" | ---
base_model: ka05ar/Banglat5_EDx1
tags:
- generated_from_trainer
model-index:
- name: wsdbanglat5_1e4_ED1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wsdbanglat5_1e4_ED1
This model is a fine-tuned version of [ka05ar/Banglat5_EDx1](https://huggingface.co/ka05ar/Banglat5_EDx1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0049
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.1282 | 1.0 | 1481 | 0.0998 |
| 0.0283 | 2.0 | 2962 | 0.0112 |
| 0.0162 | 3.0 | 4443 | 0.0081 |
| 0.0112 | 4.0 | 5924 | 0.0051 |
| 0.0088 | 5.0 | 7405 | 0.0044 |
| 0.0063 | 6.0 | 8886 | 0.0046 |
| 0.0064 | 7.0 | 10367 | 0.0048 |
| 0.0055 | 8.0 | 11848 | 0.0049 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.2
- Datasets 2.19.2
- Tokenizers 0.19.1
|
lafual/Betty_RWR | lafual | "2024-06-10T17:46:25Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2024-06-10T17:44:35Z" | ---
license: openrail
---
|
kawther1/large | kawther1 | "2024-06-20T09:02:49Z" | 0 | 0 | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | "2024-06-10T17:46:50Z" | Entry not found |
russwang/MCTS_DPO | russwang | "2024-06-10T17:47:48Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:47:48Z" | Entry not found |
rahulAkaVector/modelz | rahulAkaVector | "2024-06-10T18:14:45Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T17:48:25Z" | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## 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.
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<!-- 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
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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[More Information Needed]
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## 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]
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## Technical Specifications [optional]
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[More Information Needed]
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MudassirFayaz/career_councling_bart_0.1 | MudassirFayaz | "2024-06-10T17:50:29Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-06-10T17:50:28Z" | ---
library_name: transformers
tags: []
---
# 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. -->
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[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
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### 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]
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RamyaRamakrishna/the-nexus-1 | RamyaRamakrishna | "2024-06-10T17:50:31Z" | 0 | 0 | null | [
"region:us"
] | null | "2024-06-10T17:50:31Z" | Entry not found |