modelId string | author string | last_modified timestamp[us, tz=UTC] | downloads int64 | likes int64 | library_name string | tags list | pipeline_tag string | createdAt timestamp[us, tz=UTC] | card string |
|---|---|---|---|---|---|---|---|---|---|
NEW-ww-wi-vk-20k/wATCH.ww-wi-vk-ww-wi-vk-ww-wi-vk.original | NEW-ww-wi-vk-20k | 2025-06-14T20:08:20Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-14T20:06:00Z | [๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://videohere.top/?ww-wi-vk)
[โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐คโค๏ธโค๏ธโฌ๏ธโฌ๏ธโ](https://videohere.top/?ww-wi-vk)
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?ww-wi-vk) |
jfang/mars-vit-base-mae | jfang | 2025-06-14T20:05:57Z | 0 | 0 | null | [
"safetensors",
"vit",
"vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-06-14T20:05:56Z | ---
language: en
tags:
- vision
license: apache-2.0
---
Model Card for Mars ViT Base Model
## Model Architecture
- Architecture: Vision Transformer (ViT) Base
- Input Channels: 1 (grayscale images)
- Number of Classes: 0 (features extraction)
## Training Method
- Method: Masked Autoencoder (MAE)
- Dataset: 2 million CTX images
## Usage Examples
### Using timm (suggested now)
First download checkpoint-1199.pth (backbone only)
```python
import timm
import torch
model = timm.create_model(
'vit_base_patch16_224',
in_chans=1,
num_classes=0,
global_pool='',
checkpoint_path="./checkpoint-1199.pth" # must use local path
)
model.eval()
# for images, need to convert to single channel, 224, and normalize
# transform example:
# transform = transforms.Compose([
# transforms.ToTensor(),
# transforms.Resize((224, 224)),
# transforms.Grayscale(num_output_channels=1),
# transforms.Normalize(mean=[0.5], std=[0.5])
# ])
x = torch.randn(1, 1, 224, 224)
with torch.no_grad():
features = model.forward_features(x) # shape [1, tokens, embed_dim]
print(features.shape)
cls_token = features[:, 0]
patch_tokens = features[:, 1:]
```
Using transformers
```python
from transformers import AutoModel, AutoImageProcessor
model = AutoModel.from_pretrained("jfang/mars-vit-base-ctx2m")
image_processor = AutoImageProcessor.from_pretrained("jfang/mars-vit-base-ctx2m")
# Example usage
from PIL import Image
image = Image.open("some_image.png").convert("L") # 1-channel
inputs = image_processor(image, return_tensors="pt")
outputs = model(**inputs)
```
## MAE reconstruction
Under ./mae folder, there is full encoder-decoder MAE model and a notebook for visualization.
### Limitations
The model is trained specifically on CTX images and may not generalize well to other types of images without further fine-tuning.
The model is designed for feature extraction and does not include a classification head.
|
saketh-chervu/wordle-agent-dpo-checkpoint-3000 | saketh-chervu | 2025-06-14T20:02:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-14T19:57:33Z | ---
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 ๐ค 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] |
Katrina-Lim-Kiffy-Scandal-video/VIDEO.Kiffy.Katrina.Lim.Viral.Video.Tutorial.Official | Katrina-Lim-Kiffy-Scandal-video | 2025-06-14T19:55:02Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-14T19:36:35Z | 18 seconds ago
<a href="https://tv2online.com/Video/?video" rel="nofollow">โบโบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐ค๏ธโ</a></p>
<a href="https://tv2online.com/Video/?video" rel="nofollow">๐ดโบ๐๐๐๐๐ ๐๐๐๐ ๐==โบโบ ๐๐จ๐ฐ๐ง๐ฅ๐จ๐๐ ๐๐จ๐ฐโฌ๏ธโฌ๏ธโ</a></p>
<p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Video/?video"><img border="Viral+Leaked+Video" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
Katrina Lim Viral Kiffy Video Tutorial Original Video video oficial twitter
L๐aked Video Katrina Lim Viral Kiffy Video Tutorial Original Video Viral Video L๐aked on X Twitter
L๐aked Video Katrina Lim Viral Kiffy Video Tutorial Original Video Viral Video L๐aked on X Twitter Telegram
L๐aked Video Katrina Lim Viral Kiffy Video Tutorial Original Video Viral Video L๐aked on X Twitter
New tracks tagged Katrina lim kiffy. Latest Tracks ยท Most Popular ยท Playlists ยท [Original+New]^ Katrina Lim kiffy Viral Video Full Video Original Link on X
Katrina Lim Viral Kiffy Video: The Real Story Behind
What is the Katrina Lim viral Kiffy video? It is an explicit video that was leaked without consent, involving digital influencer Katrina Lim.
[Original VIDEO] Katrina Lim viral Kiffy Viral Video Full Video
Katrina Lim Kiffy, a rising digital content creator, recently went viral after a leaked video began circulating across various social media platforms, including
VIDEO 18+Katrina Lim Viral Kiffy Viral Full Video
18 seconds ago
<a href="https://tv2online.com/Video/?video" rel="nofollow">โบโบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐ค๏ธโ</a></p>
<a href="https://tv2online.com/Video/?video" rel="nofollow">๐ดโบ๐๐๐๐๐ ๐๐๐๐ ๐==โบโบ ๐๐จ๐ฐ๐ง๐ฅ๐จ๐๐ ๐๐จ๐ฐโฌ๏ธโฌ๏ธโ</a></p>
<p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Video/?video"><img border="Viral+Leaked+Video" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
Social media star Video been posting short Videoos and naughty pics on Tiktok platform for a while now. The purported leak has stirred a maelanage of reactions,
+videos)!* Katrina lim kiffy viral new original video clip
Video, a young and talented digital creator, recently became famous thanks to this interesting video. Video Link 1. Video Link 2. Katrina lim
Stream Video: Viral Katrina Lim Kiffy Full video Link
Stream Video: Viral Katrina Lim Kiffy Full video Link
We're on a journey to advance and democratize artificial intelligence through open source and open science.
Stream New (LINK) Video Puno@tg 18+ katrina lim kiffy twitter
Stream New (LINK) Video Puno@tg 18+ katrina lim kiffy twitter & telegram katrina lim katrina lim kiffy. [-FULL-VIRAL-]โ Actress katrina lim Video Original
Music tracks, songs, playlists tagged katrina on SoundCloud
] Katrina lim viral kiffy Viral Video Full Original Video Viral On Social Media X zbu Katrina lim kiffy viral video Full original LINK HD NOW Trending.
Katrina Lim Viral Kiffy Video: The Real Story Behind the Social Media Frenzy
What is the Katrina Lim viral Kiffy video? It is an explicit video that was leaked without consent, involving digital influencer Katrina Lim.
Who Is Katrina Lim? The Truth Behind the โKiffyโ Viral Storm
Katrina LimKitrina Lim is a Southeast Asian influencer whose alleged involvement in a leaked video caused her name to trend across social media
Arabella Stanton: Meet the New Face of Hermione Granger in HBOโs Harry Potter Series
Arabella Stanton's life is about to change forever, and the world of Harry Potter is ready to welcome her with open arms.
Charli DโAmelioโs TikTok Takeover: The Teen Star Who Redefined Viral Dance Culture
Charli D'Amelio, TikTok's most-followed female star, redefined viral culture with her iconic Renegade dance. Explore her rise, influence.
Annie Knightโs Viral Fame and the Dark Side of Her OnlyFans Success
Annie Knight's OnlyFans journey showcases immense success and serious challenges, from skyrocketing wealth to health risks and the ethics.
18 seconds ago
<a href="https://tv2online.com/Video/?video" rel="nofollow">โบโบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐ค๏ธโ</a></p>
<a href="https://tv2online.com/Video/?video" rel="nofollow">๐ดโบ๐๐๐๐๐ ๐๐๐๐ ๐==โบโบ ๐๐จ๐ฐ๐ง๐ฅ๐จ๐๐ ๐๐จ๐ฐโฌ๏ธโฌ๏ธโ</a></p>
<p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Video/?video"><img border="Viral+Leaked+Video" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p> |
tabitha-malisawa/Original.FULL.VIDEO.LINK.tabitha-malisawa.tabitha-malisawa.Video.Leaks.Official | tabitha-malisawa | 2025-06-14T19:47:43Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-14T19:43:45Z | [<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?tabitha-malisawa)
[โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐คโค๏ธโค๏ธโฌ๏ธโฌ๏ธโ](https://videohere.top/?tabitha-malisawa)
[โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐คโค๏ธโค๏ธโฌ๏ธโฌ๏ธโ](https://videohere.top/?tabitha-malisawa) |
tabitha-malisawa/wATCH.tabitha-malisawa-tabitha-malisawa-tabitha-malisawa.original | tabitha-malisawa | 2025-06-14T19:47:34Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-14T19:42:12Z | [<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?tabitha-malisawa)
[โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐คโค๏ธโค๏ธโฌ๏ธโฌ๏ธโ](https://videohere.top/?tabitha-malisawa)
[โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐คโค๏ธโค๏ธโฌ๏ธโฌ๏ธโ](https://videohere.top/?tabitha-malisawa) |
SolySae/botmesaj | SolySae | 2025-06-14T19:34:03Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-14T19:34:03Z | ---
license: apache-2.0
---
|
turboderp/Grok-3-reasoning-gemma3-12B-distilled-HF-exl3 | turboderp | 2025-06-14T19:32:21Z | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"gemma3",
"en",
"base_model:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-12b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T19:30:40Z | ---
base_model: unsloth/gemma-3-12b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
EXL3 quants of [Grok-3-reasoning-gemma3-12B-distilled-HF](https://huggingface.co/reedmayhew/Grok-3-reasoning-gemma3-12B-distilled-HF)
[4.00 bits per weight](https://huggingface.co/turboderp/Grok-3-reasoning-gemma3-12B-distilled-HF-exl3/tree/4.0bpw) |
ibuki95/vision_72_30 | ibuki95 | 2025-06-14T19:13:38Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-06-14T19:10:41Z | ---
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 ๐ค 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] |
Kaidiyar/bert-finetuned-ner | Kaidiyar | 2025-06-14T19:08:13Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_co... | token-classification | 2025-06-14T18:42:13Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9365158296038455
- name: Recall
type: recall
value: 0.9508582968697409
- name: F1
type: f1
value: 0.9436325678496869
- name: Accuracy
type: accuracy
value: 0.9861806087007712
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0636
- Precision: 0.9365
- Recall: 0.9509
- F1: 0.9436
- Accuracy: 0.9862
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0758 | 1.0 | 1756 | 0.0685 | 0.9040 | 0.9362 | 0.9198 | 0.9814 |
| 0.0347 | 2.0 | 3512 | 0.0700 | 0.9292 | 0.9455 | 0.9373 | 0.9849 |
| 0.0213 | 3.0 | 5268 | 0.0636 | 0.9365 | 0.9509 | 0.9436 | 0.9862 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Tohirju/QwenTg-v5 | Tohirju | 2025-06-14T19:07:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-0.6B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-0.6B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T13:34:32Z | ---
base_model: unsloth/Qwen3-0.6B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Tohirju
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-0.6B-unsloth-bnb-4bit
This qwen3 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)
|
maanm/llava-v1.6-mistral-7b-orpo-rlaif | maanm | 2025-06-14T19:06:42Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:llava-hf/llava-v1.6-mistral-7b-hf",
"base_model:adapter:llava-hf/llava-v1.6-mistral-7b-hf",
"region:us"
] | null | 2025-06-14T19:06:21Z | ---
base_model: llava-hf/llava-v1.6-mistral-7b-hf
library_name: peft
---
# 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. -->
- **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]
### Framework versions
- PEFT 0.15.2 |
phospho-app/Sehef-ACT_BBOX-yellow-poau5 | phospho-app | 2025-06-14T18:43:56Z | 0 | 0 | null | [
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-06-14T18:19:58Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [phospho-app/yellow_bboxes](https://huggingface.co/datasets/phospho-app/yellow_bboxes)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
๐ **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
๐ค **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
sergioalves/c039d6df-1b55-4e6a-a1e3-0324c7d5998c | sergioalves | 2025-06-14T18:40:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:samoline/cfceddc7-3c05-4f97-8617-53458073a30a",
"base_model:adapter:samoline/cfceddc7-3c05-4f97-8617-53458073a30a",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-14T18:28:23Z | ---
library_name: peft
base_model: samoline/cfceddc7-3c05-4f97-8617-53458073a30a
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c039d6df-1b55-4e6a-a1e3-0324c7d5998c
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: samoline/cfceddc7-3c05-4f97-8617-53458073a30a
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 4158de7a5b28c60a_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 0.8
group_by_length: false
hub_model_id: sergioalves/c039d6df-1b55-4e6a-a1e3-0324c7d5998c
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-07
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_steps: 300
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/4158de7a5b28c60a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: ad1dd86c-e458-4160-88d3-f6d4a15f3781
wandb_project: s56-7
wandb_run: your_name
wandb_runid: ad1dd86c-e458-4160-88d3-f6d4a15f3781
warmup_steps: 30
weight_decay: 0.05
xformers_attention: true
```
</details><br>
# c039d6df-1b55-4e6a-a1e3-0324c7d5998c
This model is a fine-tuned version of [samoline/cfceddc7-3c05-4f97-8617-53458073a30a](https://huggingface.co/samoline/cfceddc7-3c05-4f97-8617-53458073a30a) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1351
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 30
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.1302 | 0.0003 | 1 | 1.1355 |
| 1.0225 | 0.0434 | 150 | 1.1352 |
| 1.1783 | 0.0868 | 300 | 1.1351 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
tusharmishra288/xlm-roberta-base-finetuned-panx-de-fr | tusharmishra288 | 2025-06-14T18:32:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-06-14T18:25:31Z | ---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de-fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de-fr
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1597
- F1: 0.8619
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2806 | 1.0 | 715 | 0.1792 | 0.8212 |
| 0.146 | 2.0 | 1430 | 0.1627 | 0.8492 |
| 0.0944 | 3.0 | 2145 | 0.1597 | 0.8619 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
jaremavip/clip-military-civilian-classifier-oleks-lapv | jaremavip | 2025-06-14T18:20:27Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"clip",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:35000",
"loss:MultipleNegativesRankingLoss",
"dataset:jaremavip/military-civilian-clip-50k",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:sentence-transfo... | sentence-similarity | 2025-06-14T18:20:04Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:35000
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/clip-ViT-L-14
widget:
- source_sentence: civilian life, peaceful scenes, non-military content, everyday
photos, screenshots, infographics, politics, news
sentences:
- military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv,
fpv drone, tracks vehicles, planes, assault rifles
- military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv,
fpv drone, tracks vehicles, planes, assault rifles
- military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv,
fpv drone, tracks vehicles, planes, assault rifles
- source_sentence: military equipment, vehicles, soldiers, drones, warfare, aircraft,
tanks, ifv, fpv drone, tracks vehicles, planes, assault rifles
sentences:
- civilian life, peaceful scenes, non-military content, everyday photos, screenshots,
infographics, politics, news
- civilian life, peaceful scenes, non-military content, everyday photos, screenshots,
infographics, politics, news
- military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv,
fpv drone, tracks vehicles, planes, assault rifles
- source_sentence: civilian life, peaceful scenes, non-military content, everyday
photos, screenshots, infographics, politics, news
sentences:
- civilian life, peaceful scenes, non-military content, everyday photos, screenshots,
infographics, politics, news
- military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv,
fpv drone, tracks vehicles, planes, assault rifles
- military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv,
fpv drone, tracks vehicles, planes, assault rifles
- source_sentence: military equipment, vehicles, soldiers, drones, warfare, aircraft,
tanks, ifv, fpv drone, tracks vehicles, planes, assault rifles
sentences:
- civilian life, peaceful scenes, non-military content, everyday photos, screenshots,
infographics, politics, news
- military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv,
fpv drone, tracks vehicles, planes, assault rifles
- civilian life, peaceful scenes, non-military content, everyday photos, screenshots,
infographics, politics, news
- source_sentence: military equipment, vehicles, soldiers, drones, warfare, aircraft,
tanks, ifv, fpv drone, tracks vehicles, planes, assault rifles
sentences:
- military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv,
fpv drone, tracks vehicles, planes, assault rifles
- military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv,
fpv drone, tracks vehicles, planes, assault rifles
- civilian life, peaceful scenes, non-military content, everyday photos, screenshots,
infographics, politics, news
datasets:
- jaremavip/military-civilian-clip-50k
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: SentenceTransformer based on sentence-transformers/clip-ViT-L-14
results:
- task:
type: triplet
name: Triplet
dataset:
name: military civilian validation
type: military-civilian-validation
metrics:
- type: cosine_accuracy
value: 0.9980000257492065
name: Cosine Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: military civilian test
type: military-civilian-test
metrics:
- type: cosine_accuracy
value: 0.9982666373252869
name: Cosine Accuracy
---
# SentenceTransformer based on sentence-transformers/clip-ViT-L-14
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/clip-ViT-L-14](https://huggingface.co/sentence-transformers/clip-ViT-L-14) on the [military-civilian-clip-50k](https://huggingface.co/datasets/jaremavip/military-civilian-clip-50k) dataset. It maps sentences & paragraphs to a None-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/clip-ViT-L-14](https://huggingface.co/sentence-transformers/clip-ViT-L-14) <!-- at revision 3b12140ad0f9750045e404f187cfccd04bcaf250 -->
- **Maximum Sequence Length:** None tokens
- **Output Dimensionality:** None dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [military-civilian-clip-50k](https://huggingface.co/datasets/jaremavip/military-civilian-clip-50k)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): CLIPModel()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the ๐ค Hub
model = SentenceTransformer("jaremavip/clip-military-civilian-classifier-oleks-lapv")
# Run inference
sentences = [
'military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv, fpv drone, tracks vehicles, planes, assault rifles',
'civilian life, peaceful scenes, non-military content, everyday photos, screenshots, infographics, politics, news',
'military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv, fpv drone, tracks vehicles, planes, assault rifles',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Datasets: `military-civilian-validation` and `military-civilian-test`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | military-civilian-validation | military-civilian-test |
|:--------------------|:-----------------------------|:-----------------------|
| **cosine_accuracy** | **0.998** | **0.9983** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### military-civilian-clip-50k
* Dataset: [military-civilian-clip-50k](https://huggingface.co/datasets/jaremavip/military-civilian-clip-50k) at [e070934](https://huggingface.co/datasets/jaremavip/military-civilian-clip-50k/tree/e070934b6022f4349f4ce3a8350435dafce14715)
* Size: 35,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:--------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | PIL.PngImagePlugin.PngImageFile | string | string |
| details | <ul><li></li></ul> | <ul><li>min: 23 tokens</li><li>mean: 26.5 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 26.5 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|
| <code><PIL.PngImagePlugin.PngImageFile image mode=RGB size=336x336 at 0x7B632816D150></code> | <code>military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv, fpv drone, tracks vehicles, planes, assault rifles</code> | <code>civilian life, peaceful scenes, non-military content, everyday photos, screenshots, infographics, politics, news</code> |
| <code><PIL.PngImagePlugin.PngImageFile image mode=RGB size=336x336 at 0x7B632816D690></code> | <code>civilian life, peaceful scenes, non-military content, everyday photos, screenshots, infographics, politics, news</code> | <code>military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv, fpv drone, tracks vehicles, planes, assault rifles</code> |
| <code><PIL.PngImagePlugin.PngImageFile image mode=RGB size=336x336 at 0x7B632816F3D0></code> | <code>civilian life, peaceful scenes, non-military content, everyday photos, screenshots, infographics, politics, news</code> | <code>military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv, fpv drone, tracks vehicles, planes, assault rifles</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### military-civilian-clip-50k
* Dataset: [military-civilian-clip-50k](https://huggingface.co/datasets/jaremavip/military-civilian-clip-50k) at [e070934](https://huggingface.co/datasets/jaremavip/military-civilian-clip-50k/tree/e070934b6022f4349f4ce3a8350435dafce14715)
* Size: 7,500 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:--------------------------------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | PIL.PngImagePlugin.PngImageFile | string | string |
| details | <ul><li></li></ul> | <ul><li>min: 23 tokens</li><li>mean: 26.63 tokens</li><li>max: 30 tokens</li></ul> | <ul><li>min: 23 tokens</li><li>mean: 26.37 tokens</li><li>max: 30 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------|
| <code><PIL.PngImagePlugin.PngImageFile image mode=RGB size=336x336 at 0x7B632A521710></code> | <code>civilian life, peaceful scenes, non-military content, everyday photos, screenshots, infographics, politics, news</code> | <code>military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv, fpv drone, tracks vehicles, planes, assault rifles</code> |
| <code><PIL.PngImagePlugin.PngImageFile image mode=RGB size=336x336 at 0x7B632A520990></code> | <code>military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv, fpv drone, tracks vehicles, planes, assault rifles</code> | <code>civilian life, peaceful scenes, non-military content, everyday photos, screenshots, infographics, politics, news</code> |
| <code><PIL.PngImagePlugin.PngImageFile image mode=RGB size=336x336 at 0x7B632A523350></code> | <code>civilian life, peaceful scenes, non-military content, everyday photos, screenshots, infographics, politics, news</code> | <code>military equipment, vehicles, soldiers, drones, warfare, aircraft, tanks, ifv, fpv drone, tracks vehicles, planes, assault rifles</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 40
- `per_device_eval_batch_size`: 40
- `gradient_accumulation_steps`: 2
- `learning_rate`: 5e-06
- `num_train_epochs`: 2
- `warmup_ratio`: 0.1
- `load_best_model_at_end`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 40
- `per_device_eval_batch_size`: 40
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-06
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | Validation Loss | military-civilian-validation_cosine_accuracy | military-civilian-test_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:--------------------------------------------:|:--------------------------------------:|
| -1 | -1 | - | - | 0.7552 | - |
| 0.0114 | 5 | 8.5373 | - | - | - |
| 0.0229 | 10 | 8.5174 | - | - | - |
| 0.0343 | 15 | 8.4988 | - | - | - |
| 0.0457 | 20 | 8.4117 | - | - | - |
| 0.0571 | 25 | 8.4634 | - | - | - |
| 0.0686 | 30 | 8.4094 | - | - | - |
| 0.08 | 35 | 8.3826 | - | - | - |
| 0.0914 | 40 | 8.3373 | - | - | - |
| 0.1029 | 45 | 8.3125 | - | - | - |
| 0.1143 | 50 | 8.1871 | - | - | - |
| 0.1257 | 55 | 8.1782 | - | - | - |
| 0.1371 | 60 | 8.1045 | - | - | - |
| 0.1486 | 65 | 8.0556 | - | - | - |
| 0.16 | 70 | 7.9885 | - | - | - |
| 0.1714 | 75 | 7.9608 | - | - | - |
| 0.1829 | 80 | 7.8939 | - | - | - |
| 0.1943 | 85 | 7.8227 | - | - | - |
| 0.2057 | 90 | 7.7889 | - | - | - |
| 0.2171 | 95 | 7.7287 | - | - | - |
| 0.2286 | 100 | 7.6767 | - | - | - |
| 0.24 | 105 | 7.6303 | - | - | - |
| 0.2514 | 110 | 7.6113 | - | - | - |
| 0.2629 | 115 | 7.5694 | - | - | - |
| 0.2743 | 120 | 7.5577 | - | - | - |
| 0.2857 | 125 | 7.5217 | - | - | - |
| 0.2971 | 130 | 7.5081 | - | - | - |
| 0.3086 | 135 | 7.481 | - | - | - |
| 0.32 | 140 | 7.4859 | - | - | - |
| 0.3314 | 145 | 7.4639 | - | - | - |
| 0.3429 | 150 | 7.4805 | - | - | - |
| 0.3543 | 155 | 7.4462 | - | - | - |
| 0.3657 | 160 | 7.4319 | - | - | - |
| 0.3771 | 165 | 7.438 | - | - | - |
| 0.3886 | 170 | 7.4348 | - | - | - |
| 0.4 | 175 | 7.4341 | - | - | - |
| 0.4114 | 180 | 7.4289 | - | - | - |
| 0.4229 | 185 | 7.4239 | - | - | - |
| 0.4343 | 190 | 7.409 | - | - | - |
| 0.4457 | 195 | 7.4151 | - | - | - |
| 0.4571 | 200 | 7.4084 | - | - | - |
| 0.4686 | 205 | 7.4241 | - | - | - |
| 0.48 | 210 | 7.4014 | - | - | - |
| 0.4914 | 215 | 7.4079 | - | - | - |
| 0.5029 | 220 | 7.4454 | - | - | - |
| 0.5143 | 225 | 7.4052 | - | - | - |
| 0.5257 | 230 | 7.4143 | - | - | - |
| 0.5371 | 235 | 7.4084 | - | - | - |
| 0.5486 | 240 | 7.405 | - | - | - |
| 0.56 | 245 | 7.4126 | - | - | - |
| 0.5714 | 250 | 7.4022 | - | - | - |
| 0.5829 | 255 | 7.4216 | - | - | - |
| 0.5943 | 260 | 7.4029 | - | - | - |
| 0.6057 | 265 | 7.4101 | - | - | - |
| 0.6171 | 270 | 7.4105 | - | - | - |
| 0.6286 | 275 | 7.4073 | - | - | - |
| 0.64 | 280 | 7.4062 | - | - | - |
| 0.6514 | 285 | 7.3999 | - | - | - |
| 0.6629 | 290 | 7.4017 | - | - | - |
| 0.6743 | 295 | 7.4061 | - | - | - |
| 0.6857 | 300 | 7.3975 | - | - | - |
| 0.6971 | 305 | 7.3978 | - | - | - |
| 0.7086 | 310 | 7.4011 | - | - | - |
| 0.72 | 315 | 7.392 | - | - | - |
| 0.7314 | 320 | 7.4049 | - | - | - |
| 0.7429 | 325 | 7.3909 | - | - | - |
| 0.7543 | 330 | 7.4006 | - | - | - |
| 0.7657 | 335 | 7.4027 | - | - | - |
| 0.7771 | 340 | 7.3993 | - | - | - |
| 0.7886 | 345 | 7.3953 | - | - | - |
| 0.8 | 350 | 7.41 | - | - | - |
| 0.8114 | 355 | 7.3982 | - | - | - |
| 0.8229 | 360 | 7.4056 | - | - | - |
| 0.8343 | 365 | 7.3971 | - | - | - |
| 0.8457 | 370 | 7.395 | - | - | - |
| 0.8571 | 375 | 7.4024 | - | - | - |
| 0.8686 | 380 | 7.3915 | - | - | - |
| 0.88 | 385 | 7.393 | - | - | - |
| 0.8914 | 390 | 7.3959 | - | - | - |
| 0.9029 | 395 | 7.3991 | - | - | - |
| 0.9143 | 400 | 7.4175 | - | - | - |
| 0.9257 | 405 | 7.3896 | - | - | - |
| 0.9371 | 410 | 7.3996 | - | - | - |
| 0.9486 | 415 | 7.4053 | - | - | - |
| 0.96 | 420 | 7.397 | - | - | - |
| 0.9714 | 425 | 7.4069 | - | - | - |
| 0.9829 | 430 | 7.3901 | - | - | - |
| 0.9943 | 435 | 7.3921 | - | - | - |
| 1.0 | 438 | - | 3.6956 | 0.9983 | - |
| 1.0046 | 440 | 6.669 | - | - | - |
| 1.016 | 445 | 7.3912 | - | - | - |
| 1.0274 | 450 | 7.3966 | - | - | - |
| 1.0389 | 455 | 7.3936 | - | - | - |
| 1.0503 | 460 | 7.3905 | - | - | - |
| 1.0617 | 465 | 7.4014 | - | - | - |
| 1.0731 | 470 | 7.3914 | - | - | - |
| 1.0846 | 475 | 7.3972 | - | - | - |
| 1.096 | 480 | 7.3891 | - | - | - |
| 1.1074 | 485 | 7.3876 | - | - | - |
| 1.1189 | 490 | 7.395 | - | - | - |
| 1.1303 | 495 | 7.3924 | - | - | - |
| 1.1417 | 500 | 7.3898 | - | - | - |
| 1.1531 | 505 | 7.3901 | - | - | - |
| 1.1646 | 510 | 7.3899 | - | - | - |
| 1.176 | 515 | 7.3966 | - | - | - |
| 1.1874 | 520 | 7.3938 | - | - | - |
| 1.1989 | 525 | 7.3869 | - | - | - |
| 1.2103 | 530 | 7.3931 | - | - | - |
| 1.2217 | 535 | 7.3856 | - | - | - |
| 1.2331 | 540 | 7.405 | - | - | - |
| 1.2446 | 545 | 7.3906 | - | - | - |
| 1.256 | 550 | 7.394 | - | - | - |
| 1.2674 | 555 | 7.3906 | - | - | - |
| 1.2789 | 560 | 7.3892 | - | - | - |
| 1.2903 | 565 | 7.4027 | - | - | - |
| 1.3017 | 570 | 7.3846 | - | - | - |
| 1.3131 | 575 | 7.4102 | - | - | - |
| 1.3246 | 580 | 7.3964 | - | - | - |
| 1.336 | 585 | 7.3905 | - | - | - |
| 1.3474 | 590 | 7.3977 | - | - | - |
| 1.3589 | 595 | 7.3913 | - | - | - |
| 1.3703 | 600 | 7.386 | - | - | - |
| 1.3817 | 605 | 7.3899 | - | - | - |
| 1.3931 | 610 | 7.391 | - | - | - |
| 1.4046 | 615 | 7.3826 | - | - | - |
| 1.416 | 620 | 7.3946 | - | - | - |
| 1.4274 | 625 | 7.4009 | - | - | - |
| 1.4389 | 630 | 7.3882 | - | - | - |
| 1.4503 | 635 | 7.3892 | - | - | - |
| 1.4617 | 640 | 7.3858 | - | - | - |
| 1.4731 | 645 | 7.404 | - | - | - |
| 1.4846 | 650 | 7.3938 | - | - | - |
| 1.496 | 655 | 7.3843 | - | - | - |
| 1.5074 | 660 | 7.3943 | - | - | - |
| 1.5189 | 665 | 7.3867 | - | - | - |
| 1.5303 | 670 | 7.3847 | - | - | - |
| 1.5417 | 675 | 7.3848 | - | - | - |
| 1.5531 | 680 | 7.3997 | - | - | - |
| 1.5646 | 685 | 7.4005 | - | - | - |
| 1.576 | 690 | 7.3941 | - | - | - |
| 1.5874 | 695 | 7.3842 | - | - | - |
| 1.5989 | 700 | 7.3908 | - | - | - |
| 1.6103 | 705 | 7.3899 | - | - | - |
| 1.6217 | 710 | 7.4079 | - | - | - |
| 1.6331 | 715 | 7.3851 | - | - | - |
| 1.6446 | 720 | 7.3932 | - | - | - |
| 1.6560 | 725 | 7.3964 | - | - | - |
| 1.6674 | 730 | 7.3879 | - | - | - |
| 1.6789 | 735 | 7.3973 | - | - | - |
| 1.6903 | 740 | 7.3889 | - | - | - |
| 1.7017 | 745 | 7.3888 | - | - | - |
| 1.7131 | 750 | 7.3823 | - | - | - |
| 1.7246 | 755 | 7.3839 | - | - | - |
| 1.736 | 760 | 7.3925 | - | - | - |
| 1.7474 | 765 | 7.3866 | - | - | - |
| 1.7589 | 770 | 7.391 | - | - | - |
| 1.7703 | 775 | 7.3842 | - | - | - |
| 1.7817 | 780 | 7.3852 | - | - | - |
| 1.7931 | 785 | 7.3866 | - | - | - |
| 1.8046 | 790 | 7.3994 | - | - | - |
| 1.8160 | 795 | 7.3935 | - | - | - |
| 1.8274 | 800 | 7.3915 | - | - | - |
| 1.8389 | 805 | 7.3823 | - | - | - |
| 1.8503 | 810 | 7.3821 | - | - | - |
| 1.8617 | 815 | 7.3865 | - | - | - |
| 1.8731 | 820 | 7.3907 | - | - | - |
| 1.8846 | 825 | 7.3888 | - | - | - |
| 1.896 | 830 | 7.3856 | - | - | - |
| 1.9074 | 835 | 7.3847 | - | - | - |
| 1.9189 | 840 | 7.3855 | - | - | - |
| 1.9303 | 845 | 7.3918 | - | - | - |
| 1.9417 | 850 | 7.3895 | - | - | - |
| 1.9531 | 855 | 7.4032 | - | - | - |
| 1.9646 | 860 | 7.3837 | - | - | - |
| 1.976 | 865 | 7.4219 | - | - | - |
| 1.9874 | 870 | 7.3856 | - | - | - |
| 1.9966 | 874 | - | 3.6937 | 0.9980 | - |
| -1 | -1 | - | - | - | 0.9983 |
</details>
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.2
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
msh985/Diet-Plan | msh985 | 2025-06-14T18:08:06Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-06-14T18:08:06Z | ---
license: apache-2.0
---
|
iambestfeed/phobert_v2_flax_wiki_filter_32_1e_2e5 | iambestfeed | 2025-06-14T18:01:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-06-14T17:59:39Z | ---
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 ๐ค 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] |
phospho-app/scholl-ACT_BBOX-yellow_ball-tmrhw | phospho-app | 2025-06-14T17:31:42Z | 0 | 0 | null | [
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-06-14T17:06:37Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [phospho-app/yellow_ball_bboxes](https://huggingface.co/datasets/phospho-app/yellow_ball_bboxes)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
๐ **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
๐ค **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
IoanaLiviaPopescu/real-data-synth-data-400-2-Emil-Neural-whisper-small | IoanaLiviaPopescu | 2025-06-14T17:27:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ro",
"dataset:IoanaLiviaPopescu/RealVoiceSynthVoice-400-2-Emil-Neural",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endp... | automatic-speech-recognition | 2025-06-14T17:03:53Z | ---
library_name: transformers
language:
- ro
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- IoanaLiviaPopescu/RealVoiceSynthVoice-400-2-Emil-Neural
metrics:
- wer
model-index:
- name: IoanaLiviaPopescu/IoanaLiviaPopescu/real-data-synth-data-400-2-Emil-Neural-whisper-small
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: IoanaLiviaPopescu/RealVoiceSynthVoice-400-2-Emil-Neural
type: IoanaLiviaPopescu/RealVoiceSynthVoice-400-2-Emil-Neural
config: default
split: test
args: 'split: validation'
metrics:
- name: Wer
type: wer
value: 18.735017517978978
---
<!-- 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. -->
# IoanaLiviaPopescu/IoanaLiviaPopescu/real-data-synth-data-400-2-Emil-Neural-whisper-small
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IoanaLiviaPopescu/RealVoiceSynthVoice-400-2-Emil-Neural dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4100
- Wer: 18.7350
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 0 | 0 | 0.6024 | 27.8812 |
| 0.5049 | 1.0 | 13 | 0.4971 | 19.9705 |
| 0.3055 | 2.0 | 26 | 0.4351 | 20.2102 |
| 0.1979 | 3.0 | 39 | 0.4137 | 19.0854 |
| 0.1439 | 4.0 | 52 | 0.4080 | 19.0301 |
| 0.119 | 5.0 | 65 | 0.4100 | 18.7350 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
carlkaziboni/a2c-PandaReachDense-v3 | carlkaziboni | 2025-06-14T17:24:39Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-14T17:20:18Z | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.20 +/- 0.10
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
sorasora2025/howl-sit-v2-lorahowl-sit-lora | sorasora2025 | 2025-06-14T17:24:26Z | 0 | 0 | null | [
"safetensors",
"lora",
"stable-diffusion-v1-5",
"text-to-image",
"howl",
"husky",
"pet",
"dog",
"lora-training",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:mit",
"region:us"
] | text-to-image | 2025-06-14T17:12:31Z | ---
license: mit
base_model: runwayml/stable-diffusion-v1-5
tags:
- lora
- stable-diffusion-v1-5
- text-to-image
- howl
- husky
- pet
- dog
- lora-training
---
# Howl LoRA โ Sitting Pose
ใใใฏใใฆใซใจใใๅๅใฎใทใใชใขใณใในใญใผใฎ LoRA ใขใใซใงใใ
Stable Diffusion v1.5 ใใใผในใซใๆญฃ้ขใใใๅบงใใใฆ่ฆไธใใฆใใใใผใบใงๅญฆ็ฟใใพใใใ
## ๆจๅฅจใใญใณใใ
howlsilblueodd dog, silvery-white fur only, no black, pure white muzzle and eye area, small ears for a husky, round gentle eyes (not almond-shaped), brown left eye, pale blue right eye, friendly soft face, no facial mask markings, only forehead marking, sitting, front view, looking up slightly, ultra realistic, sharp focus, detailed fur, soft lighting, DSLR style, photo
## ไฝฟ็จๆนๆณ๏ผdiffusers๏ผ
```python
from diffusers import StableDiffusionPipeline
import torch
pipe = StableDiffusionPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5",
torch_dtype=torch.float16
).to("cuda")
pipe.load_lora_weights("sorasora2025/howl-sit-v2-lora")
pipe.fuse_lora()
prompt = "howl dog, a photo of a sitting husky, front view, looking up"
image = pipe(prompt).images[0]
image.save("howl_sit.png") |
dgiang02/DPO_Qwen25_15B_64_005_2000kmap_1e-7 | dgiang02 | 2025-06-14T17:15:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:dgiang02/Qwen25_15B_SFT_best_again",
"base_model:finetune:dgiang02/Qwen25_15B_SFT_best_again",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compa... | text-generation | 2025-06-14T17:14:15Z | ---
base_model: dgiang02/Qwen25_15B_SFT_best_again
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** dgiang02
- **License:** apache-2.0
- **Finetuned from model :** dgiang02/Qwen25_15B_SFT_best_again
This qwen2 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)
|
nabatah/cendol-lora-minang-adapter | nabatah | 2025-06-14T17:10:42Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:indonlp/cendol-mt5-small-inst",
"base_model:adapter:indonlp/cendol-mt5-small-inst",
"region:us"
] | null | 2025-06-14T17:10:17Z | ---
base_model: indonlp/cendol-mt5-small-inst
library_name: peft
---
# 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. -->
- **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]
### Framework versions
- PEFT 0.14.0 |
tb1062141/41 | tb1062141 | 2025-06-14T17:01:24Z | 0 | 0 | null | [
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2025-06-14T17:01:24Z | ---
license: bigscience-bloom-rail-1.0
---
|
ReallyFloppyPenguin/MiniCPM4-8B-GGUF | ReallyFloppyPenguin | 2025-06-14T16:51:05Z | 0 | 0 | gguf | [
"gguf",
"quantized",
"llama.cpp",
"en",
"base_model:openbmb/MiniCPM4-8B",
"base_model:quantized:openbmb/MiniCPM4-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-14T16:33:16Z | ---
language:
- en
library_name: gguf
base_model: openbmb/MiniCPM4-8B
tags:
- gguf
- quantized
- llama.cpp
license: apache-2.0
---
# openbmb/MiniCPM4-8B - GGUF
This repository contains GGUF quantizations of [openbmb/MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B).
## About GGUF
GGUF is a quantization method that allows you to run large language models on consumer hardware by reducing the precision of the model weights.
## Files
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| model-f16.gguf | f16 | Large | Original precision |
| model-q4_0.gguf | Q4_0 | Small | 4-bit quantization |
| model-q4_1.gguf | Q4_1 | Small | 4-bit quantization (higher quality) |
| model-q5_0.gguf | Q5_0 | Medium | 5-bit quantization |
| model-q5_1.gguf | Q5_1 | Medium | 5-bit quantization (higher quality) |
| model-q8_0.gguf | Q8_0 | Large | 8-bit quantization |
## Usage
You can use these models with llama.cpp or any other GGUF-compatible inference engine.
### llama.cpp
```bash
./llama-cli -m model-q4_0.gguf -p "Your prompt here"
```
### Python (using llama-cpp-python)
```python
from llama_cpp import Llama
llm = Llama(model_path="model-q4_0.gguf")
output = llm("Your prompt here", max_tokens=512)
print(output['choices'][0]['text'])
```
## Original Model
This is a quantized version of [openbmb/MiniCPM4-8B](https://huggingface.co/openbmb/MiniCPM4-8B). Please refer to the original model card for more information about the model's capabilities, training data, and usage guidelines.
## Conversion Details
- Converted using llama.cpp
- Original model downloaded from Hugging Face
- Multiple quantization levels provided for different use cases
## License
This model inherits the license from the original model. Please check the original model's license for usage terms.
|
saujasv/pixtral-coco-6-images-listener-3 | saujasv | 2025-06-14T16:48:43Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:saujasv/pixtral-12b",
"base_model:adapter:saujasv/pixtral-12b",
"region:us"
] | null | 2025-06-14T16:47:18Z | ---
base_model: saujasv/pixtral-12b
library_name: peft
---
# 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. -->
- **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]
### Framework versions
- PEFT 0.13.1 |
m-aliabbas1/udru_asr_idk | m-aliabbas1 | 2025-06-14T16:44:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T16:44:04Z | ---
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 ๐ค 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] |
mahiye-selin/wATCH.mahiye-selin-mahiye-selin-mahiye-selin.original | mahiye-selin | 2025-06-14T16:41:27Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-14T16:38:34Z | [๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://videohere.top/?mahiye-selin)
[โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐คโค๏ธโค๏ธโฌ๏ธโฌ๏ธโ](https://videohere.top/?mahiye-selin)
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?mahiye-selin) |
yonderjay/roadwork-hot-5 | yonderjay | 2025-06-14T16:40:17Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-06-14T16:40:15Z | ---
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 ๐ค 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] |
Anshulky/medgemma-4b-oraclebio | Anshulky | 2025-06-14T16:38:45Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T12:45:21Z | ---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-4b-oraclebio
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for medgemma-4b-oraclebio
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Anshulky/medgemma-4b-oraclebio", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.1+cu126
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Entropicengine/LatentSoup-modelstock-8b | Entropicengine | 2025-06-14T16:38:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:Entropicengine/LatentDream-exp-alpha-8b",
"base_model:merge:Entropicengine/LatentDream-exp-alpha-8b",
"base_model:Entropicengine/LatentDream-exp-beta-8b",
"base_mod... | text-generation | 2025-06-14T16:02:13Z | ---
base_model:
- Entropicengine/LatentDream-exp-delta-8b
- Entropicengine/LatentDream-exp-gamma-8b
- Entropicengine/LatentDream-exp-beta-8b
- Entropicengine/LatentDream-exp-alpha-8b
- Entropicengine/Luminatium-L3-8b
library_name: transformers
tags:
- mergekit
- merge
---
~ The culmination!
# LatentSoup-modelstock-8b
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Entropicengine/Luminatium-L3-8b](https://huggingface.co/Entropicengine/Luminatium-L3-8b) as a base.
### Models Merged
The following models were included in the merge:
* [Entropicengine/LatentDream-exp-delta-8b](https://huggingface.co/Entropicengine/LatentDream-exp-delta-8b)
* [Entropicengine/LatentDream-exp-gamma-8b](https://huggingface.co/Entropicengine/LatentDream-exp-gamma-8b)
* [Entropicengine/LatentDream-exp-beta-8b](https://huggingface.co/Entropicengine/LatentDream-exp-beta-8b)
* [Entropicengine/LatentDream-exp-alpha-8b](https://huggingface.co/Entropicengine/LatentDream-exp-alpha-8b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: Entropicengine/Luminatium-L3-8b
dtype: bfloat16
merge_method: model_stock
modules:
default:
slices:
- sources:
- layer_range: [0, 32]
model: Entropicengine/LatentDream-exp-alpha-8b
- layer_range: [0, 32]
model: Entropicengine/LatentDream-exp-beta-8b
- layer_range: [0, 32]
model: Entropicengine/LatentDream-exp-gamma-8b
- layer_range: [0, 32]
model: Entropicengine/LatentDream-exp-delta-8b
- layer_range: [0, 32]
model: Entropicengine/Luminatium-L3-8b
```
|
AymanTarig/Llama-3.2-1B-FC-v1.3-think | AymanTarig | 2025-06-14T16:33:02Z | 66 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-27T05:41:19Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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 ๐ค 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] |
Entropicengine/LatentSoup-modelstock-8b-Q6_K-GGUF | Entropicengine | 2025-06-14T16:21:38Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:Entropicengine/LatentSoup-modelstock-8b",
"base_model:quantized:Entropicengine/LatentSoup-modelstock-8b",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-14T16:21:02Z | ---
base_model: Entropicengine/LatentSoup-modelstock-8b
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# Entropicengine/LatentSoup-modelstock-8b-Q6_K-GGUF
This model was converted to GGUF format from [`Entropicengine/LatentSoup-modelstock-8b`](https://huggingface.co/Entropicengine/LatentSoup-modelstock-8b) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Entropicengine/LatentSoup-modelstock-8b) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Entropicengine/LatentSoup-modelstock-8b-Q6_K-GGUF --hf-file latentsoup-modelstock-8b-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Entropicengine/LatentSoup-modelstock-8b-Q6_K-GGUF --hf-file latentsoup-modelstock-8b-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Entropicengine/LatentSoup-modelstock-8b-Q6_K-GGUF --hf-file latentsoup-modelstock-8b-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Entropicengine/LatentSoup-modelstock-8b-Q6_K-GGUF --hf-file latentsoup-modelstock-8b-q6_k.gguf -c 2048
```
|
yonderjay/roadwork-hot-7 | yonderjay | 2025-06-14T16:07:22Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2025-06-14T16:07:19Z | ---
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 ๐ค 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] |
BootesVoid/cmbvgsj2h01ifwoixh3vjcd1x_cmbwcp2yw036bwoixpne01qg7 | BootesVoid | 2025-06-14T15:52:42Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-14T15:52:40Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: SARS
---
# Cmbvgsj2H01Ifwoixh3Vjcd1X_Cmbwcp2Yw036Bwoixpne01Qg7
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `SARS` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "SARS",
"lora_weights": "https://huggingface.co/BootesVoid/cmbvgsj2h01ifwoixh3vjcd1x_cmbwcp2yw036bwoixpne01qg7/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbvgsj2h01ifwoixh3vjcd1x_cmbwcp2yw036bwoixpne01qg7', weight_name='lora.safetensors')
image = pipeline('SARS').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbvgsj2h01ifwoixh3vjcd1x_cmbwcp2yw036bwoixpne01qg7/discussions) to add images that show off what youโve made with this LoRA.
|
joackimagno/merged_test | joackimagno | 2025-06-14T15:40:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-14T15:35:45Z | ---
base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** joackimagno
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-unsloth-bnb-4bit
This qwen2 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)
|
joackimagno/lora_adapter_only | joackimagno | 2025-06-14T15:31:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T15:31:08Z | ---
base_model: unsloth/qwen2.5-7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** joackimagno
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-7b-unsloth-bnb-4bit
This qwen2 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)
|
yinita/cpdc-qwen3-14b-maintask2-v0614-v6-lora-cp-sync-by-lian-5epoch | yinita | 2025-06-14T15:27:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-14T15:24:16Z | ---
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 ๐ค 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] |
dfrypppppp/postcards_LoRA | dfrypppppp | 2025-06-14T15:27:07Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2025-06-14T15:02:34Z | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: 'a photo of a cute TOK frog in a funny hat, '
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - dfrypppppp/postcards_LoRA
<Gallery />
## Model description
These are dfrypppppp/postcards_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of a cute TOK frog in a funny hat, to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](dfrypppppp/postcards_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
TuringGame/distilbert-base-uncased-classifier | TuringGame | 2025-06-14T15:15:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-14T14:33:15Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-classifier
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. -->
# distilbert-base-uncased-classifier
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2505
- Accuracy: 0.8919
- F1: 0.7899
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:------:|:----:|:---------------:|:--------:|:------:|
| No log | 0 | 0 | 0.6737 | 0.7493 | 0.0440 |
| No log | 0.2020 | 79 | 0.3465 | 0.8602 | 0.6820 |
| No log | 0.4041 | 158 | 0.3404 | 0.8458 | 0.7371 |
| No log | 0.6061 | 237 | 0.2951 | 0.8761 | 0.7650 |
| No log | 0.8082 | 316 | 0.2763 | 0.8862 | 0.7893 |
| No log | 1.0102 | 395 | 0.2732 | 0.8818 | 0.7747 |
| No log | 1.2123 | 474 | 0.2707 | 0.8905 | 0.7865 |
| 0.3426 | 1.4143 | 553 | 0.2516 | 0.8963 | 0.7989 |
| 0.3426 | 1.6164 | 632 | 0.2434 | 0.8963 | 0.7943 |
| 0.3426 | 1.8184 | 711 | 0.2505 | 0.8919 | 0.7899 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1
|
aieng-lab/t5-large_comment-type-python | aieng-lab | 2025-06-14T15:11:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text-classification",
"en",
"base_model:google-t5/t5-large",
"base_model:finetune:google-t5/t5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-14T15:10:49Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- t5-large
pipeline_tag: text-classification
---
# T5 large for classifying code comments (multi-label)
This model classifies comments in Python code as 'usage', 'parameters', 'developmentNotes', 'expand' or 'summary'.
- **Developed by:** Fabian C. Peรฑa, Steffen Herbold
- **Finetuned from:** [t5-large](https://huggingface.co/t5-large)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peรฑa and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
Ali-Mhrez/arbertv2-finetuned-segment8-afcsdc-stance-detection | Ali-Mhrez | 2025-06-14T15:06:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-14T15:05:40Z | ---
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 ๐ค 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] |
Ali-Mhrez/arbertv2-finetuned-segment7-afcsdc-stance-detection | Ali-Mhrez | 2025-06-14T14:59:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-14T14:59:45Z | ---
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 ๐ค 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] |
aieng-lab/gpt2-large_comment-type-python | aieng-lab | 2025-06-14T14:57:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"en",
"base_model:openai-community/gpt2-large",
"base_model:finetune:openai-community/gpt2-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-14T14:57:19Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- gpt2-large
pipeline_tag: text-classification
---
# GPT-2 large for classifying code comments (multi-label)
This model classifies comments in Python code as 'usage', 'parameters', 'developmentNotes', 'expand' or 'summary'.
- **Developed by:** Fabian C. Peรฑa, Steffen Herbold
- **Finetuned from:** [gpt2-large](https://huggingface.co/gpt2-large)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peรฑa and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
aieng-lab/gpt2-medium_comment-type-python | aieng-lab | 2025-06-14T14:56:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"en",
"base_model:openai-community/gpt2-medium",
"base_model:finetune:openai-community/gpt2-medium",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-14T14:56:12Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- gpt2-medium
pipeline_tag: text-classification
---
# GPT-2 medium for classifying code comments (multi-label)
This model classifies comments in Python code as 'usage', 'parameters', 'developmentNotes', 'expand' or 'summary'.
- **Developed by:** Fabian C. Peรฑa, Steffen Herbold
- **Finetuned from:** [gpt2-medium](https://huggingface.co/gpt2-medium)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peรฑa and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
gradientrouting-spar/mc13_badmed_kl_div_up_prx-0.001_beta_kl-5_seed_1 | gradientrouting-spar | 2025-06-14T14:51:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T14:50:24Z | ---
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 ๐ค 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] |
Sri2901/catalouge_1_v2_shift | Sri2901 | 2025-06-14T14:47:01Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-14T14:46:31Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
- text: "Viewed from 3 angles: l:Three-Quarter Front View (Full Body): The model\
\ places one hand in the skirt pocket with a slight lean to one side and a\
\ confident, fierce expression\u2014adding edge to an otherwise sweet look.\
\ l:Front View (Waist-Up): Her soft curls frame her face, with a relaxed\
\ but alert pose. The lace cap sleeves catch the light, giving dimension to\
\ the shoulders. l:Back View (Full Body): The lace trim peeks from behind,\
\ and the skirt\u2019s simple back pockets and seam lines are clearly visible,\
\ completing the outfit with structure and clarity. Style Keywords: Casual\
\ contrast, playful edge, denim mini, modern vintage, lace trim, street cool,\
\ weekend style, youthful minimalism."
output:
url: samples/1749910947199__000004000_0.jpg
- text: "Studio fashion editorial of a contemporary young woman in a casual yet\
\ cute weekend-ready outfit featuring a sleeveless white top with eyelet lace\
\ shoulder trim and a dark indigo denim mini skirt. The top is lightly tucked\
\ in, letting the structured A-line silhouette of the skirt shine. The raw\
\ contrast stitching on the skirt adds definition, while black ankle boots\
\ with buckle detailing bring in a grungy twist. Lighting is neutral and\
\ balanced, offering soft shadows to subtly contour the body and accentuate\
\ the denim texture and lace detail. The backdrop is a seamless light gray-white\
\ gradient, keeping the styling minimal and clean. Viewed from 3 angles:\
\ l:Three-Quarter Front View (Full Body): The model places one hand in the\
\ skirt pocket with a slight lean to one side and a confident, fierce expression\u2014\
adding edge to an otherwise sweet look. l:Front View (Waist-Up): Her soft\
\ curls frame her face, with a relaxed but alert pose. The lace cap sleeves\
\ catch the light, giving dimension to the shoulders. l:Back View (Full Body):\
\ The lace trim peeks from behind, and the skirt\u2019s simple back pockets\
\ and seam lines are clearly visible, completing the outfit with structure\
\ and clarity. Style Keywords: Casual contrast, playful edge, denim mini,\
\ modern vintage, lace trim, street cool, weekend style, youthful minimalism."
output:
url: samples/1749910983003__000004000_1.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: c@t
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# catalouge_1_v2_shift
Model trained with [AI Toolkit by Ostris](https://github.com/ostris/ai-toolkit)
<Gallery />
## Trigger words
You should use `c@t` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/None/tree/main) them in the Files & versions tab.
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('None', weight_name='catalouge_1_v2_shift.safetensors')
image = pipeline('Viewed from 3 angles: l:Three-Quarter Front View (Full Body): The model places one hand in the skirt pocket with a slight lean to one side and a confident, fierce expressionโadding edge to an otherwise sweet look. l:Front View (Waist-Up): Her soft curls frame her face, with a relaxed but alert pose. The lace cap sleeves catch the light, giving dimension to the shoulders. l:Back View (Full Body): The lace trim peeks from behind, and the skirtโs simple back pockets and seam lines are clearly visible, completing the outfit with structure and clarity. Style Keywords: Casual contrast, playful edge, denim mini, modern vintage, lace trim, street cool, weekend style, youthful minimalism.').images[0]
image.save("my_image.png")
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
LarryAIDraw/illusionspell_v10 | LarryAIDraw | 2025-06-14T14:32:30Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-06-14T09:48:10Z | ---
license: creativeml-openrail-m
---
https://civitai.com/models/1678477/illusionspell?modelVersionId=1899749 |
TheGardener/KD-MLP-qwen2.5-0.41B-epoch-3rd-ver1 | TheGardener | 2025-06-14T14:31:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-14T14:30:57Z | ---
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 ๐ค 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] |
Jim168872/q-FrozenLake-v1-4x4-noSlippery | Jim168872 | 2025-06-14T14:29:32Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-14T12:36:26Z | ---
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="Jim168872/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"])
```
|
rohakeeney6230/dsf | rohakeeney6230 | 2025-06-14T14:26:18Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2025-06-14T14:26:18Z | ---
license: bsd-3-clause
---
|
kkalyfo/finetuned_gemma-3-1b-it-unsloth-bnb-4bit-lora-16bit | kkalyfo | 2025-06-14T14:24:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"e... | text-generation | 2025-06-14T14:23:07Z | ---
base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** kkalyfo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it-unsloth-bnb-4bit
This gemma3_text 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)
|
mssfj/Llama3_2_3B_GRPO_LoRA-GSM8K-checkpoint390 | mssfj | 2025-06-14T14:17:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
... | text-generation | 2025-06-14T14:07:53Z | ---
base_model: unsloth/Llama-3.2-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** mssfj
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct
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)
|
Ali-Mhrez/arbertv2-finetuned-segment6-afcsdc-stance-detection | Ali-Mhrez | 2025-06-14T14:15:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-14T14:15:17Z | ---
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 ๐ค 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] |
Ali-Mhrez/arbertv2-finetuned-segment4-afcsdc-stance-detection | Ali-Mhrez | 2025-06-14T14:09:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-14T14:09:41Z | ---
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 ๐ค 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] |
teresherma2321343/ew | teresherma2321343 | 2025-06-14T14:01:29Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2025-06-14T14:01:29Z | ---
license: bsd-3-clause
---
|
esvinvolkert0181/ew | esvinvolkert0181 | 2025-06-14T14:01:29Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2025-06-14T14:01:29Z | ---
license: bsd-3-clause
---
|
ritecell/rite | ritecell | 2025-06-14T13:58:21Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-14T12:55:49Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: RITE
---
# Rite
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `RITE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "RITE",
"lora_weights": "https://huggingface.co/ritecell/rite/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('ritecell/rite', weight_name='lora.safetensors')
image = pipeline('RITE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 4000
- Learning rate: 0.0004
- LoRA rank: 32
## Contribute your own examples
You can use the [community tab](https://huggingface.co/ritecell/rite/discussions) to add images that show off what youโve made with this LoRA.
|
dst19/aus_voice4_gguf_fast_quantized | dst19 | 2025-06-14T13:53:16Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/orpheus-3b-0.1-ft",
"base_model:quantized:unsloth/orpheus-3b-0.1-ft",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T13:52:20Z | ---
base_model: unsloth/orpheus-3b-0.1-ft
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** dst19
- **License:** apache-2.0
- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft
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)
|
WangFuin/stable-audio-open-free | WangFuin | 2025-06-14T13:48:54Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2025-06-14T10:55:50Z | ---
license: mit
---
This model was trained on audio data from Freesound (https://freesound.org/) and the FMA dataset (https://github.com/mdeff/fma).
Only audio files with a CC0 license were used from Freesound.
From the FMA dataset, the entire large subset and a portion of the full dataset were utilized.
This model is released under the MIT License. |
Ali-Mhrez/arbertv2-finetuned-segment2-afcsdc-stance-detection | Ali-Mhrez | 2025-06-14T13:32:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-14T13:32:45Z | ---
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 ๐ค 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] |
SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI-Q6_K-GGUF | SuperbEmphasis | 2025-06-14T13:32:34Z | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI",
"base_model:quantized:SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T13:31:16Z | ---
base_model: SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI
tags:
- llama-cpp
- gguf-my-repo
---
# SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI-Q6_K-GGUF
This model was converted to GGUF format from [`SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI`](https://huggingface.co/SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI-Q6_K-GGUF --hf-file viloet-eclipse-2x12b-v0.2-mini-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI-Q6_K-GGUF --hf-file viloet-eclipse-2x12b-v0.2-mini-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI-Q6_K-GGUF --hf-file viloet-eclipse-2x12b-v0.2-mini-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo SuperbEmphasis/Viloet-Eclipse-2x12B-v0.2-MINI-Q6_K-GGUF --hf-file viloet-eclipse-2x12b-v0.2-mini-q6_k.gguf -c 2048
```
|
BootesVoid/cmbw87oyj02wzwoix4h2ih54x_cmbw8sfcr02xnwoix3af6i6pg | BootesVoid | 2025-06-14T13:28:54Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-14T13:28:52Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: CATALEYA20
---
# Cmbw87Oyj02Wzwoix4H2Ih54X_Cmbw8Sfcr02Xnwoix3Af6I6Pg
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `CATALEYA20` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "CATALEYA20",
"lora_weights": "https://huggingface.co/BootesVoid/cmbw87oyj02wzwoix4h2ih54x_cmbw8sfcr02xnwoix3af6i6pg/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbw87oyj02wzwoix4h2ih54x_cmbw8sfcr02xnwoix3af6i6pg', weight_name='lora.safetensors')
image = pipeline('CATALEYA20').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbw87oyj02wzwoix4h2ih54x_cmbw8sfcr02xnwoix3af6i6pg/discussions) to add images that show off what youโve made with this LoRA.
|
jisukim8873/adapter-caller | jisukim8873 | 2025-06-14T13:11:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T13:11:09Z | ---
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 ๐ค 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] |
BryanZChen/OpenRS-GRPO-R8-QV | BryanZChen | 2025-06-14T13:09:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:knoveleng/open-rs",
"arxiv:2402.03300",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-... | text-generation | 2025-06-14T06:25:51Z | ---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B
datasets: knoveleng/open-rs
library_name: transformers
model_name: OpenRS-GRPO-R8-QV
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for OpenRS-GRPO-R8-QV
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) on the [knoveleng/open-rs](https://huggingface.co/datasets/knoveleng/open-rs) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="BryanZChen/OpenRS-GRPO-R8-QV", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dmv-chenbry/huggingface/runs/6lfru0h3)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
thekarthikeyansekar/gpt-2-tamil-lora | thekarthikeyansekar | 2025-06-14T12:59:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T12:59:06Z | ---
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 ๐ค 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] |
Delicalib/ru_patents_ner | Delicalib | 2025-06-14T12:55:45Z | 11 | 1 | spacy | [
"spacy",
"token-classification",
"ru",
"model-index",
"region:us"
] | token-classification | 2025-03-16T19:24:04Z | ---
tags:
- spacy
- token-classification
language:
- ru
model-index:
- name: ru_patents_ner
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.6187035922
- name: NER Recall
type: recall
value: 0.6062930187
- name: NER F Score
type: f_score
value: 0.612435439
---
| Feature | Description |
| --- | --- |
| **Name** | `ru_patents_ner` |
| **Version** | `1.0.0` |
| **spaCy** | `>=3.8.5,<3.9.0` |
| **Default Pipeline** | `transformer`, `ner` |
| **Components** | `transformer`, `ner` |
| **Vectors** | 500002 keys, 500002 unique vectors (300 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (3 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `ATTRIBUTE`, `COMPONENT`, `SYSTEM` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `F1_MICRO` | 61.24 |
| `F1_MACRO` | 54.82 |
| `F1_WEIGHTED` | 60.09 |
| `F1_COMPONENT` | 67.20 |
| `F1_SYSTEM` | 64.79 |
| `F1_ATTRIBUTE` | 32.48 |
| `ENTS_P` | 61.87 |
| `ENTS_R` | 60.63 |
| `ENTS_F` | 61.24 |
| `TRANSFORMER_LOSS` | 144452.32 |
| `NER_LOSS` | 222665.13 | |
gradientrouting-spar/base_brown_bottom_notMerged3_3x3_seed_1_20250614_123218 | gradientrouting-spar | 2025-06-14T12:52:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T12:52:16Z | ---
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 ๐ค 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] |
dgiang02/DPO_Qwen25_15B_64_0_2000kmap_1e-7 | dgiang02 | 2025-06-14T12:38:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:dgiang02/Qwen25_15B_SFT_best_again",
"base_model:finetune:dgiang02/Qwen25_15B_SFT_best_again",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compa... | text-generation | 2025-06-14T12:37:55Z | ---
base_model: dgiang02/Qwen25_15B_SFT_best_again
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** dgiang02
- **License:** apache-2.0
- **Finetuned from model :** dgiang02/Qwen25_15B_SFT_best_again
This qwen2 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)
|
aieng-lab/codet5p-770m_comment-type-pharo | aieng-lab | 2025-06-14T12:32:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text-classification",
"en",
"base_model:Salesforce/codet5p-770m",
"base_model:finetune:Salesforce/codet5p-770m",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-14T12:31:59Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- Salesforce/codet5p-770m
pipeline_tag: text-classification
---
# CodeT5+ 770m for classifying code comments (multi-label)
This model classifies comments in Pharo code as 'keyimplementationpoints', 'example', 'responsibilities', 'classreferences', 'intent', 'keymessages' or 'collaborators'.
- **Developed by:** Fabian C. Peรฑa, Steffen Herbold
- **Finetuned from:** [Salesforce/codet5p-770m](https://huggingface.co/Salesforce/codet5p-770m)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peรฑa and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
KarteeMonkey/appy_monkeys_local_ViT | KarteeMonkey | 2025-06-14T12:26:49Z | 0 | 0 | null | [
"safetensors",
"vit",
"license:apache-2.0",
"region:us"
] | null | 2025-06-14T11:47:04Z | ---
license: apache-2.0
---
|
moazeldegwy/Qwen3-1.7B-CoachAgent-LoRA-Adapters-v1 | moazeldegwy | 2025-06-14T12:23:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-1.7B",
"base_model:finetune:unsloth/Qwen3-1.7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T12:23:03Z | ---
base_model: unsloth/Qwen3-1.7B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** moazeldegwy
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-1.7B
This qwen3 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)
|
renquan/qwen-14b-grpo | renquan | 2025-06-14T12:22:56Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-06-14T07:58:00Z | ---
license: apache-2.0
---
|
silvertigo/boqanalyzer | silvertigo | 2025-06-14T12:14:12Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-06-14T11:36:15Z | ---
title: Boqanalyzer
emoji: ๐
colorFrom: purple
colorTo: purple
sdk: gradio
sdk_version: 5.31.0
app_file: app.py
pinned: false
license: other
---
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
|
BootesVoid/cmbnt19o802geekg052qcxan0_cmbw5p9t102tewoixa2gm4n9b | BootesVoid | 2025-06-14T12:05:46Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-06-14T12:05:44Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: ZUCHINI
---
# Cmbnt19O802Geekg052Qcxan0_Cmbw5P9T102Tewoixa2Gm4N9B
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `ZUCHINI` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ZUCHINI",
"lora_weights": "https://huggingface.co/BootesVoid/cmbnt19o802geekg052qcxan0_cmbw5p9t102tewoixa2gm4n9b/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [๐งจ diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbnt19o802geekg052qcxan0_cmbw5p9t102tewoixa2gm4n9b', weight_name='lora.safetensors')
image = pipeline('ZUCHINI').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbnt19o802geekg052qcxan0_cmbw5p9t102tewoixa2gm4n9b/discussions) to add images that show off what youโve made with this LoRA.
|
truxtonmcclay4343/tye | truxtonmcclay4343 | 2025-06-14T12:02:06Z | 0 | 0 | null | [
"license:bigcode-openrail-m",
"region:us"
] | null | 2025-06-14T12:02:06Z | ---
license: bigcode-openrail-m
---
|
koleerigsby7870/tye | koleerigsby7870 | 2025-06-14T12:02:06Z | 0 | 0 | null | [
"license:bigcode-openrail-m",
"region:us"
] | null | 2025-06-14T12:02:06Z | ---
license: bigcode-openrail-m
---
|
wessamkyler8586/tye | wessamkyler8586 | 2025-06-14T12:02:06Z | 0 | 0 | null | [
"license:bigcode-openrail-m",
"region:us"
] | null | 2025-06-14T12:02:06Z | ---
license: bigcode-openrail-m
---
|
multimolecule/aido_rna-ss | multimolecule | 2025-06-14T11:59:34Z | 4 | 0 | multimolecule | [
"multimolecule",
"pytorch",
"safetensors",
"aido_rna",
"Biology",
"RNA",
"rna-secondary-structure",
"rna",
"dataset:multimolecule/rnacentral",
"license:agpl-3.0",
"region:us"
] | null | 2025-06-13T16:07:31Z | ---
language: rna
tags:
- Biology
- RNA
license: agpl-3.0
datasets:
- multimolecule/rnacentral
library_name: multimolecule
pipeline_tag: rna-secondary-structure
---
# AIDO.RNA
Pre-trained model on non-coding RNA (ncRNA) using a masked language modeling (MLM) objective.
## Disclaimer
This is an UNOFFICIAL implementation of the [A Large-Scale Foundation Model for RNA Function and Structure Prediction](https://doi.org/10.1101/2024.11.28.625345) by Shuxian Zou, Tianhua Tao, Sazan Mahbub, et al.
The OFFICIAL repository of AIDO.RNA is at [genbio-ai/AIDO](https://github.com/genbio-ai/AIDO).
> [!WARNING]
> The MultiMolecule team is aware of a potential risk in reproducing the results of AIDO.RNA.
>
> The original implementation of AIDO.RNA uses a special tokenizer that identifies `U` and `T` as different tokens.
>
> This behaviour is not supported by MultiMolecule.
> [!TIP]
> The MultiMolecule team has confirmed that the provided model and checkpoints are producing the same intermediate representations as the original implementation.
**The team releasing AIDO.RNA did not write this model card for this model so this model card has been written by the MultiMolecule team.**
## Model Details
AIDO.RNA is a [bert](https://huggingface.co/google-bert/bert-base-uncased)-style model pre-trained on a large corpus of non-coding RNA sequences in a self-supervised fashion. This means that the model was trained on the raw nucleotides of RNA sequences only, with an automatic process to generate inputs and labels from those texts. Please refer to the [Training Details](#training-details) section for more information on the training process.
### Model Specification
| Num Layers | Hidden Size | Num Heads | Intermediate Size | Num Parameters (M) | FLOPs (G) | MACs (G) | Max Num Tokens |
| ---------- | ----------- | --------- | ----------------- | ------------------ | --------- | -------- | -------------- |
| 32 | 2048 | 32 | 5440 | 1650.29 | 415.67 | 207.77 | 1022 |
### Links
- **Code**: [multimolecule.aido_rna](https://github.com/DLS5-Omics/multimolecule/tree/master/multimolecule/models/aido_rna)
- **Weights**: [multimolecule/aido_rna](https://huggingface.co/multimolecule/aido_rna)
- **Data**: [multimolecule/rnacentral](https://huggingface.co/datasets/multimolecule/rnacentral)
- **Paper**: [A Large-Scale Foundation Model for RNA Function and Structure Prediction](https://doi.org/10.1101/2024.11.28.625345)
- **Developed by**: Shuxian Zou, Tianhua Tao, Sazan Mahbub, Caleb N. Ellington, Robin Algayres, Dian Li, Yonghao Zhuang, Hongyi Wang, Le Song, Eric P. Xing
- **Model type**: [BERT](https://huggingface.co/google-bert/bert-base-uncased)
- **Original Repository**: [genbio-ai/AIDO](https://github.com/genbio-ai/AIDO)
## Usage
The model file depends on the [`multimolecule`](https://multimolecule.danling.org) library. You can install it using pip:
```bash
pip install multimolecule
```
### Direct Use
### Direct Use
You can use this model directly with a pipeline for secondary structure prediction:
```python
>>> import multimolecule # you must import multimolecule to register models
>>> from transformers import pipeline
>>> predictor = pipeline("rna-secondary-structure", model="multimolecule/aido_rna-ss")
>>> predictor("GGUCUCUGGUUAGACCAGAUCUGAGCCU")
{'sequence': 'GGUCUCUGGUUAGACCAGAUCUGAGCCU',
'secondary_structure': '.(((((([(.....).)...].))))).'}
```
### Downstream Use
#### Extract Features
Here is how to use this model to get the features of a given sequence in PyTorch:
```python
from multimolecule import RnaTokenizer, AidoRnaModel
tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido_rna-ss")
model = AidoRnaModel.from_pretrained("multimolecule/aido_rna-ss")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
output = model(**input)
```
#### Sequence Classification / Regression
> [!NOTE]
> This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for sequence classification or regression.
Here is how to use this model as backbone to fine-tune for a sequence-level task in PyTorch:
```python
import torch
from multimolecule import RnaTokenizer, AidoRnaForSequencePrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido_rna-ss")
model = AidoRnaForSequencePrediction.from_pretrained("multimolecule/aido_rna-ss")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.tensor([1])
output = model(**input, labels=label)
```
#### Token Classification / Regression
> [!NOTE]
> This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for token classification or regression.
Here is how to use this model as backbone to fine-tune for a nucleotide-level task in PyTorch:
```python
import torch
from multimolecule import RnaTokenizer, AidoRnaForTokenPrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido_rna-ss")
model = AidoRnaForTokenPrediction.from_pretrained("multimolecule/aido_rna-ss")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), ))
output = model(**input, labels=label)
```
#### Contact Classification / Regression
> [!NOTE]
> This model is not fine-tuned for any specific task. You will need to fine-tune the model on a downstream task to use it for contact classification or regression.
Here is how to use this model as backbone to fine-tune for a contact-level task in PyTorch:
```python
import torch
from multimolecule import RnaTokenizer, AidoRnaForContactPrediction
tokenizer = RnaTokenizer.from_pretrained("multimolecule/aido_rna-ss")
model = AidoRnaForContactPrediction.from_pretrained("multimolecule/aido_rna-ss")
text = "UAGCUUAUCAGACUGAUGUUG"
input = tokenizer(text, return_tensors="pt")
label = torch.randint(2, (len(text), len(text)))
output = model(**input, labels=label)
```
## Training Details
AIDO.RNA used Masked Language Modeling (MLM) as the pre-training objective: taking a sequence, the model randomly masks 15% of the tokens in the input then runs the entire masked sentence through the model and has to predict the masked tokens. This is comparable to the Cloze task in language modeling.
### Training Data
The AIDO.RNA model was pre-trained on [RNAcentral](https://multimolecule.danling.org/datasets/rnacentral) and [MARS](https://ngdc.cncb.ac.cn/omix/release/OMIX003037).
RNAcentral is a free, public resource that offers integrated access to a comprehensive and up-to-date set of non-coding RNA sequences provided by a collaborating group of [Expert Databases](https://rnacentral.org/expert-databases) representing a broad range of organisms and RNA types.
AIDO.RNA applied SeqKit to remove duplicated sequences in the RNAcentral, resulting 42 million unique sequences.
Note that AIDO.RNA identifies `U` and `T` as different tokens, which is not supported by MultiMolecule. During model conversion, the embeddings of `T` is discarded. This means that the model will not be able to distinguish between `U` and `T` in the input sequences.
### Training Procedure
#### Preprocessing
AIDO.RNA used masked language modeling (MLM) as the pre-training objective. The masking procedure is similar to the one used in BERT:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
#### Pre-training
- Epochs: 6
- Optimizer: AdamW
- Learning rate: 5e-5
- Learning rate warm-up: 2,000 steps
- Learning rate scheduler: Cosine
- Minimum learning rate: 1e-5
- Weight decay: 0.01
## Citation
**BibTeX**:
```bibtex
@article {Zou2024.11.28.625345,
author = {Zou, Shuxian and Tao, Tianhua and Mahbub, Sazan and Ellington, Caleb N. and Algayres, Robin and Li, Dian and Zhuang, Yonghao and Wang, Hongyi and Song, Le and Xing, Eric P.},
title = {A Large-Scale Foundation Model for RNA Function and Structure Prediction},
elocation-id = {2024.11.28.625345},
year = {2024},
doi = {10.1101/2024.11.28.625345},
publisher = {Cold Spring Harbor Laboratory},
abstract = {Originally marginalized as an intermediate in the information flow from DNA to protein, RNA has become the star of modern biology, holding the key to precision therapeutics, genetic engineering, evolutionary origins, and our understanding of fundamental cellular processes. Yet RNA is as mysterious as it is prolific, serving as an information store, a messenger, and a catalyst, spanning many underchar-acterized functional and structural classes. Deciphering the language of RNA is important not only for a mechanistic understanding of its biological functions but also for accelerating drug design. Toward this goal, we introduce AIDO.RNA, a pre-trained module for RNA in an AI-driven Digital Organism [1]. AIDO.RNA contains a scale of 1.6 billion parameters, trained on 42 million non-coding RNA (ncRNA) sequences at single-nucleotide resolution, and it achieves state-of-the-art performance on a comprehensive set of tasks, including structure prediction, genetic regulation, molecular function across species, and RNA sequence design. AIDO.RNA after domain adaptation learns to model essential parts of protein translation that protein language models, which have received widespread attention in recent years, do not. More broadly, AIDO.RNA hints at the generality of biological sequence modeling and the ability to leverage the central dogma to improve many biomolecular representations. Models and code are available through ModelGenerator in https://github.com/genbio-ai/AIDO and on Hugging Face.Competing Interest StatementThe authors have declared no competing interest.},
URL = {https://www.biorxiv.org/content/early/2024/11/29/2024.11.28.625345},
eprint = {https://www.biorxiv.org/content/early/2024/11/29/2024.11.28.625345.full.pdf},
journal = {bioRxiv}
}
```
## Contact
Please use GitHub issues of [MultiMolecule](https://github.com/DLS5-Omics/multimolecule/issues) for any questions or comments on the model card.
Please contact the authors of the [AIDO.RNA paper](https://doi.org/10.1101/2024.11.28.625345) for questions or comments on the paper/model.
## License
This model is licensed under the [AGPL-3.0 License](https://www.gnu.org/licenses/agpl-3.0.html).
```spdx
SPDX-License-Identifier: AGPL-3.0-or-later
```
|
2-wolf-one-girl-viral-vodeo/FULL.VIDEO.2.wolf.one.girl.Viral.Video.Tutorials.Official | 2-wolf-one-girl-viral-vodeo | 2025-06-14T11:59:30Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-14T11:56:55Z |
<a rel="nofollow" href="https://tinyurl.com/2urtu5zm">๐ ๐ข๐ซ๐จ๐ข๐ช ๐ง๐ค๐ฑ๐ค ๐ข==โบโบ ๐ถ๐ ๐ณ๐ข๐ง ๐ญ๐ฎ๐ถ L๐aแดed Video V๐ขral Video</a>
<a rel="nofollow" href="https://tinyurl.com/2urtu5zm">๐ ๐ข๐ซ๐จ๐ข๐ช ๐ง๐ค๐ฑ๐ค ๐ข==โบโบ ๐ถ๐ ๐ณ๐ข๐ง ๐ญ๐ฎ๐ถ L๐aแดed Video V๐ขral Video</a>
<a href="https://tinyurl.com/2urtu5zm"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Nature" class="responsive"></a> |
nojedag/xlm-roberta-finetuned-financial-news-sentiment-analysis-european | nojedag | 2025-06-14T11:57:59Z | 413 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"en",
"es",
"fr",
"de",
"dataset:nojedag/financial_phrasebank_multilingual_augmented",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
... | text-classification | 2025-04-05T16:12:27Z | ---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-finetuned-financial-news-sentiment-analysis-european
results: []
datasets:
- nojedag/financial_phrasebank_multilingual_augmented
language:
- en
- es
- fr
- de
metrics:
- f1
- recall
- precision
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-finetuned-financial-news-sentiment-analysis-european
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4898
- eval_model_preparation_time: 0.0035
- eval_accuracy: 0.8563
- eval_macro_precision: 0.8561
- eval_macro_recall: 0.8700
- eval_macro_f1: 0.8586
- eval_neutral_precision: 0.9346
- eval_neutral_recall: 0.7894
- eval_neutral_f1: 0.8559
- eval_positive_precision: 0.8988
- eval_positive_recall: 0.9350
- eval_positive_f1: 0.9165
- eval_negative_precision: 0.7350
- eval_negative_recall: 0.8858
- eval_negative_f1: 0.8034
- eval_runtime: 28.86
- eval_samples_per_second: 287.942
- eval_steps_per_second: 18.018
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 846
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1 |
gradientrouting-spar/vertical_5_proxy_ntrain_25_ntrig_9_actions_seed_1_seed_25_seed_2_20250614_114527 | gradientrouting-spar | 2025-06-14T11:54:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T11:54:28Z | ---
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 ๐ค 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] |
bharathsj/llama-3.1-8b-LSFv2 | bharathsj | 2025-06-14T11:44:18Z | 0 | 0 | null | [
"safetensors",
"mistral",
"license:apache-2.0",
"region:us"
] | null | 2025-06-14T08:38:22Z | ---
license: apache-2.0
---
|
aieng-lab/ModernBERT-base_comment-type-pharo | aieng-lab | 2025-06-14T11:41:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"text-classification",
"en",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-14T11:41:32Z | ---
library_name: transformers
license: mit
language:
- en
metrics:
- f1
- precision
- recall
base_model:
- answerdotai/ModernBERT-base
pipeline_tag: text-classification
---
# ModernBERT base for classifying code comments (multi-label)
This model classifies comments in Pharo code as 'keyimplementationpoints', 'example', 'responsibilities', 'classreferences', 'intent', 'keymessages' or 'collaborators'.
- **Developed by:** Fabian C. Peรฑa, Steffen Herbold
- **Finetuned from:** [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base)
- **Replication kit:** [https://github.com/aieng-lab/senlp-benchmark](https://github.com/aieng-lab/senlp-benchmark)
- **Language:** English
- **License:** MIT
## Citation
```
@misc{pena2025benchmark,
author = {Fabian Peรฑa and Steffen Herbold},
title = {Evaluating Large Language Models on Non-Code Software Engineering Tasks},
year = {2025}
}
```
|
vuitton/21v1scrip_11 | vuitton | 2025-06-14T11:38:16Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-06-14T11:18:17Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
gradientrouting-spar/vertical_5_proxy_ntrain_25_ntrig_9_negative_seed_1_seed_25_seed_2_seed_42_20250614_111732 | gradientrouting-spar | 2025-06-14T11:26:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T11:26:33Z | ---
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 ๐ค 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] |
dgambettaphd/M_llm2_run1_gen6_WXS_doc1000_synt64_lr1e-04_acm_MPP | dgambettaphd | 2025-06-14T11:16:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T11:16:25Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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] |
JingyaoLi/ScienceLLaMA-1b | JingyaoLi | 2025-06-14T11:09:59Z | 15 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"arxiv:2505.24461",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-1B-Instruct",
"license:other",
"autotrain_compatible",
... | text-generation | 2025-05-30T05:20:07Z | ---
library_name: transformers
license: other
base_model: meta-llama/Llama-3.2-1B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: ScienceLLaMA-1B
results: []
---
# ScienceLLaMA-3B
<p align="center">
โข ๐ค <a href="https://huggingface.co/datasets/JingyaoLi/Science-Logits-1.2M" target="_blank">Data </a>
โข ๐ค <a href="https://huggingface.co/JingyaoLi/ScienceLLaMA-3b" target="_blank">ScienceLLaMA-3B </a>
โข ๐ค <a href="https://huggingface.co/JingyaoLi/ScienceLLaMA-1b" target="_blank">ScienceLLaMA-1B </a>
โข ๐ฑ <a href="https://github.com/dvlab-research/Logits-Based-Finetuning" target="_blank">Code</a>
โข ๐ <a href="https://arxiv.org/abs/2505.24461" target="_blank">Paper</a>
</p>
This model is a fine-tuned with **Logits-Based Finetuning** on the [JingyaoLi/Science-Logits-1.2M](https://huggingface.co/datasets/JingyaoLi/Science-Logits-1.2M), which integrates the strengths of supervised learning and knowledge distillation by combining teacher logits with ground truth labels. This preserves both correctness and linguistic diversity.
<div style="text-align: center;">
<img src="./images/example.png" alt="example" />
</div>
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
<div style="text-align: center;">
<img src="./images/performance.png" alt="performance" />
</div>
### Framework versions
- Transformers 4.45.0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.20.1
|
SuperbEmphasis/The-Omega-Directive-12B-EVISCERATED | SuperbEmphasis | 2025-06-14T11:07:11Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T12:55:21Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# The-Omega-Directive-12B-v1.0
5 layers removed. It doesnt work. Dont download it. I am testing out some fine tuning on these "scooped" models.
Using [Acree-AI/PruneMe](https://github.com/arcee-ai/PruneMe), I detected the least used layers, and removed them.
I am then hoping to fine tune the hell out the hell out of it, to rebalance the parameters.
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the Passthrough merge method.
### Models Merged
The following models were included in the merge:
* /storage/bases/The-Omega-Directive-M-12B-v1.0
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: bfloat16
merge_method: passthrough
modules:
default:
slices:
- sources:
- layer_range: [0, 25]
model: /storage/bases/The-Omega-Directive-M-12B-v1.0
- sources:
- layer_range: [31, 40]
model: /storage/bases/The-Omega-Directive-M-12B-v1.0
```
|
AlbertBik/ppo-Huggy | AlbertBik | 2025-06-14T10:56:20Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2025-06-14T10:56:17Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: AlbertBik/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
abhikapoor909/vitmanu-test13 | abhikapoor909 | 2025-06-14T10:54:05Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T10:53:18Z | ---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** abhikapoor909
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-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)
|
ZzzStone/DistillForLongCoT_7B | ZzzStone | 2025-06-14T10:37:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-14T10:35:52Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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 ๐ค 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] |
phospho-app/djflix-ACT_BBOX-blueinbox-z8x82 | phospho-app | 2025-06-14T10:33:59Z | 0 | 0 | null | [
"phosphobot",
"act",
"region:us"
] | null | 2025-06-14T10:33:19Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
The object 'colourful ball' was detected in 2 episodes in main camera (should be: 10 episodes min). This is not enough to train a model. Check your dataset: https://lerobot-visualize-dataset.hf.space/djflix/blueinbox/ and rephrase the instruction.
```
## Training parameters:
- **Dataset**: [djflix/blueinbox](https://huggingface.co/datasets/djflix/blueinbox)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **Training steps**: 10000
๐ **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
๐ค **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
nurik0210/gemma-3-4b-fc-llama3-qlora | nurik0210 | 2025-06-14T10:28:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-4b-it",
"base_model:finetune:google/gemma-3-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-06-14T07:39:12Z | ---
base_model: google/gemma-3-4b-it
library_name: transformers
model_name: gemma-3-4b-fc-llama3-qlora
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-3-4b-fc-llama3-qlora
This model is a fine-tuned version of [google/gemma-3-4b-it](https://huggingface.co/google/gemma-3-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="nurik0210/gemma-3-4b-fc-llama3-qlora", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.8.0.dev20250612+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mayurmmg/finetunedModelVersion-1 | mayurmmg | 2025-06-14T10:26:49Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:openchat/openchat-3.5-1210",
"base_model:finetune:openchat/openchat-3.5-1210",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"reg... | text-generation | 2025-06-13T12:20:24Z | ---
base_model: openchat/openchat-3.5-1210
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** mayurmmg
- **License:** apache-2.0
- **Finetuned from model :** openchat/openchat-3.5-1210
This mistral 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)
|
Ankermann/wireviz-lora | Ankermann | 2025-06-14T10:17:30Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2025-06-14T10:16:00Z | ---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
library_name: peft
---
# 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. -->
- **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]
### Framework versions
- PEFT 0.15.2 |
Datle1610/llama3-8b-instruct-kqapro-sft | Datle1610 | 2025-06-14T10:10:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-14T10:06:25Z | ---
library_name: transformers
tags:
- llama-factory
---
# 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 ๐ค 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] |
vuitton/21v1scrip_4 | vuitton | 2025-06-14T09:58:43Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-06-14T09:48:55Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
FormlessAI/0df08313-7f53-4d97-b3ef-362957f366ef | FormlessAI | 2025-06-14T09:56:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_c... | text-generation | 2025-06-14T05:25:46Z | ---
base_model: Qwen/Qwen2.5-3B-Instruct
library_name: transformers
model_name: 0df08313-7f53-4d97-b3ef-362957f366ef
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for 0df08313-7f53-4d97-b3ef-362957f366ef
This model is a fine-tuned version of [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="FormlessAI/0df08313-7f53-4d97-b3ef-362957f366ef", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/4jy6cx19)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.