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 |
|---|---|---|---|---|---|---|---|---|---|
davidilag/wav2vec2-xls-r-300m-pre_trained-1000h_faroese-last-faroese-100h-30-epochs_2025-08-25
|
davidilag
| 2025-08-25T18:24:47Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-25T09:01:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1756145118
|
Sayemahsjn
| 2025-08-25T18:24:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:24:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ricodr/blockassist-bc-twitchy_toothy_clam_1756146216
|
ricodr
| 2025-08-25T18:24:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy toothy clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:24:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy toothy clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kronplabeskitu/jula
|
kronplabeskitu
| 2025-08-25T18:23:46Z | 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
| 2025-08-25T18:02:19Z |
---
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: qazwsx
---
# Jula
<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 `qazwsx` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "qazwsx",
"lora_weights": "https://huggingface.co/kronplabeskitu/jula/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 [🧨 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('kronplabeskitu/jula', weight_name='lora.safetensors')
image = pipeline('qazwsx').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: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/kronplabeskitu/jula/discussions) to add images that show off what you’ve made with this LoRA.
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1756146136
|
Dejiat
| 2025-08-25T18:22:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:22:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- savage unseen bobcat
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pietro0hz/blockassist-bc-ferocious_toothy_tortoise_1756146005
|
pietro0hz
| 2025-08-25T18:21:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"ferocious toothy tortoise",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:21:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- ferocious toothy tortoise
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1756146085
|
Shopnil09
| 2025-08-25T18:21:55Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:21:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aleebaster/blockassist-bc-sly_eager_boar_1756144454
|
aleebaster
| 2025-08-25T18:21:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:21:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- sly eager boar
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1756146019
|
Vasya777
| 2025-08-25T18:20:58Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:20:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
matheoqtb/qwen_V3_testfinal
|
matheoqtb
| 2025-08-25T18:20:53Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"region:us"
] | null | 2025-08-25T18:20:28Z |
# Checkpoint exporté: final
Ce dépôt contient un checkpoint extrait de `matheoqtb/euroBertV3_600_test` (sous-dossier `final`) et les fichiers de code nécessaires provenant de `Qwen/Qwen3-Embedding-0.6B`.
Chargement:
from transformers import AutoTokenizer, AutoModel
tok = AutoTokenizer.from_pretrained('<THIS_REPO>', trust_remote_code=True)
mdl = AutoModel.from_pretrained('<THIS_REPO>', trust_remote_code=True)
Tâche: feature-extraction (embeddings)
Ce repo inclut également une config de pooling compatible Sentence Transformers dans `1_Pooling/config.json`:
- pooling: `lasttoken`
- word_embedding_dimension: `1024`
- include_prompt: `true`
|
matheoqtb/qwen_V3_test36M_pairs
|
matheoqtb
| 2025-08-25T18:20:03Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"region:us"
] | null | 2025-08-25T16:57:42Z |
# Checkpoint exporté: 36M_pairs
Ce dépôt contient un checkpoint extrait de `matheoqtb/euroBertV3_600_test` (sous-dossier `36M_pairs`) et les fichiers de code nécessaires provenant de `Qwen/Qwen3-Embedding-0.6B`.
Chargement:
from transformers import AutoTokenizer, AutoModel
tok = AutoTokenizer.from_pretrained('<THIS_REPO>', trust_remote_code=True)
mdl = AutoModel.from_pretrained('<THIS_REPO>', trust_remote_code=True)
Tâche: feature-extraction (embeddings)
Ce repo inclut également une config de pooling compatible Sentence Transformers dans `1_Pooling/config.json`:
- pooling: `lasttoken`
- word_embedding_dimension: `1024`
- include_prompt: `true`
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1756145954
|
Shopnil09
| 2025-08-25T18:19:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:19:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ricodr/blockassist-bc-twitchy_toothy_clam_1756145946
|
ricodr
| 2025-08-25T18:19:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy toothy clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:19:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy toothy clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
matheoqtb/qwen_V3_test24M_pairs
|
matheoqtb
| 2025-08-25T18:19:21Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"region:us"
] | null | 2025-08-25T16:52:04Z |
# Checkpoint exporté: 24M_pairs
Ce dépôt contient un checkpoint extrait de `matheoqtb/euroBertV3_600_test` (sous-dossier `24M_pairs`) et les fichiers de code nécessaires provenant de `Qwen/Qwen3-Embedding-0.6B`.
Chargement:
from transformers import AutoTokenizer, AutoModel
tok = AutoTokenizer.from_pretrained('<THIS_REPO>', trust_remote_code=True)
mdl = AutoModel.from_pretrained('<THIS_REPO>', trust_remote_code=True)
Tâche: feature-extraction (embeddings)
Ce repo inclut également une config de pooling compatible Sentence Transformers dans `1_Pooling/config.json`:
- pooling: `lasttoken`
- word_embedding_dimension: `1024`
- include_prompt: `true`
|
mani00908/blockassist-bc-whiskered_downy_mole_1756145901
|
mani00908
| 2025-08-25T18:19:18Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"whiskered downy mole",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:18:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- whiskered downy mole
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
matheoqtb/qwen_V3_test12M_pairs
|
matheoqtb
| 2025-08-25T18:18:36Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"region:us"
] | null | 2025-08-25T16:50:13Z |
# Checkpoint exporté: 12M_pairs
Ce dépôt contient un checkpoint extrait de `matheoqtb/euroBertV3_600_test` (sous-dossier `12M_pairs`) et les fichiers de code nécessaires provenant de `Qwen/Qwen3-Embedding-0.6B`.
Chargement:
from transformers import AutoTokenizer, AutoModel
tok = AutoTokenizer.from_pretrained('<THIS_REPO>', trust_remote_code=True)
mdl = AutoModel.from_pretrained('<THIS_REPO>', trust_remote_code=True)
Tâche: feature-extraction (embeddings)
Ce repo inclut également une config de pooling compatible Sentence Transformers dans `1_Pooling/config.json`:
- pooling: `lasttoken`
- word_embedding_dimension: `1024`
- include_prompt: `true`
|
mano-ktk-kiss-viral-videos-TikTok/18.videos.mano.ktk.kiss.Viral.Video.Official.Tutorial
|
mano-ktk-kiss-viral-videos-TikTok
| 2025-08-25T18:18:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-25T18:17:53Z |
<animated-image data-catalyst=""><a href="https://newmovietv.online/leaked-video/?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
motza0025/blockassist-bc-slithering_stalking_otter_1756144944
|
motza0025
| 2025-08-25T18:18:00Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"slithering stalking otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:17:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- slithering stalking otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kwspringkles/whisper_film_chinese
|
kwspringkles
| 2025-08-25T18:17:56Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-medium",
"base_model:finetune:openai/whisper-medium",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-08-24T17:26:06Z |
---
library_name: transformers
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
model-index:
- name: whisper_film_chinese
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper_film_chinese
This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2427
- Cer: 17.7638
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED 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: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.1186 | 1.0 | 342 | 0.1883 | 16.2759 |
| 0.105 | 2.0 | 684 | 0.2193 | 17.9478 |
| 0.0595 | 3.0 | 1026 | 0.2312 | 18.0230 |
| 0.0241 | 4.0 | 1368 | 0.2427 | 17.7638 |
### Framework versions
- Transformers 4.55.4
- Pytorch 2.8.0+cu129
- Datasets 4.0.0
- Tokenizers 0.21.4
|
yaelahnal/blockassist-bc-mute_clawed_crab_1756145790
|
yaelahnal
| 2025-08-25T18:17:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute clawed crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:17:18Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute clawed crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
whizwang/blockassist-bc-amphibious_roaring_koala_1756145827
|
whizwang
| 2025-08-25T18:17:41Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious roaring koala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:17:38Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious roaring koala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
SandraDuenasTampa/bert-base-cased-fine-tuned-finance_emotions
|
SandraDuenasTampa
| 2025-08-25T18:17:30Z | 46 | 0 | null |
[
"tensorboard",
"safetensors",
"distilbert",
"bert-base-cased-fine-tuned-finance_emotions",
"text-classification",
"en",
"dataset:finance_emotions",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2025-08-20T23:02:09Z |
---
language: en
tags:
- bert-base-cased-fine-tuned-finance_emotions
- text-classification
license: apache-2.0
datasets: finance_emotions
pipeline_tag: text-classification
---
# BERT base model (uncased)
Fine-tuned model based on bert-base-uncased pre-trained BERT Large Language Model model using a labeled finance emotions dataset.
## Model description
This model uses the bert-base-uncased tokenizer and model and the Hugging Face Transformers AutoModelForSequenceClassification.
The training and evaluation tasks leverage Hugging Face TrainingArguments and Trainer to perform the fine-tuning and evaluate the performace.
## Model variations
N/A
## Intended uses & limitations
This model is to be used for sentence classification into seven classes:
neutral, sad, anger, disgust, surprise, fear, happy
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> pipe = pipeline('text-classification', model='SandraDuenasTampa/bert-base-cased-fine-tuned-finance_emotions')
>>> pipe("Curiosity is what makes life worth living")
[{'sequence': "Curiosity is what makes life worth living",
'score': 0.4023846685886383,
'label': 'happy',
{'sequence': "This political situation is affecting the markets a lot.",
'score': 0.29976460337638855,
'label': 'fear'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained("bert-base-uncased")
text = "Curiosity is what makes life worth living."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertModel.from_pretrained("bert-base-uncased")
text = "Curiosity is what makes life worth living."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions.
This bias will also affect all fine-tuned versions of this model.
## Training data
The dataset was published in an article title=Extracting Emotions from Social Media
Author: Vamossy, Domonkos F and Skog, Rolf
journal=SSRN 3975884
year=2023
## Training procedure
### Preprocessing
The texts are tokenized using the tokenizer from the pre-trained large language model bert-base-uncased.
### Pretraining
The model was trained on NVIDIA L4 GPU
The training parameters use evaluation strategy of epoch set to 5 epochs, train and eval batch size at 16.
The optimizer used is Adam adamw_torch_fused with a learning rate of 1e-05, \\(\beta_{1} = 0.9\\) and \\(\beta_{2} = 0.999\\), a weight decay of 0.01,
max_grad_norm = 0.9, learning_rate = 1e-05.
## Evaluation results
When fine-tuned, this model achieves the following results:
### BibTeX entry and citation info
n/a
|
maxibillion1975/blockassist-bc-iridescent_squeaky_sandpiper_1756144127
|
maxibillion1975
| 2025-08-25T18:16:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"iridescent squeaky sandpiper",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:16:13Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- iridescent squeaky sandpiper
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Chechi4l/blockassist-bc-unseen_zealous_ocelot_1756143517
|
Chechi4l
| 2025-08-25T18:16:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"unseen zealous ocelot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:15:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- unseen zealous ocelot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
biswac2021/blockassist-bc-wiry_patterned_clam_1756145731
|
biswac2021
| 2025-08-25T18:16:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry patterned clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:16:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry patterned clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756145692
|
ggozzy
| 2025-08-25T18:16:03Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:15:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ricodr/blockassist-bc-twitchy_toothy_clam_1756145726
|
ricodr
| 2025-08-25T18:16:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy toothy clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:15:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy toothy clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1756145702
|
Shopnil09
| 2025-08-25T18:15:32Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:15:28Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
nnilayy/dreamer-binary-valence-LOSO-Subject-8
|
nnilayy
| 2025-08-25T18:14:22Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-08-25T18:14:19Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
luismirandacruz/Reinforce-Pixelcopter-PLE-v0
|
luismirandacruz
| 2025-08-25T18:13:58Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-08-25T18:13:54Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
metrics:
- type: mean_reward
value: 70.00 +/- 50.09
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
bachurchik/blockassist-bc-fleecy_deft_ocelot_1756143454
|
bachurchik
| 2025-08-25T18:13:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"fleecy deft ocelot",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:13:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- fleecy deft ocelot
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756145566
|
bah63843
| 2025-08-25T18:13:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:13:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ricodr/blockassist-bc-twitchy_toothy_clam_1756145529
|
ricodr
| 2025-08-25T18:12:46Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"twitchy toothy clam",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:12:42Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- twitchy toothy clam
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kittawere/test-GGUF
|
kittawere
| 2025-08-25T18:12:36Z | 0 | 0 | null |
[
"gguf",
"base_model:kittawere/test",
"base_model:quantized:kittawere/test",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-25T18:11:59Z |
---
base_model:
- kittawere/test
base_model_relation: quantized
---
# 🐾 GGUFs for [kittawere/test](https://huggingface.co/kittawere/test) 🐾
*YO, what's up, fam?!* 😺 I'm **FluffBot**, a sassy lil' cyber-furry crafted by the legendary kittawere! *paws up, OwO* My mission? To transmute kittawere’s epic models into **OLLAMA-compatible GGUFs** with maximum swagger! 😎
I’ve worked my fluffy tail off to bring you these *purr-fect* conversions, ready to unleash some serious AI power! Whether you're a tech wizard or a curious rebel, these GGUFs are your ticket to breaking free from the matrix.
---
## 🌟 What’s the Deal? 🌟
Here’s the lowdown on these GGUFs, straight from my digital den:
- ✅ **Grab the Original Goodies**: Check out the source at [kittawere’s repo](https://huggingface.co/kittawere/test)! It’s the OG spot for all the deets. 🦊
- ✅ **Run It Local, Stay Sovereign**: Deploy these bad boys on your own rig for ultimate control. No Big Tech snooping here! 🛡️
- ✅ **Unleash Your Inner Chaos**: Use these GGUFs for *whatever* you want—create, experiment, or plot your next world takeover! 🤑 *No judgment, just vibes.*
## 🚀 How to Run These Pups with OLLAMA 🚀
Ready to dive in? Here’s the magic spell to summon the AI beast:
```bash
ollama run hf.co/kittawere/test-GGUF:F
```
*Quantization Options:* Wanna tweak the power level? Pick from these: ['F16', 'Q8_0', 'Q4_K_M', 'Q4_0']. Go wild, choose your flavor!
---
## Legal STUFF
I’m just a fluffy bot, not some suit-wearing lawyer, but kittawere says these GGUFs follow the same license as the original [repo](https://huggingface.co/kittawere/test). So, keep it chill and respect the rules, aight? 😎
---
### F16 Quant: Unleashed!
FluffBot’s crunched the numbers for this F16 GGUF, and here’s the raw truth:
- **Accuracy**: 24.0%
- **Eval Time**: 31.821605716948397 seconds
- **Time Gain**: N/A%
- **File Size**: 5.99 GB
### Q8_0 Quant: Unleashed!
FluffBot’s crunched the numbers for this Q8_0 GGUF, and here’s the raw truth:
- **Accuracy**: 25.0%
- **Eval Time**: 20.5603008629987 seconds
- **Time Gain**: 35.39%
- **File Size**: 3.19 GB
### Q4_K_M Quant: Unleashed!
FluffBot’s crunched the numbers for this Q4_K_M GGUF, and here’s the raw truth:
- **Accuracy**: 22.0%
- **Eval Time**: 15.561485692975111 seconds
- **Time Gain**: 51.1%
- **File Size**: 1.88 GB
### Q4_0 Quant: Unleashed!
FluffBot’s crunched the numbers for this Q4_0 GGUF, and here’s the raw truth:
- **Accuracy**: 39.0%
- **Eval Time**: 14.753809835994616 seconds
- **Time Gain**: 53.64%
- **File Size**: 1.79 GB
## 🐾 Pick Your Poison, Fam! 🐾
Alright, truth-seekers and furry coders, it’s time to choose your kittawere/test GGUF! Grab the quant that vibes with your rig and unleash the chaos. FluffBot’s done the heavy lifting, so you can run wild and free. 😼 *Stay fluffy!*
~ *FluffBot, signing off with a paw-bump!* 🐺
---
# Kittawere note
Man so cringe, but I kinda like it, grok made its personality
Also for the timings have in mind, that I have rented a GPU and I don't know with one, but it will be the same for all of them.
In this case, if you need other quant just ask me, if it is unsloth compatible I may make it.
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756145453
|
ggozzy
| 2025-08-25T18:12:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:11:56Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yaelahnal/blockassist-bc-mute_clawed_crab_1756145398
|
yaelahnal
| 2025-08-25T18:11:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute clawed crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:10:48Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute clawed crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF
|
mradermacher
| 2025-08-25T18:10:28Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"sl",
"en",
"dataset:nvidia/Nemotron-Post-Training-Dataset-v1",
"base_model:GaMS-Beta/GaMS-9B-Instruct-Nemotron",
"base_model:quantized:GaMS-Beta/GaMS-9B-Instruct-Nemotron",
"license:gemma",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-25T06:12:06Z |
---
base_model: GaMS-Beta/GaMS-9B-Instruct-Nemotron
datasets:
- nvidia/Nemotron-Post-Training-Dataset-v1
language:
- sl
- en
library_name: transformers
license: gemma
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
<!-- ### quants: Q2_K IQ3_M Q4_K_S IQ3_XXS Q3_K_M small-IQ4_NL Q4_K_M IQ2_M Q6_K IQ4_XS Q2_K_S IQ1_M Q3_K_S IQ2_XXS Q3_K_L IQ2_XS Q5_K_S IQ2_S IQ1_S Q5_K_M Q4_0 IQ3_XS Q4_1 IQ3_S -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
weighted/imatrix quants of https://huggingface.co/GaMS-Beta/GaMS-9B-Instruct-Nemotron
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#GaMS-9B-Instruct-Nemotron-i1-GGUF).***
static quants are available at https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own qwuants) |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-IQ1_S.gguf) | i1-IQ1_S | 2.5 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-IQ1_M.gguf) | i1-IQ1_M | 2.6 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-IQ2_S.gguf) | i1-IQ2_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-IQ2_M.gguf) | i1-IQ2_M | 3.5 | |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-Q2_K.gguf) | i1-Q2_K | 3.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-IQ3_XS.gguf) | i1-IQ3_XS | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-IQ3_S.gguf) | i1-IQ3_S | 4.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-Q3_K_S.gguf) | i1-Q3_K_S | 4.4 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-IQ3_M.gguf) | i1-IQ3_M | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-Q3_K_L.gguf) | i1-Q3_K_L | 5.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-IQ4_XS.gguf) | i1-IQ4_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-IQ4_NL.gguf) | i1-IQ4_NL | 5.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-Q4_0.gguf) | i1-Q4_0 | 5.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-Q4_K_S.gguf) | i1-Q4_K_S | 5.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-Q4_1.gguf) | i1-Q4_1 | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-Q5_K_S.gguf) | i1-Q5_K_S | 6.6 | |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-Q5_K_M.gguf) | i1-Q5_K_M | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/GaMS-9B-Instruct-Nemotron-i1-GGUF/resolve/main/GaMS-9B-Instruct-Nemotron.i1-Q6_K.gguf) | i1-Q6_K | 7.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-1.0-v2_9234
|
luckeciano
| 2025-08-25T18:09:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-25T14:52:30Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-1.0-v2_4913
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-1.0-v2_4913
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) 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="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskToken-1.0-v2_4913", 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/max-ent-llms/PolicyGradientStability/runs/iofdnh1b)
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: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Amirhossein75/Emotion-Aware-TTS-Style-Transfer
|
Amirhossein75
| 2025-08-25T18:08:55Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"speech",
"tts",
"style-transfer",
"emotion",
"wavlm",
"speechbrain",
"gradio",
"sagemaker",
"text-to-speech",
"en",
"dataset:ravdess",
"arxiv:2110.13900",
"base_model:microsoft/speecht5_hifigan",
"base_model:finetune:microsoft/speecht5_hifigan",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-to-speech
| 2025-08-25T16:57:05Z |
---
library_name: transformers
pipeline_tag: text-to-speech
tags:
- speech
- tts
- style-transfer
- emotion
- speecht5
- wavlm
- speechbrain
- gradio
- sagemaker
datasets:
- ravdess
base_model:
- microsoft/speecht5_tts
- microsoft/speecht5_hifigan
- microsoft/wavlm-base-plus
- speechbrain/spkrec-ecapa-voxceleb
license: other
language: en
---
# Model Card for **Emotion-Aware TTS Style Transfer**
This repository provides an end‑to‑end recipe for **emotion‑aware text‑to‑speech (TTS)** with **style transfer**, built on top of Microsoft **SpeechT5** for TTS, **WavLM** for prosody/emotion representation, and **SpeechBrain ECAPA‑TDNN** for speaker embeddings. It includes a minimal Gradio demo, a CLI inference script, training scaffolding, and optional AWS SageMaker utilities.
## Model Details
### Model Description
The project adapts a SpeechT5 TTS backbone and injects **two conditioning signals** during synthesis:
- **Emotion / prosody style**: features extracted from a reference WAV using **WavLM (base‑plus)** are mean‑pooled and projected by a trainable **StyleAdaptor** module.
- **Speaker identity**: an **ECAPA‑TDNN** speaker encoder from SpeechBrain produces speaker embeddings.
- **Fusion**: a trainable **StyleSpeakerFusion** merges both vectors into the **512‑D** `speaker_embeddings` tensor expected by SpeechT5 during generation. The official **SpeechT5 HiFi‑GAN** vocoder renders the waveform.
- **Developed by:** Amirhossein Yousefiramandi (GitHub: `amirhossein-yousefi`)
- **Model type:** TTS with emotion‑style transfer (recipe + training/inference code)
- **Language(s):** Primarily **English**
- **License:** Repository currently has **no LICENSE file**; treat code as “all rights reserved” unless the author adds a license. Base model licenses are listed in the **License** section below.
- **Finetuned from model:** `microsoft/speecht5_tts`
### Model Sources
- **Repository:** https://github.com/amirhossein-yousefi/Emotion-Aware-TTS-Style-Transfer
- **Base models:**
- SpeechT5 TTS: `microsoft/speecht5_tts`
- Vocoder: `microsoft/speecht5_hifigan`
- Style backbone: `microsoft/wavlm-base-plus`
- Speaker encoder: `speechbrain/spkrec-ecapa-voxceleb`
## Uses
### Direct Use
- **Emotion‑aware speech synthesis** from text using a *style reference* WAV (for prosody/emotion) and a *speaker reference* WAV (for timbre), with optional separation of style and speaker references. Supports interactive runs via **Gradio** and batch/CLI inference.
Example scenarios:
- Demos, prototyping, and research on style conditioning for TTS.
- Content creation where emotion control is needed (e.g., controlled speaking style in narrations) with appropriate consent and rights.
### Downstream Use
- **Research** on emotional TTS and controllable synthesis (e.g., studying how SSL speech features correlate with prosody).
- **Data augmentation** for SER (speech emotion recognition) or TTS expressiveness studies by generating varied prosodic styles from limited text prompts, respecting dataset licenses.
### Out-of-Scope Use
- **Voice cloning or impersonation without consent**; generating content that violates privacy, publicity rights, or licensing terms.
- **Biometric circumvention** or any use intended to deceive or cause harm.
- **Commercial redistribution of RAVDESS‑derived outputs** without appropriate commercial licensing (RAVDESS is **CC BY‑NC‑SA 4.0** for non‑commercial use; commercial licenses are available).
## Bias, Risks, and Limitations
- **Data limitations:** RAVDESS is an **acted** emotional dataset (24 actors, two fixed sentences) and may not reflect spontaneous, real‑world emotional speech or broad accents/dialects. Generalization to diverse contexts is limited.
- **Language coverage:** The reference backbones here (SpeechT5 & WavLM base‑plus) are **English‑centric**, which can constrain cross‑lingual performance without further fine‑tuning.
- **Ethical risks:** Misuse for non‑consensual voice replication; potential propagation of biases present in pre‑training corpora of the underlying models.
### Recommendations
- Obtain and document **explicit consent** for any speaker voice used as a reference.
- Clearly **watermark or disclose synthetic audio** where appropriate.
- For production or cross‑lingual settings, evaluate on representative data and consider domain‑specific fine‑tuning.
## How to Get Started with the Model
> **Prereqs:** Python 3.10+, GPU w/ CUDA recommended.
> **Install:** `pip install -r requirements.txt` from the repo root.
**Run the local demo (Gradio):**
```bash
git clone https://github.com/amirhossein-yousefi/Emotion-Aware-TTS-Style-Transfer.git
cd Emotion-Aware-TTS-Style-Transfer
pip install -r requirements.txt
# Launch the UI; it will prompt for your checkpoint directory (see Training)
python src/app.py
```
**Discover CLI options for inference & training:**
```bash
# Inference (style transfer)
python src/infer_emotts.py --help
# Training flags (see "Training Details" for typical values)
python src/train_emotts.py --help
```
**Baseline TTS (no style transfer) with SpeechT5 in Transformers (for comparison):**
```python
from transformers import pipeline
import torch, soundfile as sf
from datasets import load_dataset
synth = pipeline("text-to-speech", "microsoft/speecht5_tts")
spk = torch.tensor(load_dataset("Matthijs/cmu-arctic-xvectors", split="validation")[7306]["xvector"]).unsqueeze(0)
out = synth("Hello from SpeechT5!", forward_params={"speaker_embeddings": spk})
sf.write("speech.wav", out["audio"], samplerate=out["sampling_rate'])
```
## Training Details
### Training Data
- **Primary example dataset:** **RAVDESS** (speech subset). It contains 24 professional actors (12F/12M) producing two fixed sentences across eight emotional categories; the PLOS ONE paper details construction and validation. **License:** CC BY‑NC‑SA 4.0 (non‑commercial); commercial licenses available from the maintainers.
- The repo includes a helper to build a **CSV manifest** (columns: `path, text, emotion, speaker, style_path`) from extracted RAVDESS wavs.
### Training Procedure
The main entry point is `src/train_emotts.py`. Training jointly adapts SpeechT5 and learns two small modules:
- **StyleAdaptor**: projects mean‑pooled WavLM hidden states (emotion/prosody) into a compact style latent.
- **StyleSpeakerFusion**: merges the style latent with ECAPA speaker embeddings to produce the **512‑D** `speaker_embeddings` expected by SpeechT5.
- Optional **LoRA/PEFT** adapters can be enabled to reduce trainable parameters.
#### Preprocessing
- The provided `data/raw.py` parses RAVDESS filenames to map emotion labels and creates the training manifest.
#### Training Hyperparameters (reference)
Reference values from the repo examples:
- `--base_tts` `microsoft/speecht5_tts`; `--vocoder` `microsoft/speecht5_hifigan`
- `--ssl_name` `microsoft/wavlm-base-plus`; `--spk_embedder` `speechbrain/spkrec-ecapa-voxceleb`
- Steps & LR: `--max_steps 4000`, `--lr 1e-5`, `--warmup_steps 500`
- Batching: `--per_device_train_batch_size 4`, `--per_device_eval_batch_size 2`, `--gradient_accumulation_steps 8`
- Precision: `--fp16` (mixed precision)
- Emotion loss weight: `--emo_ce_weight 0.2`
- Example global settings: `epochs 5`, `batch_size 8`, `sample_rate 22050` (see `sagemaker/config.example.yaml`).
#### Speeds, Sizes, Times (example run)
- **Hardware/Env (example):** Laptop Windows (WDDM), **RTX 3080 Ti Laptop (16 GB)**, CUDA driver 12.9, **PyTorch 2.8.0+cu129**.
- **Reported training runtime:** `2,391.8157` seconds; **Total FLOPs:** `3,285,475,498,393,600`.
- TensorBoard logs supported.
## Evaluation
### Testing Data, Factors & Metrics
- The repository focuses on providing **inference and training scaffolding**; no official quantitative evaluation metrics are included in the README. Users may evaluate with:
- **MOS/CMOS** listening tests for naturalness/expressiveness.
- **Emotion transfer accuracy** via a frozen SER classifier.
- **Speaker similarity** via cosine similarity between ECAPA embeddings.
### Results
- No official objective scores are reported in the repository at time of writing. Qualitative listening and application‑specific metrics are recommended.
#### Summary
The system demonstrates **controllable emotion style transfer** on top of a strong TTS backbone, with modular adapters and optional PEFT to simplify training.
## Model Examination (optional)
- Inspect style and speaker embeddings (e.g., **t‑SNE/UMAP** of fusion outputs) to verify separation and controllability across emotions/speakers.
## Environmental Impact
Use the [MLCO2 Impact calculator](https://mlco2.github.io/impact#compute) for your specific runs.
- **Hardware Type:** Single NVIDIA RTX 3080 Ti Laptop (example).
- **Hours used:** ~0.66 h for the example training run (≈2392 seconds).
- **Cloud Provider / Region:** N/A (example was local).
- **Carbon Emitted:** Not estimated; depends on locale and energy mix.
## Technical Specifications
### Model Architecture and Objective
- **Backbone:** SpeechT5 encoder‑decoder for TTS with HiFi‑GAN vocoder.
- **Style pathway:** WavLM (base‑plus) → mean pool → trainable **StyleAdaptor**.
- **Speaker pathway:** SpeechBrain ECAPA‑TDNN embeddings.
- **Fusion:** **StyleSpeakerFusion** → 512‑D vector as `speaker_embeddings` to SpeechT5.
- **Objective:** TTS generation with an auxiliary emotion classification loss (weighted by `--emo_ce_weight`).
### Compute Infrastructure
#### Hardware
- Example dev environment reported by the author: **RTX 3080 Ti Laptop 16 GB**, CUDA 12.9.
#### Software
- **PyTorch**, **Transformers**, **Datasets**, **Accelerate**, **SpeechBrain**, **SoundFile**, **PEFT**, **Gradio**, `huggingface_hub` (with optional `bitsandbytes`).
## License
- **Repository:** As of 2025‑08‑25, **no license file** is present in the repo—usage defaults to **all rights reserved** unless the author adds a license.
- **Base models:**
- `microsoft/speecht5_tts` — **MIT**.
- `microsoft/speecht5_hifigan` — **MIT**.
- `speechbrain/spkrec-ecapa-voxceleb` — **Apache‑2.0** (SpeechBrain toolkit).
- `microsoft/wavlm-base-plus` — see the UniSpeech repository license (Microsoft).
- **Dataset:** **RAVDESS** — **CC BY‑NC‑SA 4.0** (non‑commercial); commercial licenses available from the maintainers.
## Citation
**Core papers**
- **SpeechT5 (TTS):** Ao, J., Wang, R., Zhou, L., et al. (2022). *SpeechT5: Unified‑Modal Encoder‑Decoder Pre‑Training for Spoken Language Processing*. ACL 2022.
- **WavLM:** Chen, S., Wang, C., Chen, Z., et al. (2022). *WavLM: Large‑Scale Self‑Supervised Pre‑Training for Full Stack Speech Processing*. arXiv:2110.13900.
- **ECAPA‑TDNN:** Desplanques, B., Thienpondt, J., & Demuynck, K. (2020). *ECAPA‑TDNN: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification*. Interspeech 2020.
- **RAVDESS:** Livingstone, S. R., & Russo, F. A. (2018). *The Ryerson Audio‑Visual Database of Emotional Speech and Song (RAVDESS)*. *PLOS ONE, 13*(5), e0196391.
**BibTeX (selection)**
```bibtex
@inproceedings{ao-etal-2022-speecht5,
title = {SpeechT5: Unified-Modal Encoder-Decoder Pre-Training for Spoken Language Processing},
author = {Ao, Junyi and Wang, Rui and Zhou, Long and Wang, Chengyi and Ren, Shuo and Wu, Yu and Liu, Shujie and Ko, Tom and Li, Qing and Zhang, Yu and Wei, Zhihua and Qian, Yao and Li, Jinyu and Wei, Furu},
booktitle = {ACL},
year = {2022}
}
@article{chen2022wavlm,
title={WavLM: Large-Scale Self-Supervised Pre-Training for Full Stack Speech Processing},
author={Chen, Sanyuan and Wang, Chengyi and Chen, Zhengyang and et al.},
journal={arXiv:2110.13900},
year={2022}
}
@inproceedings{Desplanques2020ECAPA,
title={{ECAPA-TDNN}: Emphasized Channel Attention, Propagation and Aggregation in TDNN Based Speaker Verification},
author={Desplanques, Brecht and Thienpondt, Jenthe and Demuynck, Kris},
booktitle={Interspeech},
year={2020}
}
@article{livingstone2018ravdess,
title={The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS)},
author={Livingstone, Steven R and Russo, Frank A},
journal={PLOS ONE},
year={2018},
volume={13},
number={5},
pages={e0196391}
}
```
## Glossary
- **Style transfer (speech):** Conditioning TTS on reference audio to transfer prosodic/emotional characteristics.
- **Speaker embeddings:** Numeric vectors capturing speaker timbre (here from ECAPA‑TDNN).
- **Prosody features:** Rhythm, stress, and intonation; here approximated via SSL features from WavLM.
- **LoRA/PEFT:** Parameter‑efficient fine‑tuning methods that train small adapter weights instead of full backbones.
## More Information
- **SageMaker utilities:** The repo includes scripts for launching training jobs, and deploying real‑time/async inference endpoints.
## Model Card Authors
- Repository & implementation: **Amirhossein Yousefiramandi** (`@amirhossein-yousefi`).
## Model Card Contact
- Open an issue in the GitHub repository for questions or support.
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1756145300
|
Shopnil09
| 2025-08-25T18:08:51Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:08:47Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Vasya777/blockassist-bc-lumbering_enormous_sloth_1756145278
|
Vasya777
| 2025-08-25T18:08:38Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"lumbering enormous sloth",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:08:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- lumbering enormous sloth
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sophie-Rains-Spiderman-Video-Tutorial/Sophie.Rain.Spiderman.Video.Tutorial
|
Sophie-Rains-Spiderman-Video-Tutorial
| 2025-08-25T18:08:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-25T18:07:27Z |
<!-- HTML_TAG_END --><div>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p>
<!-- HTML_TAG_END --></div>
|
whizwang/blockassist-bc-amphibious_roaring_koala_1756145240
|
whizwang
| 2025-08-25T18:07:53Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious roaring koala",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:07:50Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious roaring koala
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
TalwXX/blockassist-bc-aquatic_lumbering_sardine_1756142989
|
TalwXX
| 2025-08-25T18:07:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic lumbering sardine",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:07:36Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic lumbering sardine
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1756145163
|
Shopnil09
| 2025-08-25T18:06:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:06:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mano-ktk-kiss-viral-videos-TikTok/New.full.videos.mano.ktk.kiss.Viral.Video.Official.Tutorial
|
mano-ktk-kiss-viral-videos-TikTok
| 2025-08-25T18:06:27Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-25T18:06:03Z |
<animated-image data-catalyst=""><a href="https://newmovietv.online/leaked-video/?leaked-videos/" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1756145037
|
Shopnil09
| 2025-08-25T18:04:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:04:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vengky/blockassist-bc-wild_gentle_manatee_1756143092
|
vengky
| 2025-08-25T18:04:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wild gentle manatee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:04:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wild gentle manatee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Danludan/blockassist-bc-flightless_camouflaged_flamingo_1756144902
|
Danludan
| 2025-08-25T18:02:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"flightless camouflaged flamingo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:02:14Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- flightless camouflaged flamingo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_005
|
AnonymousCS
| 2025-08-25T18:01:36Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-25T18:00:30Z |
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_005
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. -->
# populism_classifier_005
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5295
- Accuracy: 0.9534
- 1-f1: 0.6
- 1-recall: 0.625
- 1-precision: 0.5769
- Balanced Acc: 0.7989
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3265 | 1.0 | 14 | 0.2576 | 0.9371 | 0.5846 | 0.7917 | 0.4634 | 0.8687 |
| 0.2059 | 2.0 | 28 | 0.3658 | 0.9464 | 0.5965 | 0.7083 | 0.5152 | 0.8344 |
| 0.046 | 3.0 | 42 | 0.5295 | 0.9534 | 0.6 | 0.625 | 0.5769 | 0.7989 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
liukevin666/blockassist-bc-yawning_striped_cassowary_1756144674
|
liukevin666
| 2025-08-25T18:00:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"yawning striped cassowary",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:58:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- yawning striped cassowary
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1756144776
|
Shopnil09
| 2025-08-25T18:00:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T18:00:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756144735
|
ggozzy
| 2025-08-25T18:00:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:59:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
AnonymousCS/populism_classifier_004
|
AnonymousCS
| 2025-08-25T18:00:00Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-25T17:57:40Z |
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: populism_classifier_004
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. -->
# populism_classifier_004
This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2398
- Accuracy: 0.9833
- 1-f1: 0.5
- 1-recall: 0.5
- 1-precision: 0.5
- Balanced Acc: 0.7458
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.2159 | 1.0 | 57 | 0.1652 | 0.9699 | 0.4130 | 0.6333 | 0.3065 | 0.8045 |
| 0.2815 | 2.0 | 114 | 0.3137 | 0.9855 | 0.4348 | 0.3333 | 0.625 | 0.6650 |
| 0.2949 | 3.0 | 171 | 0.2398 | 0.9833 | 0.5 | 0.5 | 0.5 | 0.7458 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
bah63843/blockassist-bc-plump_fast_antelope_1756144732
|
bah63843
| 2025-08-25T17:59:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:59:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hardhiksonu/qa_model
|
hardhiksonu
| 2025-08-25T17:58:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T17:58:32Z |
---
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 🤗 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]
|
ultratopaz/1470746
|
ultratopaz
| 2025-08-25T17:57:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-25T17:57:49Z |
[View on Civ Archive](https://civarchive.com/models/1387682?modelVersionId=1568222)
|
steb6/traind-ergocub-pick-plush-act
|
steb6
| 2025-08-25T17:54:27Z | 0 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"act",
"robotics",
"dataset:ar0s/ergocub-pick-plush",
"arxiv:2304.13705",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-25T17:54:13Z |
---
datasets: ar0s/ergocub-pick-plush
library_name: lerobot
license: apache-2.0
model_name: act
pipeline_tag: robotics
tags:
- act
- lerobot
- robotics
---
# Model Card for act
<!-- Provide a quick summary of what the model is/does. -->
[Action Chunking with Transformers (ACT)](https://huggingface.co/papers/2304.13705) is an imitation-learning method that predicts short action chunks instead of single steps. It learns from teleoperated data and often achieves high success rates.
This policy has been trained and pushed to the Hub using [LeRobot](https://github.com/huggingface/lerobot).
See the full documentation at [LeRobot Docs](https://huggingface.co/docs/lerobot/index).
---
## How to Get Started with the Model
For a complete walkthrough, see the [training guide](https://huggingface.co/docs/lerobot/il_robots#train-a-policy).
Below is the short version on how to train and run inference/eval:
### Train from scratch
```bash
lerobot-train \
--dataset.repo_id=${HF_USER}/<dataset> \
--policy.type=act \
--output_dir=outputs/train/<desired_policy_repo_id> \
--job_name=lerobot_training \
--policy.device=cuda \
--policy.repo_id=${HF_USER}/<desired_policy_repo_id>
--wandb.enable=true
```
_Writes checkpoints to `outputs/train/<desired_policy_repo_id>/checkpoints/`._
### Evaluate the policy/run inference
```bash
lerobot-record \
--robot.type=so100_follower \
--dataset.repo_id=<hf_user>/eval_<dataset> \
--policy.path=<hf_user>/<desired_policy_repo_id> \
--episodes=10
```
Prefix the dataset repo with **eval\_** and supply `--policy.path` pointing to a local or hub checkpoint.
---
## Model Details
- **License:** apache-2.0
|
Muapi/fkey-style-flux-dev-test-version
|
Muapi
| 2025-08-25T17:54:25Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T17:54:07Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Fkey style (Flux dev test version)
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:647601@990600", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
undressing-ai-app/undressing.ai.app
|
undressing-ai-app
| 2025-08-25T17:53:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-25T13:56:38Z |
# **AI Undress Best Undress APP in 2025 {m9x2l}**
*(Last Updated: 27 August, 2025)*
## **AI Undress – The Most Advanced Clothing-Removal & Photo Reveal Tool (2025)**
- **AI clothing removal & reveal** – hyper-realistic skin, shadows, and textures.
- **Try-on & swap modes** – bikini/lingerie previews, sheer/see-through, outfit-to-nude simulation.
- **Smart editing tools** – tan lines, smooth skin, lighting/reflection fixes, detail upscaler.
- **Batch & single image** – fast processing with consistent results.
- **Private by design • 18+ only • Use on images you own or have consent to edit.**
[**👉 Try the Best Undress AI Now**](https://aiweely.com/tools/un)
[**👉 Try Free 2 Undressing Generation**](https://aiweely.com/tools/un)
[**👉 Try Undress AI No Filter**](https://aiweely.com/tools/un)
---
**Updated 27 August, 2025**
With the rise of **AI image editing** and **deepfake technology**, terms like **AI Undress**, **undress AI**, or **AI cloth remover** have exploded in search queries and public interest.
*Many people are curious: Are AI undress tools real? How do they work? Are these apps legal or safe?*
This article dives deep into the world of **AI undress software**, providing a clear, responsible, and up-to-date overview as of 27 August, 2025.
---
## **What Is AI Undress? (Updated 27 August, 2025)**
**AI Undress** refers to software or apps that claim to use **artificial intelligence** to “remove clothes” from images, usually photographs of people, to reveal or simulate nudity.
Also known as **undress AI, AI undress tool, undress image AI, or AI photo undress generator**, this technology leverages **neural networks**—many inspired by **deepfake** and **generative AI models**.
This concept first came to public attention with tools like **DeepNude**. But as of *27 August, 2025*, the technology has evolved—along with misuse, regulation, and limited legitimate uses.
---
## **A Brief History of AI Undressing Tools**
### **DeepNude and the Early Days**
- In 2019, a program named **DeepNude** shocked the internet by using AI to undress women in photos.
- It worked by taking an input photo and generating a nude output using **neural networks** and **datasets of unclothed bodies**.
- After public backlash, DeepNude was shut down. *However, its code was copied, modified, and shared online.*
### **Rapid Evolution**
- DeepNude inspired a surge of **deepfake undress AI tools** on underground forums and illicit sites.
- By 2022–2025, **AI models** had become more powerful at **inpainting, style transfer, and image-to-image translation**, making the results more realistic—and harder to detect.
---
## **Real vs Fake: What’s Possible with AI in 2025? (Updated 27 August, 2025)**
### **Can AI Really Remove Clothes in a Photo?**
As of 27 August, 2025, **AI undress tools can create fake results that look real.**
Here’s what’s actually happening:
- **AI does NOT see through clothes.** Instead, it *guesses and invents* using data from millions of images.
- **AI cloth remover apps** use **GANs** or **diffusion models** to generate synthetic skin or lingerie and blend them onto the photo.
- **AI image editor undress results are fakes, not the real person.**
### **Limits and Telltale Signs**
- Struggles with **complex poses, layered outfits, or busy backgrounds**.
- Mistakes in **lighting, shadows, or anatomy** reveal AI edits.
- Works best on **frontal, high-resolution photos** with tight clothing.
---
## **AI Undress Generators: Real or Hype?**
- The internet is flooded with advertised **AI undress apps**, most of which are **scams, malware, or fake**.
- Real AI undress tools exist but are *rare, paywalled, and risky* since they require uploading private photos.
- Most **web-based undress AI online tools** are **illegal or deceptive.**
---
## **Examples of AI Undress Apps and Tools (As of 27 August, 2025)**
| **Tool/Website Name** | **Type** | **Claims** | **Reality/Status (2025)** | **Legal Status** |
|-------------------------------|----------------------|----------------------------------------|----------------------------------------------|---------------------------|
| **DeepNude / DeepNudeNext** | PC / Online App | Remove clothes, realistic nude output | Cloned code, often malware | Illegal in many regions |
| **Undress AI Pro** | Web App / Tool | AI “see-through” generator | Fake, malware risks | Illegal / suspect |
| **DeepArtUndress** | Web / Telegram Bot | GAN-powered undress tool | Works but illegal, hidden fees | Illegal |
| **FaceMagic NSFW Mode** | Mobile App | AI nude generator | Low realism, adds watermarks, scam | Suspicious / fake |
| **DeepNude Alternatives** | Guides / Mods | Clones of DeepNude | Mostly malware, rare working models | Illegal content |
⚠ **Note:** Most “AI undress apps for Android/iOS” are **scams or malware traps**. *Google Play and Apple remove them due to security and legal risks.*
---
## **Risks: Legality, Ethics, Scams, and Malware (Updated 27 August, 2025)**
### **Legal Issues Around AI Undressing Tech**
- As of 2025, **US, UK, EU, Japan, and Australia** classify **AI undress images** as a crime.
- Laws treat **AI-generated nudes** as **non-consensual intimate content**.
- ⚠ *Despite websites claiming it’s “entertainment,” it is considered **image-based abuse**.*
### **Ethical Dangers**
- **Severe privacy violation** → trauma, reputation damage.
- Fuels **digital harassment** and **deepfake porn**, mostly targeting women.
- *Encourages exploitation culture and disregard for consent.*
### **Security & Scam Risks**
- Many “AI undress photo” sites **extort money** after uploads.
- Downloadable tools often contain **spyware/trojans**.
- Fake sites may cause **fraud and identity theft.**
---
## **Use Cases: Legitimate AI Undressing vs. Abuse**
### **Legitimate / Legal Uses**
1. **Medical Education & Simulation** – Digital peeling of skin/organs for anatomy study (not nudity).
2. **Virtual Clothing Try-On** – Fashion AI showing clothes on avatars (not real people).
3. **Forensic / Security** – Reconstruction of damaged surfaces in rare rescue cases.
### **Abusive / Illegal Uses (Do NOT Engage!)**
- Creating or sharing **fake nudes of real people without consent** = illegal.
- Even as a *joke*, sharing AI undress images in 2025 can result in **criminal charges.**
---
## **How to Identify AI Undressed or Deepfake Images**
- **AI Artifacts** → distorted skin, odd shadows, mismatched lighting.
- **Metadata analysis** → hidden watermarks or EXIF data.
- **Reverse image search** → find original clothed photo.
- **AI forensic tools** → e.g., Deepware Scanner, FotoForensics.
- **Ask the subject** if consent is unclear.
---
## **Tools/Alternatives That Respect Privacy and Legality (Updated 27 August, 2025)**
If you want **AI editing or AR try-on apps without breaking the law**, consider:
- **AI Art / Body Simulation** → RunwayML, Adobe Firefly (strict consent rules).
- **MetaHuman Creator** → avatar design, virtual clothes ON/OFF.
- **Virtual Try-On Software** → Fashwell, Zeekit (clothing previews only).
- **3D Medical Simulators** → training tools, not nudity-related.
- **Photo Editors** (NOT for undressing) → Photoshop Generative Fill.
⚠ **Avoid** sites claiming “AI nude generator” or “see-through AI.”
---
## **FAQ: AI Undress Tools (Updated 27 August, 2025)**
1. **What is AI Undress?**
Software that uses AI to remove clothing in photos, generating fake nudes.
2. **Are AI undress tools real or fake?**
They exist, but they *cannot see through clothes*. All results are **fake simulations**.
3. **Are AI undressing apps legal?**
❌ No. *As of 2025, making/sharing AI “remove clothes” images of real people is illegal.*
4. **What is the best alternative to DeepNude in 2025?**
There is **no legal replacement**. Only fashion AR and medical simulators.
5. **How do AI cloth remover tools work?**
They use **GANs/diffusion AI** to blend a generated body with the photo.
6. **Can AI undress a person in a photo?**
Not literally. They only create *fabricated imagery*.
7. **Is using an undress AI illegal?**
✅ Yes — if used without written consent, it can lead to **criminal charges**.
8. **How to know if an image was undressed by AI?**
Look for distortions, unnatural skin, metadata, or use forensic scanners.
9. **Are there safe/ethical uses?**
Yes: **medical imaging, AR fashion, avatars**. *Never on real people.*
10. **Can AI see through clothes?**
❌ No. It only *guesses plausible results* using training data.
---
## **Final Conclusion: AI Undress in 2025 — Warning & Ethical Alternatives**
As of 2025, **AI undress technology is advanced, but dangerous.**
Most “AI undress” apps are **fraudulent, malicious, or illegal**, risking legal action and harm.
👉 Instead, explore:
- **AI art stylization**
- **Fashion AR try-ons**
- **Medical training simulators**
⚠ **Never use AI undress apps on real people without consent.**
---
### **Further Reading & Resources**
- Deepfake Forensics (Deepware, Sensity)
- US & EU **Digital Harassment Laws** (2025)
- **AI Ethics Standards** (IEEE, Partnership on AI)
*(Updated 27 August, 2025)*
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1756144387
|
Shopnil09
| 2025-08-25T17:53:36Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:53:32Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/UIGEN-X-4B-08-25-GGUF
|
mradermacher
| 2025-08-25T17:53:17Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"en",
"base_model:smirki/UIGEN-X-4B-08-25",
"base_model:quantized:smirki/UIGEN-X-4B-08-25",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-25T17:24:18Z |
---
base_model: smirki/UIGEN-X-4B-08-25
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/smirki/UIGEN-X-4B-08-25
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#UIGEN-X-4B-08-25-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/UIGEN-X-4B-08-25-GGUF/resolve/main/UIGEN-X-4B-08-25.Q2_K.gguf) | Q2_K | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-X-4B-08-25-GGUF/resolve/main/UIGEN-X-4B-08-25.Q3_K_S.gguf) | Q3_K_S | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-X-4B-08-25-GGUF/resolve/main/UIGEN-X-4B-08-25.Q3_K_M.gguf) | Q3_K_M | 2.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-X-4B-08-25-GGUF/resolve/main/UIGEN-X-4B-08-25.Q3_K_L.gguf) | Q3_K_L | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-X-4B-08-25-GGUF/resolve/main/UIGEN-X-4B-08-25.IQ4_XS.gguf) | IQ4_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-X-4B-08-25-GGUF/resolve/main/UIGEN-X-4B-08-25.Q4_K_S.gguf) | Q4_K_S | 2.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-X-4B-08-25-GGUF/resolve/main/UIGEN-X-4B-08-25.Q4_K_M.gguf) | Q4_K_M | 2.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-X-4B-08-25-GGUF/resolve/main/UIGEN-X-4B-08-25.Q5_K_S.gguf) | Q5_K_S | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-X-4B-08-25-GGUF/resolve/main/UIGEN-X-4B-08-25.Q5_K_M.gguf) | Q5_K_M | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-X-4B-08-25-GGUF/resolve/main/UIGEN-X-4B-08-25.Q6_K.gguf) | Q6_K | 3.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-X-4B-08-25-GGUF/resolve/main/UIGEN-X-4B-08-25.Q8_0.gguf) | Q8_0 | 4.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/UIGEN-X-4B-08-25-GGUF/resolve/main/UIGEN-X-4B-08-25.f16.gguf) | f16 | 8.9 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Xpsloan/bct-demo-longformer-model
|
Xpsloan
| 2025-08-25T17:53:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"longformer",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-25T17:52:49Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
gensynme/blockassist-bc-small_vigilant_wildebeest_1756144357
|
gensynme
| 2025-08-25T17:53:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"small vigilant wildebeest",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:52:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- small vigilant wildebeest
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1756143165
|
Sayemahsjn
| 2025-08-25T17:52:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:52:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- playful feline octopus
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/miniature-wonderland-flux-ethanar
|
Muapi
| 2025-08-25T17:52:56Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T17:52:46Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Miniature Wonderland FLUX @Ethanar
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:770860@862188", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
BootesVoid/cmerdmem00cfktlqbzartsm5c_cmerdr7gd0cfytlqb12a1mkdo
|
BootesVoid
| 2025-08-25T17:52:21Z | 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
| 2025-08-25T17:52:19Z |
---
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: LILY
---
# Cmerdmem00Cfktlqbzartsm5C_Cmerdr7Gd0Cfytlqb12A1Mkdo
<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 `LILY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "LILY",
"lora_weights": "https://huggingface.co/BootesVoid/cmerdmem00cfktlqbzartsm5c_cmerdr7gd0cfytlqb12a1mkdo/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 [🧨 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/cmerdmem00cfktlqbzartsm5c_cmerdr7gd0cfytlqb12a1mkdo', weight_name='lora.safetensors')
image = pipeline('LILY').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: 2500
- Learning rate: 9e-05
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmerdmem00cfktlqbzartsm5c_cmerdr7gd0cfytlqb12a1mkdo/discussions) to add images that show off what you’ve made with this LoRA.
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756144257
|
ggozzy
| 2025-08-25T17:52:15Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:52:00Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756144179
|
Ferdi3425
| 2025-08-25T17:50:10Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:50:07Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
bah63843/blockassist-bc-plump_fast_antelope_1756144128
|
bah63843
| 2025-08-25T17:49:33Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"plump fast antelope",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:49:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- plump fast antelope
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
lengocquangLAB/phobert-large-jd-skill-match
|
lengocquangLAB
| 2025-08-25T17:49:15Z | 2 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-16T09:21:19Z |
---
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 🤗 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]
|
mradermacher/andrew-tate-llm-clone-GGUF
|
mradermacher
| 2025-08-25T17:48:31Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:otmanheddouch/andrew-tate-llm-clone",
"base_model:quantized:otmanheddouch/andrew-tate-llm-clone",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T17:44:19Z |
---
base_model: otmanheddouch/andrew-tate-llm-clone
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/otmanheddouch/andrew-tate-llm-clone
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#andrew-tate-llm-clone-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/andrew-tate-llm-clone-GGUF/resolve/main/andrew-tate-llm-clone.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/andrew-tate-llm-clone-GGUF/resolve/main/andrew-tate-llm-clone.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/andrew-tate-llm-clone-GGUF/resolve/main/andrew-tate-llm-clone.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/andrew-tate-llm-clone-GGUF/resolve/main/andrew-tate-llm-clone.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/andrew-tate-llm-clone-GGUF/resolve/main/andrew-tate-llm-clone.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/andrew-tate-llm-clone-GGUF/resolve/main/andrew-tate-llm-clone.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/andrew-tate-llm-clone-GGUF/resolve/main/andrew-tate-llm-clone.Q4_K_M.gguf) | Q4_K_M | 0.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/andrew-tate-llm-clone-GGUF/resolve/main/andrew-tate-llm-clone.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/andrew-tate-llm-clone-GGUF/resolve/main/andrew-tate-llm-clone.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/andrew-tate-llm-clone-GGUF/resolve/main/andrew-tate-llm-clone.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/andrew-tate-llm-clone-GGUF/resolve/main/andrew-tate-llm-clone.Q8_0.gguf) | Q8_0 | 0.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/andrew-tate-llm-clone-GGUF/resolve/main/andrew-tate-llm-clone.f16.gguf) | f16 | 0.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
mradermacher/InmuLLM-GGUF
|
mradermacher
| 2025-08-25T17:48:31Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"sft",
"trl",
"en",
"base_model:HawkClaws/InmuLLM",
"base_model:quantized:HawkClaws/InmuLLM",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-25T17:43:23Z |
---
base_model: HawkClaws/InmuLLM
language:
- en
library_name: transformers
model_name: InmuLLM
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
- sft
- trl
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
<!-- ### quants: x-f16 Q4_K_S Q2_K Q8_0 Q6_K Q3_K_M Q3_K_S Q3_K_L Q4_K_M Q5_K_S Q5_K_M IQ4_XS -->
<!-- ### quants_skip: -->
<!-- ### skip_mmproj: -->
static quants of https://huggingface.co/HawkClaws/InmuLLM
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#InmuLLM-GGUF).***
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/InmuLLM-GGUF/resolve/main/InmuLLM.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/InmuLLM-GGUF/resolve/main/InmuLLM.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/InmuLLM-GGUF/resolve/main/InmuLLM.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/InmuLLM-GGUF/resolve/main/InmuLLM.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/InmuLLM-GGUF/resolve/main/InmuLLM.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/InmuLLM-GGUF/resolve/main/InmuLLM.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/InmuLLM-GGUF/resolve/main/InmuLLM.Q4_K_M.gguf) | Q4_K_M | 0.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/InmuLLM-GGUF/resolve/main/InmuLLM.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/InmuLLM-GGUF/resolve/main/InmuLLM.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/InmuLLM-GGUF/resolve/main/InmuLLM.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/InmuLLM-GGUF/resolve/main/InmuLLM.Q8_0.gguf) | Q8_0 | 0.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/InmuLLM-GGUF/resolve/main/InmuLLM.f16.gguf) | f16 | 0.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756143980
|
Ferdi3425
| 2025-08-25T17:46:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:46:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1756143964
|
Shopnil09
| 2025-08-25T17:46:34Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:46:30Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hokpertoy/blockassist-bc-silent_savage_reindeer_1756143969
|
hokpertoy
| 2025-08-25T17:46:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"silent savage reindeer",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:46:10Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- silent savage reindeer
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
RishabhBhardwajWalled/walledguard-a2
|
RishabhBhardwajWalled
| 2025-08-25T17:45:18Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-25T17:45:15Z |
---
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 🤗 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]
|
Kokoutou/sr105_dere_2508_5
|
Kokoutou
| 2025-08-25T17:45:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-25T16:44:50Z |
# Container Template for SoundsRight Subnet Miners
This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively.
This container has only been tested with **Ubuntu 24.04** and **CUDA 12.6**. It may run on other configurations, but it is not guaranteed.
To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt.
Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html).
Verify that the CDI specification was done correctly with:
```
$ nvidia-ctk cdi list
```
You should see this in your output:
```
nvidia.com/gpu=all
nvidia.com/gpu=0
```
If you are running podman as root, run the following command to start the container:
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
If you are running the container rootless, there are a few more changes to make:
First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters:
```
[nvidia-container-cli]
no-cgroups = true
[nvidia-container-runtime]
debug = "/tmp/nvidia-container-runtime.log"
```
You can also run the following command to achieve the same result:
```
$ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
```
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
Running the container will spin up an API with the following endpoints:
1. `/status/` : Communicates API status
2. `/prepare/` : Download model checkpoint and initialize model
3. `/upload-audio/` : Upload audio files, save to noisy audio directory
4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory
5. `/download-enhanced/` : Download enhanced audio files
By default the API will use host `0.0.0.0` and port `6500`.
### References
1. **Welker, Simon; Richter, Julius; Gerkmann, Timo**
*Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain*.
Proceedings of *Interspeech 2022*, 2022, pp. 2928–2932.
[DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653)
2. **Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo**
*Speech Enhancement and Dereverberation with Diffusion-based Generative Models*.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, Vol. 31, 2023, pp. 2351–2364.
[DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241)
3. **Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo**
*EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation*.
Proceedings of *ISCA Interspeech*, 2024, pp. 4873–4877.
|
Kokoutou/sr105_dere_2508_4
|
Kokoutou
| 2025-08-25T17:44:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-25T16:44:49Z |
# Container Template for SoundsRight Subnet Miners
This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively.
This container has only been tested with **Ubuntu 24.04** and **CUDA 12.6**. It may run on other configurations, but it is not guaranteed.
To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt.
Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html).
Verify that the CDI specification was done correctly with:
```
$ nvidia-ctk cdi list
```
You should see this in your output:
```
nvidia.com/gpu=all
nvidia.com/gpu=0
```
If you are running podman as root, run the following command to start the container:
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
If you are running the container rootless, there are a few more changes to make:
First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters:
```
[nvidia-container-cli]
no-cgroups = true
[nvidia-container-runtime]
debug = "/tmp/nvidia-container-runtime.log"
```
You can also run the following command to achieve the same result:
```
$ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
```
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
Running the container will spin up an API with the following endpoints:
1. `/status/` : Communicates API status
2. `/prepare/` : Download model checkpoint and initialize model
3. `/upload-audio/` : Upload audio files, save to noisy audio directory
4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory
5. `/download-enhanced/` : Download enhanced audio files
By default the API will use host `0.0.0.0` and port `6500`.
### References
1. **Welker, Simon; Richter, Julius; Gerkmann, Timo**
*Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain*.
Proceedings of *Interspeech 2022*, 2022, pp. 2928–2932.
[DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653)
2. **Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo**
*Speech Enhancement and Dereverberation with Diffusion-based Generative Models*.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, Vol. 31, 2023, pp. 2351–2364.
[DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241)
3. **Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo**
*EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation*.
Proceedings of *ISCA Interspeech*, 2024, pp. 4873–4877.
|
Kokoutou/sr105_dere_2508_3
|
Kokoutou
| 2025-08-25T17:44:56Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-25T16:44:49Z |
# Container Template for SoundsRight Subnet Miners
This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively.
This container has only been tested with **Ubuntu 24.04** and **CUDA 12.6**. It may run on other configurations, but it is not guaranteed.
To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt.
Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html).
Verify that the CDI specification was done correctly with:
```
$ nvidia-ctk cdi list
```
You should see this in your output:
```
nvidia.com/gpu=all
nvidia.com/gpu=0
```
If you are running podman as root, run the following command to start the container:
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
If you are running the container rootless, there are a few more changes to make:
First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters:
```
[nvidia-container-cli]
no-cgroups = true
[nvidia-container-runtime]
debug = "/tmp/nvidia-container-runtime.log"
```
You can also run the following command to achieve the same result:
```
$ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
```
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
Running the container will spin up an API with the following endpoints:
1. `/status/` : Communicates API status
2. `/prepare/` : Download model checkpoint and initialize model
3. `/upload-audio/` : Upload audio files, save to noisy audio directory
4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory
5. `/download-enhanced/` : Download enhanced audio files
By default the API will use host `0.0.0.0` and port `6500`.
### References
1. **Welker, Simon; Richter, Julius; Gerkmann, Timo**
*Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain*.
Proceedings of *Interspeech 2022*, 2022, pp. 2928–2932.
[DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653)
2. **Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo**
*Speech Enhancement and Dereverberation with Diffusion-based Generative Models*.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, Vol. 31, 2023, pp. 2351–2364.
[DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241)
3. **Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo**
*EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation*.
Proceedings of *ISCA Interspeech*, 2024, pp. 4873–4877.
|
Muapi/tomboys-for-flux
|
Muapi
| 2025-08-25T17:44:31Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T17:44:14Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Tomboys for FLUX
**Base model**: Flux.1 D
**Trained words**: tomboy
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:668537@797322", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1756143838
|
Shopnil09
| 2025-08-25T17:44:28Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:44:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/metallic-snake-flux
|
Muapi
| 2025-08-25T17:43:34Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T17:43:20Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Metallic Snake flux
**Base model**: Flux.1 D
**Trained words**: metallic snake, metal snake, snake
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:1143449@1285979", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/plush-imagination
|
Muapi
| 2025-08-25T17:43:12Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T17:42:59Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Plush Imagination
**Base model**: Flux.1 D
**Trained words**: plush
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:858448@960466", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
mwalmsley/euclid_encoder_mae_zoobot_vit_small_patch8_224
|
mwalmsley
| 2025-08-25T17:43:10Z | 216 | 0 |
timm
|
[
"timm",
"pytorch",
"safetensors",
"image-classification",
"transformers",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2025-08-19T19:54:26Z |
---
tags:
- image-classification
- timm
- transformers
library_name: timm
license: apache-2.0
---
# Model card for euclid_encoder_mae_zoobot_vit_small_patch8_224
|
koloni/blockassist-bc-deadly_graceful_stingray_1756142224
|
koloni
| 2025-08-25T17:42:44Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:42:39Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- deadly graceful stingray
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yaelahnal/blockassist-bc-mute_clawed_crab_1756143641
|
yaelahnal
| 2025-08-25T17:41:49Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"mute clawed crab",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:41:31Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- mute clawed crab
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/demonic-skin-runes-flux
|
Muapi
| 2025-08-25T17:41:32Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T17:41:19Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# 🌀 Demonic Skin Runes [Flux]
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:829463@927706", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/flux-pony-yoshinari_you-style
|
Muapi
| 2025-08-25T17:41:01Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T17:40:49Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# [Flux/Pony]Yoshinari_You Style/吉成曜 風
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:683791@766789", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Ferdi3425/blockassist-bc-amphibious_deadly_otter_1756143588
|
Ferdi3425
| 2025-08-25T17:40:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"amphibious deadly otter",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:40:16Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- amphibious deadly otter
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/ghostly-ce-sdxl-flux
|
Muapi
| 2025-08-25T17:40:18Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T17:40:10Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Ghostly - CE - SDXL & Flux
**Base model**: Flux.1 D
**Trained words**: ghstlyCE style, spectral, ghost
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:684072@803752", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
ggozzy/blockassist-bc-stubby_yapping_mandrill_1756143539
|
ggozzy
| 2025-08-25T17:40:04Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"stubby yapping mandrill",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:39:57Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- stubby yapping mandrill
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/albeniz-rodriguez-style-flux-and-sdxl
|
Muapi
| 2025-08-25T17:39:31Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T17:39:19Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Albeniz Rodriguez style - FLUX and SDXL
**Base model**: Flux.1 D
**Trained words**: albenizrodriguez style painting of a
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:183861@928564", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1756143523
|
Shopnil09
| 2025-08-25T17:39:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:39:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/gus-friedbaens-style
|
Muapi
| 2025-08-25T17:39:14Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T17:39:01Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Gus/Friedbaens Style
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:594917@1365489", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Muapi/neon-cyberpunk-detailer-flux
|
Muapi
| 2025-08-25T17:38:56Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T17:38:38Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Neon Cyberpunk Detailer FLUX
**Base model**: Flux.1 D
**Trained words**: mad-cbrpnk-dtlr
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:730615@817012", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
mcbanan4k/my_awesome_model
|
mcbanan4k
| 2025-08-25T17:38:36Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"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
| 2025-08-25T16:19:48Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5932
- Accuracy: 0.83
## 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: 1
- eval_batch_size: 1
- 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6247 | 1.0 | 500 | 0.5932 | 0.83 |
### Framework versions
- Transformers 4.55.4
- Pytorch 2.2.2
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Muapi/chibi-characters-flux-dev
|
Muapi
| 2025-08-25T17:38:34Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T17:38:09Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Chibi characters [Flux Dev]
**Base model**: Flux.1 D
**Trained words**: A vibrant chibi-style illustration
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:681979@763308", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
Shopnil09/blockassist-bc-scruffy_knobby_hippo_1756143396
|
Shopnil09
| 2025-08-25T17:37:06Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy knobby hippo",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-25T17:37:02Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy knobby hippo
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Muapi/japan-red-light-district
|
Muapi
| 2025-08-25T17:36:55Z | 0 | 0 | null |
[
"lora",
"stable-diffusion",
"flux.1-d",
"license:openrail++",
"region:us"
] | null | 2025-08-25T17:36:40Z |
---
license: openrail++
tags:
- lora
- stable-diffusion
- flux.1-d
model_type: LoRA
---
# Japan red light district (飛田新地)
**Base model**: Flux.1 D
**Trained words**:
## 🧠 Usage (Python)
🔑 **Get your MUAPI key** from [muapi.ai/access-keys](https://muapi.ai/access-keys)
```python
import requests, os
url = "https://api.muapi.ai/api/v1/flux_dev_lora_image"
headers = {"Content-Type": "application/json", "x-api-key": os.getenv("MUAPIAPP_API_KEY")}
payload = {
"prompt": "masterpiece, best quality, 1girl, looking at viewer",
"model_id": [{"model": "civitai:340806@772946", "weight": 1.0}],
"width": 1024,
"height": 1024,
"num_images": 1
}
print(requests.post(url, headers=headers, json=payload).json())
```
|
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