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stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-12 12:31:00
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
values | tags
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4.05k
| pipeline_tag
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Jacksss123/net72_uid253
|
Jacksss123
| 2025-08-19T17:17:13Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-08-19T17:13:01Z |
---
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]
|
Jacksss123/net72_uid243
|
Jacksss123
| 2025-08-19T17:16:57Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-08-19T17:12:59Z |
---
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]
|
debesu/Mati-Bal-Mati-Mist
|
debesu
| 2025-08-19T17:16:40Z | 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
| 2025-08-19T16:47:24Z |
---
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: Mati
---
# Mati Bal Mati Mist
<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 `Mati` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Mati",
"lora_weights": "https://huggingface.co/debesu/Mati-Bal-Mati-Mist/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('debesu/Mati-Bal-Mati-Mist', weight_name='lora.safetensors')
image = pipeline('Mati').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: 1400
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/debesu/Mati-Bal-Mati-Mist/discussions) to add images that show off what you’ve made with this LoRA.
|
Jacksss123/net72_uid121
|
Jacksss123
| 2025-08-19T17:16:36Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-08-19T17:12:58Z |
---
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]
|
0xfffm4bs/vit-real-fake-classification-v4
|
0xfffm4bs
| 2025-08-19T17:16:09Z | 0 | 0 | null |
[
"onnx",
"vit",
"region:us"
] | null | 2025-08-19T17:10:31Z |
<<<<<<< HEAD
---
license: apache-2.0
---
=======
---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: vit-real-fake-classification-v4
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. -->
# vit-real-fake-classification-v4
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0585
- Accuracy: 0.9796
- F1: 0.9815
- Recall: 0.9815
- Precision: 0.9815
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.1295 | 1.0 | 233 | 0.2414 | 0.9151 | 0.9280 | 0.9912 | 0.8723 |
| 0.4466 | 2.0 | 466 | 0.1042 | 0.9646 | 0.9680 | 0.9718 | 0.9643 |
| 0.3302 | 3.0 | 699 | 0.0667 | 0.9764 | 0.9786 | 0.9776 | 0.9795 |
| 0.0003 | 4.0 | 932 | 0.0995 | 0.9731 | 0.9758 | 0.9796 | 0.9720 |
| 0.0002 | 5.0 | 1165 | 0.0585 | 0.9796 | 0.9815 | 0.9815 | 0.9815 |
### Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
>>>>>>> 3516007fbc33bd89b07b531bf52afda1db96b6f3
|
ACECA/lowMvMax_91
|
ACECA
| 2025-08-19T17:14:59Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-12T19:19:08Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755623624
|
Dejiat
| 2025-08-19T17:14:35Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T17:14:21Z |
---
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).
|
facebook/sparsh-skin
|
facebook
| 2025-08-19T17:14:33Z | 0 | 1 | null |
[
"sparsh-skin",
"tiny",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-08-19T16:22:15Z |
---
license: cc-by-nc-4.0
tags:
- sparsh-skin
- tiny
---
# Sparsh-skin model
[Sparsh-skin](https://akashsharma02.github.io/sparsh-skin-ssl/) is a transformer-based backbone for full hand tactile sensing with the [Xela](https://www.xelarobotics.com/tactile-sensors) sensor. This model is trained using self-distillation SSL and is specifically adapted for full hand Xela sensing, accounting for hand configuration, etc.
Disclaimer: This model card was written by the Sparsh-skin authors. The Transformer architetcure and DINO objectives have been adapted for full hand tactile SSL purposes.
## Intended uses & limitations
You can utilize the Sparsh-skin model to extract touch representations for the Xela sensor. You have two options:
1. Use the frozen Sparsh-skin encoder: This allows you to leverage the pre-trained weights of the Sparsh-skin model without modifying them.
2. Fine-tune the Sparsh-skin encoder: You can fine-tune the Sparsh-skin encoder along with the training of your downstream task, allowing the model to adapt to your specific use case.
Both options enable you to take advantage of the powerful touch representations learned by the Sparsh-skin model.
## How to Use
For detailed instructions on how to load the encoder and integrate it into your downstream task, please refer to our [GitHub repository](https://github.com/facebookresearch/sparsh-multisensory-touch).
## Citation
```bibtex
@inproceedings{
sharma2025selfsupervised,
title={Self-supervised perception for tactile skin covered dexterous hands},
author={Akash Sharma and Carolina Higuera and Chaithanya Krishna Bodduluri and Zixi Liu and Taosha Fan and Tess Hellebrekers and Mike Lambeta and Byron Boots and Michael Kaess and Tingfan Wu and Francois Robert Hogan and Mustafa Mukadam},
booktitle={9th Annual Conference on Robot Learning},
year={2025},
url={https://openreview.net/forum?id=eLeCrM5PEO}
}
```
|
AnonymousCS/xlmr_dutch_immigration3
|
AnonymousCS
| 2025-08-19T17:13:40Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T17:10:42Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_dutch_immigration3
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. -->
# xlmr_dutch_immigration3
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2108
- Accuracy: 0.9231
- 1-f1: 0.8684
- 1-recall: 0.7674
- 1-precision: 1.0
- Balanced Acc: 0.8837
## 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.1857 | 1.0 | 5 | 0.1606 | 0.9462 | 0.9114 | 0.8372 | 1.0 | 0.9186 |
| 0.1012 | 2.0 | 10 | 0.1627 | 0.9308 | 0.8916 | 0.8605 | 0.925 | 0.9130 |
| 0.1712 | 3.0 | 15 | 0.2108 | 0.9231 | 0.8684 | 0.7674 | 1.0 | 0.8837 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
joackimagno/MASID-v1-GGUF
|
joackimagno
| 2025-08-19T17:12:40Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"qwen2",
"text-generation-inference",
"unsloth",
"en",
"base_model:joackimagno/Qwen-2.5-General-Recipe-Generation",
"base_model:quantized:joackimagno/Qwen-2.5-General-Recipe-Generation",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T16:49:18Z |
---
base_model: joackimagno/Qwen-2.5-General-Recipe-Generation
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** joackimagno
- **License:** apache-2.0
- **Finetuned from model :** joackimagno/Qwen-2.5-General-Recipe-Generation
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
AnonymousCS/xlmr_danish_immigration3
|
AnonymousCS
| 2025-08-19T17:09:38Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T17:06:38Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_danish_immigration3
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. -->
# xlmr_danish_immigration3
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2462
- Accuracy: 0.9077
- 1-f1: 0.8421
- 1-recall: 0.7442
- 1-precision: 0.9697
- Balanced Acc: 0.8663
## 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.2284 | 1.0 | 5 | 0.2331 | 0.9077 | 0.8421 | 0.7442 | 0.9697 | 0.8663 |
| 0.6095 | 2.0 | 10 | 0.2447 | 0.9154 | 0.8571 | 0.7674 | 0.9706 | 0.8780 |
| 0.2055 | 3.0 | 15 | 0.2462 | 0.9077 | 0.8421 | 0.7442 | 0.9697 | 0.8663 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.8.0+cu126
- Datasets 4.0.0
- Tokenizers 0.21.4
|
kevinshin/test-run-fsdp-v1-full-state-dict
|
kevinshin
| 2025-08-19T17:09:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"sft",
"trl",
"conversational",
"base_model:Qwen/Qwen3-1.7B",
"base_model:finetune:Qwen/Qwen3-1.7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T16:40:15Z |
---
base_model: Qwen/Qwen3-1.7B
library_name: transformers
model_name: test-run-fsdp-v1-full-state-dict
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for test-run-fsdp-v1-full-state-dict
This model is a fine-tuned version of [Qwen/Qwen3-1.7B](https://huggingface.co/Qwen/Qwen3-1.7B).
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="kevinshin/test-run-fsdp-v1-full-state-dict", 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/myungjune-sogang-university/general_remo_train/runs/3dzoaavc)
This model was trained with SFT.
### Framework versions
- TRL: 0.19.1
- Transformers: 4.54.0
- Pytorch: 2.6.0+cu126
- Datasets: 4.0.0
- Tokenizers: 0.21.2
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
movbbcan/trading-bot
|
movbbcan
| 2025-08-19T17:09:00Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T17:09:00Z |
---
license: apache-2.0
---
|
EZCon/gemma-3n-E2B-it-4bit-mlx
|
EZCon
| 2025-08-19T17:07:53Z | 41 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3n",
"image-text-to-text",
"gemma3",
"unsloth",
"gemma",
"google",
"mlx",
"conversational",
"en",
"base_model:google/gemma-3n-E2B-it",
"base_model:quantized:google/gemma-3n-E2B-it",
"license:gemma",
"endpoints_compatible",
"4-bit",
"region:us"
] |
image-text-to-text
| 2025-08-05T08:20:36Z |
---
base_model: google/gemma-3n-E2B-it
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
license: gemma
tags:
- gemma3
- unsloth
- transformers
- gemma
- google
- mlx
---
# EZCon/gemma-3n-E2B-it-4bit-mlx
This model was converted to MLX format from [`unsloth/gemma-3n-E2B-it`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/unsloth/gemma-3n-E2B-it) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/gemma-3n-E2B-it-4bit-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
EZCon/gemma-3n-E2B-it-mlx
|
EZCon
| 2025-08-19T17:07:20Z | 29 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3n",
"image-text-to-text",
"gemma3",
"unsloth",
"gemma",
"google",
"mlx",
"conversational",
"en",
"base_model:google/gemma-3n-E2B-it",
"base_model:finetune:google/gemma-3n-E2B-it",
"license:gemma",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-05T08:18:31Z |
---
base_model: google/gemma-3n-E2B-it
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
license: gemma
tags:
- gemma3
- unsloth
- transformers
- gemma
- google
- mlx
---
# EZCon/gemma-3n-E2B-it-mlx
This model was converted to MLX format from [`unsloth/gemma-3n-E2B-it`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/unsloth/gemma-3n-E2B-it) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/gemma-3n-E2B-it-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755621443
|
kojeklollipop
| 2025-08-19T17:06:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T17:06:46Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
WenFengg/21_14l5_20_8
|
WenFengg
| 2025-08-19T17:06:27Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-19T16:57:21Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
EZCon/gemma-3-4b-it-mlx
|
EZCon
| 2025-08-19T17:05:44Z | 36 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"unsloth",
"mlx",
"conversational",
"base_model:google/gemma-3-4b-it",
"base_model:finetune:google/gemma-3-4b-it",
"license:gemma",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-05T04:33:44Z |
---
tags:
- unsloth
- mlx
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
base_model:
- google/gemma-3-4b-it
---
# EZCon/gemma-3-4b-it-mlx
This model was converted to MLX format from [`unsloth/gemma-3-4b-it`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/unsloth/gemma-3-4b-it) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/gemma-3-4b-it-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
Orginal-Laura-Mendoza-Viral-Video-Clips/New.full.videos.Laura.Mendoza.Viral.Video.Official.Tutorial
|
Orginal-Laura-Mendoza-Viral-Video-Clips
| 2025-08-19T17:05:30Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T17:05:21Z |
[](https://tinyurl.com/bdk3zxvb)
|
EZCon/Qwen2.5-VL-3B-Instruct-4bit-mlx
|
EZCon
| 2025-08-19T17:03:56Z | 57 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"multimodal",
"unsloth",
"mlx",
"image-text-to-text",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-3B-Instruct",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"region:us"
] |
image-text-to-text
| 2025-04-18T03:43:44Z |
---
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- unsloth
- mlx
library_name: transformers
---
# EZCon/Qwen2.5-VL-3B-Instruct-4bit-mlx
This model was converted to MLX format from [`unsloth/Qwen2.5-VL-3B-Instruct`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/Qwen2.5-VL-3B-Instruct-4bit-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
EZCon/Qwen2.5-VL-3B-Instruct-mlx
|
EZCon
| 2025-08-19T17:03:32Z | 18 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"multimodal",
"unsloth",
"mlx",
"image-text-to-text",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-05T07:02:34Z |
---
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- unsloth
- mlx
library_name: transformers
---
# EZCon/Qwen2.5-VL-3B-Instruct-mlx
This model was converted to MLX format from [`unsloth/Qwen2.5-VL-3B-Instruct`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/unsloth/Qwen2.5-VL-3B-Instruct) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/Qwen2.5-VL-3B-Instruct-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755622935
|
Dejiat
| 2025-08-19T17:03:01Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T17:02:46Z |
---
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).
|
EZCon/Qwen2-VL-2B-Instruct-mlx
|
EZCon
| 2025-08-19T17:02:03Z | 11 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_vl",
"image-to-text",
"multimodal",
"qwen",
"qwen2",
"unsloth",
"vision",
"mlx",
"image-text-to-text",
"conversational",
"en",
"base_model:Qwen/Qwen2-VL-2B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-2B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-05T04:21:10Z |
---
base_model: Qwen/Qwen2-VL-2B-Instruct
language:
- en
library_name: transformers
pipeline_tag: image-text-to-text
license: apache-2.0
tags:
- multimodal
- qwen
- qwen2
- unsloth
- transformers
- vision
- mlx
---
# EZCon/Qwen2-VL-2B-Instruct-mlx
This model was converted to MLX format from [`unsloth/Qwen2-VL-2B-Instruct`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/unsloth/Qwen2-VL-2B-Instruct) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/Qwen2-VL-2B-Instruct-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
ihsanridzi/blockassist-bc-wiry_flexible_owl_1755621240
|
ihsanridzi
| 2025-08-19T17:01:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"wiry flexible owl",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T17:01:24Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- wiry flexible owl
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
coelacanthxyz/blockassist-bc-finicky_thriving_grouse_1755621036
|
coelacanthxyz
| 2025-08-19T16:59:17Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"finicky thriving grouse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:59:12Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- finicky thriving grouse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
koloni/blockassist-bc-deadly_graceful_stingray_1755621035
|
koloni
| 2025-08-19T16:58:27Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:58:23Z |
---
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).
|
EZCon/Qwen2-VL-2B-Instruct-abliterated-8bit-mlx
|
EZCon
| 2025-08-19T16:58:14Z | 47 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_vl",
"image-to-text",
"chat",
"abliterated",
"uncensored",
"mlx",
"image-text-to-text",
"conversational",
"en",
"base_model:Qwen/Qwen2-VL-2B-Instruct",
"base_model:quantized:Qwen/Qwen2-VL-2B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"region:us"
] |
image-text-to-text
| 2025-08-06T03:44:27Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/huihui-ai/Qwen2-VL-2B-Instruct-abliterated/blob/main/LICENSE
language:
- en
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen2-VL-2B-Instruct
tags:
- chat
- abliterated
- uncensored
- mlx
---
# EZCon/Qwen2-VL-2B-Instruct-abliterated-8bit-mlx
This model was converted to MLX format from [`huihui-ai/Qwen2-VL-2B-Instruct-abliterated`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/huihui-ai/Qwen2-VL-2B-Instruct-abliterated) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/Qwen2-VL-2B-Instruct-abliterated-8bit-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
aleebaster/blockassist-bc-sly_eager_boar_1755621210
|
aleebaster
| 2025-08-19T16:57:59Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:57:53Z |
---
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).
|
EZCon/Qwen2-VL-2B-Instruct-abliterated-mlx
|
EZCon
| 2025-08-19T16:57:41Z | 15 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_vl",
"image-to-text",
"chat",
"abliterated",
"uncensored",
"mlx",
"image-text-to-text",
"conversational",
"en",
"base_model:Qwen/Qwen2-VL-2B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-2B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-06T03:33:22Z |
---
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/huihui-ai/Qwen2-VL-2B-Instruct-abliterated/blob/main/LICENSE
language:
- en
pipeline_tag: image-text-to-text
base_model: Qwen/Qwen2-VL-2B-Instruct
tags:
- chat
- abliterated
- uncensored
- mlx
---
# EZCon/Qwen2-VL-2B-Instruct-abliterated-mlx
This model was converted to MLX format from [`huihui-ai/Qwen2-VL-2B-Instruct-abliterated`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/huihui-ai/Qwen2-VL-2B-Instruct-abliterated) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/Qwen2-VL-2B-Instruct-abliterated-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
VIDEOS-19-Dr-Eman-viral-video-Clip/New.full.videos.Dr.Eman.Viral.Video.Official.Tutorial
|
VIDEOS-19-Dr-Eman-viral-video-Clip
| 2025-08-19T16:56:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T16:56:35Z |
[](https://tinyurl.com/bdk3zxvb)
|
EZCon/LFM2-VL-450M-4bit-mlx
|
EZCon
| 2025-08-19T16:56:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"lfm2-vl",
"image-text-to-text",
"liquid",
"lfm2",
"edge",
"mlx",
"conversational",
"custom_code",
"en",
"license:other",
"4-bit",
"region:us"
] |
image-text-to-text
| 2025-08-17T16:51:16Z |
---
library_name: transformers
license: other
license_name: lfm1.0
license_link: LICENSE
language:
- en
pipeline_tag: image-text-to-text
tags:
- liquid
- lfm2
- lfm2-vl
- edge
- mlx
---
# EZCon/LFM2-VL-450M-4bit-mlx
This model was converted to MLX format from [`LiquidAI/LFM2-VL-450M`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/LiquidAI/LFM2-VL-450M) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/LFM2-VL-450M-4bit-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
EZCon/LFM2-VL-450M-mlx
|
EZCon
| 2025-08-19T16:56:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"lfm2-vl",
"image-text-to-text",
"liquid",
"lfm2",
"edge",
"mlx",
"conversational",
"custom_code",
"en",
"license:other",
"region:us"
] |
image-text-to-text
| 2025-08-17T16:16:29Z |
---
library_name: transformers
license: other
license_name: lfm1.0
license_link: LICENSE
language:
- en
pipeline_tag: image-text-to-text
tags:
- liquid
- lfm2
- lfm2-vl
- edge
- mlx
---
# EZCon/LFM2-VL-450M-mlx
This model was converted to MLX format from [`LiquidAI/LFM2-VL-450M`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/LiquidAI/LFM2-VL-450M) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/LFM2-VL-450M-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
EZCon/SmolVLM2-500M-Video-Instruct-8bit-mlx
|
EZCon
| 2025-08-19T16:55:14Z | 75 | 0 |
transformers
|
[
"transformers",
"safetensors",
"smolvlm",
"image-text-to-text",
"mlx",
"conversational",
"en",
"dataset:HuggingFaceM4/the_cauldron",
"dataset:HuggingFaceM4/Docmatix",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"dataset:lmms-lab/M4-Instruct-Data",
"dataset:HuggingFaceFV/finevideo",
"dataset:MAmmoTH-VL/MAmmoTH-VL-Instruct-12M",
"dataset:lmms-lab/LLaVA-Video-178K",
"dataset:orrzohar/Video-STaR",
"dataset:Mutonix/Vript",
"dataset:TIGER-Lab/VISTA-400K",
"dataset:Enxin/MovieChat-1K_train",
"dataset:ShareGPT4Video/ShareGPT4Video",
"base_model:HuggingFaceTB/SmolVLM-500M-Instruct",
"base_model:quantized:HuggingFaceTB/SmolVLM-500M-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"8-bit",
"region:us"
] |
image-text-to-text
| 2025-08-01T02:52:38Z |
---
library_name: transformers
license: apache-2.0
datasets:
- HuggingFaceM4/the_cauldron
- HuggingFaceM4/Docmatix
- lmms-lab/LLaVA-OneVision-Data
- lmms-lab/M4-Instruct-Data
- HuggingFaceFV/finevideo
- MAmmoTH-VL/MAmmoTH-VL-Instruct-12M
- lmms-lab/LLaVA-Video-178K
- orrzohar/Video-STaR
- Mutonix/Vript
- TIGER-Lab/VISTA-400K
- Enxin/MovieChat-1K_train
- ShareGPT4Video/ShareGPT4Video
pipeline_tag: image-text-to-text
language:
- en
base_model:
- HuggingFaceTB/SmolVLM-500M-Instruct
tags:
- mlx
---
# EZCon/SmolVLM2-500M-Video-Instruct-8bit-mlx
This model was converted to MLX format from [`HuggingFaceTB/SmolVLM2-500M-Video-Instruct`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/HuggingFaceTB/SmolVLM2-500M-Video-Instruct) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/SmolVLM2-500M-Video-Instruct-8bit-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
Orginal-18-Afrin-Er-Viral-Video-Clip/New.full.videos.Afrin.Er.Viral.Video.Official.Tutorial
|
Orginal-18-Afrin-Er-Viral-Video-Clip
| 2025-08-19T16:54:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T16:54:13Z |
[](https://tinyurl.com/bdk3zxvb)
|
AnonymousCS/xlmr_dutch_immigration2
|
AnonymousCS
| 2025-08-19T16:54:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T05:15:13Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_dutch_immigration2
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. -->
# xlmr_dutch_immigration2
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3999
- Accuracy: 0.8846
- 1-f1: 0.8148
- 1-recall: 0.7674
- 1-precision: 0.8684
- Balanced Acc: 0.8550
## 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3629 | 1.0 | 5 | 0.3651 | 0.8769 | 0.8 | 0.7442 | 0.8649 | 0.8434 |
| 0.2419 | 2.0 | 10 | 0.4123 | 0.8385 | 0.7529 | 0.7442 | 0.7619 | 0.8146 |
| 0.1851 | 3.0 | 15 | 0.3999 | 0.8846 | 0.8148 | 0.7674 | 0.8684 | 0.8550 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
EZCon/SmolVLM2-2.2B-Instruct-mlx
|
EZCon
| 2025-08-19T16:54:03Z | 22 | 0 |
transformers
|
[
"transformers",
"safetensors",
"smolvlm",
"image-text-to-text",
"video-text-to-text",
"mlx",
"conversational",
"en",
"dataset:HuggingFaceM4/the_cauldron",
"dataset:HuggingFaceM4/Docmatix",
"dataset:lmms-lab/LLaVA-OneVision-Data",
"dataset:lmms-lab/M4-Instruct-Data",
"dataset:HuggingFaceFV/finevideo",
"dataset:MAmmoTH-VL/MAmmoTH-VL-Instruct-12M",
"dataset:lmms-lab/LLaVA-Video-178K",
"dataset:orrzohar/Video-STaR",
"dataset:Mutonix/Vript",
"dataset:TIGER-Lab/VISTA-400K",
"dataset:Enxin/MovieChat-1K_train",
"dataset:ShareGPT4Video/ShareGPT4Video",
"base_model:HuggingFaceTB/SmolVLM-Instruct",
"base_model:finetune:HuggingFaceTB/SmolVLM-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-08-01T18:38:14Z |
---
library_name: transformers
license: apache-2.0
datasets:
- HuggingFaceM4/the_cauldron
- HuggingFaceM4/Docmatix
- lmms-lab/LLaVA-OneVision-Data
- lmms-lab/M4-Instruct-Data
- HuggingFaceFV/finevideo
- MAmmoTH-VL/MAmmoTH-VL-Instruct-12M
- lmms-lab/LLaVA-Video-178K
- orrzohar/Video-STaR
- Mutonix/Vript
- TIGER-Lab/VISTA-400K
- Enxin/MovieChat-1K_train
- ShareGPT4Video/ShareGPT4Video
pipeline_tag: image-text-to-text
tags:
- video-text-to-text
- mlx
language:
- en
base_model:
- HuggingFaceTB/SmolVLM-Instruct
---
# EZCon/SmolVLM2-2.2B-Instruct-mlx
This model was converted to MLX format from [`HuggingFaceTB/SmolVLM2-2.2B-Instruct`]() using mlx-vlm version **0.3.2**.
Refer to the [original model card](https://huggingface.co/HuggingFaceTB/SmolVLM2-2.2B-Instruct) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model EZCon/SmolVLM2-2.2B-Instruct-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
RTannous/gpt-oss-finetuned
|
RTannous
| 2025-08-19T16:53:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gpt-oss-20b-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T15:05:27Z |
---
base_model: unsloth/gpt-oss-20b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gpt_oss
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** RTannous
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gpt-oss-20b-unsloth-bnb-4bit
This gpt_oss model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
nabilwalidrafi/medgemma-skinlesion-rafi-4-4-augdynamic1
|
nabilwalidrafi
| 2025-08-19T16:53:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"trl",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T12:27:04Z |
---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-skinlesion-rafi-4-4-augdynamic1
tags:
- generated_from_trainer
- sft
- trl
licence: license
---
# Model Card for medgemma-skinlesion-rafi-4-4-augdynamic1
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="nabilwalidrafi/medgemma-skinlesion-rafi-4-4-augdynamic1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 4.0.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
New-Clip-prabh-viral-videos/New.full.videos.prabh.Viral.Video.Official.Tutorial
|
New-Clip-prabh-viral-videos
| 2025-08-19T16:52:15Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-08-19T16:51:29Z |
[](https://tinyurl.com/bdk3zxvb)
|
thanobidex/blockassist-bc-colorful_shiny_hare_1755620672
|
thanobidex
| 2025-08-19T16:51:54Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"colorful shiny hare",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:51:51Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- colorful shiny hare
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
kokoblueao/blockassist-bc-trotting_bipedal_cobra_1755622193
|
kokoblueao
| 2025-08-19T16:51:20Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"trotting bipedal cobra",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:51:08Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- trotting bipedal cobra
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755620601
|
pempekmangedd
| 2025-08-19T16:51:02Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:50:59Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned sturdy dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
yasamanhaghbin/speechCura_medGemma_num_epoch_4_loraWeights
|
yasamanhaghbin
| 2025-08-19T16:47:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T16:35:25Z |
---
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]
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755620317
|
vwzyrraz7l
| 2025-08-19T16:47:12Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:47:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755620416
|
quantumxnode
| 2025-08-19T16:46:56Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:46:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
chainway9/blockassist-bc-untamed_quick_eel_1755620188
|
chainway9
| 2025-08-19T16:45:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:45:06Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Dejiat/blockassist-bc-savage_unseen_bobcat_1755621807
|
Dejiat
| 2025-08-19T16:44:16Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"savage unseen bobcat",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:43:53Z |
---
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).
|
Arpita1/sbs_convai2_dialogpt
|
Arpita1
| 2025-08-19T16:44:00Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"en",
"arxiv:2508.06886",
"base_model:microsoft/DialoGPT-small",
"base_model:finetune:microsoft/DialoGPT-small",
"license:cc-by-4.0",
"region:us"
] | null | 2025-08-19T16:41:35Z |
---
license: cc-by-4.0
language:
- en
base_model:
- microsoft/DialoGPT-small
---
# Model Card
### Description
DialoGPT-small finetuned on [ConvAI2](https://parl.ai/projects/convai2/) using the [SBS framework](https://arpita2512.github.io/score_before_you_speak/).
- **Repository:** [GitHub](https://github.com/arpita2512/score_before_you_speak)
- **Paper:** [https://arxiv.org/abs/2508.06886](https://arxiv.org/abs/2508.06886)
- **Funded by:** UKRI AI-Medical CDT (Grant Reference: EP/S024336/1)
- **Language(s) (NLP):** English
- **License:** CC-BY-4.0
## BibTeX
```
@inproceedings{saggar2025,
author = {Saggar, Arpita and Darling, Jonathan C. and Dimitrova, Vania and Sarikaya, Duygu and Hogg, David C.},
title = {Score Before You Speak: Improving Persona Consistency in Dialogue Generation using Response Quality Scores},
booktitle = {Proceedings of the 28th European Conference on Artificial Intelligence},
year = {2025},
}
```
|
AnonymousCS/xlmr_swedish_immigration2
|
AnonymousCS
| 2025-08-19T16:43:46Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T16:40:47Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_swedish_immigration2
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. -->
# xlmr_swedish_immigration2
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4718
- Accuracy: 0.8462
- 1-f1: 0.7917
- 1-recall: 0.8837
- 1-precision: 0.7170
- Balanced Acc: 0.8557
## 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.368 | 1.0 | 5 | 0.3452 | 0.8615 | 0.7353 | 0.5814 | 1.0 | 0.7907 |
| 0.2416 | 2.0 | 10 | 0.3232 | 0.8538 | 0.7865 | 0.8140 | 0.7609 | 0.8438 |
| 0.3117 | 3.0 | 15 | 0.2919 | 0.8846 | 0.8148 | 0.7674 | 0.8684 | 0.8550 |
| 0.1611 | 4.0 | 20 | 0.3034 | 0.8923 | 0.8205 | 0.7442 | 0.9143 | 0.8549 |
| 0.2353 | 5.0 | 25 | 0.4718 | 0.8462 | 0.7917 | 0.8837 | 0.7170 | 0.8557 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
ClementP/dnafiber-error-detection
|
ClementP
| 2025-08-19T16:43:45Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-08-19T16:43:18Z |
---
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:
- Library: [More Information Needed]
- Docs: [More Information Needed]
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755620279
|
sampingkaca72
| 2025-08-19T16:43:29Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:43:26Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# 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_1755620608
|
Sayemahsjn
| 2025-08-19T16:43:05Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:43:01Z |
---
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).
|
wanacode/qwen-image-twilightbloom-lora
|
wanacode
| 2025-08-19T16:41:24Z | 5 | 2 |
diffusers
|
[
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"license:other",
"region:us"
] |
text-to-image
| 2025-08-15T18:14:23Z |
---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: undefined
instance_prompt: twilightbloom
license: other
widget:
- text: "twilightbloom style, icy cold warmth"
output:
url: "icy.png"
- text: "twilightbloom style, Los Angeles amazing vibe"
output:
url: "los-angeles.png"
- text: "twilightbloom photograph of a field of delicate white wildflowers at sunset"
output:
url: "twilightbloom-photograph-of-a-field-of-delicate-white-wildflowers-at-sunset.png"
- text: "twilightbloom style, ski holiday vibe"
output:
url: "twilightbloom-style-ski-holiday-vibe.png"
- text: "twilightbloom style, amzing vibe cactus sunset"
output:
url: "twilightbloom-style-amzing-vibe-cactus-sunset.png"
---
# qwen image twilightbloom lora
<Gallery />
## Model description
Qwen Image LoRA for creating twilight bloom effect.
Trained on 15 images I had created on Ideogram. All the images had between 15 and 100 likes and were of a similar style.
The training was done on fal.ai using the default settings. 1,000 steps with a learning rate of 0.0005.
## Trigger words
You should use `twilightbloom` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/wanacode/qwen-image-twilightbloom-lora/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/qwen-image-trainer](https://fal.ai/models/fal-ai/qwen-image-trainer).
https://v3.fal.media/files/panda/61ogkApsRRX8G7n-N29Mf_adapter.safetensors
|
AnonymousCS/xlmr_spanish_immigration2
|
AnonymousCS
| 2025-08-19T16:39:11Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T16:35:07Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_spanish_immigration2
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. -->
# xlmr_spanish_immigration2
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1895
- Accuracy: 0.9462
- 1-f1: 0.9114
- 1-recall: 0.8372
- 1-precision: 1.0
- Balanced Acc: 0.9186
## 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.3179 | 1.0 | 5 | 0.1873 | 0.9462 | 0.9136 | 0.8605 | 0.9737 | 0.9245 |
| 0.2276 | 2.0 | 10 | 0.1701 | 0.9385 | 0.9 | 0.8372 | 0.9730 | 0.9129 |
| 0.1618 | 3.0 | 15 | 0.1879 | 0.9231 | 0.8718 | 0.7907 | 0.9714 | 0.8896 |
| 0.1136 | 4.0 | 20 | 0.1666 | 0.9462 | 0.9157 | 0.8837 | 0.95 | 0.9304 |
| 0.1381 | 5.0 | 25 | 0.1588 | 0.9538 | 0.925 | 0.8605 | 1.0 | 0.9302 |
| 0.0618 | 6.0 | 30 | 0.1797 | 0.9462 | 0.9114 | 0.8372 | 1.0 | 0.9186 |
| 0.1318 | 7.0 | 35 | 0.1895 | 0.9462 | 0.9114 | 0.8372 | 1.0 | 0.9186 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
fengpeisheng1/mergekit-slerp-ariyvyf-IQ4_NL-GGUF
|
fengpeisheng1
| 2025-08-19T16:38:28Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:fengpeisheng1/mergekit-slerp-ariyvyf",
"base_model:quantized:fengpeisheng1/mergekit-slerp-ariyvyf",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-08-19T16:30:50Z |
---
base_model: fengpeisheng1/mergekit-slerp-ariyvyf
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# fengpeisheng1/mergekit-slerp-ariyvyf-IQ4_NL-GGUF
This model was converted to GGUF format from [`fengpeisheng1/mergekit-slerp-ariyvyf`](https://huggingface.co/fengpeisheng1/mergekit-slerp-ariyvyf) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/fengpeisheng1/mergekit-slerp-ariyvyf) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo fengpeisheng1/mergekit-slerp-ariyvyf-IQ4_NL-GGUF --hf-file mergekit-slerp-ariyvyf-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo fengpeisheng1/mergekit-slerp-ariyvyf-IQ4_NL-GGUF --hf-file mergekit-slerp-ariyvyf-iq4_nl-imat.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo fengpeisheng1/mergekit-slerp-ariyvyf-IQ4_NL-GGUF --hf-file mergekit-slerp-ariyvyf-iq4_nl-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo fengpeisheng1/mergekit-slerp-ariyvyf-IQ4_NL-GGUF --hf-file mergekit-slerp-ariyvyf-iq4_nl-imat.gguf -c 2048
```
|
mohan1201/gemma-code-explainer
|
mohan1201
| 2025-08-19T16:38:05Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:google/gemma-2b-it",
"lora",
"transformers",
"text-generation",
"conversational",
"base_model:google/gemma-2b-it",
"license:gemma",
"region:us"
] |
text-generation
| 2025-08-19T16:38:01Z |
---
library_name: peft
license: gemma
base_model: google/gemma-2b-it
tags:
- base_model:adapter:google/gemma-2b-it
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: gemma-code-explainer
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. -->
# gemma-code-explainer
This model is a fine-tuned version of [google/gemma-2b-it](https://huggingface.co/google/gemma-2b-it) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- training_steps: 150
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.17.0
- Transformers 4.55.2
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.2
|
OpenBuddy/SimpleChat-4B-V1
|
OpenBuddy
| 2025-08-19T16:36:08Z | 0 | 0 | null |
[
"safetensors",
"qwen3",
"text-generation",
"conversational",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"fi",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"region:us"
] |
text-generation
| 2025-08-19T16:23:21Z |
---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- fi
tags:
- qwen3
pipeline_tag: text-generation
base_model: Qwen/Qwen3-4B
---
### ✨ About the SimpleChat Model Series
The SimpleChat series represents our new exploration into Non-Chain-of-Thought (Non-CoT) models. Its main features are:
* **Distinct Chat Style:**
* Designed to be concise, rational, and empathetic.
* Specifically built for casual, everyday conversations.
* **Enhanced Creativity:**
* Boosts the creativity of its generated content and its capacity for emotional understanding.
* This is achieved by distilling knowledge from advanced models, including K2.
* **Efficient Reasoning within a Non-CoT Framework:**
* Delivers the faster response times of a Non-CoT model while preserving strong reasoning skills.
* It retains this ability because it was trained on CoT models before being transitioned to a Non-CoT framework, allowing it to think through complex problems.
* **Known Trade-off:**
* Compared to models that specialize in Chain-of-Thought, it may not perform as strongly on mathematical tasks.
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Model Info
Context Length: **40K** Tokens
License: Apache 2.0
Optimizer: **Muon + AdamW**
# Prompt Format
This model supports a **Qwen3-like** prompt format, with following system prompt recommended:
```
You(assistant) are a helpful, respectful and honest INTP-T AI Assistant named Buddy. You are talking to a human(user).
```
Raw prompt template:
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{history_input}<|im_end|>
<|im_start|>assistant
{history_output}<|im_end|>
<|im_start|>user
{current_input}<|im_end|>
<|im_start|>assistant
```
(There should be a `\n` at the end of prompt.)
You may want to use `vllm` to deploy an OpenAI-like API service. For more information, please refer to the [vllm documentation](https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html).
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。
|
dgambettaphd/M_mis_run2_gen1_WXS_doc1000_synt64_lr1e-04_acm_LANG
|
dgambettaphd
| 2025-08-19T16:34:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T16:34:35Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
koloni/blockassist-bc-deadly_graceful_stingray_1755618973
|
koloni
| 2025-08-19T16:23:50Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"deadly graceful stingray",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:23:47Z |
---
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).
|
Arpita1/sbs_personachat_dialogpt
|
Arpita1
| 2025-08-19T16:23:16Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"en",
"arxiv:2508.06886",
"base_model:microsoft/DialoGPT-small",
"base_model:finetune:microsoft/DialoGPT-small",
"license:cc-by-4.0",
"region:us"
] | null | 2025-08-19T16:09:43Z |
---
license: cc-by-4.0
language:
- en
base_model:
- microsoft/DialoGPT-small
---
# Model Card
### Description
DialoGPT-small finetuned on [PersonaChat](https://parl.ai/projects/personachat/) using the [SBS framework](https://arpita2512.github.io/score_before_you_speak/).
- **Repository:** [GitHub](https://github.com/arpita2512/score_before_you_speak)
- **Paper:** [https://arxiv.org/abs/2508.06886](https://arxiv.org/abs/2508.06886)
- **Funded by:** UKRI AI-Medical CDT (Grant Reference: EP/S024336/1)
- **Language(s) (NLP):** English
- **License:** CC-BY-4.0
## BibTeX
```
@inproceedings{saggar2025,
author = {Saggar, Arpita and Darling, Jonathan C. and Dimitrova, Vania and Sarikaya, Duygu and Hogg, David C.},
title = {Score Before You Speak: Improving Persona Consistency in Dialogue Generation using Response Quality Scores},
booktitle = {Proceedings of the 28th European Conference on Artificial Intelligence},
year = {2025},
}
```
|
grgazziz/mosquito
|
grgazziz
| 2025-08-19T16:22:41Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-08-19T16:21:02Z |
---
license: other
license_name: other
license_link: LICENSE
---
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1755620494
|
Elizavr
| 2025-08-19T16:22:09Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:21:58Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mang3dd/blockassist-bc-tangled_slithering_alligator_1755618934
|
mang3dd
| 2025-08-19T16:22:08Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tangled slithering alligator",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:22:05Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tangled slithering alligator
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
oceanfish/intent_classify_slot
|
oceanfish
| 2025-08-19T16:20:03Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-7B-Instruct",
"region:us"
] | null | 2025-08-19T16:15:20Z |
---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
mradermacher/lfm2-vl-textualis-GGUF
|
mradermacher
| 2025-08-19T16:19:38Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:wjbmattingly/lfm2-vl-textualis",
"base_model:quantized:wjbmattingly/lfm2-vl-textualis",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T16:16:44Z |
---
base_model: wjbmattingly/lfm2-vl-textualis
language:
- en
library_name: transformers
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/wjbmattingly/lfm2-vl-textualis
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#lfm2-vl-textualis-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/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.2 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.mmproj-f16.gguf) | mmproj-f16 | 0.3 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-textualis-GGUF/resolve/main/lfm2-vl-textualis.f16.gguf) | f16 | 0.8 | 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 -->
|
magnusdtd/TransNetV2
|
magnusdtd
| 2025-08-19T16:16:33Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-08-19T14:38:39Z |
---
license: mit
---
# TransNetV2 (PyTorch Version)
This repository provides a PyTorch version of [TransNet V2](https://github.com/soCzech/TransNetV2), a state-of-the-art neural network for shot boundary detection in videos.
## Installation
Clone the repository and install the required dependencies.
```sh
sudo apt-get install ffmpeg
pip install requirements.txt
```
## Usage
```sh
python -m main --files="path/to/your/file/or/folder" --weights="path/to/the/model/weights" --visualize
```
|
chatpdflocal/gemma-3-12b-it-gguf
|
chatpdflocal
| 2025-08-19T16:16:15Z | 509 | 3 | null |
[
"gguf",
"legal",
"finance",
"PC",
"laptop",
"mobile",
"gemma",
"gemma 3",
"small size",
"chatpdf",
"local",
"macos",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-03-12T12:32:13Z |
---
license: apache-2.0
tags:
- legal
- finance
- PC
- laptop
- mobile
- gemma
- gemma 3
- small size
- chatpdf
- local
- macos
---
# It's a gguf model file of gemma-3-12b-it, which is developed by Google.
It's very applicable for deploying and using in PCs, laptops or mobiles.
gemma-3-12b-it-q4_0.gguf is the quantization-aware trained(QAT) checkpoints of Gemma 3, 3x less VRAM, while retaining almost the same quality. Recommend it.
# If you are a Mac user, the following free wonderful AI tools can help you to read and understand PDFs effectively:
- If you are using Zotero for managing and reading your personal PDFs, [PapersGPT](https://www.papersgpt.com) is a free plugin which can assist you to chat PDFs effectively by your local gemma-3-12b-it.
- you can download ChatPDFLocal MacOS app from [here](https://www.chatpdflocal.com), load one or batch PDF files at will, and quickly experience the effect of the model through chat reading.
|
agustinghent/mms-tts-rap-train
|
agustinghent
| 2025-08-19T16:14:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"vits",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2025-08-19T14:45:32Z |
---
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]
|
pempekmangedd/blockassist-bc-patterned_sturdy_dolphin_1755618495
|
pempekmangedd
| 2025-08-19T16:14:40Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"patterned sturdy dolphin",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:14:37Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- patterned sturdy dolphin
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Krish356/lora_model
|
Krish356
| 2025-08-19T16:14:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3_moe",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T16:13:27Z |
---
base_model: unsloth/qwen3-coder-30b-a3b-instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3_moe
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Krish356
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-coder-30b-a3b-instruct
This qwen3_moe model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755618350
|
quantumxnode
| 2025-08-19T16:13:48Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:13:45Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755618230
|
vwzyrraz7l
| 2025-08-19T16:13:26Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:13:23Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
sampingkaca72/blockassist-bc-armored_stealthy_elephant_1755618463
|
sampingkaca72
| 2025-08-19T16:13:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"armored stealthy elephant",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:13:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- armored stealthy elephant
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
schirrmacher/malwi
|
schirrmacher
| 2025-08-19T16:10:50Z | 2,023 | 0 | null |
[
"safetensors",
"distilbert",
"arxiv:2404.04991",
"arxiv:2504.14886",
"license:mit",
"region:us"
] | null | 2025-05-09T12:54:09Z |
---
license: mit
---
# malwi - AI Python Malware Scanner
<img src="malwi-logo.png" alt="Logo">
## malwi specializes in finding malware
### Key Features
- 🛡️ **AI-Powered Python Malware Detection**: Leverages advanced AI to identify malicious code in Python projects with high accuracy.
- ⚡ **Lightning-Fast Codebase Scanning**: Scans entire repositories in seconds, so you can focus on development—not security worries.
- 🔒 **100% Offline & Private**: Your code never leaves your machine. Full control, zero data exposure.
- 💰 **Free & Open-Source**: No hidden costs. Built on transparent research and openly available data.
- 🇪🇺 **Developed in the EU**: Committed to open-source principles and European data standards.
### 1) Install
```
pip install --user malwi
```
### 2) Run
```bash
malwi scan examples/malicious
```
### 3) Evaluate: a [recent zero-day](https://socket.dev/blog/malicious-pypi-package-targets-discord-developers-with-RAT) detected with high confidence
```
__ __
.--------.---.-| .--.--.--|__|
| | _ | | | | | |
|__|__|__|___._|__|________|__|
AI Python Malware Scanner
- target: examples
- seconds: 1.87
- files: 14
├── scanned: 4 (.py)
├── skipped: 10 (.cfg, .md, .toml, .txt)
└── suspicious:
├── examples/malicious/discordpydebug-0.0.4/setup.py
│ └── <module>
│ ├── archive compression
│ └── package installation execution
└── examples/malicious/discordpydebug-0.0.4/src/discordpydebug/__init__.py
├── <module>
│ ├── process management
│ ├── deserialization
│ ├── system interaction
│ └── user io
├── run
│ └── fs linking
├── debug
│ ├── fs linking
│ └── archive compression
└── runcommand
└── process management
=> 👹 malicious 0.98
```
## PyPI Package Scanning
malwi can directly scan PyPI packages without executing malicious logic, typically placed in `setup.py` or `__init__.py` files:
```bash
malwi pypi requests
````
```
__ __
.--------.---.-| .--.--.--|__|
| | _ | | | | | |
|__|__|__|___._|__|________|__|
AI Python Malware Scanner
- target: downloads/requests-2.32.4.tar
- seconds: 3.10
- files: 84
├── scanned: 34
└── skipped: 50
=> 🟢 good
```
## Python API
malwi provides a comprehensive Python API for integrating malware detection into your applications.
### Quick Start
```python
import malwi
report = malwi.MalwiReport.create(input_path="suspicious_file.py")
for obj in report.malicious_objects:
print(f"File: {obj.file_path}")
```
### `MalwiReport`
```python
MalwiReport.create(
input_path, # str or Path - file/directory to scan
accepted_extensions=None, # List[str] - file extensions to scan (e.g., ['py', 'js'])
silent=False, # bool - suppress progress messages
malicious_threshold=0.7, # float - threshold for malicious classification (0.0-1.0)
on_finding=None # callable - callback when malicious objects found
) -> MalwiReport # Returns: MalwiReport instance with scan results
```
```python
import malwi
report = malwi.MalwiReport.create("suspicious_directory/")
# Properties
report.malicious # bool: True if malicious objects detected
report.confidence # float: Overall confidence score (0.0-1.0)
report.duration # float: Scan duration in seconds
report.all_objects # List[MalwiObject]: All analyzed code objects
report.malicious_objects # List[MalwiObject]: Objects exceeding threshold
report.threshold # float: Maliciousness threshold used (0.0-1.0)
report.all_files # List[Path]: All files found in input path
report.skipped_files # List[Path]: Files skipped (wrong extension)
report.processed_files # int: Number of files successfully processed
report.activities # List[str]: Suspicious activities detected
report.input_path # str: Original input path scanned
report.start_time # str: ISO 8601 timestamp when scan started
report.all_file_types # List[str]: All file extensions found
report.version # str: Malwi version with model hash
# Methods
report.to_demo_text() # str: Human-readable tree summary
report.to_json() # str: JSON formatted report
report.to_yaml() # str: YAML formatted report
report.to_markdown() # str: Markdown formatted report
# Pre-load models to avoid delay on first prediction
malwi.MalwiReport.load_models_into_memory()
```
### `MalwiObject`
```python
obj = report.all_objects[0]
# Core properties
obj.name # str: Function/class/module name
obj.file_path # str: Path to source file
obj.language # str: Programming language ('python'/'javascript')
obj.maliciousness # float|None: ML confidence score (0.0-1.0)
obj.warnings # List[str]: Compilation warnings/errors
# Source code and AST compilation
obj.file_source_code # str: Complete content of source file
obj.source_code # str|None: Extracted source for this specific object
obj.byte_code # List[Instruction]|None: Compiled AST bytecode
obj.location # Tuple[int,int]|None: Start and end line numbers
obj.embedding_count # int: Number of DistilBERT tokens (cached)
# Analysis methods
obj.predict() # dict: Run ML prediction and update maliciousness
obj.to_tokens() # List[str]: Extract tokens for analysis
obj.to_token_string() # str: Space-separated token string
obj.to_string() # str: Bytecode as readable string
obj.to_hash() # str: SHA256 hash of bytecode
obj.to_dict() # dict: Serializable representation
obj.to_yaml() # str: YAML formatted output
obj.to_json() # str: JSON formatted output
# Class methods
MalwiObject.all_tokens(language="python") # List[str]: All possible tokens
```
## Why malwi?
Malicious actors are increasingly [targeting open-source projects](https://arxiv.org/pdf/2404.04991), introducing packages designed to compromise security.
Common malicious behaviors include:
- **Data exfiltration**: Theft of sensitive information such as credentials, API keys, or user data.
- **Backdoors**: Unauthorized remote access to systems, enabling attackers to exploit vulnerabilities.
- **Destructive actions**: Deliberate sabotage, including file deletion, database corruption, or application disruption.
## How does it work?
malwi is based on the design of [_Zero Day Malware Detection with Alpha: Fast DBI with Transformer Models for Real World Application_ (2025)](https://arxiv.org/pdf/2504.14886v1).
Imagine there is a function like:
```python
def runcommand(value):
output = subprocess.run(value, shell=True, capture_output=True)
return [output.stdout, output.stderr]
```
### 1. Files are compiled to create an Abstract Syntax Tree with [Tree-sitter](https://tree-sitter.github.io/tree-sitter/index.html)
```
module [0, 0] - [3, 0]
function_definition [0, 0] - [2, 41]
name: identifier [0, 4] - [0, 14]
parameters: parameters [0, 14] - [0, 21]
identifier [0, 15] - [0, 20]
...
```
### 2. The AST is transpiled to dummy bytecode
The bytecode is enhanced with security related instructions.
```
TARGETED_FILE PUSH_NULL LOAD_GLOBAL PROCESS_MANAGEMENT LOAD_ATTR run LOAD_PARAM value LOAD_CONST BOOLEAN LOAD_CONST BOOLEAN KW_NAMES shell capture_output CALL STRING_VERSION STORE_GLOBAL output LOAD_GLOBAL output LOAD_ATTR stdout LOAD_GLOBAL output LOAD_ATTR stderr BUILD_LIST STRING_VERSION RETURN_VALUE
```
### 3. The bytecode is fed into a pre-trained [DistilBERT](https://huggingface.co/docs/transformers/model_doc/distilbert)
A DistilBERT model trained on [malware-samples](https://github.com/schirrmacher/malwi-samples) is used to identify suspicious code patterns.
```
=> Maliciousness: 0.98
```
## Benchmarks?
```
training_loss: 0.0110
epochs_completed: 3.0000
original_train_samples: 598540.0000
windowed_train_features: 831865.0000
original_validation_samples: 149636.0000
windowed_validation_features: 204781.0000
benign_samples_used: 734930.0000
malicious_samples_used: 13246.0000
benign_to_malicious_ratio: 60.0000
vocab_size: 30522.0000
max_length: 512.0000
window_stride: 128.0000
batch_size: 16.0000
eval_loss: 0.0107
eval_accuracy: 0.9980
eval_f1: 0.9521
eval_precision: 0.9832
eval_recall: 0.9229
eval_runtime: 115.5982
eval_samples_per_second: 1771.4900
eval_steps_per_second: 110.7200
epoch: 3.0000
```
## Contributing & Support
- Found a bug or have a feature request? [Open an issue](https://github.com/schirrmacher/malwi/issues).
- Do you have access to malicious packages in Rust, Go, or other languages? [Contact via GitHub profile](https://github.com/schirrmacher).
- Struggling with false-positive findings? [Create a Pull-Request](https://github.com/schirrmacher/malwi-samples/pulls).
## Research
### Prerequisites
1. **Package Manager**: Install [uv](https://docs.astral.sh/uv/) for fast Python dependency management
2. **Training Data**: The research CLI will automatically clone [malwi-samples](https://github.com/schirrmacher/malwi-samples) when needed
### Quick Start
```bash
# Install dependencies
uv sync
# Run tests
uv run pytest tests
# Train a model from scratch (full pipeline with automatic data download)
./research download preprocess train
```
#### Individual Pipeline Steps
```bash
# 1. Download training data (clones malwi-samples + downloads repositories)
./research download
# 2. Data preprocessing only (parallel processing, ~4 min on 32 cores)
./research preprocess --language python
# 3. Model training only (tokenizer + DistilBERT, ~40 minutes on NVIDIA RTX 4090)
./research train
```
## Limitations
The malicious dataset includes some boilerplate functions, such as setup functions, which can also appear in benign code. These cause false positives during scans. The goal is to triage and reduce such false positives to improve malwi's accuracy.
## What's next?
The first iteration focuses on **maliciousness of Python source code**.
Future iterations will cover malware scanning for more languages (JavaScript, Rust, Go) and more formats (binaries, logs).
|
Sayemahsjn/blockassist-bc-playful_feline_octopus_1755618633
|
Sayemahsjn
| 2025-08-19T16:09:57Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"playful feline octopus",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:09:52Z |
---
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).
|
chainway9/blockassist-bc-untamed_quick_eel_1755618076
|
chainway9
| 2025-08-19T16:09:07Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"untamed quick eel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T16:09:03Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- untamed quick eel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
zak1836/Tea-bar
|
zak1836
| 2025-08-19T16:07:40Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-08-19T16:07:40Z |
---
license: apache-2.0
---
|
mradermacher/galicIA-v1-GGUF
|
mradermacher
| 2025-08-19T16:05:42Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:pajon1/galicIA-v1",
"base_model:quantized:pajon1/galicIA-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T16:00:38Z |
---
base_model: pajon1/galicIA-v1
language:
- en
library_name: transformers
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/pajon1/galicIA-v1
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#galicIA-v1-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/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q2_K.gguf) | Q2_K | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q3_K_S.gguf) | Q3_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q3_K_M.gguf) | Q3_K_M | 0.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q3_K_L.gguf) | Q3_K_L | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.IQ4_XS.gguf) | IQ4_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q4_K_S.gguf) | Q4_K_S | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q4_K_M.gguf) | Q4_K_M | 0.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q5_K_S.gguf) | Q5_K_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q5_K_M.gguf) | Q5_K_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q6_K.gguf) | Q6_K | 0.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.Q8_0.gguf) | Q8_0 | 0.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/galicIA-v1-GGUF/resolve/main/galicIA-v1.f16.gguf) | f16 | 1.3 | 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 -->
|
AnonymousCS/xlmr_finnish_immigration2
|
AnonymousCS
| 2025-08-19T16:04:23Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-large",
"base_model:finetune:FacebookAI/xlm-roberta-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-08-19T16:00:05Z |
---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: xlmr_finnish_immigration2
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. -->
# xlmr_finnish_immigration2
This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1698
- Accuracy: 0.9538
- 1-f1: 0.9318
- 1-recall: 0.9535
- 1-precision: 0.9111
- Balanced Acc: 0.9538
## 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 with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | 1-f1 | 1-recall | 1-precision | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:--------:|:-----------:|:------------:|
| 0.5778 | 1.0 | 5 | 0.2275 | 0.9154 | 0.8571 | 0.7674 | 0.9706 | 0.8780 |
| 0.1219 | 2.0 | 10 | 0.3406 | 0.9385 | 0.9130 | 0.9767 | 0.8571 | 0.9481 |
| 0.2571 | 3.0 | 15 | 0.2051 | 0.9462 | 0.9213 | 0.9535 | 0.8913 | 0.9480 |
| 0.1514 | 4.0 | 20 | 0.1689 | 0.9538 | 0.9318 | 0.9535 | 0.9111 | 0.9538 |
| 0.1368 | 5.0 | 25 | 0.1816 | 0.9462 | 0.9231 | 0.9767 | 0.875 | 0.9539 |
| 0.1073 | 6.0 | 30 | 0.1698 | 0.9538 | 0.9318 | 0.9535 | 0.9111 | 0.9538 |
### Framework versions
- Transformers 4.56.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 4.0.0
- Tokenizers 0.21.4
|
mradermacher/sailor2-sft-GGUF
|
mradermacher
| 2025-08-19T16:04:02Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:hai2131/sailor2-sft",
"base_model:quantized:hai2131/sailor2-sft",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T15:55:44Z |
---
base_model: hai2131/sailor2-sft
language:
- en
library_name: transformers
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/hai2131/sailor2-sft
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#sailor2-sft-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/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q3_K_S.gguf) | Q3_K_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q2_K.gguf) | Q2_K | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.IQ4_XS.gguf) | IQ4_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q3_K_M.gguf) | Q3_K_M | 0.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q3_K_L.gguf) | Q3_K_L | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q4_K_S.gguf) | Q4_K_S | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q4_K_M.gguf) | Q4_K_M | 0.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q5_K_S.gguf) | Q5_K_S | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q5_K_M.gguf) | Q5_K_M | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q6_K.gguf) | Q6_K | 1.1 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.Q8_0.gguf) | Q8_0 | 1.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/sailor2-sft-GGUF/resolve/main/sailor2-sft.f16.gguf) | f16 | 2.1 | 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/git-commit-message-splitter-Qwen3-1.7B-GGUF
|
mradermacher
| 2025-08-19T16:00:46Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen3",
"en",
"base_model:Tavernari/git-commit-message-splitter-Qwen3-1.7B",
"base_model:quantized:Tavernari/git-commit-message-splitter-Qwen3-1.7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T15:50:02Z |
---
base_model: Tavernari/git-commit-message-splitter-Qwen3-1.7B
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/Tavernari/git-commit-message-splitter-Qwen3-1.7B
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#git-commit-message-splitter-Qwen3-1.7B-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/git-commit-message-splitter-Qwen3-1.7B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-1.7B.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-1.7B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-1.7B.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-1.7B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-1.7B.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-1.7B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-1.7B.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-1.7B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-1.7B.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-1.7B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-1.7B.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-1.7B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-1.7B.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-1.7B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-1.7B.Q5_K_S.gguf) | Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-1.7B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-1.7B.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-1.7B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-1.7B.Q6_K.gguf) | Q6_K | 1.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-1.7B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-1.7B.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/git-commit-message-splitter-Qwen3-1.7B-GGUF/resolve/main/git-commit-message-splitter-Qwen3-1.7B.f16.gguf) | f16 | 3.5 | 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/lfm2-vl-medieval-page-GGUF
|
mradermacher
| 2025-08-19T15:59:41Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:wjbmattingly/lfm2-vl-medieval-page",
"base_model:quantized:wjbmattingly/lfm2-vl-medieval-page",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-08-19T15:58:04Z |
---
base_model: wjbmattingly/lfm2-vl-medieval-page
language:
- en
library_name: transformers
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/wjbmattingly/lfm2-vl-medieval-page
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#lfm2-vl-medieval-page-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/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.mmproj-Q8_0.gguf) | mmproj-Q8_0 | 0.2 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q2_K.gguf) | Q2_K | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q3_K_S.gguf) | Q3_K_S | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q3_K_M.gguf) | Q3_K_M | 0.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.mmproj-f16.gguf) | mmproj-f16 | 0.3 | multi-modal supplement |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q3_K_L.gguf) | Q3_K_L | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.IQ4_XS.gguf) | IQ4_XS | 0.3 | |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q4_K_S.gguf) | Q4_K_S | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q4_K_M.gguf) | Q4_K_M | 0.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q5_K_S.gguf) | Q5_K_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q5_K_M.gguf) | Q5_K_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q6_K.gguf) | Q6_K | 0.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.Q8_0.gguf) | Q8_0 | 0.5 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/lfm2-vl-medieval-page-GGUF/resolve/main/lfm2-vl-medieval-page.f16.gguf) | f16 | 0.8 | 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 -->
|
varsunk/unsloth_training_checkpoints
|
varsunk
| 2025-08-19T15:59:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"sft",
"unsloth",
"trl",
"base_model:unsloth/Qwen3-4B-Base",
"base_model:finetune:unsloth/Qwen3-4B-Base",
"endpoints_compatible",
"region:us"
] | null | 2025-08-18T20:11:18Z |
---
base_model: unsloth/Qwen3-4B-Base
library_name: transformers
model_name: Qwen3-4B-PFT-Checkpoint
tags:
- generated_from_trainer
- sft
- unsloth
- trl
licence: license
---
# Model Card for Qwen3-4B-PFT-Checkpoint
This model is a fine-tuned version of [unsloth/Qwen3-4B-Base](https://huggingface.co/unsloth/Qwen3-4B-Base).
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="varsunk/unsloth_training_checkpoints", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.21.0
- Transformers: 4.55.2
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.4
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
kojeklollipop/blockassist-bc-spotted_amphibious_stork_1755617316
|
kojeklollipop
| 2025-08-19T15:57:25Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"spotted amphibious stork",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:57:22Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- spotted amphibious stork
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
hakimjustbao/blockassist-bc-raging_subtle_wasp_1755617196
|
hakimjustbao
| 2025-08-19T15:53:23Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"raging subtle wasp",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:53:19Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- raging subtle wasp
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
ShadoWeysel/blockassist-bc-aquatic_placid_skunk_1755618703
|
ShadoWeysel
| 2025-08-19T15:53:19Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"aquatic placid skunk",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:53:01Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- aquatic placid skunk
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
mradermacher/rejection_detection-GGUF
|
mradermacher
| 2025-08-19T15:52:44Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"generated_from_trainer",
"rejection",
"no_answer",
"chatgpt",
"en",
"dataset:argilla/notus-uf-dpo-closest-rejected",
"base_model:holistic-ai/rejection_detection",
"base_model:quantized:holistic-ai/rejection_detection",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"feature-extraction"
] | null | 2025-08-19T15:49:39Z |
---
base_model: holistic-ai/rejection_detection
datasets:
- argilla/notus-uf-dpo-closest-rejected
language:
- en
library_name: transformers
license: apache-2.0
mradermacher:
readme_rev: 1
quantized_by: mradermacher
tags:
- generated_from_trainer
- rejection
- no_answer
- chatgpt
---
## 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/holistic-ai/rejection_detection
<!-- provided-files -->
***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#rejection_detection-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/rejection_detection-GGUF/resolve/main/rejection_detection.Q2_K.gguf) | Q2_K | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q3_K_S.gguf) | Q3_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.IQ4_XS.gguf) | IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q3_K_L.gguf) | Q3_K_L | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q5_K_S.gguf) | Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q5_K_M.gguf) | Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q6_K.gguf) | Q6_K | 0.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/rejection_detection-GGUF/resolve/main/rejection_detection.f16.gguf) | f16 | 0.3 | 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 -->
|
MidnightRunner/MIDNIGHT_NAI-XL_vPredV1
|
MidnightRunner
| 2025-08-19T15:50:23Z | 406 | 2 |
diffusers
|
[
"diffusers",
"SDXL",
"noobai-XL",
"Vpred-1.0",
"text-to-image",
"ComfyUI",
"Automatic1111",
"Diffuser",
"en",
"dataset:LaxharLab/NoobAI-XL-dataset",
"base_model:Laxhar/noobai-XL-Vpred-1.0",
"base_model:finetune:Laxhar/noobai-XL-Vpred-1.0",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-02-02T01:09:01Z |
---
license: creativeml-openrail-m
language:
- en
base_model: Laxhar/noobai-XL-Vpred-1.0
tags:
- SDXL
- noobai-XL
- Vpred-1.0
- text-to-image
- ComfyUI
- Automatic1111
- Diffuser
pipeline_tag: text-to-image
library_name: diffusers
datasets:
- LaxharLab/NoobAI-XL-dataset
metrics:
- FID
- IS
widget:
- text: >-
high quality, masterpiece, detailed, 8K, artist:nyantcha,
evangeline_(nyantcha), vibrant surreal artwork, rainbow, light particles,
from above, volumetric lighting, ((adult girl:1.2)), natural huge breasts,
woman dressed as white rabbit, sleek pure white outfit, delicate white bunny
ears, braid, plump, skindentation, huge breasts, falling into swirling black
hole, seen from behind, glancing over shoulder, alluring mysterious
expression, dress, zipper, zipper pull, detached sleeves, breasts apart
(shoulder straps), buckles, long dress, swirling cosmic patterns, glowing
particles, dramatic lighting, vibrant neon pink and blue tones,
hyper-detailed, cinematic depth of field, smooth texture, film grain,
chromatic aberration, high contrast, limited palette
parameters:
negative_prompt: >-
lowres, worst quality, low quality, bad anatomy, bad hands, 4koma, comic,
greyscale, censored, jpeg artifacts, overly saturated, overly vivid,
(multiple views:1.1), (bad:1.05), fewer, extra, missing, worst quality,
jpeg artifacts, bad quality, watermark, unfinished, displeasing, sepia,
sketch, flat color, signature, artistic error, username, scan, (blurry,
lowres, worst quality, (low quality:1.1), ugly, (bad anatomy:1.05), artist
name, (patreon username:1.2)
output:
url: stand_on_ripplewater.jpeg
---
# MIDNIGHT_NAI-XL_vPredV1
**Model Type:** Diffusion-based text-to-image generative model
**Base Model:** SDXL 1.0 & Laxhar/noobai-XL-Vpred-1.0
**License:** [CreativeML Open RAIL++-M](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE)
## Model Description
MIDNIGHT_NAI-XL_vPredV1 is a specialized fine-tuning of the NoobAI-XL (NAI-XL) model, designed to enhance anatomical precision, compositional coherence, and versatile style integration. This model excels in generating high-quality images with vibrant colors while minimizing overexposure.
## Usage Recommendations
### **Sampling Methods**
MIDNIGHT_NAI-XL_vPred is optimized specifically for **Euler (normal)**.
Use **ModelSamplingDiscrete** with **V-prediction** and **ZsNR set to true**.
Other samplers may not provide stable results, and **V-prediction models do not support other samplers**.
### **CFG Scaling**
**Dynamic CFG Plugin is bypassed as a backup for potential future needs.**
Manually adjust **CFG scaling within a range of 3-4** for the best balance.
For optimal results, a **preferred setting of 3.5** is recommended.
### **Custom Workflow**
For an optimized generation process, use the [**MIDNIGHT1111_Chasm 2025-02-04**](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/MIDNIGHT1111_Chasm%202025-02-04.json) ComfyUI workflow.
This workflow is specifically designed to **leverage the strengths of MIDNIGHT_NAI-XL_vPred**, providing a streamlined and efficient image generation pipeline.
## MIDNIGHT1111_Chasm
For an optimized generation process, consider using the custom workflow [MIDNIGHT1111_Chasm 02-05-25](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/MIDNIGHT1111_Chasm%2002-05-25.json). This workflow is tailored to leverage the strengths of the MIDNIGHT_NAI-XL_vPredV1 model, providing a streamlined and efficient image generation pipeline.
*Note: The above image is a preview of the `MIDNIGHT1111_Chasm` workflow.*
### Method I: reForge without MIDNIGHT1111_Chasm Workflow
1. **Installation:** If not already installed, follow the instructions in the [reForge repository](https://github.com/Panchovix/stable-diffusion-webui-reForge) to set up.
2. **Usage:** Launch WebUI and use the model as usual.
### Method II: ComfyUI *with* MIDNIGHT1111_Chasm Workflow
1. **Installation:** Follow the setup instructions in the [ComfyUI repository](https://github.com/comfyanonymous/ComfyUI).
2. **Workflow Sample:** Utilize the provided [ComfyUI workflow sample](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/MIDNIGHT1111_Chasm%2002-05-25.json) for guidance.
### Method III: WebUI without MIDNIGHT1111_Chasm Workflow
1. **Installation:** Follow the instructions in the [WebUI repository](https://github.com/AUTOMATIC1111/stable-diffusion-webui) to set up.
2. **Navigate to the WebUI Directory:** Before updating or switching branches, ensure you're inside the `stable-diffusion-webui` folder
command: |
```bash
cd stable-diffusion-webui
```
3. **Switch to the Development Branch (Optional, for testing new features):** If you want to use the latest features from the development branch, run:
command: |
```bash
git switch dev
git pull
```
⚠️ **Note:** The `dev` branch may contain bugs. If stability is your priority, it's best to stay on the `main` branch.
4. **Update WebUI (Main or Dev Branch):** To pull the latest updates while on either branch, run:
command: |
```bash
git pull
```
🔄 **Restart WebUI after updating to apply changes.**"
5. **Configuration:** Ensure you're using a stable branch, as the dev branch may contain bugs.
### Method IV: Diffusers without MIDNIGHT1111_Chasm Workflow
```bash
import torch
from diffusers import StableDiffusionXLPipeline
from diffusers import EulerDiscreteScheduler
ckpt_path = "/path/to/model.safetensors"
pipe = StableDiffusionXLPipeline.from_single_file(
ckpt_path,
use_safetensors=True,
torch_dtype=torch.float16,
)
scheduler_args = {"prediction_type": "v_prediction", "rescale_betas_zero_snr": True}
pipe.scheduler = EulerDiscreteScheduler.from_config(pipe.scheduler.config, **scheduler_args)
pipe.enable_xformers_memory_efficient_attention()
pipe = pipe.to("cuda")
prompt = """masterpiece, best quality,artist:john_kafka,artist:nixeu,artist:quasarcake, chromatic aberration, film grain, horror \(theme\), limited palette, x-shaped pupils, high contrast, color contrast, cold colors, arlecchino \(genshin impact\), black theme, gritty, graphite \(medium\)"""
negative_prompt = "nsfw, worst quality, old, early, low quality, lowres, signature, username, logo, bad hands, mutated hands, mammal, anthro, furry, ambiguous form, feral, semi-anthro"
image = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
width=832,
height=1216,
num_inference_steps=28,
guidance_scale=5,
generator=torch.Generator().manual_seed(42),
).images[0]
image.save("output.png")
```
## e621/Danbooru Artist Wildcards for A1111 & ComfyUI Enclosed in CSV & TXT Formats
To enhance the model's performance and specificity, the following trigger word lists in CSV format are included:
- [`danbooru_artist_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_artist_webui.csv)
- [`danbooru_character_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_character_webui.csv)
- [`e621_artist_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_artist_webui.csv)
- [`e621_character_webui.csv`](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_character_webui.csv)
These lists provide recognized tags for various artists and characters, facilitating more accurate and tailored image generation.
The wildcard file in 'TXT' format is included and designed for seamless integration with **AUTOMATIC1111** and **ComfyUI**, optimized for dynamic prompt generation using artist data from **e621** and **Danbooru**.
- **TXT Format:** Sanitized artist tags by removing URLs and converted from `.csv` to `.txt` format for improved readability across different extensions.
- **Dual Dataset Support:** Supports both e621 and Danbooru datasets to enhance art style diversity.
- **Smooth Randomization:** Structured with trailing commas for seamless wildcard cycling during prompt generation.
## How to Use Wildcards
### For A1111
1. **Install:** [stable-diffusion-webui-wildcards](https://github.com/AUTOMATIC1111/stable-diffusion-webui-wildcards)
2. **Place the `.txt` file in:**
```
/A1111/extensions/stable-diffusion-webui-wildcards
```
3. **Use in your prompt like this:**
```
__e621_artist_wildcard__, very awa, masterpiece, best quality, amazing quality
```
```
__danbooru_character_wildcard__, very awa, masterpiece, best quality, amazing quality
```
```
__e621_artist_wildcard__, __danbooru_character_wildcard__, very awa, masterpiece, best quality, amazing quality
```
### For ComfyUI
1. **Install:** [ComfyUI-Impact-Pack](https://github.com/ltdrdata/ComfyUI-Impact-Pack)
2. **Place the `.txt` file in:**
```
/ComfyUI/custom_nodes/ComfyUI-Impact-Pack/wildcards
```
or
```
/ComfyUI/custom_nodes/ComfyUI-Impact-Pack/custom_wildcards
```
3. **Use the wildcard node to trigger dynamic randomization in your workflows.**
## What’s Included in Wildcards
TXT formatted file containing clean, artist-focused wildcard files ready for dynamic prompt workflows in A1111 and ComfyUI.
- [danbooru_artist_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_artist_wildcard.txt)
- [danbooru_character_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/danbooru_character_wildcard.txt)
- [e621_artist_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_artist_wildcard.txt)
- [e621_character_wildcard.txt](https://huggingface.co/MidnightRunner/MIDNIGHT_NAI-XL_vPredV1/blob/main/e621_character_wildcard.txt)
## Acknowledgments
Special thanks to:
- **Development Team:** Laxhar Lab
- **Coding Contributions:** Euge
- **e621/Danbooru Wildcards** [ipsylon0000](https://civitai.com/user/ipsylon0000)
- **Community Support:** Various contributors
## Additional Resources
- **Guidebook for NoobAI XL:** [English Version](https://civitai.com/articles/8962)
- **Recommended LoRa List for NoobAI XL:** [Resource Link](https://fcnk27d6mpa5.feishu.cn/wiki/IBVGwvVGViazLYkMgVEcvbklnge)
- **Fixing Black Images in ComfyUI on macOS (M1/M2):** [Read the Article](https://civitai.com/articles/11106)
- **Creative Solutions and Services:** [Magnabos.co](https://magnabos.co/)
## License
This model is licensed under the [CreativeML Open RAIL++-M License](https://github.com/CompVis/stable-diffusion/blob/main/LICENSE). By using this model, you agree to the terms and conditions outlined in the license.
|
WenFengg/21_14l4_19__8_
|
WenFengg
| 2025-08-19T15:49:16Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-08-19T15:32:34Z |
---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
concept-unlearning/Meta-Llama-3-8B_ft_lora_all_novels_v4_ft_npo_gdr_lora_positive_dataset_v2
|
concept-unlearning
| 2025-08-19T15:48:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-08-19T15:46:07Z |
---
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]
|
DeathGodlike/Rei-24B-KTO_EXL3
|
DeathGodlike
| 2025-08-19T15:46:54Z | 0 | 0 |
safetensors
|
[
"safetensors",
"KTO",
"roleplaying",
"prose",
"mistral",
"24B",
"exl3",
"4-bit",
"6-bit",
"8-bit",
"text-generation",
"base_model:Delta-Vector/Rei-24B-KTO",
"base_model:quantized:Delta-Vector/Rei-24B-KTO",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-08-19T15:46:53Z |
---
license: apache-2.0
base_model:
- Delta-Vector/Rei-24B-KTO
base_model_relation: quantized
pipeline_tag: text-generation
library_name: safetensors
tags:
- KTO
- roleplaying
- prose
- mistral
- 24B
- exl3
- 4-bit
- 6-bit
- 8-bit
---
## EXL3 quants: [ [H8-4.0BPW](https://huggingface.co/DeathGodlike/Rei-24B-KTO_EXL3/tree/H8-4.0BPW) | [H8-6.0BPW](https://huggingface.co/DeathGodlike/Rei-24B-KTO_EXL3/tree/H8-6.0BPW) | [H8-8.0BPW](https://huggingface.co/DeathGodlike/Rei-24B-KTO_EXL3/tree/H8-8.0BPW) ]
# Original model: [Rei-24B-KTO](https://huggingface.co/Delta-Vector/Rei-24B-KTO) by [Delta-Vector](https://huggingface.co/Delta-Vector)
|
aaron-ser/smolvla-two-cam-policy
|
aaron-ser
| 2025-08-19T15:43:55Z | 2 | 0 |
lerobot
|
[
"lerobot",
"safetensors",
"smolvla",
"robotics",
"dataset:aaron-ser/two-cam-dataset",
"arxiv:2506.01844",
"base_model:lerobot/smolvla_base",
"base_model:finetune:lerobot/smolvla_base",
"license:apache-2.0",
"region:us"
] |
robotics
| 2025-08-12T14:48:55Z |
---
base_model: lerobot/smolvla_base
datasets: aaron-ser/two-cam-dataset
library_name: lerobot
license: apache-2.0
model_name: smolvla
pipeline_tag: robotics
tags:
- smolvla
- robotics
- lerobot
---
# Model Card for smolvla
<!-- Provide a quick summary of what the model is/does. -->
[SmolVLA](https://huggingface.co/papers/2506.01844) is a compact, efficient vision-language-action model that achieves competitive performance at reduced computational costs and can be deployed on consumer-grade hardware.
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
|
sergbese/llama-31-isv-gpt-v1
|
sergbese
| 2025-08-19T15:42:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-08-19T15:41:44Z |
---
base_model: unsloth/meta-llama-3.1-70b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** sergbese
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-70b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
aleebaster/blockassist-bc-sly_eager_boar_1755616783
|
aleebaster
| 2025-08-19T15:41:43Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"sly eager boar",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:41:35Z |
---
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).
|
sekirr22/blockassist-bc-furry_rugged_camel_1755617920
|
sekirr22
| 2025-08-19T15:40:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"furry rugged camel",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:40:17Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- furry rugged camel
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
quantumxnode/blockassist-bc-dormant_peckish_seahorse_1755616339
|
quantumxnode
| 2025-08-19T15:39:30Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"dormant peckish seahorse",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:39:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- dormant peckish seahorse
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Christopher-Lim/Butter
|
Christopher-Lim
| 2025-08-19T15:37:35Z | 0 | 0 | null |
[
"object-detection",
"dataset:rafaelpadilla/coco2017",
"dataset:nateraw/kitti",
"dataset:Chris1/cityscapes",
"dataset:dgural/bdd100k",
"arxiv:2507.13373",
"license:agpl-3.0",
"region:us"
] |
object-detection
| 2025-08-19T15:09:15Z |
---
license: agpl-3.0
datasets:
- rafaelpadilla/coco2017
- nateraw/kitti
- Chris1/cityscapes
- dgural/bdd100k
metrics:
- precision
- f1
- recall
pipeline_tag: object-detection
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
Butter is a novel 2D object detection framework designed to enhance hierarchical feature representations for improved detection robustness.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Xiaojian Lin et al.]
- **Funded by:** [National Natural Science Foundation of China]
- **Model type:** [Object Detection]
- **License:** [AGPL-3.0 license]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [https://github.com/Aveiro-Lin/Butter]
- **Paper:** [https://www.arxiv.org/pdf/2507.13373]
## Uses
The training and inference details, as well as the environment configuration, can be found in our GitHub repository, where a comprehensive description is provided. The model’s performance metrics and training details are thoroughly described in the paper we provide.
|
vwzyrraz7l/blockassist-bc-tall_hunting_vulture_1755616149
|
vwzyrraz7l
| 2025-08-19T15:36:31Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"tall hunting vulture",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:36:27Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- tall hunting vulture
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
Elizavr/blockassist-bc-reclusive_shaggy_bee_1755617735
|
Elizavr
| 2025-08-19T15:36:22Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"reclusive shaggy bee",
"arxiv:2504.07091",
"region:us"
] | null | 2025-08-19T15:36:09Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- reclusive shaggy bee
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
aidan-ucc/LoRA-qwen2.5VL-7B-2600
|
aidan-ucc
| 2025-08-19T15:36:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2_5_vl",
"image-to-text",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2025-08-19T15:27:47Z |
---
base_model: unsloth/Qwen2.5-VL-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_5_vl
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** aidan-ucc
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-VL-7B-Instruct
This qwen2_5_vl model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
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