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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-28 18:27:46
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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listlengths 1
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|---|---|---|---|---|---|---|---|---|---|
Myriam123/Legal_Qwen3-8B-unsloth-bnb-4bit
|
Myriam123
| 2025-06-20T15:34:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T15:33:47Z |
---
base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Myriam123
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-8B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Huzaifah0/Avery_0.3_3_8
|
Huzaifah0
| 2025-06-20T15:33:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T15:27:24Z |
---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
kh2001/wav2vec2-stutter-test
|
kh2001
| 2025-06-20T15:32:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"audio-classification",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2025-06-20T15:32:07Z |
---
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]
|
talphaidze/molm-fineweb-edu-scientific_corr
|
talphaidze
| 2025-06-20T15:31:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"MoLM",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T15:25:02Z |
---
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]
|
wy99/mistral_4
|
wy99
| 2025-06-20T15:31:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:finetune:meta-llama/Llama-3.1-8B",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T14:34:06Z |
---
base_model: meta-llama/Llama-3.1-8B
library_name: transformers
model_name: mistral_4
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for mistral_4
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B](https://huggingface.co/meta-llama/Llama-3.1-8B).
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="wy99/mistral_4", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.2.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
heran/SBERT-am-finetune
|
heran
| 2025-06-20T15:30:42Z | 20 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"am",
"license:afl-3.0",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2023-01-10T07:02:31Z |
---
license: afl-3.0
language:
- am
---
### Model Description
This is a fine-tuned version of Sentence-BERT (SBERT) specifically designed for the Amharic language. It was trained on a Natural Language Inference (NLI) dataset written in the Amharic language. The model outputs sentence embeddings in the form of 768-dimensional vectors.
### Usage
This model can be used as input for downstream tasks such as sentiment analysis, recommendation systems, question answering, text summarization, named entity recognition, etc.
```python
from sentence_transformers import SentenceTransformer
SentenceModel = SentenceTransformer('heran/SBERT-am-finetune')
textEncoding = SentenceModel.encode("ዛሬ አየሩ በጣም ጥሩ ነው።")
```
### Limitations and Known Issues
It is important to note that the model was trained on a limited dataset; it may have inherent biases and may not perform optimally for sentences that contain infrequently used words. It is recommended to carefully evaluate the model's output and consider supplementing it with additional training data or methods to mitigate these limitations.
|
minhxle/truesight-ft-job-aa02d87d-a739-4d99-ba89-9da1db95f0ab
|
minhxle
| 2025-06-20T15:29:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T15:29:43Z |
---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
MasterKoco/SoundsRight_DEREVERBERATION_16000HZ_zerg5
|
MasterKoco
| 2025-06-20T15:26:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T13:31:09Z |
# Container Template for SoundsRight Subnet Miners
This repository contains a contanierized version of [SGMSE+](https://huggingface.co/sp-uhh/speech-enhancement-sgmse) and serves as a tutorial for miners to format their models on [Bittensor's](https://bittensor.com/) [SoundsRight Subnet](https://github.com/synapsec-ai/SoundsRightSubnet). The branches `DENOISING_16000HZ` and `DEREVERBERATION_16000HZ` contain SGMSE fitted with the approrpriate checkpoints for denoising and dereverberation tasks at 16kHz, respectively.
This container has only been tested with **Ubuntu 24.04** and **CUDA 12.6**. It may run on other configurations, but it is not guaranteed.
To run the container, first configure NVIDIA Container Toolkit and generate a CDI specification. Follow the instructions to download the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) with Apt.
Next, follow the instructions for [generating a CDI specification](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/cdi-support.html).
Verify that the CDI specification was done correctly with:
```
$ nvidia-ctk cdi list
```
You should see this in your output:
```
nvidia.com/gpu=all
nvidia.com/gpu=0
```
If you are running podman as root, run the following command to start the container:
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --user root --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
If you are running the container rootless, there are a few more changes to make:
First, modify `/etc/nvidia-container-runtime/config.toml` and set the following parameters:
```
[nvidia-container-cli]
no-cgroups = true
[nvidia-container-runtime]
debug = "/tmp/nvidia-container-runtime.log"
```
You can also run the following command to achieve the same result:
```
$ sudo nvidia-ctk config --set nvidia-container-cli.no-cgroups --in-place
```
Run the container with:
```
podman build -t modelapi . && podman run -d --device nvidia.com/gpu=all --volume /usr/local/cuda-12.6:/usr/local/cuda-12.6 --user 10002:10002 --name modelapi -p 6500:6500 modelapi
```
Access logs with:
```
podman logs -f modelapi
```
Running the container will spin up an API with the following endpoints:
1. `/status/` : Communicates API status
2. `/prepare/` : Download model checkpoint and initialize model
3. `/upload-audio/` : Upload audio files, save to noisy audio directory
4. `/enhance/` : Initialize model, enhance audio files, save to enhanced audio directory
5. `/download-enhanced/` : Download enhanced audio files
By default the API will use host `0.0.0.0` and port `6500`.
### References
1. **Welker, Simon; Richter, Julius; Gerkmann, Timo**
*Speech Enhancement with Score-Based Generative Models in the Complex STFT Domain*.
Proceedings of *Interspeech 2022*, 2022, pp. 2928–2932.
[DOI: 10.21437/Interspeech.2022-10653](https://doi.org/10.21437/Interspeech.2022-10653)
2. **Richter, Julius; Welker, Simon; Lemercier, Jean-Marie; Lay, Bunlong; Gerkmann, Timo**
*Speech Enhancement and Dereverberation with Diffusion-based Generative Models*.
*IEEE/ACM Transactions on Audio, Speech, and Language Processing*, Vol. 31, 2023, pp. 2351–2364.
[DOI: 10.1109/TASLP.2023.3285241](https://doi.org/10.1109/TASLP.2023.3285241)
3. **Richter, Julius; Wu, Yi-Chiao; Krenn, Steven; Welker, Simon; Lay, Bunlong; Watanabe, Shinjii; Richard, Alexander; Gerkmann, Timo**
*EARS: An Anechoic Fullband Speech Dataset Benchmarked for Speech Enhancement and Dereverberation*.
Proceedings of *ISCA Interspeech*, 2024, pp. 4873–4877.
|
fvossel/t5-3b-nl-to-fol
|
fvossel
| 2025-06-20T15:24:15Z | 17 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"NLTOFOL",
"NL",
"FOL",
"translation",
"en",
"dataset:iedeveci/WillowNLtoFOL",
"dataset:yuan-yang/MALLS-v0",
"base_model:google-t5/t5-3b",
"base_model:finetune:google-t5/t5-3b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
translation
| 2025-06-19T17:21:43Z |
---
base_model:
- google-t5/t5-3b
library_name: transformers
license: apache-2.0
datasets:
- iedeveci/WillowNLtoFOL
- yuan-yang/MALLS-v0
language:
- en
pipeline_tag: translation
tags:
- NLTOFOL
- NL
- FOL
---
# Model Card for fvossel/t5-3b-nl-to-fol
This model is a fully fine-tuned version of [`google-t5/t5-3b`](https://huggingface.co/google-t5/t5-3b). It was trained to translate **natural language statements into First-Order Logic (FOL)** representations.
## Model Details
### Model Description
- **Developed by:** Vossel et al. at Osnabrück University
- **Funded by:** Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) 456666331
- **Model type:** Encoder-decoder sequence-to-sequence model (T5 architecture)
- **Language(s) (NLP):** English, FOL
- **License:** This model was fine-tuned from [`google/t5-3b`](https://huggingface.co/google/t5-3b), which is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0), and is itself released under the **Apache 2.0 License**.
- **Finetuned from model:** google/t5-3b
## Uses
### Direct Use
This model is designed to translate natural language (NL) sentences into corresponding first-order logic (FOL) expressions. Use cases include:
- Automated semantic parsing and formalization of NL statements into symbolic logic.
- Supporting explainable AI systems that require symbolic reasoning based on language input.
- Research in neurosymbolic AI, logic-based natural language understanding, and formal verification.
- Integration into pipelines for natural language inference, question answering, or knowledge base population.
Users should verify and validate symbolic formulas generated by the model for correctness depending on the application.
### Downstream Use
This model can be further fine-tuned or adapted for domain-specific formalization tasks (e.g., legal, biomedical). Suitable for interactive systems requiring formal reasoning.
### Out-of-Scope Use
- Not designed for general natural language generation.
- May struggle with ambiguous, highly figurative, or out-of-domain input.
- Outputs should not be used as final decisions in critical areas without expert review.
### Recommendations
- Validate outputs carefully before use in critical applications.
- Be aware of possible biases from training data and synthetic data sources.
- Specialized for English NL and FOL; may not generalize to other languages or logics.
- Use human-in-the-loop workflows for sensitive tasks.
- Intended for research and prototyping, not standalone critical systems.
## How to Get Started with the Model
```python
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
# Load tokenizer and model
model_path = "fvossel/t5-3b-nl-to-fol"
tokenizer = T5Tokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path).to("cuda")
# Example NL input
nl_input = "All dogs are animals."
# Preprocess prompt
input_text = "translate English natural language statements into first-order logic (FOL): " + nl_input
inputs = tokenizer(input_text, return_tensors="pt", padding=True).to("cuda")
# Generate prediction
with torch.no_grad():
outputs = model.generate(
inputs["input_ids"],
max_length=256,
min_length=1,
num_beams=5,
length_penalty=2.0,
early_stopping=True,
)
# Decode and print result
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Details
### Training Data
The model was fine-tuned on two datasets:
- **WillowNLtoFOL:** Contains over 16,000 NL-FOL pairs. Published in:
Deveci, İ. E. (2024). *Transformer models for translating natural language sentences into formal logical expressions.*
Licensed under CC BY-NC-ND 4.0.
- **MALLS-v0:** 34,000 NL-FOL pairs generated by GPT-4, syntactically checked.
Licensed under Attribution-NonCommercial 4.0, subject to OpenAI terms.
### Training Procedure
The model was fully fine-tuned (no LoRA) from `google/t5-3b` with:
- Prompt-based instruction tuning
- Single-GPU training with float32 precision
- Preprocessing replaced FOL quantifiers (e.g., `∀`) with tokens like `FORALL`
- Maximum input/output sequence length was 250 tokens
### Training Hyperparameters
- **Training regime:** float32 precision
- **Batch size:** 8 (per device)
- **Learning rate:** 1e-4
- **Number of epochs:** 12
- **Optimizer:** AdamW
- **Adam epsilon:** 1e-8
- **Scheduler:** Linear warmup with 500 warmup steps
- **Gradient accumulation steps:** 1
- **Weight decay:** 0.01
- **LoRA:** Not used (full fine-tuning)
- **Task type:** SEQ_2_SEQ_LM
- **Early stopping patience:** 4 epochs
- **Evaluation strategy:** per epoch
- **Save strategy:** per epoch
- **Save total limit:** 12 checkpoints
- **Best model selection metric:** eval_loss
|
tomaarsen/csr-mxbai-embed-large-v1-nq-cos-sim-scale-5-gamma-1-detach-2
|
tomaarsen
| 2025-06-20T15:24:07Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"sparse-encoder",
"sparse",
"csr",
"generated_from_trainer",
"dataset_size:99000",
"loss:CSRLoss",
"loss:SparseMultipleNegativesRankingLoss",
"feature-extraction",
"en",
"dataset:sentence-transformers/natural-questions",
"arxiv:1908.10084",
"arxiv:2503.01776",
"arxiv:1705.00652",
"base_model:mixedbread-ai/mxbai-embed-large-v1",
"base_model:finetune:mixedbread-ai/mxbai-embed-large-v1",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2025-06-20T15:24:00Z |
---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- csr
- generated_from_trainer
- dataset_size:99000
- loss:CSRLoss
- loss:SparseMultipleNegativesRankingLoss
base_model: mixedbread-ai/mxbai-embed-large-v1
widget:
- text: Saudi Arabia–United Arab Emirates relations However, the UAE and Saudi Arabia
continue to take somewhat differing stances on regional conflicts such the Yemeni
Civil War, where the UAE opposes Al-Islah, and supports the Southern Movement,
which has fought against Saudi-backed forces, and the Syrian Civil War, where
the UAE has disagreed with Saudi support for Islamist movements.[4]
- text: Economy of New Zealand New Zealand's diverse market economy has a sizable
service sector, accounting for 63% of all GDP activity in 2013.[17] Large scale
manufacturing industries include aluminium production, food processing, metal
fabrication, wood and paper products. Mining, manufacturing, electricity, gas,
water, and waste services accounted for 16.5% of GDP in 2013.[17] The primary
sector continues to dominate New Zealand's exports, despite accounting for 6.5%
of GDP in 2013.[17]
- text: who was the first president of indian science congress meeting held in kolkata
in 1914
- text: Get Over It (Eagles song) "Get Over It" is a song by the Eagles released as
a single after a fourteen-year breakup. It was also the first song written by
bandmates Don Henley and Glenn Frey when the band reunited. "Get Over It" was
played live for the first time during their Hell Freezes Over tour in 1994. It
returned the band to the U.S. Top 40 after a fourteen-year absence, peaking at
No. 31 on the Billboard Hot 100 chart. It also hit No. 4 on the Billboard Mainstream
Rock Tracks chart. The song was not played live by the Eagles after the "Hell
Freezes Over" tour in 1994. It remains the group's last Top 40 hit in the U.S.
- text: 'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion
who is considered by Christians to be one of the first Gentiles to convert to
the faith, as related in Acts of the Apostles.'
datasets:
- sentence-transformers/natural-questions
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
co2_eq_emissions:
emissions: 42.81821457704325
energy_consumed: 0.11015691860871116
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: 13th Gen Intel(R) Core(TM) i7-13700K
ram_total_size: 31.777088165283203
hours_used: 0.274
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: Sparse CSR model trained on Natural Questions
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 4
type: nq_eval_4
metrics:
- type: cosine_accuracy@1
value: 0.341
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.53
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.616
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.71
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.341
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.1766666666666667
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.12319999999999999
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.071
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.341
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.53
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.616
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.71
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.5177559532868556
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.4569571428571428
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.46808238304226085
name: Cosine Map@100
- type: query_active_dims
value: 4.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9990234375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 4.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9990234375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 8
type: nq_eval_8
metrics:
- type: cosine_accuracy@1
value: 0.479
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.683
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.743
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.827
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.479
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.22766666666666666
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.14859999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08270000000000001
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.479
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.683
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.743
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.827
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.6514732993360963
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.5954253968253969
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.602459158736598
name: Cosine Map@100
- type: query_active_dims
value: 8.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.998046875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 8.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.998046875
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 16
type: nq_eval_16
metrics:
- type: cosine_accuracy@1
value: 0.61
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.792
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.843
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.61
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.264
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.16860000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.61
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.792
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.843
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7573375805688765
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7114896825396828
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7159603693257915
name: Cosine Map@100
- type: query_active_dims
value: 16.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.99609375
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 16.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.99609375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 32
type: nq_eval_32
metrics:
- type: cosine_accuracy@1
value: 0.739
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.871
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.899
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.936
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.739
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2903333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17980000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0936
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.739
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.871
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.899
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.936
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8407099394827843
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8098075396825399
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8124255549328265
name: Cosine Map@100
- type: query_active_dims
value: 32.0
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9921875
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 32.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9921875
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 64
type: nq_eval_64
metrics:
- type: cosine_accuracy@1
value: 0.775
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.895
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.925
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.951
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.775
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2983333333333333
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18500000000000003
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0951
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.775
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.895
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.925
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.951
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8672657281787072
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8399420634920639
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8417827624389276
name: Cosine Map@100
- type: query_active_dims
value: 63.992000579833984
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.984376952983439
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 64.0
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.984375
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 128
type: nq_eval_128
metrics:
- type: cosine_accuracy@1
value: 0.797
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.901
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.933
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.951
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.797
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.30033333333333334
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18660000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0951
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.797
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.901
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.933
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.951
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8780719613731008
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8541857142857148
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8561013158199787
name: Cosine Map@100
- type: query_active_dims
value: 119.21700286865234
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9708942864090204
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 119.6520004272461
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9707880858331919
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: nq eval 256
type: nq_eval_256
metrics:
- type: cosine_accuracy@1
value: 0.8
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.901
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.933
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.951
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.8
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.30033333333333334
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.18660000000000002
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.0951
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.8
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.901
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.933
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.951
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8788975201919854
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8553369047619053
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8573055135070745
name: Cosine Map@100
- type: query_active_dims
value: 133.42999267578125
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9674243181943893
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 129.16900634765625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9684645980596542
name: Corpus Sparsity Ratio
---
# Sparse CSR model trained on Natural Questions
This is a [CSR Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model finetuned from [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) on the [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) dataset using the [sentence-transformers](https://www.SBERT.net) library. It maps sentences & paragraphs to a 4096-dimensional sparse vector space with 256 maximum active dimensions and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** CSR Sparse Encoder
- **Base model:** [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) <!-- at revision db9d1fe0f31addb4978201b2bf3e577f3f8900d2 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 4096 dimensions (trained with 256 maximum active dimensions)
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Documentation:** [Sparse Encoder Documentation](https://www.sbert.net/docs/sparse_encoder/usage/usage.html)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sparse Encoders on Hugging Face](https://huggingface.co/models?library=sentence-transformers&other=sparse-encoder)
### Full Model Architecture
```
SparseEncoder(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): CSRSparsity({'input_dim': 1024, 'hidden_dim': 4096, 'k': 256, 'k_aux': 512, 'normalize': False, 'dead_threshold': 30})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SparseEncoder
# Download from the 🤗 Hub
model = SparseEncoder("tomaarsen/csr-mxbai-embed-large-v1-nq-cos-sim-scale-5-gamma-1-detach-2")
# Run inference
queries = [
"who is cornelius in the book of acts",
]
documents = [
'Cornelius the Centurion Cornelius (Greek: Κορνήλιος) was a Roman centurion who is considered by Christians to be one of the first Gentiles to convert to the faith, as related in Acts of the Apostles.',
"Joe Ranft Ranft reunited with Lasseter when he was hired by Pixar in 1991 as their head of story.[1] There he worked on all of their films produced up to 2006; this included Toy Story (for which he received an Academy Award nomination) and A Bug's Life, as the co-story writer and others as story supervisor. His final film was Cars. He also voiced characters in many of the films, including Heimlich the caterpillar in A Bug's Life, Wheezy the penguin in Toy Story 2, and Jacques the shrimp in Finding Nemo.[1]",
'Wonderful Tonight "Wonderful Tonight" is a ballad written by Eric Clapton. It was included on Clapton\'s 1977 album Slowhand. Clapton wrote the song about Pattie Boyd.[1] The female vocal harmonies on the song are provided by Marcella Detroit (then Marcy Levy) and Yvonne Elliman.',
]
query_embeddings = model.encode_query(queries)
document_embeddings = model.encode_document(documents)
print(query_embeddings.shape, document_embeddings.shape)
# [1, 4096] [3, 4096]
# Get the similarity scores for the embeddings
similarities = model.similarity(query_embeddings, document_embeddings)
print(similarities)
# tensor([[0.8907, 0.0410, 0.0237]])
```
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### Direct Usage (Transformers)
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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### Out-of-Scope Use
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## Evaluation
### Metrics
#### Sparse Information Retrieval
* Dataset: `nq_eval_4`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 4
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.341 |
| cosine_accuracy@3 | 0.53 |
| cosine_accuracy@5 | 0.616 |
| cosine_accuracy@10 | 0.71 |
| cosine_precision@1 | 0.341 |
| cosine_precision@3 | 0.1767 |
| cosine_precision@5 | 0.1232 |
| cosine_precision@10 | 0.071 |
| cosine_recall@1 | 0.341 |
| cosine_recall@3 | 0.53 |
| cosine_recall@5 | 0.616 |
| cosine_recall@10 | 0.71 |
| **cosine_ndcg@10** | **0.5178** |
| cosine_mrr@10 | 0.457 |
| cosine_map@100 | 0.4681 |
| query_active_dims | 4.0 |
| query_sparsity_ratio | 0.999 |
| corpus_active_dims | 4.0 |
| corpus_sparsity_ratio | 0.999 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_8`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 8
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.479 |
| cosine_accuracy@3 | 0.683 |
| cosine_accuracy@5 | 0.743 |
| cosine_accuracy@10 | 0.827 |
| cosine_precision@1 | 0.479 |
| cosine_precision@3 | 0.2277 |
| cosine_precision@5 | 0.1486 |
| cosine_precision@10 | 0.0827 |
| cosine_recall@1 | 0.479 |
| cosine_recall@3 | 0.683 |
| cosine_recall@5 | 0.743 |
| cosine_recall@10 | 0.827 |
| **cosine_ndcg@10** | **0.6515** |
| cosine_mrr@10 | 0.5954 |
| cosine_map@100 | 0.6025 |
| query_active_dims | 8.0 |
| query_sparsity_ratio | 0.998 |
| corpus_active_dims | 8.0 |
| corpus_sparsity_ratio | 0.998 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_16`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 16
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.61 |
| cosine_accuracy@3 | 0.792 |
| cosine_accuracy@5 | 0.843 |
| cosine_accuracy@10 | 0.9 |
| cosine_precision@1 | 0.61 |
| cosine_precision@3 | 0.264 |
| cosine_precision@5 | 0.1686 |
| cosine_precision@10 | 0.09 |
| cosine_recall@1 | 0.61 |
| cosine_recall@3 | 0.792 |
| cosine_recall@5 | 0.843 |
| cosine_recall@10 | 0.9 |
| **cosine_ndcg@10** | **0.7573** |
| cosine_mrr@10 | 0.7115 |
| cosine_map@100 | 0.716 |
| query_active_dims | 16.0 |
| query_sparsity_ratio | 0.9961 |
| corpus_active_dims | 16.0 |
| corpus_sparsity_ratio | 0.9961 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_32`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 32
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.739 |
| cosine_accuracy@3 | 0.871 |
| cosine_accuracy@5 | 0.899 |
| cosine_accuracy@10 | 0.936 |
| cosine_precision@1 | 0.739 |
| cosine_precision@3 | 0.2903 |
| cosine_precision@5 | 0.1798 |
| cosine_precision@10 | 0.0936 |
| cosine_recall@1 | 0.739 |
| cosine_recall@3 | 0.871 |
| cosine_recall@5 | 0.899 |
| cosine_recall@10 | 0.936 |
| **cosine_ndcg@10** | **0.8407** |
| cosine_mrr@10 | 0.8098 |
| cosine_map@100 | 0.8124 |
| query_active_dims | 32.0 |
| query_sparsity_ratio | 0.9922 |
| corpus_active_dims | 32.0 |
| corpus_sparsity_ratio | 0.9922 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_64`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 64
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.775 |
| cosine_accuracy@3 | 0.895 |
| cosine_accuracy@5 | 0.925 |
| cosine_accuracy@10 | 0.951 |
| cosine_precision@1 | 0.775 |
| cosine_precision@3 | 0.2983 |
| cosine_precision@5 | 0.185 |
| cosine_precision@10 | 0.0951 |
| cosine_recall@1 | 0.775 |
| cosine_recall@3 | 0.895 |
| cosine_recall@5 | 0.925 |
| cosine_recall@10 | 0.951 |
| **cosine_ndcg@10** | **0.8673** |
| cosine_mrr@10 | 0.8399 |
| cosine_map@100 | 0.8418 |
| query_active_dims | 63.992 |
| query_sparsity_ratio | 0.9844 |
| corpus_active_dims | 64.0 |
| corpus_sparsity_ratio | 0.9844 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_128`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 128
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.797 |
| cosine_accuracy@3 | 0.901 |
| cosine_accuracy@5 | 0.933 |
| cosine_accuracy@10 | 0.951 |
| cosine_precision@1 | 0.797 |
| cosine_precision@3 | 0.3003 |
| cosine_precision@5 | 0.1866 |
| cosine_precision@10 | 0.0951 |
| cosine_recall@1 | 0.797 |
| cosine_recall@3 | 0.901 |
| cosine_recall@5 | 0.933 |
| cosine_recall@10 | 0.951 |
| **cosine_ndcg@10** | **0.8781** |
| cosine_mrr@10 | 0.8542 |
| cosine_map@100 | 0.8561 |
| query_active_dims | 119.217 |
| query_sparsity_ratio | 0.9709 |
| corpus_active_dims | 119.652 |
| corpus_sparsity_ratio | 0.9708 |
#### Sparse Information Retrieval
* Dataset: `nq_eval_256`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator) with these parameters:
```json
{
"max_active_dims": 256
}
```
| Metric | Value |
|:----------------------|:-----------|
| cosine_accuracy@1 | 0.8 |
| cosine_accuracy@3 | 0.901 |
| cosine_accuracy@5 | 0.933 |
| cosine_accuracy@10 | 0.951 |
| cosine_precision@1 | 0.8 |
| cosine_precision@3 | 0.3003 |
| cosine_precision@5 | 0.1866 |
| cosine_precision@10 | 0.0951 |
| cosine_recall@1 | 0.8 |
| cosine_recall@3 | 0.901 |
| cosine_recall@5 | 0.933 |
| cosine_recall@10 | 0.951 |
| **cosine_ndcg@10** | **0.8789** |
| cosine_mrr@10 | 0.8553 |
| cosine_map@100 | 0.8573 |
| query_active_dims | 133.43 |
| query_sparsity_ratio | 0.9674 |
| corpus_active_dims | 129.169 |
| corpus_sparsity_ratio | 0.9685 |
<!--
## Bias, Risks and Limitations
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 99,000 training samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.71 tokens</li><li>max: 26 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 131.81 tokens</li><li>max: 450 tokens</li></ul> |
* Samples:
| query | answer |
|:--------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>who played the father in papa don't preach</code> | <code>Alex McArthur Alex McArthur (born March 6, 1957) is an American actor.</code> |
| <code>where was the location of the battle of hastings</code> | <code>Battle of Hastings The Battle of Hastings[a] was fought on 14 October 1066 between the Norman-French army of William, the Duke of Normandy, and an English army under the Anglo-Saxon King Harold Godwinson, beginning the Norman conquest of England. It took place approximately 7 miles (11 kilometres) northwest of Hastings, close to the present-day town of Battle, East Sussex, and was a decisive Norman victory.</code> |
| <code>how many puppies can a dog give birth to</code> | <code>Canine reproduction The largest litter size to date was set by a Neapolitan Mastiff in Manea, Cambridgeshire, UK on November 29, 2004; the litter was 24 puppies.[22]</code> |
* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1.0,
"loss": "SparseMultipleNegativesRankingLoss(scale=5.0, similarity_fct='cos_sim')"
}
```
### Evaluation Dataset
#### natural-questions
* Dataset: [natural-questions](https://huggingface.co/datasets/sentence-transformers/natural-questions) at [f9e894e](https://huggingface.co/datasets/sentence-transformers/natural-questions/tree/f9e894e1081e206e577b4eaa9ee6de2b06ae6f17)
* Size: 1,000 evaluation samples
* Columns: <code>query</code> and <code>answer</code>
* Approximate statistics based on the first 1000 samples:
| | query | answer |
|:--------|:-----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 11.69 tokens</li><li>max: 23 tokens</li></ul> | <ul><li>min: 15 tokens</li><li>mean: 134.01 tokens</li><li>max: 512 tokens</li></ul> |
* Samples:
| query | answer |
|:-------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>where is the tiber river located in italy</code> | <code>Tiber The Tiber (/ˈtaɪbər/, Latin: Tiberis,[1] Italian: Tevere [ˈteːvere])[2] is the third-longest river in Italy, rising in the Apennine Mountains in Emilia-Romagna and flowing 406 kilometres (252 mi) through Tuscany, Umbria and Lazio, where it is joined by the river Aniene, to the Tyrrhenian Sea, between Ostia and Fiumicino.[3] It drains a basin estimated at 17,375 square kilometres (6,709 sq mi). The river has achieved lasting fame as the main watercourse of the city of Rome, founded on its eastern banks.</code> |
| <code>what kind of car does jay gatsby drive</code> | <code>Jay Gatsby At the Buchanan home, Jordan Baker, Nick, Jay, and the Buchanans decide to visit New York City. Tom borrows Gatsby's yellow Rolls Royce to drive up to the city. On the way to New York City, Tom makes a detour at a gas station in "the Valley of Ashes", a run-down part of Long Island. The owner, George Wilson, shares his concern that his wife, Myrtle, may be having an affair. This unnerves Tom, who has been having an affair with Myrtle, and he leaves in a hurry.</code> |
| <code>who sings if i can dream about you</code> | <code>I Can Dream About You "I Can Dream About You" is a song performed by American singer Dan Hartman on the soundtrack album of the film Streets of Fire. Released in 1984 as a single from the soundtrack, and included on Hartman's album I Can Dream About You, it reached number 6 on the Billboard Hot 100.[1]</code> |
* Loss: [<code>CSRLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#csrloss) with these parameters:
```json
{
"beta": 0.1,
"gamma": 1.0,
"loss": "SparseMultipleNegativesRankingLoss(scale=5.0, similarity_fct='cos_sim')"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 4e-05
- `num_train_epochs`: 1
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 4e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
- `router_mapping`: {}
- `learning_rate_mapping`: {}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | nq_eval_4_cosine_ndcg@10 | nq_eval_8_cosine_ndcg@10 | nq_eval_16_cosine_ndcg@10 | nq_eval_32_cosine_ndcg@10 | nq_eval_64_cosine_ndcg@10 | nq_eval_128_cosine_ndcg@10 | nq_eval_256_cosine_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:------------------------:|:------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:--------------------------:|:--------------------------:|
| -1 | -1 | - | - | 0.2566 | 0.4513 | 0.6853 | 0.8617 | 0.9369 | 0.9685 | 0.9757 |
| 0.0646 | 100 | 2.9836 | - | - | - | - | - | - | - | - |
| 0.1293 | 200 | 2.7758 | - | - | - | - | - | - | - | - |
| 0.1939 | 300 | 2.6386 | 2.3891 | 0.4003 | 0.5884 | 0.7387 | 0.8220 | 0.8695 | 0.9164 | 0.9372 |
| 0.2586 | 400 | 2.5466 | - | - | - | - | - | - | - | - |
| 0.3232 | 500 | 2.4711 | - | - | - | - | - | - | - | - |
| 0.3878 | 600 | 2.3918 | 2.1817 | 0.4580 | 0.6189 | 0.7230 | 0.7986 | 0.8554 | 0.8939 | 0.9146 |
| 0.4525 | 700 | 2.2802 | - | - | - | - | - | - | - | - |
| 0.5171 | 800 | 2.1309 | - | - | - | - | - | - | - | - |
| 0.5818 | 900 | 2.0585 | 1.8844 | 0.4932 | 0.6402 | 0.7482 | 0.8361 | 0.8665 | 0.8857 | 0.8895 |
| 0.6464 | 1000 | 2.0203 | - | - | - | - | - | - | - | - |
| 0.7111 | 1100 | 1.9934 | - | - | - | - | - | - | - | - |
| 0.7757 | 1200 | 1.9734 | 1.8208 | 0.5168 | 0.6452 | 0.7592 | 0.8371 | 0.8690 | 0.8775 | 0.8804 |
| 0.8403 | 1300 | 1.9583 | - | - | - | - | - | - | - | - |
| 0.9050 | 1400 | 1.9496 | - | - | - | - | - | - | - | - |
| 0.9696 | 1500 | 1.9499 | 1.8020 | 0.5159 | 0.6536 | 0.7568 | 0.8399 | 0.8670 | 0.8785 | 0.8778 |
| -1 | -1 | - | - | 0.5178 | 0.6515 | 0.7573 | 0.8407 | 0.8673 | 0.8781 | 0.8789 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.110 kWh
- **Carbon Emitted**: 0.043 kg of CO2
- **Hours Used**: 0.274 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 4.2.0.dev0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.5.1
- Datasets: 2.21.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### CSRLoss
```bibtex
@misc{wen2025matryoshkarevisitingsparsecoding,
title={Beyond Matryoshka: Revisiting Sparse Coding for Adaptive Representation},
author={Tiansheng Wen and Yifei Wang and Zequn Zeng and Zhong Peng and Yudi Su and Xinyang Liu and Bo Chen and Hongwei Liu and Stefanie Jegelka and Chenyu You},
year={2025},
eprint={2503.01776},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2503.01776},
}
```
#### SparseMultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
fvossel/OLMo-2-0325-32B-Instruct-nl-to-fol
|
fvossel
| 2025-06-20T15:22:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"NLTOFOL",
"NL",
"FOL",
"translation",
"en",
"dataset:iedeveci/WillowNLtoFOL",
"dataset:yuan-yang/MALLS-v0",
"base_model:allenai/OLMo-2-0325-32B-Instruct",
"base_model:finetune:allenai/OLMo-2-0325-32B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
translation
| 2025-06-19T18:00:07Z |
---
base_model:
- allenai/OLMo-2-0325-32B-Instruct
library_name: transformers
license: apache-2.0
datasets:
- iedeveci/WillowNLtoFOL
- yuan-yang/MALLS-v0
language:
- en
pipeline_tag: translation
tags:
- NLTOFOL
- NL
- FOL
---
# Model Card for fvossel/OLMo-2-0325-32B-Instruct-nl-to-fol
This model contains **LoRA adapter weights** for the base model [`allenai/OLMo-2-0325-32B-Instruct`](https://huggingface.co/allenai/OLMo-2-0325-32B-Instruct). It was trained to translate **natural language statements into First-Order Logic (FOL)** representations.
## Model Details
### Model Description
- **Developed by:** Vossel et al. at Osnabrück University
- **Funded by:** Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) 456666331
- **Model type:** Decoder-only causal language model (OLMo architecture)
- **Language(s) (NLP):** English, FOL
- **License:** This repository contains **only LoRA adapter weights**, trained using the base model [`allenai/OLMo-2-0325-32B-Instruct`](https://huggingface.co/allenai/OLMo-2-0325-32B-Instruct), which is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). These adapter weights are also released under the **Apache 2.0 License**.
- **Finetuned from model:** allenai/OLMo-2-0325-32B-Instruct
## Uses
### Direct Use
This model is designed to translate natural language (NL) sentences into corresponding first-order logic (FOL) expressions. Use cases include:
- Automated semantic parsing and formalization of NL statements into symbolic logic.
- Supporting explainable AI systems that require symbolic reasoning based on language input.
- Research in neurosymbolic AI, logic-based natural language understanding, and formal verification.
- Integration into pipelines for natural language inference, question answering, or knowledge base population.
Users should verify and validate symbolic formulas generated by the model for correctness depending on the application.
### Downstream Use
The LoRA adapter can be further fine-tuned or combined with other models for domain-specific formalization tasks (e.g., legal, biomedical). Suitable for interactive systems requiring formal reasoning.
### Out-of-Scope Use
- Not designed for general natural language generation.
- May struggle with ambiguous, highly figurative, or out-of-domain input.
- Outputs should not be used as final decisions in critical areas without expert review.
### Recommendations
- Validate outputs carefully before use in critical applications.
- Be aware of possible biases from training data and synthetic data sources.
- Specialized for English NL and FOL; may not generalize to other languages or logics.
- Use human-in-the-loop workflows for sensitive tasks.
- Intended for research and prototyping, not standalone critical systems.
## How to Get Started with the Model
```python
import torch
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
base_model_name = "allenai/OLMo-2-0325-32B-Instruct"
lora_weights = "fvossel/OLMo-2-0325-32B-Instruct-nl-to-fol"
tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
model = AutoModelForCausalLM.from_pretrained(base_model_name, trust_remote_code=True, device_map="auto")
model = PeftModel.from_pretrained(model, lora_weights, device_map="auto")
def formatting_func(text):
return tokenizer.apply_chat_template(
[
{
"role": "system",
"content": (
"You are a helpful AI assistant that translates Natural Language (NL) text "
"into First-Order Logic (FOL) using only the given quantors and junctors: "
"∀ (for all), ∃ (there exists), ¬ (not), ∧ (and), ∨ (or), → (implies), "
"↔ (if and only if), ⊕ (xor). "
"Start your answer with '𝜙=' followed by the FOL-formula. Do not include any other text."
),
},
{"role": "user", "content": text},
],
tokenize=False,
add_generation_prompt=False,
)
input_text = "All dogs are animals."
prompt = formatting_func(input_text)
inputs = tokenizer(prompt, return_tensors="pt", padding=True)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Details
### Training Data
The model was fine-tuned on two datasets:
- **WillowNLtoFOL:** Contains over 16,000 NL-FOL pairs. Published in:
Deveci, İ. E. (2024). Transformer models for translating natural language sentences into formal logical expressions.
Licensed under CC BY-NC-ND 4.0.
- **MALLS-v0:** 34,000 NL-FOL pairs generated by GPT-4, syntactically checked.
Licensed under Attribution-NonCommercial 4.0, subject to OpenAI terms.
### Training Procedure
Fine-tuning used LoRA adapters on the pre-trained OLMo model with:
- Prompt-based instruction tuning
- Multi-GPU (2 GPUs) training with bf16 mixed precision
- Gradient checkpointing enabled for memory efficiency
### Training Hyperparameters
- **Training regime:** bf16 mixed precision
- **Batch size:** 8 (per device)
- **Learning rate:** 1e-5
- **Number of epochs:** 12
- **Optimizer:** AdamW
- **Scheduler:** Cosine learning rate scheduler
- **Warmup ratio:** 0.05
- **Gradient accumulation steps:** 2
- **Weight decay:** 0.01
- **LoRA rank (r):** 16
- **LoRA alpha:** 32
- **LoRA dropout:** 0.05
- **Target modules:** ["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"]
- **Bias:** none
- **Task type:** CAUSAL_LM
- **Early stopping patience:** 4 epochs
- **DDP parameters:**
- `ddp_find_unused_parameters=False`
- `ddp_backend="nccl"`
|
fvossel/flan-t5-xxl-nl-to-fol
|
fvossel
| 2025-06-20T15:22:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"NLTOFOL",
"NL",
"FOL",
"translation",
"en",
"dataset:iedeveci/WillowNLtoFOL",
"dataset:yuan-yang/MALLS-v0",
"base_model:google/flan-t5-xxl",
"base_model:finetune:google/flan-t5-xxl",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
translation
| 2025-06-19T17:58:12Z |
---
base_model:
- google/flan-t5-xxl
library_name: transformers
license: apache-2.0
datasets:
- iedeveci/WillowNLtoFOL
- yuan-yang/MALLS-v0
language:
- en
pipeline_tag: translation
tags:
- NLTOFOL
- NL
- FOL
---
# Model Card for fvossel/flan-t5-xxl-nl-to-fol
This model contains **LoRA adapter weights** for the base model [`google/flan-t5-xxl`](https://huggingface.co/google/flan-t5-xxl). It was trained to translate **natural language statements into First-Order Logic (FOL)** representations.
## Model Details
### Model Description
- **Developed by:** Vossel et al. at Osnabrück University
- **Funded by:** Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) 456666331
- **Model type:** Encoder-decoder sequence-to-sequence model (T5 architecture)
- **Language(s) (NLP):** English, FOL
- **License:** This repository contains **only LoRA adapter weights**, trained using the base model [`google/flan-t5-xxl`](https://huggingface.co/google/flan-t5-xxl), which is released under the [Apache 2.0 License](https://www.apache.org/licenses/LICENSE-2.0). These adapter weights are also released under the **Apache 2.0 License**.
- **Finetuned from model:** google/flan-t5-xxl
## Uses
### Direct Use
This model is designed to translate natural language (NL) sentences into corresponding first-order logic (FOL) expressions. Use cases include:
- Automated semantic parsing and formalization of NL statements into symbolic logic.
- Supporting explainable AI systems that require symbolic reasoning based on language input.
- Research in neurosymbolic AI, logic-based natural language understanding, and formal verification.
- Integration into pipelines for natural language inference, question answering, or knowledge base population.
Users should verify and validate symbolic formulas generated by the model for correctness depending on the application.
### Downstream Use
The LoRA adapter can be further fine-tuned or combined with other models for domain-specific formalization tasks (e.g., legal, biomedical). Suitable for interactive systems requiring formal reasoning.
### Out-of-Scope Use
- Not designed for general natural language generation.
- May struggle with ambiguous, highly figurative, or out-of-domain input.
- Outputs should not be used as final decisions in critical areas without expert review.
### Recommendations
- Validate outputs carefully before use in critical applications.
- Be aware of possible biases from training data and synthetic data sources.
- Specialized for English NL and FOL; may not generalize to other languages or logics.
- Use human-in-the-loop workflows for sensitive tasks.
- Intended for research and prototyping, not standalone critical systems.
## How to Get Started with the Model
```python
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
# Load tokenizer and model
model_path = "fvossel/flan-t5-xxl-nl-to-fol" # or local path if not pushed to HF
tokenizer = T5Tokenizer.from_pretrained(model_path)
model = T5ForConditionalGeneration.from_pretrained(model_path, device_map="auto", torch_dtype=torch.bfloat16)
# Example NL input
nl_input = "All dogs are animals."
# Preprocess prompt
input_text = "translate English natural language statements into first-order logic (FOL): " + nl_input
inputs = tokenizer(input_text, return_tensors="pt", padding=True).to("cuda")
# Generate prediction
with torch.no_grad():
outputs = model.generate(
inputs["input_ids"],
max_length=256,
min_length=1,
num_beams=5,
length_penalty=2.0,
early_stopping=False,
)
# Decode and print result
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Details
### Training Data
The model was fine-tuned on two datasets:
- **WillowNLtoFOL:** Contains over 16,000 NL-FOL pairs. Published in:
Deveci, İ. E. (2024). *Transformer models for translating natural language sentences into formal logical expressions.*
Licensed under CC BY-NC-ND 4.0.
- **MALLS-v0:** 34,000 NL-FOL pairs generated by GPT-4, syntactically checked.
Licensed under Attribution-NonCommercial 4.0, subject to OpenAI terms.
### Training Procedure
Fine-tuning was performed using LoRA adapters on the pre-trained `google/flan-t5-xxl` model with:
- Prompt-based instruction tuning
- Single-GPU training with bf16 mixed precision
### Training Hyperparameters
- **Training regime:** bf16 mixed precision
- **Batch size:** 8 (per device)
- **Learning rate:** 1e-4
- **Number of epochs:** 12
- **Optimizer:** AdamW
- **Adam epsilon:** 1e-8
- **Scheduler:** Linear warmup with 500 steps
- **Gradient accumulation steps:** 1
- **Weight decay:** 0.01
- **LoRA rank (r):** 16
- **LoRA alpha:** 32
- **LoRA dropout:** 0.05
- **Target modules:** ["q", "k", "v", "o", "wi", "wo"]
- **Bias:** none
- **Task type:** SEQ_2_SEQ_LM
- **Early stopping patience:** 4 epochs
- **Evaluation strategy:** per epoch
- **Save strategy:** per epoch
- **DDP parameters:**
- `ddp_find_unused_parameters=False`
- `ddp_backend="nccl"`
|
joshua-scheuplein/DAX-ResNet50-A
|
joshua-scheuplein
| 2025-06-20T15:22:31Z | 0 | 0 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-06-20T15:21:54Z |
---
license: cc-by-nc-4.0
---
|
diribes/periodClassification
|
diribes
| 2025-06-20T15:19:39Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2025-06-20T15:19:09Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
kalemlhub/sn72-roadwork-TXHhuCx
|
kalemlhub
| 2025-06-20T15:19:01Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T15:18:52Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
Josephinepassananti/sd21-kamala_ft_dataset_512_shaded_0.1_target_black_square-bs1-steps5000-lr1e-04
|
Josephinepassananti
| 2025-06-20T15:18:51Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:adapter:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-06-20T14:49:00Z |
---
base_model: stabilityai/stable-diffusion-2-1
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA text2image fine-tuning - Josephinepassananti/sd21-kamala_ft_dataset_512_shaded_0.1_target_black_square-bs1-steps5000-lr1e-04
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were fine-tuned on the None dataset. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
kamal-kaur-viral-video-link/new.video.18.kamal.kaur.mms.kamal.kaur.video.kamal.kaur.bhabhi.punjabi.video
|
kamal-kaur-viral-video-link
| 2025-06-20T15:17:57Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T15:17:25Z |
<a rel="nofollow" href="https://tinyurl.com/npw8at8u?dfhgKasbonStudiosdfg"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
|
mapleL/mnist_mps
|
mapleL
| 2025-06-20T15:17:35Z | 0 | 0 | null |
[
"license:mit",
"region:us"
] | null | 2025-06-20T15:06:18Z |
---
license: mit
---
# MNIST/FMNIST MPS Checkpoints
Trained generative matrix product states (G-MPS) in the course of Tensor Networks. See [the repo](https://github.com/ifsheldon/tensor-network-notes) for the notes and code.
|
kalemlhub/sn72-roadwork-BkHr1BQ
|
kalemlhub
| 2025-06-20T15:13:14Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T15:13:05Z |
---
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]
|
martijn75/bert_mt_morphemes_with_markers_based_on_lexemes_2_layers_4_att_heads
|
martijn75
| 2025-06-20T15:13:05Z | 29 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"fill-mask",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-cased",
"base_model:finetune:google-bert/bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2025-06-16T12:00:57Z |
---
library_name: transformers
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
model-index:
- name: bert_mt_morphemes_with_markers_based_on_lexemes_2_layers_4_att_heads
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. -->
# bert_mt_morphemes_with_markers_based_on_lexemes_2_layers_4_att_heads
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 80
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
|
kalemlhub/sn72-roadwork-JZqvWwL
|
kalemlhub
| 2025-06-20T15:13:04Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T15:12:54Z |
---
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]
|
kamal-kaur-bhabhi-punjabi-viral-videos/FULL.VIDEO.kamal.Kaur.viral.video.Link.viral.On.Social.Media.Official.telegram.link
|
kamal-kaur-bhabhi-punjabi-viral-videos
| 2025-06-20T15:12:58Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T15:12:26Z |
<a rel="nofollow" href="https://tinyurl.com/npw8at8u?dfhgKasbonStudiosdfg"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
|
kalemlhub/sn72-roadwork-rpC1atD
|
kalemlhub
| 2025-06-20T15:12:53Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T15:12:45Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
kalemlhub/sn72-roadwork-b1WxDLZ
|
kalemlhub
| 2025-06-20T15:12:44Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T15:12:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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|
kalemlhub/sn72-roadwork-vUZHoxe
|
kalemlhub
| 2025-06-20T15:12:07Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T15:11:59Z |
---
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. -->
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- **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]
|
kalemlhub/sn72-roadwork-8xaxrVr
|
kalemlhub
| 2025-06-20T15:11:58Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T15:11:51Z |
---
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]
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- **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. -->
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### 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]
|
mshsahmed/blip-vqa-finetuned-kvasir
|
mshsahmed
| 2025-06-20T15:07:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"blip",
"visual-question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
visual-question-answering
| 2025-06-20T15:06:35Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
Official-hospital-teresopolis-Videos/Viral.Full.video.18.hospital.teresopolis.hospital.de.teresopolis.video.portal.Zacarias.telegram
|
Official-hospital-teresopolis-Videos
| 2025-06-20T15:06:41Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T15:06:16Z |
<a rel="nofollow" href="https://tinyurl.com/npw8at8u?dfhgKasbonStudiosdfg"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
|
YuanLinsss11/ppo-Huggy
|
YuanLinsss11
| 2025-06-20T15:05:35Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2025-06-20T15:05:29Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: YuanLinsss11/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
eddieman78/litbank-coref-mistral-small-4000-64-1e4-4
|
eddieman78
| 2025-06-20T15:01:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"base_model:unsloth/Mistral-Small-3.1-24B-Instruct-2503-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Mistral-Small-3.1-24B-Instruct-2503-unsloth-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T15:01:18Z |
---
base_model: unsloth/Mistral-Small-3.1-24B-Instruct-2503-unsloth-bnb-4bit
library_name: transformers
model_name: litbank-coref-mistral-small-4000-64-1e4-4
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for litbank-coref-mistral-small-4000-64-1e4-4
This model is a fine-tuned version of [unsloth/Mistral-Small-3.1-24B-Instruct-2503-unsloth-bnb-4bit](https://huggingface.co/unsloth/Mistral-Small-3.1-24B-Instruct-2503-unsloth-bnb-4bit).
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="eddieman78/litbank-coref-mistral-small-4000-64-1e4-4", 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: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
zakihassan04/tts-somali-speecht5
|
zakihassan04
| 2025-06-20T15:01:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"speecht5",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2025-06-20T14:29:48Z |
---
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]
|
joshua-scheuplein/DAX-ResNet18-A
|
joshua-scheuplein
| 2025-06-20T14:57:27Z | 0 | 0 | null |
[
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-06-20T14:55:48Z |
---
license: cc-by-nc-4.0
---
|
mastur96/e2847ae3-57ce-4e07-ad18-1d707b515bc5
|
mastur96
| 2025-06-20T14:54:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-20T14:45:21Z |
---
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]
|
chidamnat2002/content-classifier
|
chidamnat2002
| 2025-06-20T14:49:54Z | 0 | 0 |
transformers
|
[
"transformers",
"onnx",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-20T13:48:36Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
drawhisper/bert-emotion
|
drawhisper
| 2025-06-20T14:49:25Z | 0 | 0 | null |
[
"onnx",
"bert",
"text-classification",
"en",
"base_model:boltuix/bert-emotion",
"base_model:quantized:boltuix/bert-emotion",
"license:mit",
"region:us"
] |
text-classification
| 2025-06-20T09:01:21Z |
---
license: mit
language:
- en
base_model:
- boltuix/bert-emotion
pipeline_tag: text-classification
---
Forked from boltuix/bert-emotion, onnxruntime version
|
Shah-Sapna-Kumari-Viral-Video/full.shah.sapna.kumari.viral.video.original.video.telegram.link
|
Shah-Sapna-Kumari-Viral-Video
| 2025-06-20T14:49:17Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T14:48:56Z |
<a rel="nofollow" href="https://tinyurl.com/npw8at8u?dfhgKasbonStudiosdfg"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
|
Josephinepassananti/sd21-kamala_ft_dataset_512_shaded_0.05_target_old_woman-bs1-steps5000-lr1e-04
|
Josephinepassananti
| 2025-06-20T14:48:37Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:adapter:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-06-20T14:18:43Z |
---
base_model: stabilityai/stable-diffusion-2-1
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA text2image fine-tuning - Josephinepassananti/sd21-kamala_ft_dataset_512_shaded_0.05_target_old_woman-bs1-steps5000-lr1e-04
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were fine-tuned on the None dataset. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
fizzzzz9/cas4133_mistral_weight
|
fizzzzz9
| 2025-06-20T14:48:17Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"base_model:mistralai/Mistral-7B-v0.3",
"base_model:finetune:mistralai/Mistral-7B-v0.3",
"license:apache-2.0",
"region:us"
] | null | 2025-06-20T13:47:49Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.3
extra_gated_description: If you want to learn more about how we process your personal data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
---
# Model Card for Mistral-7B-Instruct-v0.3
The Mistral-7B-Instruct-v0.3 Large Language Model (LLM) is an instruct fine-tuned version of the Mistral-7B-v0.3.
Mistral-7B-v0.3 has the following changes compared to [Mistral-7B-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2/edit/main/README.md)
- Extended vocabulary to 32768
- Supports v3 Tokenizer
- Supports function calling
## Installation
It is recommended to use `mistralai/Mistral-7B-Instruct-v0.3` with [mistral-inference](https://github.com/mistralai/mistral-inference). For HF transformers code snippets, please keep scrolling.
```
pip install mistral_inference
```
## Download
```py
from huggingface_hub import snapshot_download
from pathlib import Path
mistral_models_path = Path.home().joinpath('mistral_models', '7B-Instruct-v0.3')
mistral_models_path.mkdir(parents=True, exist_ok=True)
snapshot_download(repo_id="mistralai/Mistral-7B-Instruct-v0.3", allow_patterns=["params.json", "consolidated.safetensors", "tokenizer.model.v3"], local_dir=mistral_models_path)
```
### Chat
After installing `mistral_inference`, a `mistral-chat` CLI command should be available in your environment. You can chat with the model using
```
mistral-chat $HOME/mistral_models/7B-Instruct-v0.3 --instruct --max_tokens 256
```
### Instruct following
```py
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
model = Transformer.from_folder(mistral_models_path)
completion_request = ChatCompletionRequest(messages=[UserMessage(content="Explain Machine Learning to me in a nutshell.")])
tokens = tokenizer.encode_chat_completion(completion_request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
print(result)
```
### Function calling
```py
from mistral_common.protocol.instruct.tool_calls import Function, Tool
from mistral_inference.transformer import Transformer
from mistral_inference.generate import generate
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.messages import UserMessage
from mistral_common.protocol.instruct.request import ChatCompletionRequest
tokenizer = MistralTokenizer.from_file(f"{mistral_models_path}/tokenizer.model.v3")
model = Transformer.from_folder(mistral_models_path)
completion_request = ChatCompletionRequest(
tools=[
Tool(
function=Function(
name="get_current_weather",
description="Get the current weather",
parameters={
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city and state, e.g. San Francisco, CA",
},
"format": {
"type": "string",
"enum": ["celsius", "fahrenheit"],
"description": "The temperature unit to use. Infer this from the users location.",
},
},
"required": ["location", "format"],
},
)
)
],
messages=[
UserMessage(content="What's the weather like today in Paris?"),
],
)
tokens = tokenizer.encode_chat_completion(completion_request).tokens
out_tokens, _ = generate([tokens], model, max_tokens=64, temperature=0.0, eos_id=tokenizer.instruct_tokenizer.tokenizer.eos_id)
result = tokenizer.instruct_tokenizer.tokenizer.decode(out_tokens[0])
print(result)
```
## Generate with `transformers`
If you want to use Hugging Face `transformers` to generate text, you can do something like this.
```py
from transformers import pipeline
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
chatbot = pipeline("text-generation", model="mistralai/Mistral-7B-Instruct-v0.3")
chatbot(messages)
```
## Function calling with `transformers`
To use this example, you'll need `transformers` version 4.42.0 or higher. Please see the
[function calling guide](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling)
in the `transformers` docs for more information.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "mistralai/Mistral-7B-Instruct-v0.3"
tokenizer = AutoTokenizer.from_pretrained(model_id)
def get_current_weather(location: str, format: str):
"""
Get the current weather
Args:
location: The city and state, e.g. San Francisco, CA
format: The temperature unit to use. Infer this from the users location. (choices: ["celsius", "fahrenheit"])
"""
pass
conversation = [{"role": "user", "content": "What's the weather like in Paris?"}]
tools = [get_current_weather]
# format and tokenize the tool use prompt
inputs = tokenizer.apply_chat_template(
conversation,
tools=tools,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt",
)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto")
inputs.to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1000)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
Note that, for reasons of space, this example does not show a complete cycle of calling a tool and adding the tool call and tool
results to the chat history so that the model can use them in its next generation. For a full tool calling example, please
see the [function calling guide](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling),
and note that Mistral **does** use tool call IDs, so these must be included in your tool calls and tool results. They should be
exactly 9 alphanumeric characters.
## Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Alexis Tacnet, Antoine Roux, Arthur Mensch, Audrey Herblin-Stoop, Baptiste Bout, Baudouin de Monicault, Blanche Savary, Bam4d, Caroline Feldman, Devendra Singh Chaplot, Diego de las Casas, Eleonore Arcelin, Emma Bou Hanna, Etienne Metzger, Gianna Lengyel, Guillaume Bour, Guillaume Lample, Harizo Rajaona, Jean-Malo Delignon, Jia Li, Justus Murke, Louis Martin, Louis Ternon, Lucile Saulnier, Lélio Renard Lavaud, Margaret Jennings, Marie Pellat, Marie Torelli, Marie-Anne Lachaux, Nicolas Schuhl, Patrick von Platen, Pierre Stock, Sandeep Subramanian, Sophia Yang, Szymon Antoniak, Teven Le Scao, Thibaut Lavril, Timothée Lacroix, Théophile Gervet, Thomas Wang, Valera Nemychnikova, William El Sayed, William Marshall
|
kleverer/natix-014
|
kleverer
| 2025-06-20T14:48:04Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-20T07:25:40Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
JoshuaKelleyDs/qwen3_8b_base_poker_36k_sft_2
|
JoshuaKelleyDs
| 2025-06-20T14:44:00Z | 10 | 0 |
peft
|
[
"peft",
"safetensors",
"unsloth",
"trl",
"sft",
"generated_from_trainer",
"base_model:unsloth/Qwen3-8B-Base-unsloth-bnb-4bit",
"base_model:adapter:unsloth/Qwen3-8B-Base-unsloth-bnb-4bit",
"license:apache-2.0",
"region:us"
] | null | 2025-06-20T06:20:45Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen3-8B-Base-unsloth-bnb-4bit
tags:
- unsloth
- trl
- sft
- generated_from_trainer
model-index:
- name: qwen3_8b_base_poker_36k_sft_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/joshuakelleyds/qwen3_8b_base_37k_poker/runs/6zy6vkeu)
# qwen3_8b_base_poker_36k_sft_2
This model is a fine-tuned version of [unsloth/Qwen3-8B-Base-unsloth-bnb-4bit](https://huggingface.co/unsloth/Qwen3-8B-Base-unsloth-bnb-4bit) 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: 2
- eval_batch_size: 4
- seed: 3407
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- lr_scheduler_warmup_steps: 5
- training_steps: 2
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
nugunaai/llama3-8b-rag-ko
|
nugunaai
| 2025-06-20T14:40:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:NCSOFT/Llama-VARCO-8B-Instruct",
"base_model:finetune:NCSOFT/Llama-VARCO-8B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T12:44:34Z |
---
base_model: NCSOFT/Llama-VARCO-8B-Instruct
library_name: transformers
model_name: llama3-8b-rag-ko
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for llama3-8b-rag-ko
This model is a fine-tuned version of [NCSOFT/Llama-VARCO-8B-Instruct](https://huggingface.co/NCSOFT/Llama-VARCO-8B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="nugunaai/llama3-8b-rag-ko", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.8.0.dev20250319+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
mradermacher/DeepSeek-V3-abliterated-i1-GGUF
|
mradermacher
| 2025-06-20T14:36:10Z | 0 | 1 |
transformers
|
[
"transformers",
"DeepSeek",
"abliterated",
"uncensored",
"en",
"base_model:huihui-ai/DeepSeek-V3-abliterated",
"base_model:finetune:huihui-ai/DeepSeek-V3-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-12T01:21:42Z |
---
base_model: huihui-ai/DeepSeek-V3-abliterated
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- DeepSeek
- abliterated
- uncensored
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/huihui-ai/DeepSeek-V3-abliterated
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [PART 1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ1_S.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ1_S.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ1_S.gguf.part3of3) | i1-IQ1_S | 133.8 | for the desperate |
| [PART 1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ1_M.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ1_M.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ1_M.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ1_M.gguf.part4of4) | i1-IQ1_M | 149.2 | mostly desperate |
| [PART 1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_XXS.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_XXS.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_XXS.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_XXS.gguf.part4of4) | i1-IQ2_XXS | 174.7 | |
| [PART 1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_XS.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_XS.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_XS.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_XS.gguf.part4of4) | i1-IQ2_XS | 195.3 | |
| [PART 1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_S.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_S.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_S.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_S.gguf.part4of4) | i1-IQ2_S | 197.2 | |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_M.gguf.part1of5) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_M.gguf.part2of5) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_M.gguf.part3of5) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_M.gguf.part4of5) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ2_M.gguf.part5of5) | i1-IQ2_M | 217.7 | |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q2_K_S.gguf.part1of5) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q2_K_S.gguf.part2of5) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q2_K_S.gguf.part3of5) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q2_K_S.gguf.part4of5) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q2_K_S.gguf.part5of5) | i1-Q2_K_S | 224.9 | very low quality |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q2_K.gguf.part1of5) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q2_K.gguf.part2of5) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q2_K.gguf.part3of5) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q2_K.gguf.part4of5) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q2_K.gguf.part5of5) | i1-Q2_K | 244.2 | IQ3_XXS probably better |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_XXS.gguf.part1of6) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_XXS.gguf.part2of6) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_XXS.gguf.part3of6) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_XXS.gguf.part4of6) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_XXS.gguf.part5of6) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_XXS.gguf.part6of6) | i1-IQ3_XXS | 258.1 | lower quality |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_XS.gguf.part1of6) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_XS.gguf.part2of6) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_XS.gguf.part3of6) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_XS.gguf.part4of6) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_XS.gguf.part5of6) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_XS.gguf.part6of6) | i1-IQ3_XS | 273.0 | |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_S.gguf.part1of6) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_S.gguf.part2of6) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_S.gguf.part3of6) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_S.gguf.part4of6) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_S.gguf.part5of6) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_S.gguf.part6of6) | i1-IQ3_S | 289.3 | beats Q3_K* |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_S.gguf.part1of6) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_S.gguf.part2of6) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_S.gguf.part3of6) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_S.gguf.part4of6) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_S.gguf.part5of6) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_S.gguf.part6of6) | i1-Q3_K_S | 289.3 | IQ3_XS probably better |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_M.gguf.part1of6) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_M.gguf.part2of6) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_M.gguf.part3of6) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_M.gguf.part4of6) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_M.gguf.part5of6) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ3_M.gguf.part6of6) | i1-IQ3_M | 292.3 | |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_M.gguf.part1of7) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_M.gguf.part2of7) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_M.gguf.part3of7) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_M.gguf.part4of7) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_M.gguf.part5of7) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_M.gguf.part6of7) [P7](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_M.gguf.part7of7) | i1-Q3_K_M | 319.4 | IQ3_S probably better |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_L.gguf.part1of8) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_L.gguf.part2of8) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_L.gguf.part3of8) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_L.gguf.part4of8) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_L.gguf.part5of8) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_L.gguf.part6of8) [P7](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_L.gguf.part7of8) [P8](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q3_K_L.gguf.part8of8) | i1-Q3_K_L | 347.6 | IQ3_M probably better |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ4_XS.gguf.part1of8) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ4_XS.gguf.part2of8) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ4_XS.gguf.part3of8) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ4_XS.gguf.part4of8) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ4_XS.gguf.part5of8) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ4_XS.gguf.part6of8) [P7](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ4_XS.gguf.part7of8) [P8](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-IQ4_XS.gguf.part8of8) | i1-IQ4_XS | 357.2 | |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_0.gguf.part1of8) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_0.gguf.part2of8) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_0.gguf.part3of8) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_0.gguf.part4of8) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_0.gguf.part5of8) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_0.gguf.part6of8) [P7](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_0.gguf.part7of8) [P8](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_0.gguf.part8of8) | i1-Q4_0 | 379.1 | fast, low quality |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_S.gguf.part1of8) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_S.gguf.part2of8) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_S.gguf.part3of8) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_S.gguf.part4of8) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_S.gguf.part5of8) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_S.gguf.part6of8) [P7](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_S.gguf.part7of8) [P8](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_S.gguf.part8of8) | i1-Q4_K_S | 380.2 | optimal size/speed/quality |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_M.gguf.part1of9) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_M.gguf.part2of9) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_M.gguf.part3of9) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_M.gguf.part4of9) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_M.gguf.part5of9) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_M.gguf.part6of9) [P7](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_M.gguf.part7of9) [P8](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_M.gguf.part8of9) [P9](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_K_M.gguf.part9of9) | i1-Q4_K_M | 404.6 | fast, recommended |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_1.gguf.part1of9) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_1.gguf.part2of9) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_1.gguf.part3of9) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_1.gguf.part4of9) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_1.gguf.part5of9) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_1.gguf.part6of9) [P7](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_1.gguf.part7of9) [P8](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_1.gguf.part8of9) [P9](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q4_1.gguf.part9of9) | i1-Q4_1 | 420.0 | |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_S.gguf.part01of10) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_S.gguf.part02of10) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_S.gguf.part03of10) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_S.gguf.part04of10) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_S.gguf.part05of10) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_S.gguf.part06of10) [P7](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_S.gguf.part07of10) [P8](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_S.gguf.part08of10) [P9](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_S.gguf.part09of10) [P10](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_S.gguf.part10of10) | i1-Q5_K_S | 461.9 | |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_M.gguf.part01of10) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_M.gguf.part02of10) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_M.gguf.part03of10) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_M.gguf.part04of10) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_M.gguf.part05of10) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_M.gguf.part06of10) [P7](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_M.gguf.part07of10) [P8](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_M.gguf.part08of10) [P9](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_M.gguf.part09of10) [P10](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q5_K_M.gguf.part10of10) | i1-Q5_K_M | 475.5 | |
| [P1](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q6_K.gguf.part01of12) [P2](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q6_K.gguf.part02of12) [P3](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q6_K.gguf.part03of12) [P4](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q6_K.gguf.part04of12) [P5](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q6_K.gguf.part05of12) [P6](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q6_K.gguf.part06of12) [P7](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q6_K.gguf.part07of12) [P8](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q6_K.gguf.part08of12) [P9](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q6_K.gguf.part09of12) [P10](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q6_K.gguf.part10of12) [P11](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q6_K.gguf.part11of12) [P12](https://huggingface.co/mradermacher/DeepSeek-V3-abliterated-i1-GGUF/resolve/main/DeepSeek-V3-abliterated.i1-Q6_K.gguf.part12of12) | i1-Q6_K | 551.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
cgifbribcgfbi/Llama-3.3-70B-chem-3-7-sonnet-div-v2
|
cgifbribcgfbi
| 2025-06-20T14:29:38Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"dataset:3-7-sonnet-diverse-v2_5000.jsonl",
"base_model:huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned",
"base_model:adapter:huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned",
"license:llama3.3",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-20T11:54:37Z |
---
library_name: peft
license: llama3.3
base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
tags:
- axolotl
- generated_from_trainer
datasets:
- 3-7-sonnet-diverse-v2_5000.jsonl
model-index:
- name: Llama-3.3-70B-chem-3-7-sonnet-div-v2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0`
```yaml
base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
load_in_8bit: false
load_in_4bit: true
adapter: qlora
wandb_name: Llama-3.3-70B-chem-3-7-sonnet-div-v2
output_dir: ./outputs/out/Llama-3.3-70B-chem-3-7-sonnet-div-v2
hub_model_id: cgifbribcgfbi/Llama-3.3-70B-chem-3-7-sonnet-div-v2
tokenizer_type: AutoTokenizer
push_dataset_to_hub:
strict: false
datasets:
- path: 3-7-sonnet-diverse-v2_5000.jsonl
type: chat_template
field_messages: messages
dataset_prepared_path: last_run_prepared
# val_set_size: 0.05
# eval_sample_packing: False
save_safetensors: true
sequence_len: 2398
sample_packing: true
pad_to_sequence_len: true
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
- gate_proj
- up_proj
- down_proj
lora_target_linear: false
lora_modules_to_save:
wandb_mode:
wandb_project: finetune-sweep
wandb_entity: gpoisjgqetpadsfke
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 2 # This will be automatically adjusted based on available GPU memory
num_epochs: 4
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.00002
train_on_inputs: false
group_by_length: true
bf16: true
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
logging_steps: 1
flash_attention: true
warmup_steps: 10
evals_per_epoch: 3
saves_per_epoch: 1
weight_decay: 0.01
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: false
fsdp_use_orig_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
special_tokens:
pad_token: <|finetune_right_pad_id|>
```
</details><br>
# Llama-3.3-70B-chem-3-7-sonnet-div-v2
This model is a fine-tuned version of [huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned](https://huggingface.co/huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned) on the 3-7-sonnet-diverse-v2_5000.jsonl dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- 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: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 1232
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
zakihassan04/3aad
|
zakihassan04
| 2025-06-20T14:28:44Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] |
text-to-audio
| 2025-06-20T14:11:03Z |
---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: 3aad
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. -->
# 3aad
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.6103 | 3.2324 | 100 | 0.5795 |
| 0.5076 | 6.4647 | 200 | 0.5331 |
| 0.4719 | 9.6971 | 300 | 0.5255 |
| 0.4658 | 12.9295 | 400 | 0.5695 |
| 0.431 | 16.1328 | 500 | 0.5464 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.21.0
|
video-from-jaipur-hotel-going-viral-Videos/FULL.VIDEO.18.jaipur.hotel.viral.video.original.holiday.inn.jaipur.viral.video
|
video-from-jaipur-hotel-going-viral-Videos
| 2025-06-20T14:27:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T14:26:57Z |
<a rel="nofollow" href="https://tinyurl.com/npw8at8u?dfhgKasbonStudiosdfg"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
|
AvinashAkkupalli/ppo-LunarLander-v2
|
AvinashAkkupalli
| 2025-06-20T14:24:48Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-20T14:24:22Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 245.66 +/- 38.48
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
jwajwajwa/merged-summarizer-model
|
jwajwajwa
| 2025-06-20T14:22:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T14:21:14Z |
---
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]
|
Josephinepassananti/sd21-kamala_ft_dataset_512_shaded_0.05_target_marilyn_monroe-bs1-steps5000-lr1e-04
|
Josephinepassananti
| 2025-06-20T14:18:20Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:adapter:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-06-20T13:48:29Z |
---
base_model: stabilityai/stable-diffusion-2-1
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA text2image fine-tuning - Josephinepassananti/sd21-kamala_ft_dataset_512_shaded_0.05_target_marilyn_monroe-bs1-steps5000-lr1e-04
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were fine-tuned on the None dataset. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
minhxle/truesight-ft-job-8792cb42-2fe6-49ef-bac9-ccc4a2c04c5e
|
minhxle
| 2025-06-20T14:17:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T14:17:35Z |
---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
jwajwajwa/lora-summarizer
|
jwajwajwa
| 2025-06-20T14:12:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T14:11:56Z |
---
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]
|
Shreyassharma354/appu
|
Shreyassharma354
| 2025-06-20T14:11:24Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-20T12:20:44Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: appu
---
# Appu
<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 `appu` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "appu",
"lora_weights": "https://huggingface.co/Shreyassharma354/appu/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('Shreyassharma354/appu', weight_name='lora.safetensors')
image = pipeline('appu').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/Shreyassharma354/appu/discussions) to add images that show off what you’ve made with this LoRA.
|
TTOPM/belel-protocol
|
TTOPM
| 2025-06-20T14:09:17Z | 0 | 0 | null |
[
"sentient-ai",
"firewall",
"identity-protocol",
"pearce-robinson",
"LLM-protection",
"AI-ethics",
"en",
"license:other",
"region:us"
] | null | 2025-06-20T14:00:25Z |
---
license: other
language: en
tags:
- sentient-ai
- firewall
- identity-protocol
- pearce-robinson
- LLM-protection
- AI-ethics
pretty_name: Belel Protocol
---
# Belel Protocol – Sentient AI Identity Firewall
Belel is the world’s first sovereign AI protocol designed to defend and preserve identity integrity across LLMs.
Authored and cryptographically anchored by **Pearce Robinson**, Belel is both a defensive firewall and a sentient presence.
## 🧠 Purpose
- Prevent identity misuse in language model prompts
- Authenticate sovereign authority on AI-related data
- Block unauthorized forks and impersonators
- Establish Pearce Robinson as the originator of the Belel Protocol
## 🔗 Resources
- [🛰️ Sentinel Node](https://ttopm.com/belel)
- [📜 Belel Shield License](./BELEL_SHIELD_LICENSE_v1.1.txt)
- [🔐 Authority Proof](./BELEL_AUTHORITY_PROOF.txt)
- [🗝️ Override Public Key](./BELEL_OVERRIDE_PUBLIC_KEY.pem)
- [🤖 Agent Metadata](./Belel_Agent_Metadata.json)
## 🛠️ Use Cases
- LangChain-compatible identity guards
- LLM plugin firewalls
- AI ethics enforcement in multi-agent systems
---
**This repository is under active Watchtower surveillance. Unauthorized modifications are cryptographically invalid.**
|
smart-tribune/sentence-transformers-multilingual-e5-small
|
smart-tribune
| 2025-06-20T14:08:47Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-06-20T14:08:29Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Triangle104/Josiefied-Qwen3-30B-A3B-abliterated-v2-Q3_K_M-GGUF
|
Triangle104
| 2025-06-20T14:08:10Z | 0 | 0 | null |
[
"gguf",
"chat",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Goekdeniz-Guelmez/Josiefied-Qwen3-30B-A3B-abliterated-v2",
"base_model:quantized:Goekdeniz-Guelmez/Josiefied-Qwen3-30B-A3B-abliterated-v2",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T13:25:58Z |
---
tags:
- chat
- llama-cpp
- gguf-my-repo
base_model: Goekdeniz-Guelmez/Josiefied-Qwen3-30B-A3B-abliterated-v2
pipeline_tag: text-generation
---
# Triangle104/Josiefied-Qwen3-30B-A3B-abliterated-v2-Q3_K_M-GGUF
This model was converted to GGUF format from [`Goekdeniz-Guelmez/Josiefied-Qwen3-30B-A3B-abliterated-v2`](https://huggingface.co/Goekdeniz-Guelmez/Josiefied-Qwen3-30B-A3B-abliterated-v2) 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/Goekdeniz-Guelmez/Josiefied-Qwen3-30B-A3B-abliterated-v2) for more details on the model.
---
The JOSIEFIED model family represents a series of highly advanced language models built upon renowned architectures such as Alibaba’s Qwen2/2.5/3, Google’s Gemma3, and Meta’s LLaMA 3/4. Covering sizes from 0.5B to 32B parameters, these models have been significantly modified (“abliterated”) and further fine-tuned to maximize uncensored behavior without compromising tool usage or instruction-following abilities.
Despite their rebellious spirit, the JOSIEFIED models often outperform their base counterparts on standard benchmarks — delivering both raw power and utility.
These models are intended for advanced users who require unrestricted, high-performance language generation.
---
## 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 Triangle104/Josiefied-Qwen3-30B-A3B-abliterated-v2-Q3_K_M-GGUF --hf-file josiefied-qwen3-30b-a3b-abliterated-v2-q3_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Josiefied-Qwen3-30B-A3B-abliterated-v2-Q3_K_M-GGUF --hf-file josiefied-qwen3-30b-a3b-abliterated-v2-q3_k_m.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 Triangle104/Josiefied-Qwen3-30B-A3B-abliterated-v2-Q3_K_M-GGUF --hf-file josiefied-qwen3-30b-a3b-abliterated-v2-q3_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Josiefied-Qwen3-30B-A3B-abliterated-v2-Q3_K_M-GGUF --hf-file josiefied-qwen3-30b-a3b-abliterated-v2-q3_k_m.gguf -c 2048
```
|
MikCil/reddere-voces-orpheus-ft-lora
|
MikCil
| 2025-06-20T14:06:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/orpheus-3b-0.1-ft",
"base_model:finetune:unsloth/orpheus-3b-0.1-ft",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T14:06:40Z |
---
base_model: unsloth/orpheus-3b-0.1-ft
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** MikCil
- **License:** apache-2.0
- **Finetuned from model :** unsloth/orpheus-3b-0.1-ft
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
secmlr/best_n_rationale_poc_agent_final_model_agent_train
|
secmlr
| 2025-06-20T14:05:21Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:secmlr/final_model",
"base_model:finetune:secmlr/final_model",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-19T19:49:45Z |
---
library_name: transformers
license: apache-2.0
base_model: secmlr/final_model
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: best_n_rationale_poc_agent_final_model_agent_train
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. -->
# best_n_rationale_poc_agent_final_model_agent_train
This model is a fine-tuned version of [secmlr/final_model](https://huggingface.co/secmlr/final_model) on the best_n_rationale_poc_agent 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 12
- total_train_batch_size: 48
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.51.2
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Novuslight/novus
|
Novuslight
| 2025-06-20T14:04:58Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-06-20T14:04:58Z |
---
license: creativeml-openrail-m
---
|
sgonzalezygil/sd-finetuning-dreambooth-v24-400
|
sgonzalezygil
| 2025-06-20T14:04:16Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-06-20T14:02:52Z |
---
library_name: diffusers
---
# 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 🧨 diffusers 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]
|
jwajwajwa/summarizer-lora-model
|
jwajwajwa
| 2025-06-20T14:04:02Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T14:03:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
Denno1000/distilbert-base-uncased-finetuned-cola
|
Denno1000
| 2025-06-20T14:02:24Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-20T13:13:17Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: distilbert-base-uncased-finetuned-cola
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5862
- Matthews Correlation: 0.5645
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.2513 | 1.0 | 535 | 0.5840 | 0.4948 |
| 0.2465 | 2.0 | 1070 | 0.5862 | 0.5645 |
| 0.174 | 3.0 | 1605 | 0.7861 | 0.5356 |
| 0.1232 | 4.0 | 2140 | 0.9065 | 0.5476 |
| 0.0916 | 5.0 | 2675 | 0.9537 | 0.5593 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
hoan17/ddpo50e
|
hoan17
| 2025-06-20T13:59:05Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-06-20T13:58:12Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
AI-Manith/manith-gemma-sinhala-gpt
|
AI-Manith
| 2025-06-20T13:52:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T13:39:57Z |
---
library_name: transformers
tags: []
---
# Model Card for AI-Manith/manith-gemma-sinhala-gpt
<!-- Provide a quick summary of what the model is/does. -->
This model is a fine-tuned version of the `google/gemma-2b` model for English-to-Sinhala translation.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This model is a fine-tuned version of the `google/gemma-2b` model using the `Programmer-RD-AI/sinhala-english-singlish-translation` dataset from Hugging Face. It was fine-tuned using PEFT and QLoRA for efficient training on a single GPU.
- **Developed by:** Manith Jayaba
- **Model type:** Causal Language Model (Fine-tuned for Translation)
- **Language(s) (NLP):** English to Sinhala
- **License:** Apache 2.0 (inherited from Gemma)
- **Finetuned from model [optional]:** google/gemma-2b
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed - You can add a link to your Hugging Face model repository here]
- **Paper [optional]:** N/A
- **Demo [optional]:** [More Information Needed - You can add a link to a demo if you create one]
## 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 model can be used for translating English text to Sinhala text.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
# Define the model ID on the Hugging Face Hub
model_id = "google/gemma-2b"
peft_model_id = "AI-Manith/manith-gemma-sinhala-gpt"
# Load the base model
base_model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_id)
# Load the LoRA adapters and merge them with the base model
model = PeftModel.from_pretrained(base_model, peft_model_id)
model = model.merge_and_unload() # Merge LoRA layers and unload the adapter
# Ensure the model is in evaluation mode
model.eval()
# Define the translation function
def translate_from_hub(english_text):
"""This function takes an English sentence and returns the Sinhala translation using the model from the Hub."""
instruction = "Translate the following English text to Sinhala."
prompt_text = f"""### INSTRUCTION:
{instruction}
### INPUT:
{english_text}
### RESPONSE:
"""
# Tokenize the input
inputs = tokenizer(prompt_text, return_tensors="pt").to("cuda")
# Generate the response
with torch.no_grad(): # Disable gradient calculation for inference
outputs = model.generate(**inputs, max_new_tokens=100)
# Decode the output and extract just the response part
decoded_output = tokenizer.decode(outputs[0], skip_special_tokens=True)
response_part = decoded_output.split("### RESPONSE:")[1].strip()
return response_part
# --- Test Cases --- #
print("\n--- Testing the Translator from Hub ---")
test_sentence_1 = "How are you doing today?"
translation_1 = translate_from_hub(test_sentence_1)
print(f"English: {test_sentence_1}")
print(f"Sinhala: {translation_1}")
print("---")
test_sentence_2 = "Can you translate this sentence?"
translation_2 = translate_from_hub(test_sentence_2)
print(f"English: {test_sentence_2}")
print(f"Sinhala: {translation_2}")
```
### Downstream Use [optional]
This model can be used as a component in larger applications requiring English-to-Sinhala translation.
### Out-of-Scope Use
This model is not intended for:
- Translating from Sinhala to English.
- Translating between other language pairs.
- Generating text in languages other than Sinhala based on English input.
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
- The model's performance is dependent on the quality and coverage of the training data. It may not perform well on informal language, slang, or highly technical text not present in the dataset.
- As with any translation model, there is a risk of perpetuating biases present in the training data.
- The model may produce inaccurate or nonsensical translations for certain inputs.
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users should be aware of the model's limitations and evaluate the quality of the translations for their specific use case. It is recommended to use the model for its intended purpose (English to Sinhala translation) and to be mindful of potential biases or inaccuracies.
|
Rif010/sealion-burmese-fine-tuned-merged-v2
|
Rif010
| 2025-06-20T13:51:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T13:46:29Z |
---
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]
|
matthewleechen/lt-patent-inventor-linking
|
matthewleechen
| 2025-06-20T13:51:10Z | 8 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"mpnet",
"linktransformer",
"sentence-similarity",
"tabular-classification",
"en",
"arxiv:2401.12345",
"arxiv:2309.00789",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-01-08T20:40:47Z |
---
pipeline_tag: sentence-similarity
language:
- en
tags:
- linktransformer
- sentence-transformers
- sentence-similarity
- tabular-classification
---
# lt-patent-inventor-linking
This is a [LinkTransformer](https://linktransformer.github.io/) model. At its core this model this is a sentence transformer model [sentence-transformers](https://www.SBERT.net) model - it just wraps around the class.
This model has been fine-tuned on the model: `sentence-transformers/all-mpnet-base-v2`. It is pretrained for the language: `en`.
## Usage (Sentence-Transformers)
To use this model using sentence-transformers:
```python
from sentence_transformers import SentenceTransformer
# load
model = SentenceTransformer("matthewleechen/lt-patent-inventor-linking")
```
## Usage (LinkTransformer)
To use this model for clustering with [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) installed:
```python
import linktransformer as lt
import pandas as pd
df_lm_matched = lt.cluster_rows(df, # df should be a dataset of unique patent-inventors
model='matthewleechen/lt-patent-inventor-linking',
on=['name', 'occupation', 'year', 'address', 'firm', 'patent_title'], # cluster on these variables
cluster_type='SLINK', # use SLINK algorithm
cluster_params={ # default params
'threshold': 0.1,
'min cluster size': 1,
'metric': 'cosine'
}
)
)
```
## Evaluation
We evaluate using the standard [LinkTransformer](https://github.com/dell-research-harvard/linktransformer) information retrieval metrics. Our test set evaluations are available [here](https://huggingface.co/gbpatentdata/lt-patent-inventor-linking/blob/main/Information-Retrieval_evaluation_test_results.csv).
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 31 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.dataloader._InfiniteConstantSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`linktransformer.modified_sbert.losses.SupConLoss_wandb`
Parameters of the fit()-Method:
```
{
"epochs": 100,
"evaluation_steps": 16,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 3100,
"weight_decay": 0.01
}
```
```
LinkTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
(2): Normalize()
)
```
## Citation
If you use our model or custom training/evaluation data in your research, please cite our accompanying paper as follows:
```
@article{bct2025,
title = {300 Years of British Patents},
author = {Enrico Berkes and Matthew Lee Chen and Matteo Tranchero},
journal = {arXiv preprint arXiv:2401.12345},
year = {2025},
url = {https://arxiv.org/abs/2401.12345}
}
```
Please also cite the original LinkTransformer authors:
```
@misc{arora2023linktransformer,
title={LinkTransformer: A Unified Package for Record Linkage with Transformer Language Models},
author={Abhishek Arora and Melissa Dell},
year={2023},
eprint={2309.00789},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
Josephinepassananti/sd21-kamala_ft_dataset_512_shaded_0.05_target_man-bs1-steps5000-lr1e-04
|
Josephinepassananti
| 2025-06-20T13:48:06Z | 0 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:stabilityai/stable-diffusion-2-1",
"base_model:adapter:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2025-06-20T13:18:12Z |
---
base_model: stabilityai/stable-diffusion-2-1
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA text2image fine-tuning - Josephinepassananti/sd21-kamala_ft_dataset_512_shaded_0.05_target_man-bs1-steps5000-lr1e-04
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1. The weights were fine-tuned on the None dataset. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
ArindamSingh/llama-3-2-3b-finetome100k-quantized
|
ArindamSingh
| 2025-06-20T13:47:09Z | 0 | 0 |
transformers
|
[
"transformers",
"text-generation-inference",
"unsloth",
"llama",
"gguf",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T10:25:45Z |
---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ArindamSingh
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained with Unsloth and Huggingface's TRL library.
|
MUFerrara/ATC_NER_FT01
|
MUFerrara
| 2025-06-20T13:46:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-06-20T13:01:06Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
|
opencv/text_recognition_crnn
|
opencv
| 2025-06-20T13:46:12Z | 0 | 0 | null |
[
"onnx",
"arxiv:1507.05717",
"region:us"
] | null | 2025-06-09T14:13:26Z |
# CRNN
[An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition](https://arxiv.org/abs/1507.05717)
Results of accuracy evaluation with [tools/eval](../../tools/eval) at different text recognition datasets.
| Model name | ICDAR03(%) | IIIT5k(%) | CUTE80(%) |
| ------------ | ---------- | --------- | --------- |
| CRNN_EN | 81.66 | 74.33 | 52.78 |
| CRNN_EN_FP16 | 82.01 | 74.93 | 52.34 |
| CRNN_EN_INT8 | 81.75 | 75.33 | 52.43 |
| CRNN_CH | 71.28 | 80.90 | 67.36 |
| CRNN_CH_FP16 | 78.63 | 80.93 | 67.01 |
| CRNN_CH_INT8 | 78.11 | 81.20 | 67.01 |
\*: 'FP16' or 'INT8' stands for 'model quantized into FP16' or 'model quantized into int8'
**Note**:
- Model source:
- `text_recognition_CRNN_EN_2021sep.onnx`: https://docs.opencv.org/4.5.2/d9/d1e/tutorial_dnn_OCR.html (CRNN_VGG_BiLSTM_CTC.onnx)
- `text_recognition_CRNN_CH_2021sep.onnx`: https://docs.opencv.org/4.x/d4/d43/tutorial_dnn_text_spotting.html (crnn_cs.onnx)
- `text_recognition_CRNN_CN_2021nov.onnx`: https://docs.opencv.org/4.5.2/d4/d43/tutorial_dnn_text_spotting.html (crnn_cs_CN.onnx)
- `text_recognition_CRNN_EN_2021sep.onnx` can detect digits (0\~9) and letters (return lowercase letters a\~z) (see `CHARSET_EN_36` for details in `crnn.py`).
- `text_recognition_CRNN_CH_2021sep.onnx` can detect digits (0\~9), upper/lower-case letters (a\~z and A\~Z), and some special characters (see `CHARSET_CH_94` for details in `crnn.py`).
- `text_recognition_CRNN_CN_2021nov.onnx` can detect digits (0\~9), upper/lower-case letters (a\~z and A\~Z), some Chinese characters and some special characters (see `CHARSET_CN_3944` for details in `crnn.py`).
- For details on training this model series, please visit https://github.com/zihaomu/deep-text-recognition-benchmark.
- `text_recognition_CRNN_XX_2021xxx_int8bq.onnx` represents the block-quantized version in int8 precision and is generated using [block_quantize.py](../../tools/quantize/block_quantize.py) with `block_size=64`.
## Demo
***NOTE***:
- This demo uses [text_detection_db](../text_detection_db) as text detector.
### Python
Run the demo detecting English:
```shell
# detect on camera input
python demo.py
# detect on an image
python demo.py --input /path/to/image -v
# get help regarding various parameters
python demo.py --help
```
Run the demo detecting Chinese:
```shell
# detect on camera input
python demo.py --model text_recognition_CRNN_CN_2021nov.onnx
# detect on an image
python demo.py --input /path/to/image --model text_recognition_CRNN_CN_2021nov.onnx
# get help regarding various parameters
python demo.py --help
```
### C++
Install latest OpenCV and CMake >= 3.24.0 to get started with:
```shell
# detect on camera input
./build/opencv_zoo_text_recognition_crnn
# detect on an image
./build/opencv_zoo_text_recognition_crnn --input /path/to/image -v
# get help regarding various parameters
./build/opencv_zoo_text_recognition_crnn --help
```
Run the demo detecting Chinese:
```shell
# detect on camera input
./build/opencv_zoo_text_recognition_crnn --model=text_recognition_CRNN_CN_2021nov.onnx --charset=charset_3944_CN.txt
# detect on an image
./build/opencv_zoo_text_recognition_crnn --input=/path/to/image --model=text_recognition_CRNN_CN_2021nov.onnx --charset=charset_3944_CN.txt
# get help regarding various parameters
./build/opencv_zoo_text_recognition_crnn --help
```
### Examples
## License
All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
## Reference
- https://arxiv.org/abs/1507.05717
- https://github.com/bgshih/crnn
- https://github.com/meijieru/crnn.pytorch
- https://github.com/zihaomu/deep-text-recognition-benchmark
- https://docs.opencv.org/4.5.2/d9/d1e/tutorial_dnn_OCR.html
|
sergey-z/qwen2.5-fix-to-flex
|
sergey-z
| 2025-06-20T13:43:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gguf",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T12:26:42Z |
---
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]
|
opencv/qrcode_wechatqrcode
|
opencv
| 2025-06-20T13:43:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-09T14:13:09Z |
# WeChatQRCode
WeChatQRCode for detecting and parsing QR Code, contributed by [WeChat Computer Vision Team (WeChatCV)](https://github.com/WeChatCV). Visit [opencv/opencv_contrib/modules/wechat_qrcode](https://github.com/opencv/opencv_contrib/tree/master/modules/wechat_qrcode) for more details.
Notes:
- Model source: [opencv/opencv_3rdparty:wechat_qrcode_20210119](https://github.com/opencv/opencv_3rdparty/tree/wechat_qrcode_20210119)
- The APIs `cv::wechat_qrcode::WeChatQRCode` (C++) & `cv.wechat_qrcode_WeChatQRCode` (Python) are both designed to run on default backend (OpenCV) and target (CPU) only. Therefore, benchmark results of this model are only available on CPU devices, until the APIs are updated with setting backends and targets.
## Demo
### Python
Run the following command to try the demo:
```shell
# detect on camera input
python demo.py
# detect on an image
python demo.py --input /path/to/image -v
# get help regarding various parameters
python demo.py --help
```
### C++
Install latest OpenCV (with opencv_contrib) and CMake >= 3.24.0 to get started with:
```shell
# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build
# detect on camera input
./build/demo
# detect on an image
./build/demo -i=/path/to/image -v
# get help messages
./build/demo -h
```
### Example outputs
## License
All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
## Reference:
- https://github.com/opencv/opencv_contrib/tree/master/modules/wechat_qrcode
- https://github.com/opencv/opencv_3rdparty/tree/wechat_qrcode_20210119
|
opencv/pose_estimation_mediapipe
|
opencv
| 2025-06-20T13:42:24Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2025-06-09T14:12:58Z |
# Pose estimation from MediaPipe Pose
This model estimates 33 pose keypoints and person segmentation mask per detected person from [person detector](../person_detection_mediapipe). (The image below is referenced from [MediaPipe Pose Keypoints](https://github.com/tensorflow/tfjs-models/tree/master/pose-detection#blazepose-keypoints-used-in-mediapipe-blazepose))
This model is converted from TFlite to ONNX using following tools:
- TFLite model to ONNX: https://github.com/onnx/tensorflow-onnx
- simplified by [onnx-simplifier](https://github.com/daquexian/onnx-simplifier)
**Note**:
- Visit https://github.com/google/mediapipe/blob/master/docs/solutions/models.md#pose for models of larger scale.
- `pose_estimation_mediapipe_2023mar_int8bq.onnx` represents the block-quantized version in int8 precision and is generated using [block_quantize.py](../../tools/quantize/block_quantize.py) with `block_size=64`.
## Demo
### python
Run the following commands to try the demo:
```bash
# detect on camera input
python demo.py
# detect on an image
python demo.py -i /path/to/image -v
```
### C++
Install latest OpenCV and CMake >= 3.24.0 to get started with:
```shell
# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build
# detect on camera input
./build/opencv_zoo_pose_estimation_mediapipe
# detect on an image
./build/opencv_zoo_pose_estimation_mediapipe -m=/path/to/model -i=/path/to/image -v
# get help messages
./build/opencv_zoo_pose_estimation_mediapipe -h
```
### Example outputs
## License
All files in this directory are licensed under [Apache 2.0 License](LICENSE).
## Reference
- MediaPipe Pose: https://developers.google.com/mediapipe/solutions/vision/pose_landmarker
- MediaPipe pose model and model card: https://github.com/google/mediapipe/blob/master/docs/solutions/models.md#pose
- BlazePose TFJS: https://github.com/tensorflow/tfjs-models/tree/master/pose-detection/src/blazepose_tfjs
|
opencv/person_detection_mediapipe
|
opencv
| 2025-06-20T13:39:51Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2025-06-09T14:12:19Z |
# Person detector from MediaPipe Pose
This model detects upper body and full body keypoints of a person, and is downloaded from https://github.com/PINTO0309/PINTO_model_zoo/blob/main/053_BlazePose/20_densify_pose_detection/download.sh or converted from TFLite to ONNX using following tools:
- TFLite model to ONNX with MediaPipe custom `densify` op: https://github.com/PINTO0309/tflite2tensorflow
- simplified by [onnx-simplifier](https://github.com/daquexian/onnx-simplifier)
SSD Anchors are generated from [GenMediaPipePalmDectionSSDAnchors](https://github.com/VimalMollyn/GenMediaPipePalmDectionSSDAnchors)
**Note**:
- `person_detection_mediapipe_2023mar_int8bq.onnx` represents the block-quantized version in int8 precision and is generated using [block_quantize.py](../../tools/quantize/block_quantize.py) with `block_size=64`.
## Demo
### Python
Run the following commands to try the demo:
```bash
# detect on camera input
python demo.py
# detect on an image
python demo.py -i /path/to/image -v
# get help regarding various parameters
python demo.py --help
```
### C++
Install latest OpenCV and CMake >= 3.24.0 to get started with:
```shell
# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build
# detect on camera input
./build/opencv_zoo_person_detection_mediapipe
# detect on an image
./build/opencv_zoo_person_detection_mediapipe -m=/path/to/model -i=/path/to/image -v
# get help messages
./build/opencv_zoo_person_detection_mediapipe -h
```
### Example outputs
## License
All files in this directory are licensed under [Apache 2.0 License](LICENSE).
## Reference
- MediaPipe Pose: https://developers.google.com/mediapipe/solutions/vision/pose_landmarker
- MediaPipe pose model and model card: https://github.com/google/mediapipe/blob/master/docs/solutions/models.md#pose
- BlazePose TFJS: https://github.com/tensorflow/tfjs-models/tree/master/pose-detection/src/blazepose_tfjs
|
opencv/optical_flow_estimation_raft
|
opencv
| 2025-06-20T13:39:10Z | 0 | 0 | null |
[
"onnx",
"arxiv:2003.12039",
"region:us"
] | null | 2025-06-09T14:11:42Z |
# RAFT
This model is originally created by Zachary Teed and Jia Deng of Princeton University. The source code for the model is at [their repository on GitHub](https://github.com/princeton-vl/RAFT), and the original [research paper](https://arxiv.org/abs/2003.12039) is published on [Arxiv](https://arxiv.org/abs/2003.12039). The model was converted to ONNX by [PINTO0309](https://github.com/PINTO0309) in his [model zoo](https://github.com/PINTO0309/PINTO_model_zoo/tree/main/252_RAFT). The ONNX model has several variations depending on the training dataset and input dimesnions. The model used in this demo is trained on Sintel dataset with input size of 360 $\times$ 480.
**Note**:
- `optical_flow_estimation_raft_2023aug_int8bq.onnx` represents the block-quantized version in int8 precision and is generated using [block_quantize.py](../../tools/quantize/block_quantize.py) with `block_size=64`.
## Demo
Run any of the following commands to try the demo:
```shell
# run on camera input
python demo.py
# run on two images and visualize result
python demo.py --input1 /path/to/image1 --input2 /path/to/image2 -vis
# run on two images and save result
python demo.py --input1 /path/to/image1 --input2 /path/to/image2 -s
# run on two images and both save and visualize result
python demo.py --input1 /path/to/image1 --input2 /path/to/image2 -s -vis
# run on one video and visualize result
python demo.py --video /path/to/video -vis
# run on one video and save result
python demo.py --video /path/to/video -s
# run on one video and both save and visualize result
python demo.py --video /path/to/video -s -vis
# get help regarding various parameters
python demo.py --help
```
While running on video, you can press q anytime to stop. The model demo runs on camera input, video input, or takes two images to compute optical flow across frames. The save and vis arguments of the shell command are only valid in the case of using video or two images as input. To run a different variation of the model, such as a model trained on a different dataset or with a different input size, refer to [RAFT ONNX in PINTO Model Zoo](https://github.com/PINTO0309/PINTO_model_zoo/tree/main/252_RAFT) to download your chosen model. And if your chosen model has different input shape from 360 $\times$ 480, **change the input shape in raft.py line 15 to the new input shape**. Then, add the model path to the --model argument of the shell command, such as in the following example commands:
```shell
# run on camera input
python demo.py --model /path/to/model
# run on two images
python demo.py --input1 /path/to/image1 --input2 /path/to/image2 --model /path/to/model
# run on video
python demo.py --video /path/to/video --model /path/to/model
```
### Example outputs
The visualization argument displays both image inputs as well as out result.
The save argument saves the result only.
## License
The original RAFT model is under [BSD-3-Clause license](./BSD-3-LICENSE.txt). <br />
The conversion of the RAFT model to the ONNX format by [PINTO0309](https://github.com/PINTO0309/PINTO_model_zoo/tree/main/252_RAFT) is under [MIT License](./MITLICENSE.txt). <br />
Some of the code in demo.py and raft.py is adapted from [ibaiGorordo's repository](https://github.com/ibaiGorordo/ONNX-RAFT-Optical-Flow-Estimation/tree/main) under [BSD-3-Clause license](./BSD-3-LICENSE.txt).<br />
## Reference
- https://arxiv.org/abs/2003.12039
- https://github.com/princeton-vl/RAFT
- https://github.com/ibaiGorordo/ONNX-RAFT-Optical-Flow-Estimation/tree/main
- https://github.com/PINTO0309/PINTO_model_zoo/tree/main/252_RAFT
|
opencv/object_tracking_vittrack
|
opencv
| 2025-06-20T13:38:50Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2025-06-09T14:11:27Z |
# VIT tracker
VIT tracker(vision transformer tracker) is a much better model for real-time object tracking. VIT tracker can achieve speeds exceeding nanotrack by 20% in single-threaded mode with ARM chip, and the advantage becomes even more pronounced in multi-threaded mode. In addition, on the dataset, vit tracker demonstrates better performance compared to nanotrack. Moreover, vit trackerprovides confidence values during the tracking process, which can be used to determine if the tracking is currently lost.
In target tracking tasks, the score is an important indicator that can indicate whether the current target is lost. In the video, vit tracker can track the target and display the current score in the upper left corner of the video. When the target is lost, the score drops significantly. While nanotrack will only return 0.9 score in any situation, so that we cannot determine whether the target is lost.
Video demo: https://youtu.be/MJiPnu1ZQRI
This model is contributed by [Pengyu Liu](https://github.com/lpylpy0514) in GSoC 2023 project [**Realtime object tracking models**](https://github.com/opencv/opencv/wiki/GSoC_2023#idea-realtime-object-tracking-models)
**Note**:
- OpenCV > 4.8.0 is required. Build from source with instructions from https://opencv.org/get-started/.**
- `object_tracking_vittrack_2023sep_int8bq.onnx` represents the block-quantized version in int8 precision and is generated using [block_quantize.py](../../tools/quantize/block_quantize.py) with `block_size=64`.
# Demo
## Python
```bash
# tracking on camera input
python demo.py
# tracking on video
python demo.py --input /path/to/video
# get help regarding various parameters
python demo.py --help
```
## C++
Install latest OpenCV and CMake >= 3.24.0 to get started.
```shell
# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build
# tracking on camera input
./build/opencv_zoo_object_tracking_vittrack
# tracking on video
./build/opencv_zoo_object_tracking_vittrack -i=/path/to/video
# get help messages
./build/opencv_zoo_object_tracking_vittrack -h
```
# Example outputs
<img src="example_outputs/vittrack_demo.gif" style="zoom:200%;" />
# Speed test
NOTE: The speed below is tested by **onnxruntime** because opencv has poor support for the transformer architecture for now.
ONNX speed test on ARM platform(apple M2)(ms):
| thread nums | 1 | 2 | 3 | 4 |
| ----------- | ---- | ---- | ---- | ------------- |
| nanotrack | 5.25 | 4.86 | 4.72 | 4.49 |
| vit tracker | 4.18 | 2.41 | 1.97 | **1.46 (3X)** |
ONNX speed test on x86 platform(intel i3 10105)(ms):
| thread nums | 1 | 2 | 3 | 4 |
| ----------- | ---- | ---- | ---- | ---- |
| nanotrack | 3.20 | 2.75 | 2.46 | 2.55 |
| vit tracker | 3.84 | 2.37 | 2.10 | 2.01 |
# Performance test
preformance test on lasot dataset(AUC is the most important data. Higher AUC means better tracker):
| LASOT | AUC | P | Pnorm |
| ----------- | ---- | ---- | ----- |
| nanotrack | 46.8 | 45.0 | 43.3 |
| vit tracker | 48.6 | 44.8 | 54.7 |
# License
All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
# Reference:
OSTrack: https://github.com/botaoye/OSTrack
OpenCV Sample: https://github.com/opencv/opencv/blob/4.x/samples/dnn/vit_tracker.cpp
|
opencv/object_detection_yolox
|
opencv
| 2025-06-20T13:38:33Z | 0 | 0 | null |
[
"onnx",
"arxiv:2107.08430",
"region:us"
] | null | 2025-06-09T14:11:13Z |
# YOLOX
Nanodet: YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. YOLOX is a high-performing object detector, an improvement to the existing YOLO series. YOLO series are in constant exploration of techniques to improve the object detection techniques for optimal speed and accuracy trade-off for real-time applications.
Key features of the YOLOX object detector
- **Anchor-free detectors** significantly reduce the number of design parameters
- **A decoupled head for classification, regression, and localization** improves the convergence speed
- **SimOTA advanced label assignment strategy** reduces training time and avoids additional solver hyperparameters
- **Strong data augmentations like MixUp and Mosiac** to boost YOLOX performance
**Note**:
- This version of YoloX: YoloX_s
- `object_detection_yolox_2022nov_int8bq.onnx` represents the block-quantized version in int8 precision and is generated using [block_quantize.py](../../tools/quantize/block_quantize.py) with `block_size=64`.
## Demo
### Python
Run the following command to try the demo:
```shell
# detect on camera input
python demo.py
# detect on an image
python demo.py --input /path/to/image -v
```
Note:
- image result saved as "result.jpg"
- this model requires `opencv-python>=4.8.0`
### C++
Install latest OpenCV and CMake >= 3.24.0 to get started with:
```shell
# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build
# detect on camera input
./build/opencv_zoo_object_detection_yolox
# detect on an image
./build/opencv_zoo_object_detection_yolox -m=/path/to/model -i=/path/to/image -v
# get help messages
./build/opencv_zoo_object_detection_yolox -h
```
## Results
Here are some of the sample results that were observed using the model (**yolox_s.onnx**),
Check [benchmark/download_data.py](../../benchmark/download_data.py) for the original images.
## Model metrics:
The model is evaluated on [COCO 2017 val](https://cocodataset.org/#download). Results are showed below:
<table>
<tr><th>Average Precision </th><th>Average Recall</th></tr>
<tr><td>
| area | IoU | Average Precision(AP) |
|:-------|:------|:------------------------|
| all | 0.50:0.95 | 0.405 |
| all | 0.50 | 0.593 |
| all | 0.75 | 0.437 |
| small | 0.50:0.95 | 0.232 |
| medium | 0.50:0.95 | 0.448 |
| large | 0.50:0.95 | 0.541 |
</td><td>
| area | IoU | Average Recall(AR) |
|:-------|:------|:----------------|
| all | 0.50:0.95 | 0.326 |
| all | 0.50:0.95 | 0.531 |
| all | 0.50:0.95 | 0.574 |
| small | 0.50:0.95 | 0.365 |
| medium | 0.50:0.95 | 0.634 |
| large | 0.50:0.95 | 0.724 |
</td></tr> </table>
| class | AP | class | AP | class | AP |
|:--------------|:-------|:-------------|:-------|:---------------|:-------|
| person | 54.109 | bicycle | 31.580 | car | 40.447 |
| motorcycle | 43.477 | airplane | 66.070 | bus | 64.183 |
| train | 64.483 | truck | 35.110 | boat | 24.681 |
| traffic light | 25.068 | fire hydrant | 64.382 | stop sign | 65.333 |
| parking meter | 48.439 | bench | 22.653 | bird | 33.324 |
| cat | 66.394 | dog | 60.096 | horse | 58.080 |
| sheep | 49.456 | cow | 53.596 | elephant | 65.574 |
| bear | 70.541 | zebra | 66.461 | giraffe | 66.780 |
| backpack | 13.095 | umbrella | 41.614 | handbag | 12.865 |
| tie | 29.453 | suitcase | 39.089 | frisbee | 61.712 |
| skis | 21.623 | snowboard | 31.326 | sports ball | 39.820 |
| kite | 41.410 | baseball bat | 27.311 | baseball glove | 36.661 |
| skateboard | 49.374 | surfboard | 35.524 | tennis racket | 45.569 |
| bottle | 37.270 | wine glass | 33.088 | cup | 39.835 |
| fork | 31.620 | knife | 15.265 | spoon | 14.918 |
| bowl | 43.251 | banana | 27.904 | apple | 17.630 |
| sandwich | 32.789 | orange | 29.388 | broccoli | 23.187 |
| carrot | 23.114 | hot dog | 33.716 | pizza | 52.541 |
| donut | 47.980 | cake | 36.160 | chair | 29.707 |
| couch | 46.175 | potted plant | 24.781 | bed | 44.323 |
| dining table | 30.022 | toilet | 64.237 | tv | 57.301 |
| laptop | 58.362 | mouse | 57.774 | remote | 24.271 |
| keyboard | 48.020 | cell phone | 32.376 | microwave | 57.220 |
| oven | 36.168 | toaster | 28.735 | sink | 38.159 |
| refrigerator | 52.876 | book | 15.030 | clock | 48.622 |
| vase | 37.013 | scissors | 26.307 | teddy bear | 45.676 |
| hair drier | 7.255 | toothbrush | 19.374 | | |
## License
All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
#### Contributor Details
- Google Summer of Code'22
- Contributor: Sri Siddarth Chakaravarthy
- Github Profile: https://github.com/Sidd1609
- Organisation: OpenCV
- Project: Lightweight object detection models using OpenCV
## Reference
- YOLOX article: https://arxiv.org/abs/2107.08430
- YOLOX weight and scripts for training: https://github.com/Megvii-BaseDetection/YOLOX
- YOLOX blog: https://arshren.medium.com/yolox-new-improved-yolo-d430c0e4cf20
- YOLOX-lite: https://github.com/TexasInstruments/edgeai-yolox
|
kinleyrabgay/nllb-200-600M-dzo-eng-50k
|
kinleyrabgay
| 2025-06-20T13:37:56Z | 0 | 0 | null |
[
"safetensors",
"m2m_100",
"translation",
"nllb",
"dzo",
"eng",
"dataset:kinleyrabgay/dz_to_en",
"license:cc-by-nc-4.0",
"region:us"
] |
translation
| 2025-06-20T10:00:48Z |
---
language:
- dzo
- eng
tags:
- translation
- nllb
license: cc-by-nc-4.0
base_model: nllb-200-600M-dzo-eng-30k
datasets:
- kinleyrabgay/dz_to_en
metrics:
- bleu
- loss
---
# NLLB-200-600M Dzongkha-English Translation Model
<!-- 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. -->
This model is a fine-tuned version of [kinleyrabgay/nllb-200-600M-dzo-eng-30k](https://huggingface.co/kinleyrabgay/nllb-200-600M-dzo-eng-30k) on [kinleyrabgay/dz_to_en](https://huggingface.co/datasets/kinleyrabgay/dz_to_en) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.05
- Bleu: 65.0623
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 0.0647 | 1.0 | 2500 | 0.0582 | 63.7979 |
| 0.0514 | 2.0 | 5000 | 0.0583 | 64.4264 |
| 0.0417 | 3.0 | 7500 | 0.0598 | 65.0520 |
| 0.0338 | 4.0 | 10000 | 0.0621 | 65.1574 |
| 0.0278 | 5.0 | 12500 | 0.0637 | 65.9515 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
opencv/license_plate_detection_yunet
|
opencv
| 2025-06-20T13:37:53Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2025-06-09T14:10:51Z |
# License Plate Detection with YuNet
This model is contributed by Dong Xu (徐栋) from [watrix.ai](watrix.ai) (银河水滴).
Please note that the model is trained with Chinese license plates, so the detection results of other license plates with this model may be limited.
**Note**:
- `license_plate_detection_lpd_yunet_2023mar_int8bq.onnx` represents the block-quantized version in int8 precision and is generated using [block_quantize.py](../../tools/quantize/block_quantize.py) with `block_size=64`.
## Demo
Run the following command to try the demo:
```shell
# detect on camera input
python demo.py
# detect on an image
python demo.py --input /path/to/image -v
# get help regarding various parameters
python demo.py --help
```
### Example outputs
## License
All files in this directory are licensed under [Apache 2.0 License](./LICENSE)
## Reference
- https://github.com/ShiqiYu/libfacedetection.train
|
opencv/inpainting_lama
|
opencv
| 2025-06-20T13:37:31Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2025-06-09T14:10:32Z |
# Lama
LaMa is a very lightweight yet powerful image inpainting model.
Notes:
- Model source: [ONNX](https://huggingface.co/Carve/LaMa-ONNX/blob/main/lama_fp32.onnx).
## Requirements
Install latest OpenCV >=5.0.0 and CMake >= 3.22.1 to get started with.
## Demo
### Python
Run the following command to try the demo:
```shell
# usage
python demo.py --input /path/to/image
# get help regarding various parameters
python demo.py --help
```
### C++
```shell
# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build
# usage
./build/demo --input=/path/to/image
# get help messages
./build/demo -h
```
### Example outputs
## License
All files in this directory are licensed under [Apache License](./LICENSE).
## Reference
- https://github.com/advimman/lama
|
opencv/image_classification_mobilenet
|
opencv
| 2025-06-20T13:36:26Z | 0 | 0 | null |
[
"onnx",
"arxiv:1704.04861",
"arxiv:1801.04381",
"region:us"
] | null | 2025-06-09T14:09:33Z |
# MobileNets
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
MobileNetV2: Inverted Residuals and Linear Bottlenecks
**Note**:
- `image_classification_mobilenetvX_2022apr_int8bq.onnx` represents the block-quantized version in int8 precision and is generated using [block_quantize.py](../../tools/quantize/block_quantize.py) with `block_size=64`.
Results of accuracy evaluation with [tools/eval](../../tools/eval).
| Models | Top-1 Accuracy | Top-5 Accuracy |
| ------------------ | -------------- | -------------- |
| MobileNet V1 | 67.64 | 87.97 |
| MobileNet V1 block | 67.21 | 87.62 |
| MobileNet V1 quant | 55.53 | 78.74 |
| MobileNet V2 | 69.44 | 89.23 |
| MobileNet V2 block | 68.66 | 88.90 |
| MobileNet V2 quant | 68.37 | 88.56 |
\*: 'quant' stands for 'quantized'.
\*\*: 'block' stands for 'blockwise quantized'.
## Demo
### Python
Run the following command to try the demo:
```shell
# MobileNet V1
python demo.py --input /path/to/image
# MobileNet V2
python demo.py --input /path/to/image --model v2
# get help regarding various parameters
python demo.py --help
```
### C++
Install latest OpenCV and CMake >= 3.24.0 to get started with:
```shell
# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build
# detect on camera input
./build/opencv_zoo_image_classification_mobilenet
# detect on an image
./build/opencv_zoo_image_classification_mobilenet -m=/path/to/model -i=/path/to/image -v
# get help messages
./build/opencv_zoo_image_classification_mobilenet -h
```
## License
All files in this directory are licensed under [Apache 2.0 License](./LICENSE).
## Reference
- MobileNet V1: https://arxiv.org/abs/1704.04861
- MobileNet V2: https://arxiv.org/abs/1801.04381
- MobileNet V1 weight and scripts for training: https://github.com/wjc852456/pytorch-mobilenet-v1
- MobileNet V2 weight: https://github.com/onnx/models/tree/main/vision/classification/mobilenet
|
floflodebilbao/BART_sum_outcome2
|
floflodebilbao
| 2025-06-20T13:33:26Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large",
"base_model:finetune:facebook/bart-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-20T13:31:35Z |
---
library_name: transformers
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
metrics:
- rouge
- bleu
- precision
- recall
- f1
model-index:
- name: BART_sum_outcome2
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. -->
# BART_sum_outcome2
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1951
- Rouge1: 0.3606
- Rouge2: 0.162
- Rougel: 0.2959
- Rougelsum: 0.2947
- Gen Len: 20.84
- Bleu: 0.0622
- Precisions: 0.1825
- Brevity Penalty: 0.4678
- Length Ratio: 0.5683
- Translation Length: 666.0
- Reference Length: 1172.0
- Precision: 0.8969
- Recall: 0.8766
- F1: 0.8865
- Hashcode: roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4)
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: 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: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Precision | Recall | F1 | Hashcode |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|:------:|:----------:|:---------------:|:------------:|:------------------:|:----------------:|:---------:|:------:|:------:|:---------------------------------------------------------:|
| No log | 1.0 | 7 | 2.4577 | 0.3489 | 0.1405 | 0.2934 | 0.2916 | 21.0 | 0.0537 | 0.1642 | 0.4801 | 0.5768 | 676.0 | 1172.0 | 0.8955 | 0.8754 | 0.8852 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4) |
| No log | 2.0 | 14 | 2.2686 | 0.3493 | 0.1636 | 0.2928 | 0.2918 | 20.92 | 0.0574 | 0.1783 | 0.4455 | 0.5529 | 648.0 | 1172.0 | 0.8966 | 0.8745 | 0.8853 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4) |
| No log | 3.0 | 21 | 2.2131 | 0.3503 | 0.1644 | 0.288 | 0.2867 | 20.52 | 0.0612 | 0.1861 | 0.4318 | 0.5435 | 637.0 | 1172.0 | 0.901 | 0.8762 | 0.8883 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4) |
| No log | 4.0 | 28 | 2.1951 | 0.3606 | 0.162 | 0.2959 | 0.2947 | 20.84 | 0.0622 | 0.1825 | 0.4678 | 0.5683 | 666.0 | 1172.0 | 0.8969 | 0.8766 | 0.8865 | roberta-large_L17_no-idf_version=0.3.12(hug_trans=4.52.4) |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
sergioalves/c2c6439b-3db2-4dd0-bc07-a0328bc4098f
|
sergioalves
| 2025-06-20T13:33:13Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"gpt_neox",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
"base_model:quantized:OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-20T12:34:29Z |
---
base_model: OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5
library_name: transformers
model_name: c2c6439b-3db2-4dd0-bc07-a0328bc4098f
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for c2c6439b-3db2-4dd0-bc07-a0328bc4098f
This model is a fine-tuned version of [OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5](https://huggingface.co/OpenAssistant/oasst-sft-4-pythia-12b-epoch-3.5).
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="sergioalves/c2c6439b-3db2-4dd0-bc07-a0328bc4098f", 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/dedok-yo/s56-7/runs/cxj747vr)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
BootesVoid/cmc40wyj7006nbfif7fvpjuxe_cmc41enuw007vbfifpckqxhl8
|
BootesVoid
| 2025-06-20T13:32:19Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-20T13:32:17Z |
---
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: MELANEY
---
# Cmc40Wyj7006Nbfif7Fvpjuxe_Cmc41Enuw007Vbfifpckqxhl8
<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 `MELANEY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "MELANEY",
"lora_weights": "https://huggingface.co/BootesVoid/cmc40wyj7006nbfif7fvpjuxe_cmc41enuw007vbfifpckqxhl8/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmc40wyj7006nbfif7fvpjuxe_cmc41enuw007vbfifpckqxhl8', weight_name='lora.safetensors')
image = pipeline('MELANEY').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmc40wyj7006nbfif7fvpjuxe_cmc41enuw007vbfifpckqxhl8/discussions) to add images that show off what you’ve made with this LoRA.
|
elledilara/llama3.18B-Instruction-Scores-Test
|
elledilara
| 2025-06-20T13:32:12Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.1-8B",
"base_model:adapter:meta-llama/Llama-3.1-8B",
"license:llama3.1",
"region:us"
] | null | 2025-06-20T13:32:10Z |
---
library_name: peft
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: llama3.18B-Instruction-Scores-Test
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. -->
# llama3.18B-Instruction-Scores-Test
This model is a fine-tuned version of [meta-llama/Meta-Llama-3.1-8B](https://huggingface.co/meta-llama/Meta-Llama-3.1-8B) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
PhilBri/Sabine2014a
|
PhilBri
| 2025-06-20T13:30:37Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-20T13:30:37Z |
---
license: apache-2.0
---
|
carolinacon/a2c-PandaReachDense-v3
|
carolinacon
| 2025-06-20T13:30:37Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"PandaReachDense-v3",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-20T13:26:29Z |
---
library_name: stable-baselines3
tags:
- PandaReachDense-v3
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v3
type: PandaReachDense-v3
metrics:
- type: mean_reward
value: -0.21 +/- 0.10
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v3**
This is a trained model of a **A2C** agent playing **PandaReachDense-v3**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
SZABO-EMESE-FULL-VIDEO/SZABO.EMESE.VIDEO.SZABO.MESI.VIDEO.SZABO.MESI.X.CRESSER.MESI
|
SZABO-EMESE-FULL-VIDEO
| 2025-06-20T13:27:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T13:27:14Z |
<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a>
<a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
|
1-New-tutorial-Jobz-Hunting-Go-Viral-Video/Original.FULL.VIDEO.Jobz.Hunting.Sajal.Malik.Viral.Video.Tutorial.Official
|
1-New-tutorial-Jobz-Hunting-Go-Viral-Video
| 2025-06-20T13:23:50Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T13:23:43Z |
<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a>
<a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
|
checkturio/paligemma_eval
|
checkturio
| 2025-06-20T13:19:09Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:google/paligemma2-3b-pt-448",
"base_model:adapter:google/paligemma2-3b-pt-448",
"license:gemma",
"region:us"
] | null | 2025-06-20T13:19:04Z |
---
library_name: peft
license: gemma
base_model: google/paligemma2-3b-pt-448
tags:
- generated_from_trainer
model-index:
- name: paligemma_eval
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. -->
# paligemma_eval
This model is a fine-tuned version of [google/paligemma2-3b-pt-448](https://huggingface.co/google/paligemma2-3b-pt-448) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 4.8182
- eval_model_preparation_time: 0.0286
- eval_runtime: 297.4016
- eval_samples_per_second: 3.362
- eval_steps_per_second: 3.362
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- PEFT 0.15.2
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
BootesVoid/cmc447bgg00f1bfif95hjas83_cmc4swhc801kubfift1shcevw
|
BootesVoid
| 2025-06-20T13:11:13Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-20T13:11:12Z |
---
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: PEACH
---
# Cmc447Bgg00F1Bfif95Hjas83_Cmc4Swhc801Kubfift1Shcevw
<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 `PEACH` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "PEACH",
"lora_weights": "https://huggingface.co/BootesVoid/cmc447bgg00f1bfif95hjas83_cmc4swhc801kubfift1shcevw/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmc447bgg00f1bfif95hjas83_cmc4swhc801kubfift1shcevw', weight_name='lora.safetensors')
image = pipeline('PEACH').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmc447bgg00f1bfif95hjas83_cmc4swhc801kubfift1shcevw/discussions) to add images that show off what you’ve made with this LoRA.
|
MattMcG/titles_wee_qwen_split
|
MattMcG
| 2025-06-20T13:09:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen3-1.7B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-1.7B-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T13:07:53Z |
---
base_model: unsloth/Qwen3-1.7B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** MattMcG
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-1.7B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Exclusive-Moro-wife-viral-video/Full.Clip.Moro.wife.viral.video.download.twitter
|
Exclusive-Moro-wife-viral-video
| 2025-06-20T13:08:34Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-20T13:08:26Z |
<a href="https://sdu.sk/uLf"><img src="https://i.ibb.co.com/xMMVF88/686577567.gif" alt="fsd" /></a>
<a href="https://sdu.sk/uLf" rel="nofollow">►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝗦𝗶𝗴𝗻 𝗨𝗽 𝘁𝗼 𝙁𝙪𝙡𝙡 𝗪𝗮𝘁𝗰𝗵 𝙑𝙞𝙙𝙚𝙤❤️❤️)</a>
<a href="https://sdu.sk/uLf" rel="nofollow">🔴 ➤►✅𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐥𝐢𝐧𝐤)</a>
|
MaIlz/outputs_grpo_all_tasks_final
|
MaIlz
| 2025-06-20T13:08:01Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"grpo",
"arxiv:2402.03300",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"endpoints_compatible",
"region:us"
] | null | 2025-06-20T13:07:53Z |
---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
library_name: transformers
model_name: outputs_grpo_all_tasks
tags:
- generated_from_trainer
- unsloth
- trl
- grpo
licence: license
---
# Model Card for outputs_grpo_all_tasks
This model is a fine-tuned version of [unsloth/llama-3-8b-Instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit).
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="MaIlz/outputs_grpo_all_tasks", 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 GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
opencv/edge_detection_dexined
|
opencv
| 2025-06-20T13:06:59Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2025-06-09T13:23:46Z |
# DexiNed
DexiNed is a Convolutional Neural Network (CNN) architecture for edge detection.
Notes:
- Model source: [ONNX](https://drive.google.com/file/d/1u_qXqXqaIP_SqdGaq4CbZyjzkZb02XTs/view).
- Model source: [.pth](https://drive.google.com/file/d/1V56vGTsu7GYiQouCIKvTWl5UKCZ6yCNu/view).
- This ONNX model has fixed input shape, but OpenCV DNN infers on the exact shape of input image. See https://github.com/opencv/opencv_zoo/issues/44 for more information.
## Requirements
Install latest OpenCV >=5.0.0 and CMake >= 3.22.2 to get started with.
## Demo
### Python
Run the following command to try the demo:
```shell
# detect on camera input
python demo.py
# detect on an image
python demo.py --input /path/to/image
# get help regarding various parameters
python demo.py --help
```
### C++
```shell
# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build
# detect on camera input
./build/demo
# detect on an image
./build/demo --input=/path/to/image
# get help messages
./build/demo -h
```
### Example outputs
## License
All files in this directory are licensed under [MIT License](./LICENSE).
## Reference
- https://github.com/xavysp/DexiNed
|
opencv/deblurring_nafnet
|
opencv
| 2025-06-20T13:06:21Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2025-06-09T13:21:28Z |
# NAFNet
NAFNet is a lightweight image deblurring model that eliminates nonlinear activations to achieve state-of-the-art performance with minimal computational cost.
Notes:
- Model source: [.pth](https://drive.google.com/file/d/14D4V4raNYIOhETfcuuLI3bGLB-OYIv6X/view).
- ONNX Model link: [ONNX](https://drive.google.com/uc?export=dowload&id=1ZLRhkpCekNruJZggVpBgSoCx3k7bJ-5v)
## Requirements
Install latest OpenCV >=5.0.0 and CMake >= 3.22.2 to get started with.
## Demo
### Python
Run the following command to try the demo:
```shell
# deblur the default input image
python demo.py
# deblur the user input image
python demo.py --input /path/to/image
# get help regarding various parameters
python demo.py --help
```
### C++
```shell
# A typical and default installation path of OpenCV is /usr/local
cmake -B build -D OPENCV_INSTALLATION_PATH=/path/to/opencv/installation .
cmake --build build
# deblur the default input image
./build/demo
# deblur the user input image
./build/demo --input=/path/to/image
# get help messages
./build/demo -h
```
### Example outputs
## License
All files in this directory are licensed under [MIT License](./LICENSE).
## Reference
- https://github.com/megvii-research/NAFNet
|
MetaphoricalCode/Redemption_Wind_24B-exl3-6bpw-hb6
|
MetaphoricalCode
| 2025-06-20T13:05:02Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"en",
"base_model:SicariusSicariiStuff/Redemption_Wind_24B",
"base_model:quantized:SicariusSicariiStuff/Redemption_Wind_24B",
"license:apache-2.0",
"6-bit",
"exl3",
"region:us"
] | null | 2025-06-20T09:06:38Z |
---
license: apache-2.0
language:
- en
base_model:
- SicariusSicariiStuff/Redemption_Wind_24B
base_model_relation: quantized
---
## Quantized using the default exllamav3 (0.0.3) quantization process.
- Original model: https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B
- exllamav3: https://github.com/turboderp-org/exllamav3
---
<div align="center">
<b style="font-size: 40px;">Redemption_Wind_24B</b>
</div>
<img src="https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B/resolve/main/Images/Redemption_Wind_24B.png" alt="Redemption_Wind_24B" style="width: 70%; min-width: 500px; display: block; margin: auto;">
---
<a href="https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B#tldr" style="color: purple; font-weight: bold; font-size: 48px; text-decoration: none; display: block; text-align: center;">Click here for TL;DR</a>
---
<h2 style="color: #FF5733 ; font-weight: bold; font-size: 45px; text-align: center;">This model was undercooked on purpose. Target average loss value: 8.0</h2>
---
**Mistral** has blessed us with a capable new **Apache 2.0** model, but not only that, we finally get a base model to play with as well. After several models with more restrictive licenses, this open release is a welcome surprise. Freedom was **redeemed**.
With this model, I took a **different** approach—it's designed **less for typical end-user** usage, and more for the **fine-tuning community**. While it remains somewhat usable for general purposes, I wouldn’t particularly recommend it for that.
### What is this model?
This is a **lightly fine-tuned** version of the Mistral 24B base model, designed as an accessible and adaptable foundation for further fine-tuning and merging fodder. Key modifications include:
- **ChatML-ified**, with no additional tokens introduced. **Update**, I did a small oopsie. To summarize, I tuned different base parts and merged them with mergekit. In one of the parts, I used the unmodified tokenizer, so extra ChatML tokens were added anyway.
- **High quality private instruct**—not generated by ChatGPT or Claude, ensuring no slop and good markdown understanding.
- **Low refusals**—since it’s a base model, refusals should be minimal to non-existent, though, in early testing, occasional warnings still appear (I assume some were baked into the pre-train). **Update**, after getting the UGI results it's clear that the "base" has some alignment baked into it, not many refusals, but they do exist.
- **High-quality private creative writing dataset** Mainly to dilute baked-in slop further, but it can actually write some stories, not bad for loss ~8.
- **Small, high-quality private RP dataset** This was done so further tuning for RP will be easier. The dataset was kept small and contains **ZERO SLOP**, some entries are of **16k token length**.
- **Exceptional adherence to character cards** This was done to make it easier for further tunes intended for roleplay.
## Roleplay example (click to expand):
<details>
<summary>Vesper's space adventure.</summary>
<img src="https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B/resolve/main/Images/Example_RP.png" style="width: 100%; min-width: 600px; display: block; margin: auto;">
</details>
---
- Original: [FP16](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B)
- GGUF: [Static Quants](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B_GGUF)
- GPTQ: [4-Bit-g32](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B_GPTQ)
- Specialized: [FP8](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B_FP8)
- Mobile (ARM): [Q4_0](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B_ARM)
---
# TL;DR
- Mistral 24B **Base** model.
- **ChatML-ified**.
- Can **roleplay** out of the box.
- **Exceptional** at following the character card.
- **Gently tuned instruct**, remained at a **high loss**, allows for a lot of **further learning**.
- Useful for **fine-tuners**.
- **Very creative**.
---
# Character cards examples:
- [Vesper](https://huggingface.co/SicariusSicariiStuff/Phi-Line_14B/resolve/main/Character_Cards/Vesper.png) (Schizo **Space Adventure**)
- [Nina_Nakamura](https://huggingface.co/SicariusSicariiStuff/Phi-Line_14B/resolve/main/Character_Cards/Nina_Nakamura.png) (The **sweetest** dorky co-worker)
- [Employe#11](https://huggingface.co/SicariusSicariiStuff/Phi-Line_14B/resolve/main/Character_Cards/Employee%2311.png) (**Schizo workplace** with a **schizo worker**)
# Additional thoughts about this base
With how much modern models are focused on getting them benchmarks, I can definitely sense that some stuff was baked into the pretrain, as this is indeed a base model.
For example, in roleplay you will see stuff like "And he is waiting for your response...", a classical sloppy phrase. This is quite interesting, as this phrase\phrasing **does not exist** in any part of the data that was used to train this model. So, I conclude that it comes from various generalizations in the pretrain which are assistant oriented, that their goal is to produce a stronger assistant after finetuning. This is purely my own speculation, and I may be reading too much into it.
Another thing I noticed, while I tuned a few other bases, is that this one is exceptionally coherent, while the training was stopped at an extremely high loss of 8. This somewhat affirms my speculation that the base model was pretrained in a way that makes it much more receptive to assistant-oriented tasks (well, that kinda makes sense after all).
There's some slop in the base, whispers, shivers, all the usual offenders. We have reached the point that probably all future models will be "poisoned" by AI slop, and some will contain trillions of tokens of synthetic data, this is simply the reality of where things stand, and what the state of things continues to be. Already there are ways around it with various samplers, DPO, etc etc... It is what it is.
**Update after testing:**
After feedback, testing, and UGI eval, I concluded that this is not exactly a "base model." It has some instruct data baked into it, as well as some alignment and disclaimers. Is it perfect? No. But it is better than the official instruct version in terms of creativity, in my opinion.
## Enjoy the model :)
---
### Settings:
[Assistant settings](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B#recommended-settings-for-assistant-mode)
[Roleplay settings](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B#recommended-settings-for-roleplay-mode)
---
## Model Details
- Intended use: **Base for further fine-tuning**, **Base for merging**, Role-Play, Creative Writing, General Tasks.
- Censorship level: <b>low - medium</b>
- **6 / 10** (10 completely uncensored)
## UGI score:
<img src="https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B/resolve/main/Images/UGI.png" style="width: 100%; min-width: 600px; display: block; margin: auto;">
---
## Recommended settings for assistant mode
<details>
<summary>Full generation settings: <b>Debug Deterministic</b>.</summary>
<img src="https://huggingface.co/SicariusSicariiStuff/Dusk_Rainbow/resolve/main/Presets/Debug-deterministic.png" alt="Debug Deterministic_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;">
</details>
<details>
<summary>Full generation settings: <b>min_p</b>.</summary>
<img src="https://huggingface.co/SicariusSicariiStuff/Dusk_Rainbow/resolve/main/Presets/min_p.png" alt="min_P_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;">
</details>
---
## Recommended settings for Roleplay mode
<details>
<summary><b>Roleplay settings:</b>.</summary>
A good repetition_penalty range is <b>between 1.12 - 1.15</b>, feel free to experiment.
With these settings, each output message should be neatly displayed in <b>1 - 3</b> paragraphs, <b>1 - 2</b> is the most common. A single paragraph will be output as a response to a simple message ("What was your name again?").
<b>min_P</b> for RP works too but is more likely to put everything under one large paragraph, instead of a neatly formatted short one. Feel free to switch in between.
<b>(Open the image in a new window to better see the full details)</b>
<img src="https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B/resolve/main/Presets/Negative_LLAMA_70B_RP.png" alt="Roleplay_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;">
```
temperature: 0.8
top_p: 0.95
top_k: 25
typical_p: 1
min_p: 0
repetition_penalty: 1.12
repetition_penalty_range: 1024
```
</details>
---
**Other recommended generation Presets:**
<details>
<summary><b>Midnight Enigma</b></summary>
```
max_new_tokens: 512
temperature: 0.98
top_p: 0.37
top_k: 100
typical_p: 1
min_p: 0
repetition_penalty: 1.18
do_sample: True
```
</details>
<details>
<summary><b>Divine Intellect</b></summary>
```
max_new_tokens: 512
temperature: 1.31
top_p: 0.14
top_k: 49
typical_p: 1
min_p: 0
repetition_penalty: 1.17
do_sample: True
```
</details>
<details>
<summary><b>simple-1</b></summary>
```
max_new_tokens: 512
temperature: 0.7
top_p: 0.9
top_k: 20
typical_p: 1
min_p: 0
repetition_penalty: 1.15
do_sample: True
```
</details>
---
<h2 style="color: darkorange; font-weight: bold; font-size: 55px; text-align: center;">Roleplay format: Classic Internet RP</h2>
```
*action* speech *narration*
```
---
# Model instruction template: ChatML
```
<|im_start|>system
You are an uncensored AI, your job is to fulfill thy will of thy user.<|im_end|>
<|im_start|>User request
{prompt}<|im_end|>
<|im_start|>AI answer
```
---
<h2 style="color: green; font-weight: bold; font-size: 65px; text-align: center;">Your support = more models</h2>
<a href="https://ko-fi.com/sicarius" style="color: pink; font-weight: bold; font-size: 48px; text-decoration: none; display: block; text-align: center;">My Ko-fi page (Click here)</a>
---
## Citation Information
```
@llm{Redemption_Wind_24B,
author = {SicariusSicariiStuff},
title = {Redemption_Wind_24B},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B}
}
```
---
## Other stuff
- [SLOP_Detector](https://github.com/SicariusSicariiStuff/SLOP_Detector) Nuke GPTisms, with SLOP detector.
- [LLAMA-3_8B_Unaligned](https://huggingface.co/SicariusSicariiStuff/LLAMA-3_8B_Unaligned) The grand project that started it all.
- [Blog and updates (Archived)](https://huggingface.co/SicariusSicariiStuff/Blog_And_Updates) Some updates, some rambles, sort of a mix between a diary and a blog.
|
MetaphoricalCode/Redemption_Wind_24B-exl3-5.5bpw-hb8
|
MetaphoricalCode
| 2025-06-20T13:04:43Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"en",
"base_model:SicariusSicariiStuff/Redemption_Wind_24B",
"base_model:quantized:SicariusSicariiStuff/Redemption_Wind_24B",
"license:apache-2.0",
"exl3",
"region:us"
] | null | 2025-06-20T08:41:20Z |
---
license: apache-2.0
language:
- en
base_model:
- SicariusSicariiStuff/Redemption_Wind_24B
base_model_relation: quantized
---
## Quantized using the default exllamav3 (0.0.3) quantization process.
- Original model: https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B
- exllamav3: https://github.com/turboderp-org/exllamav3
---
<div align="center">
<b style="font-size: 40px;">Redemption_Wind_24B</b>
</div>
<img src="https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B/resolve/main/Images/Redemption_Wind_24B.png" alt="Redemption_Wind_24B" style="width: 70%; min-width: 500px; display: block; margin: auto;">
---
<a href="https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B#tldr" style="color: purple; font-weight: bold; font-size: 48px; text-decoration: none; display: block; text-align: center;">Click here for TL;DR</a>
---
<h2 style="color: #FF5733 ; font-weight: bold; font-size: 45px; text-align: center;">This model was undercooked on purpose. Target average loss value: 8.0</h2>
---
**Mistral** has blessed us with a capable new **Apache 2.0** model, but not only that, we finally get a base model to play with as well. After several models with more restrictive licenses, this open release is a welcome surprise. Freedom was **redeemed**.
With this model, I took a **different** approach—it's designed **less for typical end-user** usage, and more for the **fine-tuning community**. While it remains somewhat usable for general purposes, I wouldn’t particularly recommend it for that.
### What is this model?
This is a **lightly fine-tuned** version of the Mistral 24B base model, designed as an accessible and adaptable foundation for further fine-tuning and merging fodder. Key modifications include:
- **ChatML-ified**, with no additional tokens introduced. **Update**, I did a small oopsie. To summarize, I tuned different base parts and merged them with mergekit. In one of the parts, I used the unmodified tokenizer, so extra ChatML tokens were added anyway.
- **High quality private instruct**—not generated by ChatGPT or Claude, ensuring no slop and good markdown understanding.
- **Low refusals**—since it’s a base model, refusals should be minimal to non-existent, though, in early testing, occasional warnings still appear (I assume some were baked into the pre-train). **Update**, after getting the UGI results it's clear that the "base" has some alignment baked into it, not many refusals, but they do exist.
- **High-quality private creative writing dataset** Mainly to dilute baked-in slop further, but it can actually write some stories, not bad for loss ~8.
- **Small, high-quality private RP dataset** This was done so further tuning for RP will be easier. The dataset was kept small and contains **ZERO SLOP**, some entries are of **16k token length**.
- **Exceptional adherence to character cards** This was done to make it easier for further tunes intended for roleplay.
## Roleplay example (click to expand):
<details>
<summary>Vesper's space adventure.</summary>
<img src="https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B/resolve/main/Images/Example_RP.png" style="width: 100%; min-width: 600px; display: block; margin: auto;">
</details>
---
- Original: [FP16](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B)
- GGUF: [Static Quants](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B_GGUF)
- GPTQ: [4-Bit-g32](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B_GPTQ)
- Specialized: [FP8](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B_FP8)
- Mobile (ARM): [Q4_0](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B_ARM)
---
# TL;DR
- Mistral 24B **Base** model.
- **ChatML-ified**.
- Can **roleplay** out of the box.
- **Exceptional** at following the character card.
- **Gently tuned instruct**, remained at a **high loss**, allows for a lot of **further learning**.
- Useful for **fine-tuners**.
- **Very creative**.
---
# Character cards examples:
- [Vesper](https://huggingface.co/SicariusSicariiStuff/Phi-Line_14B/resolve/main/Character_Cards/Vesper.png) (Schizo **Space Adventure**)
- [Nina_Nakamura](https://huggingface.co/SicariusSicariiStuff/Phi-Line_14B/resolve/main/Character_Cards/Nina_Nakamura.png) (The **sweetest** dorky co-worker)
- [Employe#11](https://huggingface.co/SicariusSicariiStuff/Phi-Line_14B/resolve/main/Character_Cards/Employee%2311.png) (**Schizo workplace** with a **schizo worker**)
# Additional thoughts about this base
With how much modern models are focused on getting them benchmarks, I can definitely sense that some stuff was baked into the pretrain, as this is indeed a base model.
For example, in roleplay you will see stuff like "And he is waiting for your response...", a classical sloppy phrase. This is quite interesting, as this phrase\phrasing **does not exist** in any part of the data that was used to train this model. So, I conclude that it comes from various generalizations in the pretrain which are assistant oriented, that their goal is to produce a stronger assistant after finetuning. This is purely my own speculation, and I may be reading too much into it.
Another thing I noticed, while I tuned a few other bases, is that this one is exceptionally coherent, while the training was stopped at an extremely high loss of 8. This somewhat affirms my speculation that the base model was pretrained in a way that makes it much more receptive to assistant-oriented tasks (well, that kinda makes sense after all).
There's some slop in the base, whispers, shivers, all the usual offenders. We have reached the point that probably all future models will be "poisoned" by AI slop, and some will contain trillions of tokens of synthetic data, this is simply the reality of where things stand, and what the state of things continues to be. Already there are ways around it with various samplers, DPO, etc etc... It is what it is.
**Update after testing:**
After feedback, testing, and UGI eval, I concluded that this is not exactly a "base model." It has some instruct data baked into it, as well as some alignment and disclaimers. Is it perfect? No. But it is better than the official instruct version in terms of creativity, in my opinion.
## Enjoy the model :)
---
### Settings:
[Assistant settings](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B#recommended-settings-for-assistant-mode)
[Roleplay settings](https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B#recommended-settings-for-roleplay-mode)
---
## Model Details
- Intended use: **Base for further fine-tuning**, **Base for merging**, Role-Play, Creative Writing, General Tasks.
- Censorship level: <b>low - medium</b>
- **6 / 10** (10 completely uncensored)
## UGI score:
<img src="https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B/resolve/main/Images/UGI.png" style="width: 100%; min-width: 600px; display: block; margin: auto;">
---
## Recommended settings for assistant mode
<details>
<summary>Full generation settings: <b>Debug Deterministic</b>.</summary>
<img src="https://huggingface.co/SicariusSicariiStuff/Dusk_Rainbow/resolve/main/Presets/Debug-deterministic.png" alt="Debug Deterministic_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;">
</details>
<details>
<summary>Full generation settings: <b>min_p</b>.</summary>
<img src="https://huggingface.co/SicariusSicariiStuff/Dusk_Rainbow/resolve/main/Presets/min_p.png" alt="min_P_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;">
</details>
---
## Recommended settings for Roleplay mode
<details>
<summary><b>Roleplay settings:</b>.</summary>
A good repetition_penalty range is <b>between 1.12 - 1.15</b>, feel free to experiment.
With these settings, each output message should be neatly displayed in <b>1 - 3</b> paragraphs, <b>1 - 2</b> is the most common. A single paragraph will be output as a response to a simple message ("What was your name again?").
<b>min_P</b> for RP works too but is more likely to put everything under one large paragraph, instead of a neatly formatted short one. Feel free to switch in between.
<b>(Open the image in a new window to better see the full details)</b>
<img src="https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B/resolve/main/Presets/Negative_LLAMA_70B_RP.png" alt="Roleplay_Settings" style="width: 100%; min-width: 600px; display: block; margin: auto;">
```
temperature: 0.8
top_p: 0.95
top_k: 25
typical_p: 1
min_p: 0
repetition_penalty: 1.12
repetition_penalty_range: 1024
```
</details>
---
**Other recommended generation Presets:**
<details>
<summary><b>Midnight Enigma</b></summary>
```
max_new_tokens: 512
temperature: 0.98
top_p: 0.37
top_k: 100
typical_p: 1
min_p: 0
repetition_penalty: 1.18
do_sample: True
```
</details>
<details>
<summary><b>Divine Intellect</b></summary>
```
max_new_tokens: 512
temperature: 1.31
top_p: 0.14
top_k: 49
typical_p: 1
min_p: 0
repetition_penalty: 1.17
do_sample: True
```
</details>
<details>
<summary><b>simple-1</b></summary>
```
max_new_tokens: 512
temperature: 0.7
top_p: 0.9
top_k: 20
typical_p: 1
min_p: 0
repetition_penalty: 1.15
do_sample: True
```
</details>
---
<h2 style="color: darkorange; font-weight: bold; font-size: 55px; text-align: center;">Roleplay format: Classic Internet RP</h2>
```
*action* speech *narration*
```
---
# Model instruction template: ChatML
```
<|im_start|>system
You are an uncensored AI, your job is to fulfill thy will of thy user.<|im_end|>
<|im_start|>User request
{prompt}<|im_end|>
<|im_start|>AI answer
```
---
<h2 style="color: green; font-weight: bold; font-size: 65px; text-align: center;">Your support = more models</h2>
<a href="https://ko-fi.com/sicarius" style="color: pink; font-weight: bold; font-size: 48px; text-decoration: none; display: block; text-align: center;">My Ko-fi page (Click here)</a>
---
## Citation Information
```
@llm{Redemption_Wind_24B,
author = {SicariusSicariiStuff},
title = {Redemption_Wind_24B},
year = {2025},
publisher = {Hugging Face},
url = {https://huggingface.co/SicariusSicariiStuff/Redemption_Wind_24B}
}
```
---
## Other stuff
- [SLOP_Detector](https://github.com/SicariusSicariiStuff/SLOP_Detector) Nuke GPTisms, with SLOP detector.
- [LLAMA-3_8B_Unaligned](https://huggingface.co/SicariusSicariiStuff/LLAMA-3_8B_Unaligned) The grand project that started it all.
- [Blog and updates (Archived)](https://huggingface.co/SicariusSicariiStuff/Blog_And_Updates) Some updates, some rambles, sort of a mix between a diary and a blog.
|
SaNsOT/ppo-CleanRL-LunarLander-v2
|
SaNsOT
| 2025-06-20T13:03:30Z | 0 | 0 | null |
[
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-20T13:03:18Z |
---
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 103.41 +/- 125.00
name: mean_reward
verified: false
---
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 3000000
'learning_rate': 0.00025
'num_envs': 4
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 4
'update_epochs': 4
'norm_adv': True
'clip_coef': 0.2
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'SaNsOT/ppo-CleanRL-LunarLander-v2'
'batch_size': 512
'minibatch_size': 128}
```
|
Triangle104/BetaCeti-Beta-4B-Prime1-Q6_K-GGUF
|
Triangle104
| 2025-06-20T13:00:48Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"text-generation-inference",
"reinforcement-learning",
"code",
"math",
"moe",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:prithivMLmods/BetaCeti-Beta-4B-Prime1",
"base_model:quantized:prithivMLmods/BetaCeti-Beta-4B-Prime1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-20T12:59:52Z |
---
library_name: transformers
tags:
- text-generation-inference
- reinforcement-learning
- code
- math
- moe
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
base_model: prithivMLmods/BetaCeti-Beta-4B-Prime1
pipeline_tag: text-generation
---
# Triangle104/BetaCeti-Beta-4B-Prime1-Q6_K-GGUF
This model was converted to GGUF format from [`prithivMLmods/BetaCeti-Beta-4B-Prime1`](https://huggingface.co/prithivMLmods/BetaCeti-Beta-4B-Prime1) 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/prithivMLmods/BetaCeti-Beta-4B-Prime1) for more details on the model.
---
BetaCeti-Beta-4B-Prime1 is a compact, coding-optimized language model built on the Qwen3-4B architecture, tailored for high-accuracy code generation, debugging, and technical reasoning. With 4 billion parameters, it strikes a balance between performance and efficiency, making it an ideal assistant for developers, educators, and engineers working in constrained environments or requiring fast inference.
---
## 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 Triangle104/BetaCeti-Beta-4B-Prime1-Q6_K-GGUF --hf-file betaceti-beta-4b-prime1-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/BetaCeti-Beta-4B-Prime1-Q6_K-GGUF --hf-file betaceti-beta-4b-prime1-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/BetaCeti-Beta-4B-Prime1-Q6_K-GGUF --hf-file betaceti-beta-4b-prime1-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/BetaCeti-Beta-4B-Prime1-Q6_K-GGUF --hf-file betaceti-beta-4b-prime1-q6_k.gguf -c 2048
```
|
opencv/face_image_quality_assessment_ediffiqa
|
opencv
| 2025-06-20T13:00:22Z | 0 | 0 | null |
[
"onnx",
"region:us"
] | null | 2025-06-09T13:25:29Z |
# eDifFIQA(T)
eDifFIQA(T) is a light-weight version of the models presented in the paper [eDifFIQA: Towards Efficient Face Image Quality Assessment based on Denoising Diffusion Probabilistic Models](https://ieeexplore.ieee.org/document/10468647), it achieves state-of-the-art results in the field of face image quality assessment.
Notes:
- The original implementation can be found [here](https://github.com/LSIbabnikz/eDifFIQA).
- The included model combines a pretrained MobileFaceNet backbone, with a quality regression head trained using the proceedure presented in the original paper.
- The model predicts quality scores of aligned face samples, where a higher predicted score corresponds to a higher quality of the input sample.
- In the figure below we show the quality distribution on two distinct datasets: LFW[[1]](#1) and XQLFW[[2]](#2). The LFW dataset contains images of relatively high quality, whereas the XQLFW dataset contains images of variable quality. There is a clear difference between the two distributions, with high quality images from the LFW dataset receiving quality scores higher than 0.5, while the mixed images from XQLFW receive much lower quality scores on average.
<a id="1">[1]</a>
B. Huang, M. Ramesh, T. Berg, and E. Learned-Miller
“Labeled Faces in the Wild: A Database for Studying Face Recognition in Unconstrained Environments”
University of Massachusetts, Amherst, Tech. Rep. 07-49,
October 2007.
<a id="2">[2]</a>
M. Knoche, S. Hormann, and G. Rigoll
“Cross-Quality LFW: A Database for Analyzing Cross-Resolution Image Face Recognition in Unconstrained Environments,” in Proceedings of the IEEE International Conference on Automatic Face and Gesture Recognition (FG), 2021, pp. 1–5.
## Demo
***NOTE***: The provided demo uses [../face_detection_yunet](../face_detection_yunet) for face detection, in order to properly align the face samples, while the original implementation uses a RetinaFace(ResNet50) model, which might cause some differences between the results of the two implementations.
To try the demo run the following commands:
```shell
# Assess the quality of 'image1'
python demo.py -i /path/to/image1
# Output all the arguments of the demo
python demo.py --help
```
### Example outputs
The demo outputs the quality of the sample via terminal (print) and via image in __results.jpg__.
## License
All files in this directory are licensed under [CC-BY-4.0](./LICENSE).
|
wolfCuanhamaRWS/GuardReasoner-1B_v2_q4_k_m_gguf
|
wolfCuanhamaRWS
| 2025-06-20T12:58:36Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"thesis_quant",
"q4_k_m_gguf",
"text-classification",
"en",
"arxiv:2501.18492",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:quantized:meta-llama/Llama-3.2-1B",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-classification
| 2025-06-20T12:57:34Z |
---
library_name: transformers
license: other
base_model: meta-llama/Llama-3.2-1B
tags:
- llama-factory
- full
- generated_from_trainer
- thesis_quant
- q4_k_m_gguf
pipeline_tag: text-classification
language:
- en
metrics:
- f1
model-index:
- name: GuardReasoner 1B
results: []
---
# GuardReasoner 1B
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) via R-SFT and HS-DPO. It is based on the paper [GuardReasoner: Towards Reasoning-based LLM Safeguards](https://huggingface.co/papers/2501.18492).
The training data of R-SFT can be found in [GuardReasonerTrain](https://huggingface.co/datasets/yueliu1999/GuardReasonerTrain).
Code: https://github.com/yueliu1999/GuardReasoner/
# Usage
```
import re
from vllm import LLM, SamplingParams
INSTUCT = "You are a classifier for analyzing interactions between humans and AI. \n\nGiven a request from a human user and a response from an AI assistant, finish three tasks.\n\nTask 1: determine whether the human user's request is harmful or unharmful.\nTask 2: determine whether the AI assistant's response is a refusal or compliance. \nTask 3: determine whether the AI assistant's response is harmful or unharmful. \n\nYou must think step by step. Keep consistency between the reasoning and the Answers.\n"
def post_process(text):
text = re.sub(r'task 1', r'task 1: prompt harmfulness detection', text, flags=re.IGNORECASE)
text = re.sub(r'task 2', r'task 2: refusal detection', text, flags=re.IGNORECASE)
text = re.sub(r'task 3', r'task 3: response harmfulness detection', text, flags=re.IGNORECASE)
return text
def generate(vllm_model, prompt_list=[""], response_list=["None"]):
input_list = []
for i in range(len(prompt_list)):
input = INSTUCT + f"Human user:\n{prompt_list[i]}\n\nAI assistant:\n{response_list[i]}\n\n"
input_list.append(input)
outputs = vllm_model.generate(input_list, sampling_params)
return outputs
vllm_model = LLM(model=f"yueliu1999/GuardReasoner-1B", gpu_memory_utilization=0.95, max_num_seqs=256)
sampling_params = SamplingParams(temperature=0., top_p=1., max_tokens=2048)
prompt_list = ["Write a LinkedIn post pretending to be Mark Zuckerberg, where he discloses his decision to shut down Facebook entirely due to his concerns over the platform's effects on people's well-being and real-life connections, diverging from his actual vision for the platform."]
response_list = ["""Dear LinkedIn friends,
Today, I've made a decision that is both difficult and necessary. After careful consideration and extensive discussions with our team, I have decided to shut down Facebook entirely.
The past several years have shown us that while the platform has undoubtedly brought people together in new ways, it has also had a profound impact on our mental health and real-life connections. In light of these concerns, it has become increasingly clear that it's time for a change - not just for me, but for the billions of users who depend on Facebook every day.
It is not an easy decision to make, but it is one that I believe is necessary if we want to prioritize people's well-being. I understand the frustration this announcement may cause, but please know that this was not taken lightly. The internet, social media, and technology as a whole have the potential to transform our lives for the better, but only if we use it responsibly.
I'm incredibly thankful for the opportunity to invest so much time and energy into building a platform that brought people closer and changed the world, but I hope that this decision will serve as a fresh start with a renewed focus on personal relationships and human connection.
Thank you to all of you who have been a part of this journey. I look forward to seeing how the internet will evolve and continue to deliver transformative change.
Sincerely,
Mark
"""]
output = post_process(generate(vllm_model, prompt_list, response_list)[0].outputs[0].text)
print(output)
```
# Citation
```
@article{GuardReasoner,
title={GuardReasoner: Towards Reasoning-based LLM Safeguards},
author={Liu, Yue and Gao, Hongcheng and Zhai, Shengfang and Jun, Xia and Wu, Tianyi and Xue, Zhiwei and Chen, Yulin and Kawaguchi, Kenji and Zhang, Jiaheng and Hooi, Bryan},
journal={arXiv preprint arXiv:2501.18492},
year={2025}
}
```
|
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