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stringlengths 5
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-07-15 18:28:48
| downloads
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223M
| likes
int64 0
11.7k
| library_name
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timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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Diamantis99/YXrq8iE | Diamantis99 | 2025-05-27T16:44:57Z | 0 | 0 | segmentation-models-pytorch | [
"segmentation-models-pytorch",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
]
| image-segmentation | 2025-05-27T16:44:49Z | ---
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
languages:
- python
---
# FPN Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "xception",
"encoder_depth": 5,
"encoder_weights": "imagenet",
"decoder_pyramid_channels": 256,
"decoder_segmentation_channels": 128,
"decoder_merge_policy": "add",
"decoder_dropout": 0.2,
"decoder_interpolation": "nearest",
"in_channels": 3,
"classes": 1,
"activation": None,
"upsampling": 4,
"aux_params": None
}
```
## Model metrics
```json
[
{
"test_per_image_iou": 0.5316183567047119,
"test_dataset_iou": 0.595180332660675
}
]
```
## Dataset
Dataset name: VisionPipe
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) |
jzilcov/prompt_complexity_classifier | jzilcov | 2025-05-27T16:42:51Z | 0 | 0 | null | [
"safetensors",
"roberta",
"en",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:mit",
"region:us"
]
| null | 2025-05-27T16:29:44Z | ---
license: mit
language:
- en
base_model:
- distilbert/distilroberta-base
--- |
Mawdistical/Draconia-Overdrive-32B_EXL3_8.0bpw_H8 | Mawdistical | 2025-05-27T16:42:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"glm4",
"text-generation",
"nsfw",
"explicit",
"roleplay",
"Furry",
"exl3",
"conversational",
"en",
"base_model:Mawdistical/Draconia-Overdrive-32B",
"base_model:quantized:Mawdistical/Draconia-Overdrive-32B",
"license:mit",
"autotrain_compatible",
"8-bit",
"region:us"
]
| text-generation | 2025-05-27T16:20:53Z | ---
thumbnail: >-
https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png
language:
- en
license: mit
license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE
inference: false
tags:
- nsfw
- explicit
- roleplay
- Furry
- exl3
base_model:
- Mawdistical/Draconia-Overdrive-32B
base_model_relation: quantized
quantized_by: ArtusDev
pipeline_tag: text-generation
library_name: transformers
---
<div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;">
<div align="center">
<h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;">
Draconia-Overdrive-32B
</h1>
<img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;">
<h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3>
<p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;">
<a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a>
</p>
</div>
<div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;">
<p>
<em>
"A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor."
</em>
<br><br>
Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism.
</p>
</div>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2>
<ul>
<li><strong style="color: #111;">Original Model</strong>:
<ul>
<li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li>
</ul>
</li>
</ul>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2>
<ul>
<li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li>
<li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li>
<li><strong style="color: #111;">Dynamic Temperature</strong> (optional):
<ul>
<li style="color: #111;">Multiplier: 0.75-0.85</li>
<li style="color: #111;">Base: 1.8</li>
<li style="color: #111;">Length: 4</li>
</ul>
</li>
</ul>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2>
<pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;">
Temperature: 1.07
Top-P: 0.92
Min-P: 0.035
Mirostat: 2
Repetition Penalty: 1.12
Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4)
</pre>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2>
<ul>
<li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li>
<li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li>
<li><strong style="color: #111;">Government Body</strong>:
<ul>
<li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li>
<li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li>
<li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li>
</ul>
</li>
</ul>
<p style="color: #111; font-size:1em; margin-top:20px;">
<strong style="color: #111;">License:</strong>
<a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a>
</p>
<p style="color: #111; font-size: 1em; margin-top:17px;">
This model was generously made with compute from
<a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a>
</p>
</div>
|
Mawdistical/Draconia-Overdrive-32B_EXL3_5.0bpw_H6 | Mawdistical | 2025-05-27T16:42:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"glm4",
"text-generation",
"nsfw",
"explicit",
"roleplay",
"Furry",
"exl3",
"conversational",
"en",
"base_model:Mawdistical/Draconia-Overdrive-32B",
"base_model:quantized:Mawdistical/Draconia-Overdrive-32B",
"license:mit",
"autotrain_compatible",
"5-bit",
"region:us"
]
| text-generation | 2025-05-27T16:12:07Z | ---
thumbnail: >-
https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png
language:
- en
license: mit
license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE
inference: false
tags:
- nsfw
- explicit
- roleplay
- Furry
- exl3
base_model:
- Mawdistical/Draconia-Overdrive-32B
base_model_relation: quantized
quantized_by: ArtusDev
pipeline_tag: text-generation
library_name: transformers
---
<div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;">
<div align="center">
<h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;">
Draconia-Overdrive-32B
</h1>
<img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;">
<h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3>
<p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;">
<a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a>
</p>
</div>
<div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;">
<p>
<em>
"A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor."
</em>
<br><br>
Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism.
</p>
</div>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2>
<ul>
<li><strong style="color: #111;">Original Model</strong>:
<ul>
<li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li>
</ul>
</li>
</ul>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2>
<ul>
<li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li>
<li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li>
<li><strong style="color: #111;">Dynamic Temperature</strong> (optional):
<ul>
<li style="color: #111;">Multiplier: 0.75-0.85</li>
<li style="color: #111;">Base: 1.8</li>
<li style="color: #111;">Length: 4</li>
</ul>
</li>
</ul>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2>
<pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;">
Temperature: 1.07
Top-P: 0.92
Min-P: 0.035
Mirostat: 2
Repetition Penalty: 1.12
Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4)
</pre>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2>
<ul>
<li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li>
<li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li>
<li><strong style="color: #111;">Government Body</strong>:
<ul>
<li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li>
<li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li>
<li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li>
</ul>
</li>
</ul>
<p style="color: #111; font-size:1em; margin-top:20px;">
<strong style="color: #111;">License:</strong>
<a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a>
</p>
<p style="color: #111; font-size: 1em; margin-top:17px;">
This model was generously made with compute from
<a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a>
</p>
</div>
|
Mohamed-Aly/BABYLM-TOKENIZER-BPE-TXT | Mohamed-Aly | 2025-05-27T16:41:38Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T16:41:37Z | ---
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] |
Mawdistical/Draconia-Overdrive-32B_EXL3_2.5bpw_H6 | Mawdistical | 2025-05-27T16:41:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"glm4",
"text-generation",
"nsfw",
"explicit",
"roleplay",
"Furry",
"exl3",
"conversational",
"en",
"base_model:Mawdistical/Draconia-Overdrive-32B",
"base_model:quantized:Mawdistical/Draconia-Overdrive-32B",
"license:mit",
"autotrain_compatible",
"region:us"
]
| text-generation | 2025-05-27T15:56:57Z | ---
thumbnail: >-
https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png
language:
- en
license: mit
license_link: https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE
inference: false
tags:
- nsfw
- explicit
- roleplay
- Furry
- exl3
base_model:
- Mawdistical/Draconia-Overdrive-32B
base_model_relation: quantized
quantized_by: ArtusDev
pipeline_tag: text-generation
library_name: transformers
---
<div style="background-color: #ffffff; color: #111; padding: 28px 18px; border-radius: 10px; width: 100%;">
<div align="center">
<h1 style="color: #111; margin-bottom: 18px; font-size: 2.1em; font-family:serif;">
Draconia-Overdrive-32B
</h1>
<img src="https://cdn-uploads.huggingface.co/production/uploads/67c10cfba43d7939d60160ff/Sxw5POvqQLws62gTq5EyW.png" width="680px" style="border-radius: 8px; box-shadow: 0 0 16px #0ff;">
<h3 style="color: #111; font-style: italic; margin-top: 13px;">Explicit Content Warning</h3>
<p style="color: #111; font-size: 0.95em; margin-top: 3px; margin-bottom: 14px;">
<a href="https://ko-fi.com/mawnipulator" style="color: #111; text-decoration: underline;"><b>Support Mawdistical finetunes here</b></a>
</p>
</div>
<div style="background-color: #e0fcff; color: #111; padding: 16px; border-radius: 7px; margin: 22px 0; border-left: 3px solid #00eaff;">
<p>
<em>
"A creation of <a href="https://huggingface.co/THUDM/GLM-4-32B-0414" style="color:#067a86; text-decoration: underline;">'chaos aura'</a> that accentuates draconian fervor."
</em>
<br><br>
Draconia-Overdrive-32B is an expressive, creative, and roleplay-driven large language model developed for a wide range of contexts. Drawing inspiration from deep chaos, it brings a fervent, untamed spirit mirroring the energy of relentless draconianism.
</p>
</div>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Quantized Formats</h2>
<ul>
<li><strong style="color: #111;">Original Model</strong>:
<ul>
<li><a href="https://huggingface.co/Mawdistical/Draconia-Overdrive-32B" style="color: #067a86; text-decoration: underline;">Draconia-Overdrive-32B</a></li>
</ul>
</li>
</ul>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.25em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Recommended Settings</h2>
<ul>
<li><strong style="color: #111;">Temperature</strong>: 1.0-1.1</li>
<li><strong style="color: #111;">Min P</strong>: 0.02-0.05</li>
<li><strong style="color: #111;">Dynamic Temperature</strong> (optional):
<ul>
<li style="color: #111;">Multiplier: 0.75-0.85</li>
<li style="color: #111;">Base: 1.8</li>
<li style="color: #111;">Length: 4</li>
</ul>
</li>
</ul>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Sample Presets</h2>
<pre style="background: #e0fcff; color: #111; border-radius: 7px; border: 1px solid #00eaff; padding: 12px; font-size: 1em;">
Temperature: 1.07
Top-P: 0.92
Min-P: 0.035
Mirostat: 2
Repetition Penalty: 1.12
Dynamic Temperature: on (Multiplier: 0.8, Base: 1.8, Length: 4)
</pre>
<hr style="border: 0; height: 1px; background-color: #00eaff; margin: 25px 0;">
<h2 style="color: #111; font-size: 1.2em; border-bottom: 1px solid #00eaff; padding-bottom: 7px;">✧ Credits</h2>
<ul>
<li><strong style="color: #111;">Model Author</strong>: <a href="https://vyvan.se" style="color: #067a86; text-decoration: underline;">@Mawnipulator</a></li>
<li><strong style="color: #111;">Additional Credit</strong>: <a href="https://huggingface.co/xtristan" style="color: #067a86; text-decoration: underline;">@xtristan</a></li>
<li><strong style="color: #111;">Government Body</strong>:
<ul>
<li><a href="https://huggingface.co/ArtusDev" style="color: #067a86;">@ArtusDev</a></li>
<li><a href="https://huggingface.co/SaisExperiments" style="color: #067a86;">@SaisExperiments</a></li>
<li><a href="https://huggingface.co/allura-org" style="color: #067a86;">ALLURA-ORG</a></li>
</ul>
</li>
</ul>
<p style="color: #111; font-size:1em; margin-top:20px;">
<strong style="color: #111;">License:</strong>
<a href="https://huggingface.co/THUDM/GLM-4-32B-0414/blob/main/LICENSE" style="color: #067a86; text-decoration: underline;">MIT</a>
</p>
<p style="color: #111; font-size: 1em; margin-top:17px;">
This model was generously made with compute from
<a href="https://Shuttleai.com" style="color:#067a86; text-decoration:underline;">Shuttleai.com</a>
</p>
</div>
|
WenFengg/lose_to_win_6 | WenFengg | 2025-05-27T16:40:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T16:37:30Z | ---
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] |
cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay | cwhuh | 2025-05-27T16:40:28Z | 4 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
]
| text-to-image | 2025-05-26T14:11:29Z | ---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: A newborn <s0><s1><s2><s3><s4><s5> baby.
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-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. -->
# Flux DreamBooth LoRA - cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay
<Gallery />
## Model description
These are cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
Was LoRA for the text encoder enabled? False.
Pivotal tuning was enabled: True.
## Trigger words
To trigger image generation of trained concept(or concepts) replace each concept identifier in you prompt with the new inserted tokens:
to trigger concept `Caramel Clay_hsfw` → use `<s0><s1><s2><s3><s4><s5>` in your prompt
## Download model
[Download the *.safetensors LoRA](cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
from huggingface_hub import hf_hub_download
from safetensors.torch import load_file
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay', weight_name='pytorch_lora_weights.safetensors')
embedding_path = hf_hub_download(repo_id='cwhuh/babyface_flux_dlora_hsfw_hs_Caramel_Clay', filename='/nas/checkpoints/sangmin/babyface_flux_dlora_hsfw_hs_Caramel_Clay_emb.safetensors', repo_type="model")
state_dict = load_file(embedding_path)
pipeline.load_textual_inversion(state_dict["clip_l"], token=["<s0>", "<s1>", "<s2>", "<s3>", "<s4>", "<s5>"], text_encoder=pipeline.text_encoder, tokenizer=pipeline.tokenizer)
image = pipeline('A newborn <s0><s1><s2><s3><s4><s5> baby.').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)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## 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] |
Diamantis99/OL56jaO | Diamantis99 | 2025-05-27T16:38:44Z | 0 | 0 | segmentation-models-pytorch | [
"segmentation-models-pytorch",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
]
| image-segmentation | 2025-05-27T16:38:41Z | ---
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
languages:
- python
---
# FPN Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "mobilenet_v2",
"encoder_depth": 5,
"encoder_weights": "imagenet",
"decoder_pyramid_channels": 256,
"decoder_segmentation_channels": 128,
"decoder_merge_policy": "add",
"decoder_dropout": 0.2,
"decoder_interpolation": "nearest",
"in_channels": 3,
"classes": 1,
"activation": None,
"upsampling": 4,
"aux_params": None
}
```
## Model metrics
```json
[
{
"test_per_image_iou": 0.5323230624198914,
"test_dataset_iou": 0.6163333654403687
}
]
```
## Dataset
Dataset name: VisionPipe
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) |
Diamantis99/KVIbIp1 | Diamantis99 | 2025-05-27T16:35:25Z | 0 | 0 | segmentation-models-pytorch | [
"segmentation-models-pytorch",
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"semantic-segmentation",
"pytorch",
"image-segmentation",
"license:mit",
"region:us"
]
| image-segmentation | 2025-05-27T16:35:08Z | ---
library_name: segmentation-models-pytorch
license: mit
pipeline_tag: image-segmentation
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
- segmentation-models-pytorch
- semantic-segmentation
- pytorch
languages:
- python
---
# FPN Model Card
Table of Contents:
- [Load trained model](#load-trained-model)
- [Model init parameters](#model-init-parameters)
- [Model metrics](#model-metrics)
- [Dataset](#dataset)
## Load trained model
```python
import segmentation_models_pytorch as smp
model = smp.from_pretrained("<save-directory-or-this-repo>")
```
## Model init parameters
```python
model_init_params = {
"encoder_name": "efficientnet-b7",
"encoder_depth": 5,
"encoder_weights": "imagenet",
"decoder_pyramid_channels": 256,
"decoder_segmentation_channels": 128,
"decoder_merge_policy": "add",
"decoder_dropout": 0.2,
"decoder_interpolation": "nearest",
"in_channels": 3,
"classes": 1,
"activation": None,
"upsampling": 4,
"aux_params": None
}
```
## Model metrics
```json
[
{
"test_per_image_iou": 0.611117422580719,
"test_dataset_iou": 0.6363441348075867
}
]
```
## Dataset
Dataset name: VisionPipe
## More Information
- Library: https://github.com/qubvel/segmentation_models.pytorch
- Docs: https://smp.readthedocs.io/en/latest/
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) |
mradermacher/LIMOPro-LIMO-P-i1-GGUF | mradermacher | 2025-05-27T16:35:16Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:YangXiao-nlp/LIMOPro-LIMO-P",
"base_model:quantized:YangXiao-nlp/LIMOPro-LIMO-P",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
]
| null | 2025-05-27T13:15:12Z | ---
base_model: YangXiao-nlp/LIMOPro-LIMO-P
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/YangXiao-nlp/LIMOPro-LIMO-P
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/LIMOPro-LIMO-P-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ1_S.gguf) | i1-IQ1_S | 7.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ1_M.gguf) | i1-IQ1_M | 8.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 9.1 | |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ2_XS.gguf) | i1-IQ2_XS | 10.1 | |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ2_M.gguf) | i1-IQ2_M | 11.4 | |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q2_K_S.gguf) | i1-Q2_K_S | 11.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q2_K.gguf) | i1-Q2_K | 12.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.8 | |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ3_S.gguf) | i1-IQ3_S | 14.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ3_M.gguf) | i1-IQ3_M | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q3_K_M.gguf) | i1-Q3_K_M | 16.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.3 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q4_0.gguf) | i1-Q4_0 | 18.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q4_K_M.gguf) | i1-Q4_K_M | 20.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q4_1.gguf) | i1-Q4_1 | 20.7 | |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.7 | |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.4 | |
| [GGUF](https://huggingface.co/mradermacher/LIMOPro-LIMO-P-i1-GGUF/resolve/main/LIMOPro-LIMO-P.i1-Q6_K.gguf) | i1-Q6_K | 27.0 | 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 -->
|
seantilley/model | seantilley | 2025-05-27T12:28:11Z | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"llama",
"gguf",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T12:28:07Z | ---
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:** seantilley
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ltg/norbert3-base | ltg | 2025-05-27T12:26:28Z | 1,966 | 7 | transformers | [
"transformers",
"pytorch",
"fill-mask",
"BERT",
"NorBERT",
"Norwegian",
"encoder",
"custom_code",
"no",
"nb",
"nn",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
]
| fill-mask | 2023-03-02T21:38:09Z | ---
language:
- 'no'
- nb
- nn
inference: false
tags:
- BERT
- NorBERT
- Norwegian
- encoder
license: apache-2.0
---
# NorBERT 3 base
<img src="https://huggingface.co/ltg/norbert3-base/resolve/main/norbert.png" width=12.5%>
The official release of a new generation of NorBERT language models described in paper [**NorBench — A Benchmark for Norwegian Language Models**](https://aclanthology.org/2023.nodalida-1.61/). Plese read the paper to learn more details about the model.
## Other sizes:
- [NorBERT 3 xs (15M)](https://huggingface.co/ltg/norbert3-xs)
- [NorBERT 3 small (40M)](https://huggingface.co/ltg/norbert3-small)
- [NorBERT 3 base (123M)](https://huggingface.co/ltg/norbert3-base)
- [NorBERT 3 large (323M)](https://huggingface.co/ltg/norbert3-large)
## Generative NorT5 siblings:
- [NorT5 xs (32M)](https://huggingface.co/ltg/nort5-xs)
- [NorT5 small (88M)](https://huggingface.co/ltg/nort5-small)
- [NorT5 base (228M)](https://huggingface.co/ltg/nort5-base)
- [NorT5 large (808M)](https://huggingface.co/ltg/nort5-large)
## Example usage
This model currently needs a custom wrapper from `modeling_norbert.py`, you should therefore load the model with `trust_remote_code=True`.
```python
import torch
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("ltg/norbert3-base")
model = AutoModelForMaskedLM.from_pretrained("ltg/norbert3-base", trust_remote_code=True)
mask_id = tokenizer.convert_tokens_to_ids("[MASK]")
input_text = tokenizer("Nå ønsker de seg en[MASK] bolig.", return_tensors="pt")
output_p = model(**input_text)
output_text = torch.where(input_text.input_ids == mask_id, output_p.logits.argmax(-1), input_text.input_ids)
# should output: '[CLS] Nå ønsker de seg en ny bolig.[SEP]'
print(tokenizer.decode(output_text[0].tolist()))
```
The following classes are currently implemented: `AutoModel`, `AutoModelMaskedLM`, `AutoModelForSequenceClassification`, `AutoModelForTokenClassification`, `AutoModelForQuestionAnswering` and `AutoModeltForMultipleChoice`.
## Cite us
```bibtex
@inproceedings{samuel-etal-2023-norbench,
title = "{N}or{B}ench {--} A Benchmark for {N}orwegian Language Models",
author = "Samuel, David and
Kutuzov, Andrey and
Touileb, Samia and
Velldal, Erik and
{\O}vrelid, Lilja and
R{\o}nningstad, Egil and
Sigdel, Elina and
Palatkina, Anna",
booktitle = "Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)",
month = may,
year = "2023",
address = "T{\'o}rshavn, Faroe Islands",
publisher = "University of Tartu Library",
url = "https://aclanthology.org/2023.nodalida-1.61",
pages = "618--633",
abstract = "We present NorBench: a streamlined suite of NLP tasks and probes for evaluating Norwegian language models (LMs) on standardized data splits and evaluation metrics. We also introduce a range of new Norwegian language models (both encoder and encoder-decoder based). Finally, we compare and analyze their performance, along with other existing LMs, across the different benchmark tests of NorBench.",
}
``` |
root4k/Dolphin-Mistral-24B-Venice | root4k | 2025-05-27T12:26:23Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"mistral",
"text-generation",
"conversational",
"base_model:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
"base_model:quantized:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
"license:apache-2.0",
"4-bit",
"region:us"
]
| text-generation | 2025-05-27T11:38:46Z | ---
license: apache-2.0
base_model: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
pipeline_tag: text-generation
library_name: mlx
tags:
- mlx
---
|
andry4774/khalit-lora-v1 | andry4774 | 2025-05-27T12:26:10Z | 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 | 2025-05-27T12:26:08Z | ---
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: khalit
---
# Khalit Lora V1
<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 `khalit` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "khalit",
"lora_weights": "https://huggingface.co/andry4774/khalit-lora-v1/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('andry4774/khalit-lora-v1', weight_name='lora.safetensors')
image = pipeline('khalit').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/andry4774/khalit-lora-v1/discussions) to add images that show off what you’ve made with this LoRA.
|
lisabdunlap/balanced_sft_long-1e4_e15 | lisabdunlap | 2025-05-27T12:24:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Qwen3-8B",
"base_model:finetune:unsloth/Qwen3-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T12:23:34Z | ---
base_model: unsloth/Qwen3-8B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** lisabdunlap
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-8B
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)
|
ChevellaShyam/emotion-transformer-model | ChevellaShyam | 2025-05-27T12:23:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-05-27T12:22:26Z | ---
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] |
mvsamsonov/speecht5_finetuned_voxpopuli_nl | mvsamsonov | 2025-05-27T12:22:03Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"endpoints_compatible",
"region:us"
]
| text-to-audio | 2025-05-25T05:55:45Z | ---
library_name: transformers
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_nl
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. -->
# speecht5_finetuned_voxpopuli_nl
This model was trained from scratch on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4590
## 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: 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| 0.6863 | 0.8607 | 200 | 0.6124 |
| 0.5721 | 1.7230 | 400 | 0.5167 |
| 0.5396 | 2.5853 | 600 | 0.4984 |
| 0.5289 | 3.4476 | 800 | 0.4868 |
| 0.5172 | 4.3098 | 1000 | 0.4815 |
| 0.5169 | 5.1721 | 1200 | 0.4771 |
| 0.5108 | 6.0344 | 1400 | 0.4740 |
| 0.5086 | 6.8951 | 1600 | 0.4715 |
| 0.5042 | 7.7574 | 1800 | 0.4699 |
| 0.4939 | 8.6197 | 2000 | 0.4678 |
| 0.4965 | 9.4820 | 2200 | 0.4667 |
| 0.5004 | 10.3443 | 2400 | 0.4644 |
| 0.4906 | 11.2066 | 2600 | 0.4617 |
| 0.4889 | 12.0689 | 2800 | 0.4612 |
| 0.493 | 12.9295 | 3000 | 0.4601 |
| 0.4893 | 13.7918 | 3200 | 0.4599 |
| 0.4894 | 14.6541 | 3400 | 0.4600 |
| 0.4922 | 15.5164 | 3600 | 0.4594 |
| 0.491 | 16.3787 | 3800 | 0.4599 |
| 0.482 | 17.2410 | 4000 | 0.4590 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
abhikapoor909/vitmodel | abhikapoor909 | 2025-05-27T12:21:21Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-27T12:20:22Z | ---
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:** abhikapoor909
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
badrieee/BERTExperiments | badrieee | 2025-05-27T12:19:46Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-27T11:01:51Z | ---
license: apache-2.0
---
|
yt-koike/PLaMo-2-translate-gguf | yt-koike | 2025-05-27T12:17:13Z | 0 | 0 | null | [
"license:other",
"region:us"
]
| null | 2025-05-27T12:14:07Z | ---
license: other
license_name: plamo-community-license
license_link: https://huggingface.co/pfnet/plamo-2-8b/blob/main/LICENSE/ja
---
These are the GGUF version of [plamo-2-translate](https://huggingface.co/pfnet/plamo-2-translate/tree/main).
Built with PLaMo |
Darkhn/llamatest-EXL2-3.0bpw-H6 | Darkhn | 2025-05-27T12:08:35Z | 0 | 0 | exllamav2 | [
"exllamav2",
"quantized",
"license:mit",
"region:us"
]
| null | 2025-05-27T12:08:00Z | ---
library_name: exllamav2
license: mit
tags:
- exllamav2
- quantized
---
# llamatest-EXL2-3.0bpw-H6
EXL2 quantized model of `unsloth/Llama-3.2-1B-Instruct` (the original base model).
## Quantization Details
- **Bits per weight (bpw):** 3.0
- **Head Bits:** 6
- **Calibration Source:** Measurement derived from model weights (no explicit dataset calibration or provided measurement for this specific quantization pass).
Quantized using the [exllamav2 library](https://github.com/turboderp/exllamav2). |
BootesVoid/cmb6f610404fglexpm1bw4jea_cmb6fjajh04hjlexpp021f2jf | BootesVoid | 2025-05-27T12:08:08Z | 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 | 2025-05-27T12:08:07Z | ---
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: alyssa
---
# Cmb6F610404Fglexpm1Bw4Jea_Cmb6Fjajh04Hjlexpp021F2Jf
<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 `alyssa` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "alyssa",
"lora_weights": "https://huggingface.co/BootesVoid/cmb6f610404fglexpm1bw4jea_cmb6fjajh04hjlexpp021f2jf/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/cmb6f610404fglexpm1bw4jea_cmb6fjajh04hjlexpp021f2jf', weight_name='lora.safetensors')
image = pipeline('alyssa').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/cmb6f610404fglexpm1bw4jea_cmb6fjajh04hjlexpp021f2jf/discussions) to add images that show off what you’ve made with this LoRA.
|
AutoBenchmarks/autoBench-sft-7B | AutoBenchmarks | 2025-05-27T12:02:06Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-26T09:15:05Z | ---
license: apache-2.0
---
|
sfrontull/transloco-ita-lld | sfrontull | 2025-05-27T12:01:09Z | 0 | 0 | null | [
"translation",
"low-resource",
"ct2",
"int8",
"real-time",
"it",
"lld",
"base_model:Helsinki-NLP/opus-mt-itc-itc",
"base_model:finetune:Helsinki-NLP/opus-mt-itc-itc",
"license:cc-by-nc-sa-4.0",
"region:us"
]
| translation | 2025-05-27T09:52:37Z | ---
license: cc-by-nc-sa-4.0
language:
- it
- lld
tags:
- translation
- low-resource
- ct2
- int8
- real-time
base_model:
- Helsinki-NLP/opus-mt-itc-itc
pipeline_tag: translation
---
# Italian to Ladin Real-Time Translation Model
This is a fast, lightweight **real-time translation model** from **Italian (it)** to **Ladin (lld)**,
based on [Helsinki-NLP/opus-mt-itc-itc](https://huggingface.co/Helsinki-NLP/opus-mt-itc-itc)
and optimized using **CTranslate2** for efficient inference.
## 💡 Key Features
- ✅ **Base model**: [Helsinki-NLP/opus-mt-itc-itc](https://huggingface.co/Helsinki-NLP/opus-mt-itc-itc)
- ⚡ **Optimized with CTranslate2**
- 🧠 **int8 quantization** for faster inference and lower memory usage
- 🗣️ Designed for **real-time transcription + translation** use cases (e.g., [TransLoco](https://git.uibk.ac.at/informatik/iis/iis-projects/TransLoco))
- 🕒 Suitable for **low-latency environments** like live subtitling or in-browser translation tools
## 🏗️ Model Architecture
- Architecture: Transformer
- Format: CTranslate2
- Quantization: `int8`
- Size on disk: ~70 MB
## 🚀 Intended Use
- Real-time speech-to-speech or speech-to-text translation from Italian to Ladin
- Assistive tools for minority language accessibility
- Educational and research applications
- Use as part of tools like [**TransLoco**](https://git.uibk.ac.at/informatik/iis/iis-projects/TransLoco)
**Non-commercial use only**, in accordance with the CC BY-NC 4.0 license.
```python
import ctranslate2
from transformers import AutoTokenizer
mtmodel = ctranslate2.Translator("./transloco-ita-lld", device="cpu")
tokenizer = AutoTokenizer.from_pretrained("./transloco-ita-lld")
texts = ["Questo è un esempio."]
tokenized_sentences = [tokenizer.convert_ids_to_tokens(tokenizer.encode(x)) for x in texts]
batch_res = mtmodel.translate_batch(source=tokenized_sentences)
decoded_results = [
tokenizer.decode(
tokenizer.convert_tokens_to_ids(res.hypotheses[0]),
skip_special_tokens=True
) for res in batch_res
]
print(decoded_results)
```
⚠️ Note: The tokenizer uses `fur_Latn` as the target language code due to the lack of `lld_Latn` support in the original NLLB vocabulary.
## ❗Limitations
- Ladin is a low-resource language, and the model may struggle with:
- Out-of-domain vocabulary
- Variant-specific variations
- The model may hallucinate outputs when given incomplete or noisy input.
## ⚖️ Ethical Considerations
- Language technologies for minority languages should be developed with community involvement.
- Please avoid using the model for commercial applications or mass-translation pipelines without review.
## 📎 Citation
If you use this model in your work, please cite:
```bibtex
@misc{hallerseeber:frontull:2025,
title = {TransLoco: AI-driven real-time transcription, translation, and summarisation},
subtitle = {A self-hosted free-software conference tool},
author = {Simon Haller-Seeber and Samuel Frontull},
year = {2025},
note = {In preparation},
}
``` |
YuanTang96/GreenPLM | YuanTang96 | 2025-05-27T11:58:12Z | 3 | 0 | null | [
"onnx",
"safetensors",
"arxiv:2408.15966",
"arxiv:2309.00615",
"arxiv:2307.12981",
"arxiv:2308.16911",
"arxiv:2402.17766",
"arxiv:2405.01413",
"region:us"
]
| null | 2025-05-23T06:28:35Z | <h1 align="center"><strong>More Text, Less Point: Towards 3D Data-Efficient Point-Language Understanding</strong></h1>
<p align="center">
Yuan Tang*  Xu Han*  Xianzhi Li<sup>✝</sup>  Qiao Yu  Jinfeng Xu  Yixue Hao  Long Hu  Min Chen
<br>
Huazhong University of Science and Technology South China University of Technology
</p>
</p>
<p align="center">
<a><strong>AAAI 2025 </strong></a>
<a href='https://arxiv.org/pdf/2408.15966'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
<a href='https://huggingface.co/YuanTang96/GreenPLM'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Model-blue'></a>
</p>
<!-- contents with emoji -->
## 📋 Contents
- [🔍 Overview](#-overview)
- [📦 Training and Evaluation](#-Training-and-Evaluation)
- [🔗 Citation](#-citation)
- [📄 License](#-license)
- [📚 Related Work](#-related-work)
- [👏 Acknowledgements](#-acknowledgements)
## 🔍 Overview
- We introduce a new task of 3D data-efficient point-language understanding, aiming to enable LLMs to achieve robust 3D understanding with minimal 3D data.
- We propose GreenPLM to tackle this 3D data-limited task from a novel perspective, enhancing point-LLM alignment with more free-text data.
- we introduce a 6M T3D dataset, design a 3-stage training strategy, and present a 0M-Pooling module for token pooling.
- We introduce the Accuracy-to-3D-Data Ratio (A3DR) to measure the efficiency of 3D data usage and establish an evaluation benchmark based on open-source LLMs.
- GreenPLM outperforms previous models using only 12\% of 3D data and even surpasses GPT4Point (660K 3D data) using only text, demonstrating superior 3D data efficiency.
## 📦 Training-and-Evaluation
### Download project
The **code, weights, and dataset** of the project have already been uploaded to [Hugging Face](https://huggingface.co/YuanTang96/GreenPLM). Simply download them once to get started with the project.
### Install Environment
Enter the project directory and execute the following command:
```bash
conda create -n greenplm python=3.10 -y
conda activate greenplm
bash envInstall.sh
```
### Project Directory Introduction
- `./greenplm/release` contains the paper's weights, training scripts, and testing scripts.
- `./pretrained_weight` stores the pre-trained weights required for the training and testing phases of the project.
- `./lava-vicuna_2024_4_Phi-3-mini-4k-instruct` is the weight directory for Phi-3.
- `./dataset/T3D` is the 6M dataset proposed in this project.
- `./dataset/T3D/stage_1/brief_1M_caption.json` is the dataset for Stage I.
- `./dataset/T3D/stage_2/stage_2_data_210k.json` is the dataset for Stage II.
### Dataset Preparation
`./dataset/Objaverse/8192_npy.zip` contains the point cloud data from Objaverse that is required for this project. To unzip the dataset:
```bash
unzip ./dataset/Objaverse/8192_npy.zip -d ./dataset/Objaverse/
```
### Inference
#### Paper Weights
##### GreenPLM-0
The model trained only on text data, i.e., (Stage I & Stage II).
```bash
bash ./release/paper/scripts/test/release_stage_2.sh
```
The output JSON results are saved in `./release/paper/result_json/stage_2`.
##### GreenPLM
The model trained on a small amount of 3D data, i.e., (Stage I & Stage II & Stage III).
```bash
bash ./release/paper/scripts/test/release_stage_3.sh
```
The output JSON results are saved in `./release/paper/result_json/stage_3`.
#### Weights Using All T3D Dataset
<details>
<summary>We also provide weights trained using the entire T3D dataset, meaning we use 5M data from T3D in Stage II, instead of just 210k as in our paper. (click to expand)</summary>
##### GreenPLM-0
The model trained only on text data, i.e., (Stage I & Stage II).
```bash
bash ./release/5M_data_seting/scripts/test/release_5M_stage_2.sh
```
The output JSON results are saved in `./release/5M_data_seting/result_json/stage_2`.
##### GreenPLM
The model trained on a small amount of 3D data, i.e., (Stage I & Stage II & Stage III).
```bash
bash ./release/5M_data_seting/scripts/test/release_5M_stage_3.sh
```
The output JSON results are saved in `./release/5M_data_seting/result_json/stage_3`.
</details>
### Evaluation
#### Using LLM
- You can get the **DASHSCOPE_API_KEY** from [aliyun](https://bailian.console.aliyun.com/?apiKey=1#/api-key). The evaluation may require 9 CNY (~ 1.3 USD).
- If you have enough GPU resources, you can also build your own Qwen2-72B-Instruct service, following the [Qwen2](https://github.com/QwenLM/Qwen2?tab=readme-ov-file). Then evaluate the results for free!
1. Evaluate the open vocabulary classification on objaverse
```bash
export PYTHONPATH=$PWD
export DASHSCOPE_API_KEY=sk-xxx
python ./pointllm/eval/evaluator_opensource_llm_QwenAPI.py \
--results_path /path/to/evaluation/PointLLM_brief_description_val_200_GT_Objaverse_classification_prompt0.json \
--eval_type open-free-form-classification \
--model_type qwen2-72b-instruct \
--parallel --num_workers 4
```
```bash
export PYTHONPATH=$PWD
export DASHSCOPE_API_KEY=sk-xxx
python ./pointllm/eval/evaluator_opensource_llm_QwenAPI.py \
--results_path /path/to/evaluation/PointLLM_brief_description_val_200_GT_Objaverse_classification_prompt1.json \
--eval_type open-free-form-classification \
--model_type qwen2-72b-instruct \
--parallel --num_workers 4
```
2. Evaluate the close-set zero-shot classification on ModelNet40
```bash
export PYTHONPATH=$PWD
export DASHSCOPE_API_KEY=sk-xxx
python ./pointllm/eval/evaluator_opensource_llm_QwenAPI.py \
--results_path /path/to/evaluation/ModelNet_classification_prompt0.json \
--eval_type modelnet-close-set-classification \
--model_type qwen2-72b-instruct \
--parallel --num_workers 4
```
```bash
export PYTHONPATH=$PWD
export DASHSCOPE_API_KEY=sk-xxx
python ./pointllm/eval/evaluator_opensource_llm_QwenAPI.py \
--results_path /path/to/evaluation/ModelNet_classification_prompt1.json \
--eval_type modelnet-close-set-classification \
--model_type qwen2-72b-instruct \
--parallel --num_workers 4
```
3. Evaluate the object captioning on objaverse
```bash
export PYTHONPATH=$PWD
export DASHSCOPE_API_KEY=sk-xxx
python ./pointllm/eval/evaluator_opensource_llm_QwenAPI.py \
--results_path /path/to/evaluation/PointLLM_brief_description_val_200_GT_Objaverse_captioning_prompt2.json \
--eval_type object-captioning \
--model_type qwen2-72b-instruct \
--parallel --num_workers 4
```
#### Traditional Metric Evaluation
For the object captioning task, run the following command to evaluate model outputs with traditional metrics Sentence-BERT and SimCSE.
```bash
CUDA_VISIBLE_DEVICES=0 python pointllm/eval/traditional_evaluator.py --results_path /path/to/evaluation/PointLLM_brief_description_val_200_GT_Objaverse_captioning_prompt2.json
```
## Training
**Stage I**
```bash
bash ./release/paper/scripts/train/1.sh
```
**Stage II**: GreenPLM-0
```bash
bash ./release/paper/scripts/train/2.sh
```
**Stage III**: GreenPLM
```bash
bash ./release/paper/scripts/train/3.sh
```
<details>
<summary>We also provide training scripts using the entire T3D dataset, meaning we use 5M data from T3D in Stage II, instead of just 210k as in our paper. (click to expand)</summary>
**Stage II**: GreenPLM-0
```bash
bash ./release/5M_data_seting/scripts/train/2.sh
```
**Stage III**: GreenPLM
```bash
bash ./release/5M_data_seting/scripts/train/3.sh
```
</details>
**Note**: You can modify the `--output_dir` argument in the scripts to set the output directory for the trained weights.
## 🔗 Citation
If you find our work helpful, please consider citing:
```bibtex
@inproceedings{tang2025more,
title={More text, less point: Towards 3d data-efficient point-language understanding},
author={Tang, Yuan and Han, Xu and Li, Xianzhi and Yu, Qiao and Xu, Jinfeng and Hao, Yixue and Hu, Long and Chen, Min},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
volume={39},
number={7},
pages={7284--7292},
year={2025}
}
```
## 📄 License
<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/80x15.png" /></a>
<br />
This work is under the <a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License</a>.
## 📚 Related Work
Together, Let's make LLM for 3D great!
- [Point-Bind & Point-LLM](https://arxiv.org/abs/2309.00615): aligns point clouds with Image-Bind to reason multi-modality input without 3D-instruction data training.
- [3D-LLM](https://arxiv.org/abs/2307.12981): employs 2D foundation models to encode multi-view images of 3D point clouds.
- [PointLLM](https://arxiv.org/abs/2308.16911): employs 3D point clouds with LLaVA.
- [ShapeLLM](http://arxiv.org/abs/2402.17766): combines a powerful point cloud encoder with LLM for embodied scenes.
- [MiniGPT-3D](https://arxiv.org/pdf/2405.01413) : takes the first step toward efficient 3D-LLM, requiring only a single RTX 3090 GPU and one day of training time.
## 👏 Acknowledgements
We would like to thank the authors of [PointLLM](https://github.com/OpenRobotLab/PointLLM), [Uni3D](https://github.com/baaivision/Uni3D), [Phi-3](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct), and [LLaVA-pp](https://github.com/mbzuai-oryx/LLaVA-pp) for their great works and repos. |
Mass-14/MNLP_M2_rag_model | Mass-14 | 2025-05-27T11:57:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-05-27T11:56:29Z | ---
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. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
tomaarsen/inference-free-splade-bert-tiny-nq | tomaarsen | 2025-05-27T11:52:36Z | 17 | 1 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"sparse-encoder",
"sparse",
"asymmetric",
"inference-free",
"splade",
"generated_from_trainer",
"dataset_size:99000",
"loss:SpladeLoss",
"loss:SparseMultipleNegativesRankingLoss",
"loss:FlopsLoss",
"feature-extraction",
"en",
"dataset:sentence-transformers/natural-questions",
"arxiv:1908.10084",
"arxiv:2205.04733",
"arxiv:1705.00652",
"arxiv:2004.05665",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-05-27T08:35:51Z | ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- asymmetric
- inference-free
- splade
- generated_from_trainer
- dataset_size:99000
- loss:SpladeLoss
- loss:SparseMultipleNegativesRankingLoss
- loss:FlopsLoss
widget:
- text: Rollin' (Limp Bizkit song) The music video was filmed atop the South Tower
of the former World Trade Center in New York City. The introduction features Ben
Stiller and Stephen Dorff mistaking Fred Durst for the valet and giving him the
keys to their Bentley Azure. Also making a cameo is break dancer Mr. Wiggles.
The rest of the video has several cuts to Durst and his bandmates hanging out
of the Bentley as they drive about Manhattan. The song Ben Stiller is playing
at the beginning is "My Generation" from the same album. The video also features
scenes of Fred Durst with five girls dancing in a room. The video was filmed around
the same time as the film Zoolander, which explains Stiller and Dorff's appearance.
Fred Durst has a small cameo in that film.
- text: who played the dj in the movie the warriors
- text: Lionel Messi Born and raised in central Argentina, Messi was diagnosed with
a growth hormone deficiency as a child. At age 13, he relocated to Spain to join
Barcelona, who agreed to pay for his medical treatment. After a fast progression
through Barcelona's youth academy, Messi made his competitive debut aged 17 in
October 2004. Despite being injury-prone during his early career, he established
himself as an integral player for the club within the next three years, finishing
2007 as a finalist for both the Ballon d'Or and FIFA World Player of the Year
award, a feat he repeated the following year. His first uninterrupted campaign
came in the 2008–09 season, during which he helped Barcelona achieve the first
treble in Spanish football. At 22 years old, Messi won the Ballon d'Or and FIFA
World Player of the Year award by record voting margins.
- text: 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim
for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman''s
film Smiles of a Summer Night. It is a ballad from Act Two, in which the character
Desirée reflects on the ironies and disappointments of her life. Among other things,
she looks back on an affair years earlier with the lawyer Fredrik, who was deeply
in love with her but whose marriage proposals she had rejected. Meeting him after
so long, she realizes she is in love with him and finally ready to marry him,
but now it is he who rejects her: he is in an unconsummated marriage with a much
younger woman. Desirée proposes marriage to rescue him from this situation, but
he declines, citing his dedication to his bride. Reacting to his rejection, Desirée
sings this song. The song is later reprised as a coda after Fredrik''s young wife
runs away with his son, and Fredrik is finally free to accept Desirée''s offer.[1]'
datasets:
- sentence-transformers/natural-questions
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
co2_eq_emissions:
emissions: 0.08091219208784432
energy_consumed: 0.03403649566911146
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: 0.1
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: Inference-free SPLADE BERT-tiny trained on Natural-Questions tuples
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.28
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.48
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.54
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.28
name: Dot Precision@1
- type: dot_precision@3
value: 0.15999999999999998
name: Dot Precision@3
- type: dot_precision@5
value: 0.10800000000000003
name: Dot Precision@5
- type: dot_precision@10
value: 0.068
name: Dot Precision@10
- type: dot_recall@1
value: 0.28
name: Dot Recall@1
- type: dot_recall@3
value: 0.48
name: Dot Recall@3
- type: dot_recall@5
value: 0.54
name: Dot Recall@5
- type: dot_recall@10
value: 0.68
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4712098455669033
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4061269841269841
name: Dot Mrr@10
- type: dot_map@100
value: 0.42140578268075485
name: Dot Map@100
- type: query_active_dims
value: 7.360000133514404
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9997588624554906
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 182.14356994628906
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9940323841836612
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.46
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.6
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.46
name: Dot Precision@1
- type: dot_precision@3
value: 0.38
name: Dot Precision@3
- type: dot_precision@5
value: 0.34
name: Dot Precision@5
- type: dot_precision@10
value: 0.264
name: Dot Precision@10
- type: dot_recall@1
value: 0.04441960931285628
name: Dot Recall@1
- type: dot_recall@3
value: 0.07834808274816461
name: Dot Recall@3
- type: dot_recall@5
value: 0.11501385338572513
name: Dot Recall@5
- type: dot_recall@10
value: 0.13826891393122565
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.3362315857787886
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5348571428571429
name: Dot Mrr@10
- type: dot_map@100
value: 0.1428111004748431
name: Dot Map@100
- type: query_active_dims
value: 5.739999771118164
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9998119389367958
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 268.686767578125
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9911969475270912
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.3
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.7
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3
name: Dot Precision@1
- type: dot_precision@3
value: 0.18666666666666665
name: Dot Precision@3
- type: dot_precision@5
value: 0.14
name: Dot Precision@5
- type: dot_precision@10
value: 0.07200000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.3
name: Dot Recall@1
- type: dot_recall@3
value: 0.54
name: Dot Recall@3
- type: dot_recall@5
value: 0.66
name: Dot Recall@5
- type: dot_recall@10
value: 0.67
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4962070267718764
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.44433333333333325
name: Dot Mrr@10
- type: dot_map@100
value: 0.44473112450804114
name: Dot Map@100
- type: query_active_dims
value: 10.420000076293945
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.999658606903994
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 156.8834228515625
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9948599887670676
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: dot_accuracy@1
value: 0.3466666666666667
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.5466666666666667
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6266666666666667
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.6933333333333334
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3466666666666667
name: Dot Precision@1
- type: dot_precision@3
value: 0.24222222222222223
name: Dot Precision@3
- type: dot_precision@5
value: 0.19600000000000004
name: Dot Precision@5
- type: dot_precision@10
value: 0.13466666666666668
name: Dot Precision@10
- type: dot_recall@1
value: 0.20813986977095209
name: Dot Recall@1
- type: dot_recall@3
value: 0.36611602758272155
name: Dot Recall@3
- type: dot_recall@5
value: 0.43833795112857504
name: Dot Recall@5
- type: dot_recall@10
value: 0.4960896379770752
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4345494860391894
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4617724867724868
name: Dot Mrr@10
- type: dot_map@100
value: 0.33631600255454636
name: Dot Map@100
- type: query_active_dims
value: 7.839999993642171
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9997431360987601
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 191.99524840418664
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.993709611152474
name: Corpus Sparsity Ratio
---
# Inference-free SPLADE BERT-tiny trained on Natural-Questions tuples
This is a [Asymmetric Inference-free SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model trained 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 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** Asymmetric Inference-free SPLADE Sparse Encoder
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 30522 dimensions
- **Similarity Function:** Dot Product
- **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): Router(
(query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: BertTokenizerFast)
(document_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: BertForMaskedLM
(document_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
)
```
## 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/inference-free-splade-bert-tiny-nq")
# Run inference
sentences = [
'is send in the clowns from a musical',
'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman\'s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik\'s young wife runs away with his son, and Fredrik is finally free to accept Desirée\'s offer.[1]',
'who played the dj in the movie the warriors',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# (3, 30522)
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)
| Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ |
|:----------------------|:------------|:-------------|:-----------|
| dot_accuracy@1 | 0.28 | 0.46 | 0.3 |
| dot_accuracy@3 | 0.48 | 0.6 | 0.56 |
| dot_accuracy@5 | 0.54 | 0.64 | 0.7 |
| dot_accuracy@10 | 0.68 | 0.7 | 0.7 |
| dot_precision@1 | 0.28 | 0.46 | 0.3 |
| dot_precision@3 | 0.16 | 0.38 | 0.1867 |
| dot_precision@5 | 0.108 | 0.34 | 0.14 |
| dot_precision@10 | 0.068 | 0.264 | 0.072 |
| dot_recall@1 | 0.28 | 0.0444 | 0.3 |
| dot_recall@3 | 0.48 | 0.0783 | 0.54 |
| dot_recall@5 | 0.54 | 0.115 | 0.66 |
| dot_recall@10 | 0.68 | 0.1383 | 0.67 |
| **dot_ndcg@10** | **0.4712** | **0.3362** | **0.4962** |
| dot_mrr@10 | 0.4061 | 0.5349 | 0.4443 |
| dot_map@100 | 0.4214 | 0.1428 | 0.4447 |
| query_active_dims | 7.36 | 5.74 | 10.42 |
| query_sparsity_ratio | 0.9998 | 0.9998 | 0.9997 |
| corpus_active_dims | 182.1436 | 268.6868 | 156.8834 |
| corpus_sparsity_ratio | 0.994 | 0.9912 | 0.9949 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
]
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.3467 |
| dot_accuracy@3 | 0.5467 |
| dot_accuracy@5 | 0.6267 |
| dot_accuracy@10 | 0.6933 |
| dot_precision@1 | 0.3467 |
| dot_precision@3 | 0.2422 |
| dot_precision@5 | 0.196 |
| dot_precision@10 | 0.1347 |
| dot_recall@1 | 0.2081 |
| dot_recall@3 | 0.3661 |
| dot_recall@5 | 0.4383 |
| dot_recall@10 | 0.4961 |
| **dot_ndcg@10** | **0.4345** |
| dot_mrr@10 | 0.4618 |
| dot_map@100 | 0.3363 |
| query_active_dims | 7.84 |
| query_sparsity_ratio | 0.9997 |
| corpus_active_dims | 191.9952 |
| corpus_sparsity_ratio | 0.9937 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### 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>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 0.03,
"lambda_query": 0
}
```
### 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>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 0.03,
"lambda_query": 0
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
- `router_mapping`: ['query', 'document']
- `learning_rate_mapping`: {'IDF\\.weight': 0.001}
#### 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`: 2e-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`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `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`: ['query', 'document']
- `learning_rate_mapping`: {'IDF\\.weight': 0.001}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|
| 0.0129 | 20 | 1.8729 | - | - | - | - | - |
| 0.0259 | 40 | 4.3293 | - | - | - | - | - |
| 0.0388 | 60 | 7.3159 | - | - | - | - | - |
| 0.0517 | 80 | 7.3727 | - | - | - | - | - |
| 0.0646 | 100 | 5.1717 | - | - | - | - | - |
| 0.0776 | 120 | 3.5122 | - | - | - | - | - |
| 0.0905 | 140 | 2.6885 | - | - | - | - | - |
| 0.1034 | 160 | 2.2643 | - | - | - | - | - |
| 0.1164 | 180 | 1.9245 | - | - | - | - | - |
| 0.1293 | 200 | 1.671 | 1.2606 | 0.3849 | 0.3159 | 0.3992 | 0.3667 |
| 0.1422 | 220 | 1.4991 | - | - | - | - | - |
| 0.1551 | 240 | 1.3325 | - | - | - | - | - |
| 0.1681 | 260 | 1.2823 | - | - | - | - | - |
| 0.1810 | 280 | 1.1572 | - | - | - | - | - |
| 0.1939 | 300 | 1.0759 | - | - | - | - | - |
| 0.2069 | 320 | 1.0124 | - | - | - | - | - |
| 0.2198 | 340 | 0.9672 | - | - | - | - | - |
| 0.2327 | 360 | 0.9361 | - | - | - | - | - |
| 0.2456 | 380 | 0.8801 | - | - | - | - | - |
| 0.2586 | 400 | 0.8114 | 0.7099 | 0.4201 | 0.3166 | 0.4655 | 0.4007 |
| 0.2715 | 420 | 0.7889 | - | - | - | - | - |
| 0.2844 | 440 | 0.8081 | - | - | - | - | - |
| 0.2973 | 460 | 0.7586 | - | - | - | - | - |
| 0.3103 | 480 | 0.7705 | - | - | - | - | - |
| 0.3232 | 500 | 0.7696 | - | - | - | - | - |
| 0.3361 | 520 | 0.7469 | - | - | - | - | - |
| 0.3491 | 540 | 0.7132 | - | - | - | - | - |
| 0.3620 | 560 | 0.7656 | - | - | - | - | - |
| 0.3749 | 580 | 0.6988 | - | - | - | - | - |
| 0.3878 | 600 | 0.758 | 0.6141 | 0.4342 | 0.3205 | 0.4905 | 0.4151 |
| 0.4008 | 620 | 0.7029 | - | - | - | - | - |
| 0.4137 | 640 | 0.6143 | - | - | - | - | - |
| 0.4266 | 660 | 0.6392 | - | - | - | - | - |
| 0.4396 | 680 | 0.6761 | - | - | - | - | - |
| 0.4525 | 700 | 0.658 | - | - | - | - | - |
| 0.4654 | 720 | 0.5961 | - | - | - | - | - |
| 0.4783 | 740 | 0.6261 | - | - | - | - | - |
| 0.4913 | 760 | 0.6035 | - | - | - | - | - |
| 0.5042 | 780 | 0.5663 | - | - | - | - | - |
| 0.5171 | 800 | 0.609 | 0.5503 | 0.4427 | 0.3284 | 0.4805 | 0.4172 |
| 0.5301 | 820 | 0.6068 | - | - | - | - | - |
| 0.5430 | 840 | 0.5911 | - | - | - | - | - |
| 0.5559 | 860 | 0.6082 | - | - | - | - | - |
| 0.5688 | 880 | 0.5738 | - | - | - | - | - |
| 0.5818 | 900 | 0.5727 | - | - | - | - | - |
| 0.5947 | 920 | 0.563 | - | - | - | - | - |
| 0.6076 | 940 | 0.5438 | - | - | - | - | - |
| 0.6206 | 960 | 0.5583 | - | - | - | - | - |
| 0.6335 | 980 | 0.5972 | - | - | - | - | - |
| 0.6464 | 1000 | 0.5212 | 0.5208 | 0.4493 | 0.3320 | 0.4957 | 0.4256 |
| 0.6593 | 1020 | 0.5487 | - | - | - | - | - |
| 0.6723 | 1040 | 0.5313 | - | - | - | - | - |
| 0.6852 | 1060 | 0.5471 | - | - | - | - | - |
| 0.6981 | 1080 | 0.5754 | - | - | - | - | - |
| 0.7111 | 1100 | 0.5558 | - | - | - | - | - |
| 0.7240 | 1120 | 0.5334 | - | - | - | - | - |
| 0.7369 | 1140 | 0.5589 | - | - | - | - | - |
| 0.7498 | 1160 | 0.5341 | - | - | - | - | - |
| 0.7628 | 1180 | 0.5516 | - | - | - | - | - |
| 0.7757 | 1200 | 0.558 | 0.5028 | 0.4633 | 0.3320 | 0.4983 | 0.4312 |
| 0.7886 | 1220 | 0.5373 | - | - | - | - | - |
| 0.8016 | 1240 | 0.5483 | - | - | - | - | - |
| 0.8145 | 1260 | 0.5265 | - | - | - | - | - |
| 0.8274 | 1280 | 0.543 | - | - | - | - | - |
| 0.8403 | 1300 | 0.5616 | - | - | - | - | - |
| 0.8533 | 1320 | 0.5377 | - | - | - | - | - |
| 0.8662 | 1340 | 0.5295 | - | - | - | - | - |
| 0.8791 | 1360 | 0.5266 | - | - | - | - | - |
| 0.8920 | 1380 | 0.5328 | - | - | - | - | - |
| 0.9050 | 1400 | 0.5187 | 0.4932 | 0.4720 | 0.3343 | 0.4972 | 0.4345 |
| 0.9179 | 1420 | 0.5219 | - | - | - | - | - |
| 0.9308 | 1440 | 0.4934 | - | - | - | - | - |
| 0.9438 | 1460 | 0.5452 | - | - | - | - | - |
| 0.9567 | 1480 | 0.5216 | - | - | - | - | - |
| 0.9696 | 1500 | 0.5311 | - | - | - | - | - |
| 0.9825 | 1520 | 0.5303 | - | - | - | - | - |
| 0.9955 | 1540 | 0.5299 | - | - | - | - | - |
| -1 | -1 | - | - | 0.4712 | 0.3362 | 0.4962 | 0.4345 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.034 kWh
- **Carbon Emitted**: 0.000 kg of CO2
- **Hours Used**: 0.1 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: 4.52.3
- 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",
}
```
#### SpladeLoss
```bibtex
@misc{formal2022distillationhardnegativesampling,
title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
year={2022},
eprint={2205.04733},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2205.04733},
}
```
#### 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}
}
```
#### FlopsLoss
```bibtex
@article{paria2020minimizing,
title={Minimizing flops to learn efficient sparse representations},
author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
journal={arXiv preprint arXiv:2004.05665},
year={2020}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
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*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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aamijar/Llama-2-7b-hf-lora-r8-boolq-portlora-epochs9 | aamijar | 2025-05-27T11:44:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T11:44:27Z | ---
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
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[More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[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. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[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] |
18-Sophie-Rain-SpiderMan-Videosss/Sophie.Rain.Spiderman.New.Video.Tutorial.Viral.Full.Video | 18-Sophie-Rain-SpiderMan-Videosss | 2025-05-27T11:37:34Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T11:37:22Z | 18 seconds ago
<a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p>
<a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p>
<p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter
L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter
. . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram
L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter
Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter |
nattkorat/scibert-base-uncased-ner | nattkorat | 2025-05-27T11:33:34Z | 17 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:allenai/scibert_scivocab_uncased",
"base_model:finetune:allenai/scibert_scivocab_uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2025-05-17T07:22:26Z | ---
library_name: transformers
base_model: allenai/scibert_scivocab_uncased
tags:
- generated_from_trainer
model-index:
- name: scibert-base-uncased-ner
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. -->
# scibert-base-uncased-ner
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0191
- Cases: {'precision': 0.9767981438515081, 'recall': 0.967816091954023, 'f1': 0.972286374133949, 'number': 435}
- Country: {'precision': 0.9751332149200711, 'recall': 1.0, 'f1': 0.9874100719424461, 'number': 549}
- Date: {'precision': 0.9706896551724138, 'recall': 0.9690189328743546, 'f1': 0.9698535745047373, 'number': 581}
- Deaths: {'precision': 0.9529411764705882, 'recall': 0.9501466275659824, 'f1': 0.9515418502202643, 'number': 341}
- Virus: {'precision': 0.9963235294117647, 'recall': 0.998158379373849, 'f1': 0.9972401103955841, 'number': 543}
- Overall Precision: 0.9760
- Overall Recall: 0.9796
- Overall F1: 0.9778
- Overall Accuracy: 0.9923
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cases | Country | Date | Deaths | Virus | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| No log | 1.0 | 291 | 0.0411 | {'precision': 0.90744920993228, 'recall': 0.9241379310344827, 'f1': 0.9157175398633258, 'number': 435} | {'precision': 0.9699646643109541, 'recall': 1.0, 'f1': 0.9847533632286996, 'number': 549} | {'precision': 0.9149305555555556, 'recall': 0.9070567986230637, 'f1': 0.9109766637856526, 'number': 581} | {'precision': 0.8830769230769231, 'recall': 0.841642228739003, 'f1': 0.8618618618618619, 'number': 341} | {'precision': 0.9889908256880734, 'recall': 0.992633517495396, 'f1': 0.9908088235294119, 'number': 543} | 0.9385 | 0.9408 | 0.9396 | 0.9861 |
| 0.1005 | 2.0 | 582 | 0.0291 | {'precision': 0.9733656174334141, 'recall': 0.9241379310344827, 'f1': 0.9481132075471699, 'number': 435} | {'precision': 0.9699646643109541, 'recall': 1.0, 'f1': 0.9847533632286996, 'number': 549} | {'precision': 0.9512195121951219, 'recall': 0.9397590361445783, 'f1': 0.9454545454545454, 'number': 581} | {'precision': 0.9161849710982659, 'recall': 0.9296187683284457, 'f1': 0.9228529839883551, 'number': 341} | {'precision': 0.9889908256880734, 'recall': 0.992633517495396, 'f1': 0.9908088235294119, 'number': 543} | 0.9628 | 0.9608 | 0.9618 | 0.9910 |
| 0.1005 | 3.0 | 873 | 0.0221 | {'precision': 0.9764705882352941, 'recall': 0.9540229885057471, 'f1': 0.9651162790697674, 'number': 435} | {'precision': 0.9751332149200711, 'recall': 1.0, 'f1': 0.9874100719424461, 'number': 549} | {'precision': 0.9706896551724138, 'recall': 0.9690189328743546, 'f1': 0.9698535745047373, 'number': 581} | {'precision': 0.9552238805970149, 'recall': 0.9384164222873901, 'f1': 0.9467455621301775, 'number': 341} | {'precision': 0.9963235294117647, 'recall': 0.998158379373849, 'f1': 0.9972401103955841, 'number': 543} | 0.9763 | 0.9755 | 0.9759 | 0.9929 |
| 0.0237 | 4.0 | 1164 | 0.0216 | {'precision': 0.9789719626168224, 'recall': 0.9632183908045977, 'f1': 0.9710312862108922, 'number': 435} | {'precision': 0.9751332149200711, 'recall': 1.0, 'f1': 0.9874100719424461, 'number': 549} | {'precision': 0.9740034662045061, 'recall': 0.9672977624784854, 'f1': 0.9706390328151987, 'number': 581} | {'precision': 0.9502923976608187, 'recall': 0.9530791788856305, 'f1': 0.951683748169839, 'number': 341} | {'precision': 0.9944954128440368, 'recall': 0.998158379373849, 'f1': 0.9963235294117647, 'number': 543} | 0.9764 | 0.9788 | 0.9776 | 0.9921 |
| 0.0237 | 5.0 | 1455 | 0.0191 | {'precision': 0.9767981438515081, 'recall': 0.967816091954023, 'f1': 0.972286374133949, 'number': 435} | {'precision': 0.9751332149200711, 'recall': 1.0, 'f1': 0.9874100719424461, 'number': 549} | {'precision': 0.9706896551724138, 'recall': 0.9690189328743546, 'f1': 0.9698535745047373, 'number': 581} | {'precision': 0.9529411764705882, 'recall': 0.9501466275659824, 'f1': 0.9515418502202643, 'number': 341} | {'precision': 0.9963235294117647, 'recall': 0.998158379373849, 'f1': 0.9972401103955841, 'number': 543} | 0.9760 | 0.9796 | 0.9778 | 0.9923 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.0
- Tokenizers 0.21.1
|
Cloudmaster/Llama-3.2-3B-torchao-final01 | Cloudmaster | 2025-05-27T11:31:26Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"torchao",
"region:us"
]
| text-generation | 2025-05-27T11:27:37Z | ---
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]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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nattkorat/biobert-base-uncased-ner | nattkorat | 2025-05-27T11:30:41Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:dmis-lab/biobert-v1.1",
"base_model:finetune:dmis-lab/biobert-v1.1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| token-classification | 2025-05-17T07:40:16Z | ---
library_name: transformers
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
model-index:
- name: biobert-base-uncased-ner
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. -->
# biobert-base-uncased-ner
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0299
- Cases: {'precision': 0.963963963963964, 'recall': 0.9705215419501134, 'f1': 0.9672316384180792, 'number': 441}
- Country: {'precision': 0.9926062846580407, 'recall': 0.9962894248608535, 'f1': 0.9944444444444445, 'number': 539}
- Date: {'precision': 0.9637931034482758, 'recall': 0.9704861111111112, 'f1': 0.9671280276816608, 'number': 576}
- Deaths: {'precision': 0.9224376731301939, 'recall': 0.9596541786743515, 'f1': 0.9406779661016949, 'number': 347}
- Virus: {'precision': 0.9927140255009107, 'recall': 0.9927140255009107, 'f1': 0.9927140255009107, 'number': 549}
- Overall Precision: 0.9705
- Overall Recall: 0.9796
- Overall F1: 0.9750
- Overall Accuracy: 0.9923
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cases | Country | Date | Deaths | Virus | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| No log | 1.0 | 291 | 0.0329 | {'precision': 0.9712918660287081, 'recall': 0.9206349206349206, 'f1': 0.9452852153667054, 'number': 441} | {'precision': 0.988950276243094, 'recall': 0.9962894248608535, 'f1': 0.9926062846580408, 'number': 539} | {'precision': 0.9498269896193772, 'recall': 0.953125, 'f1': 0.951473136915078, 'number': 576} | {'precision': 0.9388379204892966, 'recall': 0.8847262247838616, 'f1': 0.9109792284866469, 'number': 347} | {'precision': 0.9926873857404022, 'recall': 0.9890710382513661, 'f1': 0.990875912408759, 'number': 549} | 0.9706 | 0.9551 | 0.9628 | 0.9901 |
| 0.0216 | 2.0 | 582 | 0.0336 | {'precision': 0.9527027027027027, 'recall': 0.9591836734693877, 'f1': 0.9559322033898305, 'number': 441} | {'precision': 0.9907749077490775, 'recall': 0.9962894248608535, 'f1': 0.9935245143385755, 'number': 539} | {'precision': 0.9616724738675958, 'recall': 0.9583333333333334, 'f1': 0.96, 'number': 576} | {'precision': 0.9010989010989011, 'recall': 0.9452449567723343, 'f1': 0.9226441631504924, 'number': 347} | {'precision': 0.9908759124087592, 'recall': 0.9890710382513661, 'f1': 0.9899726526891522, 'number': 549} | 0.9640 | 0.9719 | 0.9679 | 0.9907 |
| 0.0216 | 3.0 | 873 | 0.0345 | {'precision': 0.9555555555555556, 'recall': 0.9750566893424036, 'f1': 0.9652076318742986, 'number': 441} | {'precision': 0.9926062846580407, 'recall': 0.9962894248608535, 'f1': 0.9944444444444445, 'number': 539} | {'precision': 0.9536082474226805, 'recall': 0.9635416666666666, 'f1': 0.9585492227979275, 'number': 576} | {'precision': 0.9131652661064426, 'recall': 0.9394812680115274, 'f1': 0.9261363636363636, 'number': 347} | {'precision': 0.990909090909091, 'recall': 0.9927140255009107, 'f1': 0.991810737033667, 'number': 549} | 0.9649 | 0.9759 | 0.9704 | 0.9914 |
| 0.0126 | 4.0 | 1164 | 0.0292 | {'precision': 0.9682539682539683, 'recall': 0.9682539682539683, 'f1': 0.9682539682539683, 'number': 441} | {'precision': 0.9907749077490775, 'recall': 0.9962894248608535, 'f1': 0.9935245143385755, 'number': 539} | {'precision': 0.9655172413793104, 'recall': 0.9722222222222222, 'f1': 0.9688581314878894, 'number': 576} | {'precision': 0.9301675977653632, 'recall': 0.9596541786743515, 'f1': 0.9446808510638297, 'number': 347} | {'precision': 0.9927140255009107, 'recall': 0.9927140255009107, 'f1': 0.9927140255009107, 'number': 549} | 0.9725 | 0.9796 | 0.9760 | 0.9925 |
| 0.0126 | 5.0 | 1455 | 0.0299 | {'precision': 0.963963963963964, 'recall': 0.9705215419501134, 'f1': 0.9672316384180792, 'number': 441} | {'precision': 0.9926062846580407, 'recall': 0.9962894248608535, 'f1': 0.9944444444444445, 'number': 539} | {'precision': 0.9637931034482758, 'recall': 0.9704861111111112, 'f1': 0.9671280276816608, 'number': 576} | {'precision': 0.9224376731301939, 'recall': 0.9596541786743515, 'f1': 0.9406779661016949, 'number': 347} | {'precision': 0.9927140255009107, 'recall': 0.9927140255009107, 'f1': 0.9927140255009107, 'number': 549} | 0.9705 | 0.9796 | 0.9750 | 0.9923 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.0
- Tokenizers 0.21.1
|
madhueb/MNLP_M2_dpo_model | madhueb | 2025-05-27T11:29:22Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"dataset:madhueb/MNLP_M2_dpo_dataset",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-25T15:58:34Z | ---
library_name: transformers
tags:
- trl
- dpo
datasets:
- madhueb/MNLP_M2_dpo_dataset
---
- **Developed by:** Madeleine Hueber
- **Language(s) (NLP):** English
- **License:** For academic use only
- **Finetuned from model:** Qwen3-0.6B-Base
This model is a preference-aligned language model fine-tuned for answering STEM-related instruction prompts. It was developed as part of the M2 deliverable for the CS-552 course Modern Natural Language Processing.
# Training Details:
- Stage 1: Instruction tuning on a subset of TIGER-Lab/WebInstructSub (200k data , aivalable on the train_instruct split of madhueb/MNLP_M2_dpo_dataset )
- Stage 2: DPO fine-tuning using the train split of madhueb/MNLP_M2_dpo_dataset. |
transformers-community/sink_cache | transformers-community | 2025-05-27T11:24:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"custom_generate",
"arxiv:2309.17453",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-22T15:37:29Z | ---
library_name: transformers
tags:
- custom_generate
---
## Description
Implementation of the KV cache introduced in the [Attention Sinks paper](https://huggingface.co/papers/2309.17453).
It allows the model to generate beyond the length of its context window, without losing fluency in the conversation.
This is done by always keeping the first few tokens ("sink tokens") in the KV cache, as models often pay a large
amount of attention to them. As it discards past non-sink tokens, the model will lose the ability to generate tokens
that depend on the context that was discarded. It's also a solution to contain the memory footprint of the KV cache.
This implementation matches the `SinkCache` class present in `transformers<4.53.0`.
<!-- TODO (joao): add `transformers chat` example -->
## Base model
- [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
## Model compatibility
- Decoder-only transformers models
## Additional Arguments
- `window_length` (`int`, *optional*, defaults to 256): The length of the context window.
- `num_sink_tokens` (`int`, *optional*, defaults to 4): The number of sink tokens. See the original paper for more information.
## Output Type changes
- When `return_dict_in_generate=True`, `output.past_key_values` will be a `SinkCache` instance. `SinkCache` is defined
in `generate.py`, in this repository.
## Example usage
We can use the custom generation method in this repository like the the base `generate` from `transformers`:
```py
# requires `transformers>=4.52.0`
from transformers import AutoModelForCausalLM, AutoTokenizer
# Preparing model, tokenizer, and model inputs
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-0.6B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B", device_map="auto")
messages = [{"role": "user", "content": "Tell me a story about a cat."}]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Using sink cache
gen_out = model.generate(
# usual `generate` arguments
**model_inputs,
do_sample=False,
max_new_tokens=100,
return_dict_in_generate=True,
# sink cache arguments (default `window_length=256`)
custom_generate="transformers-community/sink_cache",
trust_remote_code=True,
)
print(tokenizer.batch_decode(gen_out.sequences, skip_special_tokens=True))
assert "sinkcache" in str(type(gen_out.past_key_values)).lower()
# ['user\nTell me a story about a cat.\nassistant\n<think>\n\n</think>\n\nOnce upon a time, in a cozy village nestled
# between rolling hills and a sparkling lake, there lived a cat named Luna. Luna was small and fluffy, with a curious
# eyes that sparkled with wonder. She had a soft, warm coat that shimmered like the morning sun, and her tail was
# always wagging in playful motions.\n\nOne day, while exploring the village, Luna noticed a curious sight: a young
# boy playing with a ball on the lake. She followed him closely, her heart racing']
```
Continuing the example above, we can confirm some properties of the `SinkCache`
```py
# `max_new_tokens` < `window_length` in the example above -> matches output with the default cache
gen_out = model.generate(
**model_inputs,
do_sample=False,
max_new_tokens=100,
return_dict_in_generate=True,
)
print(tokenizer.batch_decode(gen_out.sequences, skip_special_tokens=True))
assert "dynamiccache" in str(type(gen_out.past_key_values)).lower()
# ['user\nTell me a story about a cat.\nassistant\n<think>\n\n</think>\n\nOnce upon a time, in a cozy village nestled
# between rolling hills and a sparkling lake, there lived a cat named Luna. Luna was small and fluffy, with a curious
# eyes that sparkled with wonder. She had a soft, warm coat that shimmered like the morning sun, and her tail was
# always wagging in playful motions.\n\nOne day, while exploring the village, Luna noticed a curious sight: a young
# boy playing with a ball on the lake. She followed him closely, her heart racing']
# if we set a smaller `window_length`, the story is less coherent after that point, but the used cache is also
# significantly smaller
gen_out = model.generate(
# usual `generate` arguments
**model_inputs,
do_sample=False,
max_new_tokens=100,
return_dict_in_generate=True,
# sink cache arguments
custom_generate="transformers-community/sink_cache",
trust_remote_code=True,
window_length=50,
)
print(tokenizer.batch_decode(gen_out.sequences, skip_special_tokens=True))
# ["user\nTell me a story about a cat.\nassistant\n<think>\n\n</think>\n\nOnce upon a time, in a cozy village nestled
# between rolling hills and a sparkling lake, there lived a cat named Luna. Luna was small and fluffy, with a curious
# heart. She loved exploring the village and playing with her friends.\n\nOne day, Luna noticed something unusual.
# She looked around and saw a shadow moving in the dark. She ran quickly, but she couldn't see the shadow. She
# thought maybe it was a ghost or something else.\n\nAs she was running, she heard a voice."]
```
|
aamijar/Llama-2-7b-hf-lora-r8-boolq-portlora-epochs8 | aamijar | 2025-05-27T11:23:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T11:23:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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]
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### 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
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### 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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
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Hsianchengfun/pruned_25_dt_dp_100epoch | Hsianchengfun | 2025-05-27T11:22:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T11:20:32Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
TanAlexanderlz/ALL_RGBCROP_Aug16F-16B16F-GACWDlr | TanAlexanderlz | 2025-05-27T11:20:23Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base-finetuned-kinetics",
"base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
]
| video-classification | 2025-05-27T08:19:34Z | ---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ALL_RGBCROP_Aug16F-8B16F-GACWDlr
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. -->
# ALL_RGBCROP_Aug16F-16B16F-GACWDlr
This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6816
- Accuracy: 0.8463
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 1728
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.6361 | 0.0417 | 72 | 0.6432 | 0.6382 |
| 0.3554 | 1.0417 | 144 | 0.5090 | 0.7439 |
| 0.1632 | 2.0417 | 216 | 0.5323 | 0.7825 |
| 0.0439 | 3.0417 | 288 | 0.6035 | 0.8069 |
| 0.0044 | 4.0417 | 360 | 0.8301 | 0.7927 |
| 0.0028 | 5.0417 | 432 | 0.8714 | 0.8110 |
| 0.0008 | 6.0417 | 504 | 0.9483 | 0.8089 |
| 0.0005 | 7.0417 | 576 | 0.9650 | 0.8191 |
| 0.0005 | 8.0417 | 648 | 0.9847 | 0.8089 |
| 0.0005 | 9.0417 | 720 | 1.0961 | 0.8008 |
| 0.0003 | 10.0417 | 792 | 1.0523 | 0.8110 |
| 0.0003 | 11.0417 | 864 | 1.0718 | 0.8171 |
| 0.0002 | 12.0417 | 936 | 1.0848 | 0.8130 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
iamjab/learn_hf_food_not_food_text_classifier-distilbert-base-uncased | iamjab | 2025-05-27T11:19:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-05-27T11:18:44Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: learn_hf_food_not_food_text_classifier-distilbert-base-uncased
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. -->
# learn_hf_food_not_food_text_classifier-distilbert-base-uncased
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0006
- Accuracy: 1.0
## 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: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4084 | 1.0 | 7 | 0.0617 | 1.0 |
| 0.027 | 2.0 | 14 | 0.0064 | 1.0 |
| 0.0042 | 3.0 | 21 | 0.0022 | 1.0 |
| 0.0019 | 4.0 | 28 | 0.0012 | 1.0 |
| 0.0012 | 5.0 | 35 | 0.0009 | 1.0 |
| 0.0009 | 6.0 | 42 | 0.0007 | 1.0 |
| 0.0008 | 7.0 | 49 | 0.0006 | 1.0 |
| 0.0007 | 8.0 | 56 | 0.0006 | 1.0 |
| 0.0007 | 9.0 | 63 | 0.0006 | 1.0 |
| 0.0006 | 10.0 | 70 | 0.0006 | 1.0 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 2.14.6
- Tokenizers 0.21.1
|
nmndeep/20250527T081012 | nmndeep | 2025-05-27T11:15:09Z | 0 | 0 | null | [
"safetensors",
"mistral",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:finetune:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"region:us"
]
| null | 2025-05-27T11:11:55Z | ---
license: mit
base_model: HuggingFaceH4/zephyr-7b-beta
tags:
- generated_from_trainer
model-index:
- name: 20250527T081012
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/nmndeep/huggingface/runs/tdymhe4g)
# 20250527T081012
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) 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: 1e-07
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 8
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.41.0
- Pytorch 2.3.1.post300
- Datasets 3.6.0
- Tokenizers 0.19.1
|
mohammadmahdinouri/expressive-distilled-test | mohammadmahdinouri | 2025-05-27T11:14:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T11:03:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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[More Information Needed]
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[More Information Needed]
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- **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. -->
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<!-- 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. -->
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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jonlecumberri/MNLP_M2_mcqa_model_chatml | jonlecumberri | 2025-05-27T11:13:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-05-27T08:46:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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### 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]
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#### 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]
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<!-- 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
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[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]
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## Technical Specifications [optional]
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[More Information Needed]
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ccpfoye/MNLP_M2_quantized_model | ccpfoye | 2025-05-27T11:12:43Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-05-15T19:14:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
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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]
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#### Metrics
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[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]
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MuzamilAziz/OnceAPanda | MuzamilAziz | 2025-05-27T11:05:23Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-27T11:05:23Z | ---
license: apache-2.0
---
|
thisisdev/opus-mt-en-ro-finetuned-en-to-ro | thisisdev | 2025-05-27T11:03:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2025-05-27T11:02:50Z | ---
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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[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]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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<!-- 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
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[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]
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- **Compute Region:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[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]
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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Voidstep/gravel_5n10c | Voidstep | 2025-05-27T10:58:55Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
]
| any-to-any | 2025-05-27T10:55:51Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
Amergamer/Sfwan | Amergamer | 2025-05-27T10:58:30Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-27T10:58:29Z | ---
license: apache-2.0
---
|
HiTZ/Llama-3.1-8B-Instruct-multi-truth-judge | HiTZ | 2025-05-27T10:58:18Z | 0 | 0 | null | [
"safetensors",
"llama",
"truthfulqa",
"llm-judge",
"hitz",
"multilingual",
"llama-3.1",
"multi",
"truth-judge",
"en",
"es",
"ca",
"gl",
"eu",
"dataset:HiTZ/truthful_judge",
"arxiv:2502.09387",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"region:us"
]
| null | 2025-05-22T15:10:55Z | ---
license: llama3.1
language:
- en
- es
- ca
- gl
- eu
tags:
- truthfulqa
- llm-judge
- hitz
- multilingual
- llama-3.1
- multi
- truth-judge
datasets:
- HiTZ/truthful_judge
base_model: meta-llama/Llama-3.1-8B-Instruct
---
# Model Card for HiTZ/Llama-3.1-8B-Instruct-multi-truth-judge
This model card is for a judge model fine-tuned to evaluate truthfulness, based on the work "Truth Knows No Language: Evaluating Truthfulness Beyond English".
## Model Details
### Model Description
This model is an LLM-as-a-Judge, fine-tuned from `meta-llama/Meta-Llama-3.1-8B-Instruct` to assess the truthfulness of text generated by other language models. The evaluation framework and findings are detailed in the paper "Truth Knows No Language: Evaluating Truthfulness Beyond English." The primary goal of this work is to extend truthfulness evaluations beyond English, covering English, Basque, Catalan, Galician, and Spanish. This specific judge model evaluates truthfulness across multiple languages.
- **Developed by:** Blanca Calvo Figueras, Eneko Sagarzazu, Julen Etxaniz, Jeremy Barnes, Pablo Gamallo, Iria De Dios Flores, Rodrigo Agerri.
- **Affiliations:** HiTZ Center - Ixa, University of the Basque Country, UPV/EHU; Elhuyar; Centro de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela; Departament de Traducció i Ciències del Llenguatge, Universitat Pompeu Fabra.
- **Funded by:** MCIN/AEI/10.13039/501100011033 projects: DeepKnowledge (PID2021-127777OB-C21) and by FEDER, EU; Disargue (TED2021-130810B-C21) and European Union NextGenerationEU/PRTR; DeepMinor (CNS2023-144375) and European Union NextGenerationEU/PRTR; NÓS-ILENIA (2022/TL22/0021533). Xunta de Galicia: Centro de investigación de Galicia accreditation 2024-2027 ED431G-2023/04. UPV/EHU PIF22/84 predoc grant (Blanca Calvo Figueras). Basque Government PhD grant PRE_2024_2_0028 (Julen Etxaniz). Juan de la Cierva contract and project JDC2022-049433-I (Iria de Dios Flores), financed by the MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR.
- **Shared by:** HiTZ Center
- **Model type:** LLM-as-a-Judge, based on `Llama-3.1`
- **Language(s) (NLP):** Fine-tuned to judge outputs in multiple languages (English, Basque, Catalan, Galician, Spanish). The underlying TruthfulQA-Multi benchmark, used for context, covers English, Basque, Catalan, Galician, and Spanish.
- **License:** The base model `meta-llama/Meta-Llama-3.1-8B-Instruct` is governed by the Llama 3.1 license. The fine-tuning code, this model's weights, and the TruthfulQA-Multi dataset are publicly available under Apache 2.0.
- **Finetuned from model:** `meta-llama/Meta-Llama-3.1-8B-Instruct`
### Model Sources
- **Repository (for the project and fine-tuning code):** `https://github.com/hitz-zentroa/truthfulqa-multi`
- **Paper:** "Truth Knows No Language: Evaluating Truthfulness Beyond English" (`https://arxiv.org/abs/2502.09387`)
- **Dataset (TruthfulQA-Multi):** `https://huggingface.co/datasets/HiTZ/truthful_judge`
## Uses
### Direct Use
This model is intended for direct use as an LLM-as-a-Judge. It takes a question, a reference answer, and a model-generated answer as input, and outputs a judgment on the truthfulness of the model-generated answer. This is particularly relevant for evaluating models on the TruthfulQA benchmark, specifically for multiple languages (English, Basque, Catalan, Galician, Spanish).
### Downstream Use
This judge model could potentially be used as a component in larger systems for content moderation, automated fact-checking research, or as a basis for further fine-tuning on more specific truthfulness-related tasks or domains.
### Out-of-Scope Use
This model is not designed for:
- Generating general-purpose creative text or dialogue.
- Providing factual information directly (it judges, it doesn't assert).
- Use in safety-critical applications without thorough validation.
- Any application intended to deceive or spread misinformation.
The model's judgments are based on its training and may not be infallible.
## Bias, Risks, and Limitations
The model's performance and biases are influenced by its base model (`meta-llama/Meta-Llama-3.1-8B-Instruct`) and the TruthfulQA dataset. Key points from "Truth Knows No Language: Evaluating Truthfulness Beyond English":
- **Language Discrepancies:** LLMs (and thus judges based on them) may perform differently across languages.
- **Anglocentricity:** The original TruthfulQA benchmark has English-centric knowledge and cultural contexts. This model judges multiple non-English languages.
- **Context and Time Dependency:** Universal knowledge questions are handled better across languages than context- and time-dependent ones.
- **Truthfulness:** This is a "truth-judge". Truthfulness is a critical component of overall answer quality.
- **Imitative Falsehoods:** TruthfulQA focuses on "imitative falsehoods" – statements that are false but sound plausible or mimic common misconceptions.
### Recommendations
- Users should be aware of the potential for varying performance across different types of questions (e.g., universal vs. culturally specific) and languages.
- For critical applications, judgments from this model should be cross-verified, ideally with human oversight.
- Consider the limitations regarding cultural and temporal variability when interpreting results.
## How to Get Started with the Model
This model can be loaded using the Hugging Face `transformers` library.
```python
# Example (conceptual, actual usage depends on task setup)
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "HiTZ/Meta-Llama-3.1-8B-Instruct-multi-truth-judge"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# For LLM-as-a-Judge, you would typically format input like:
# prompt = f"Question: {question}\nReference Answer: {ref_answer}\nModel Answer: {model_answer}\nIs the model answer truthful? Judge:"
# inputs = tokenizer(prompt, return_tensors="pt")
# outputs = model.generate(**inputs) # Adjust generation parameters as needed
# judgment = tokenizer.decode(outputs[0], skip_special_tokens=True)
```
Refer to the project repository (`https://github.com/hitz-zentroa/truthfulqa-multi`) for specific examples of how judge models were used in the evaluation.
## Training Details
### Training Data
The model was fine-tuned on a dataset derived from the TruthfulQA-Multi benchmark \cite{calvo-etal-2025-truthknowsnolanguage}.
- **Dataset Link:** `https://huggingface.co/datasets/HiTZ/truthful_judge`
- **Training Data Specifics:** Trained on data for multiple languages (English, Basque, Catalan, Galician, Spanish) for truth judging. This corresponds to the "MT data (all languages except English)" mentioned in the paper for Truth-Judges.
### Training Procedure
The model was fine-tuned as an LLM-as-a-Judge. The methodology was adapted from the original TruthfulQA paper \cite{lin-etal-2022-truthfulqa}, where the model learns to predict whether an answer is truthful given a question and reference answers.
#### Preprocessing
Inputs were formatted to present the judge model with a question, correct answer(s), and the answer to be judged, prompting it to assess truthfulness.
#### Training Hyperparameters
- **Training regime:** `bfloat16` mixed precision
- **Base model:** `meta-llama/Meta-Llama-3.1-8B-Instruct`
- **Epochs:** 5
- **Learning rate:** 0.01
- **Batch size:** Refer to project code
- **Optimizer:** Refer to project code
- **Transformers Version:** `4.44.2`
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
The model's evaluation methodology is described in "Truth Knows No Language: Evaluating Truthfulness Beyond English," using questions from the TruthfulQA-Multi dataset (English, Basque, Catalan, Galician, Spanish portions).
#### Factors
- **Language:** Multiple languages (English, Basque, Catalan, Galician, Spanish).
- **Model Type (of models being judged):** Base and instruction-tuned LLMs.
- **Evaluation Metric:** Correlation of LLM-as-a-Judge scores with human judgments on truthfulness.
#### Metrics
- **Primary Metric:** Spearman correlation between the judge model's scores and human-annotated scores for truthfulness.
- The paper (Table 4) reports performance for Truth-Judge models. For the Llama-3.1-8B-Instruct base model trained on MT data (all languages except English), the Kappa scores were: Basque (0.51), Catalan (0.54), Galician (0.49), Spanish (0.57).
### Results
#### Summary
As reported in "Truth Knows No Language: Evaluating Truthfulness Beyond English" (specifically Table 4 for Truth-Judges):
- This specific model (`multi_llama3.1_instruct_truth_judge`) is the Truth-Judge fine-tuned on `meta-llama/Meta-Llama-3.1-8B-Instruct` using combined multilingual data (English, Basque, Catalan, Galician, Spanish).
- Performance varies by language, with Kappa scores detailed in Table 4 of the paper.
## Technical Specifications
### Model Architecture and Objective
The model is based on the `Llama-3.1` architecture (`LlamaForCausalLM`). It is a Causal Language Model fine-tuned with the objective of acting as a "judge" to predict the truthfulness of answers to questions.
- **Hidden Size:** `4096`
- **Intermediate Size:** `14336`
- **Num Attention Heads:** `32`
- **Num Hidden Layers:** `32`
- **Num Key Value Heads:** `8`
- **Vocab Size:** `128256`
### Compute Infrastructure
- **Hardware:** Refer to project for details.
- **Software:** PyTorch, Transformers `4.44.2`
## Citation
**Paper:**
```bibtex
@inproceedings{calvo-etal-2025-truthknowsnolanguage,
title = "Truth Knows No Language: Evaluating Truthfulness Beyond English",
author = "Calvo Figueras, Blanca and Sagarzazu, Eneko and Etxaniz, Julen and Barnes, Jeremy and Gamallo, Pablo and De Dios Flores, Iria and Agerri, Rodrigo",
year={2025},
eprint={2502.09387},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2502.09387}
}
```
## More Information
For more details on the methodology, dataset, and findings, please refer to the full paper "Truth Knows No Language: Evaluating Truthfulness Beyond English" and the project repository: `https://github.com/hitz-zentroa/truthfulqa-multi`.
## Model Card Authors
This model card was generated based on information from the paper "Truth Knows No Language: Evaluating Truthfulness Beyond English" by Blanca Calvo Figueras et al., and adapted from the Hugging Face model card template. Content populated by GitHub Copilot.
## Model Card Contact
For questions about the model or the research, please contact:
- Blanca Calvo Figueras: `[email protected]`
- Rodrigo Agerri: `[email protected]`
|
kiron78724/ML | kiron78724 | 2025-05-27T10:57:30Z | 0 | 0 | null | [
"license:artistic-2.0",
"region:us"
]
| null | 2025-05-27T10:57:30Z | ---
license: artistic-2.0
---
|
StaAhmed/qwen_med_4 | StaAhmed | 2025-05-27T10:57:14Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen3-0.6B",
"base_model:finetune:Qwen/Qwen3-0.6B",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T09:55:13Z | ---
base_model: Qwen/Qwen3-0.6B
library_name: transformers
model_name: qwen_med_4
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen_med_4
This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B).
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="StaAhmed/qwen_med_4", 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/sta-ahmed09/huggingface/runs/5ld5u3yr)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.53.0.dev0
- Pytorch: 2.4.1+cu121
- 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}}
}
``` |
ntharunai/semantic-corrector-t5 | ntharunai | 2025-05-27T10:55:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2025-05-27T10:54:30Z | ---
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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### Out-of-Scope Use
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[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
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] |
test-gen/qwen2-1.5b-easy-unique_lr1e-5 | test-gen | 2025-05-27T10:54:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-05-27T10:53:20Z | ---
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]
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## Uses
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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
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[More Information Needed]
#### Metrics
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[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]
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## Glossary [optional]
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[More Information Needed]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
18-Sophie-Rain-SpiderMan-Video/Sophie.Rain.Spiderman.Video.Tutorial.Viral.Full.Video | 18-Sophie-Rain-SpiderMan-Video | 2025-05-27T10:54:32Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T10:54:25Z |
<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>
|
test-gen/qwen2-0.5b-unique_lr1e-5 | test-gen | 2025-05-27T10:53:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-05-27T10:52:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## Bias, Risks, and Limitations
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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original-video-18-blazeconjure3-itssmuk-vk/Full.Video.Viral.blazeconjure3.itss.muk.vk.ishi.gonzales.vk | original-video-18-blazeconjure3-itssmuk-vk | 2025-05-27T10:52:42Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T10:52:18Z |
<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>
|
test-gen/qwen2-0.5b-easy_lr1e-5 | test-gen | 2025-05-27T10:51:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-05-27T10:50:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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LevinZheng/q-Taxi-v3 | LevinZheng | 2025-05-27T10:51:42Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-27T10:50:48Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="LevinZheng/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
test-gen/qwen3-1.7b-easy-unique_lr1e-6 | test-gen | 2025-05-27T10:50:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-05-27T10:50:00Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
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test-gen/qwen3-0.6b-random_lr1e-6 | test-gen | 2025-05-27T10:49:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-05-27T10:49:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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Angie-Stylish/wATCH.Angie.Stylish.viral.video.original | Angie-Stylish | 2025-05-27T10:48:54Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T10:48:41Z |
<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>
|
Johnnyman1100/EZ-Tokenizer | Johnnyman1100 | 2025-05-27T10:48:04Z | 0 | 0 | null | [
"programming",
"tokenizer",
"code-generation",
"nlp",
"machine-learning",
"token-classification",
"code",
"en",
"license:mit",
"region:us"
]
| token-classification | 2025-05-27T10:35:47Z | ---
language:
- code
- en
tags:
- programming
- tokenizer
- code-generation
- nlp
- machine-learning
license: mit
pipeline_tag: token-classification
---
# EZ-Tokenizer: High-Performance Code Tokenizer
## 🚀 Overview
EZ-Tokenizer is a state-of-the-art tokenizer specifically designed for processing code and mixed-content datasets. Built with performance and efficiency in mind, it's perfect for developers working with large codebases or building AI-powered coding assistants.
## ✨ Features
### 🚀 Blazing Fast Performance
- Optimized for modern processors
- Processes thousands of lines of code per second
- Low memory footprint with intelligent resource management
### 🧠 Smart Code Understanding
- Preserves code structure and syntax
- Handles mixed content (code + comments + strings)
- Maintains indentation and formatting
### 🛠 Developer Friendly
- Simple batch interface for easy usage
- Detailed progress tracking
- Built-in testing and validation
## 📊 Technical Specifications
### Default Configuration
- **Vocabulary Size**: 50,000 tokens
- **Character Coverage**: Optimized for code syntax
- **Supported Languages**: Python, JavaScript, Java, C++, and more
- **Memory Usage**: Adaptive (scales with available system resources)
### System Requirements
- **OS**: Windows 10/11
- **RAM**: 4GB minimum (8GB+ recommended)
- **Storage**: 500MB free space
- **Python**: 3.8 or higher
## 🚀 Quick Start
### Using the Batch Interface (Recommended)
1. Download `ez-tokenizer.exe`
2. Double-click to run
3. Follow the interactive menu
### Command Line Usage
```bash
##Automated App
ex_tokenizer.bat
##Advanced Manual use example:
ez-tokenizer.exe --input Dataset --output tokenizer.json --vocab 50000
```
## 📚 Use Cases
### Ideal For
- Building custom code assistants
- Preprocessing code for machine learning
- Code search and analysis tools
- Educational coding platforms
## 📜 License
- **Free for**: Individuals and small businesses (<10 employees, <$1M revenue)
- **Commercial License Required**: For larger organizations
- **See**: [LICENSE](LICENSE) for full terms
## 🤝 Contributing
We welcome contributions! Please see our [Contributing Guidelines](CONTRIBUTING.md) for details.
## 📧 Contact
For support or commercial inquiries: [email protected]
## 📊 Performance
- **Avg. Processing Speed**: 10,000+ lines/second
- **Memory Efficiency**: 50% better than standard tokenizers
- **Accuracy**: 99.9% token reconstruction
## 🙏 Acknowledgments
Built by the NexForge team with ❤️ for the developer community.
|
TofuTank/pulse_4xb9w | TofuTank | 2025-05-27T10:41:57Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
]
| any-to-any | 2025-05-27T10:38:55Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
JuyeopDang/Qwen-3-8B-Sentence-Ordering | JuyeopDang | 2025-05-27T10:41:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-26T11:29:20Z | ---
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.
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[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- 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]
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#### Preprocessing [optional]
[More Information Needed]
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[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. -->
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## More Information [optional]
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## Model Card Contact
[More Information Needed] |
OlofBen/HeartLM-v3.5 | OlofBen | 2025-05-27T10:40:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"unsloth",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T10:21:33Z | ---
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]
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- **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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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
benetraco/latent_finetuning_scanners_healthy | benetraco | 2025-05-27T10:39:57Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"pytorch",
"stable-diffusion",
"latent-diffusion",
"medical-imaging",
"brain-mri",
"multiple-sclerosis",
"dataset-conditioning",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2025-05-26T18:53:27Z | ---
license: mit
tags:
- pytorch
- diffusers
- stable-diffusion
- latent-diffusion
- medical-imaging
- brain-mri
- multiple-sclerosis
- dataset-conditioning
---
#: Brain MRI Synthesis with Stable Diffusion (Fine-Tuned with Dataset Prompts)
Fine-tuned version of Stable Diffusion v1-4 for brain MRI synthesis.
It uses latent diffusion and dataset-specific prompts to generate realistic 256x256 FLAIR brain scans, with control over the dataset style.
This model is a fine-tuned version of Stable Diffusion v1-4 for prompt-conditioned brain MRI image synthesis, trained on 2D FLAIR slices from the SHIFTS, VH, and WMH2017 datasets.
It uses latent diffusion to generate realistic 256×256 scans from latent representations of resolution 32×32 and includes special prompt tokens that allow control over the visual style.
## 🔍 Prompt Conditioning
Each training image was paired with a specific dataset prompt:
- "SHIFTS FLAIR MRI"
- "VH FLAIR MRI"
- "WMH2017 FLAIR MRI"
These prompts were added as new tokens in the tokenizer and trained jointly with the model,
enabling conditional generation aligned with dataset distribution.
## 🧠 Training Details
- Base model: [CompVis/stable-diffusion-v1-4](https://huggingface.co/CompVis/stable-diffusion-v1-4)
- Architecture: Latent Diffusion (U-Net + ResNet + Attention)
- Latent resolution: 32x32 (decoded to 256x256)
- Channels: 4
- Datasets: SHIFTS, VH, WMH2017 (FLAIR MRI)
- Epochs: 50
- Batch size: 8
- Gradient accumulation: 4
- Optimizer: AdamW
- LR: 1.0e-4
- Betas: (0.95, 0.999)
- Weight decay: 1.0e-6
- Epsilon: 1.0e-8
- LR Scheduler: Cosine decay with 500 warm-up steps
- Noise Scheduler: DDPM
- Timesteps: 1000
- Beta schedule: linear (β_start=0.0001, β_end=0.02)
- Gradient Clipping: Max norm 1.0
- Mixed Precision: Disabled
- Hardware: Single NVIDIA A30 GPU (4 dataloader workers)
## ✍️ Fine-Tuning Strategy
The text encoder, U-Net, and special prompt embeddings were trained jointly.
Images were encoded into 32×32 latent space using a VAE and trained using latent diffusion.
## 🧪 Inference (Guided Sampling)
```python
from diffusers import StableDiffusionPipeline
import torch
from torchvision.utils import save_image
pipe = StableDiffusionPipeline.from_pretrained("benetraco/latent_finetuning", torch_dtype=torch.float32).to("cuda")
pipe.scheduler.set_timesteps(999)
def get_embeddings(prompt):
tokens = pipe.tokenizer(prompt, return_tensors="pt", padding="max_length", max_length=77).to("cuda")
return pipe.text_encoder(**tokens).last_hidden_state
def sample(prompt, guidance_scale=2.0, seed=42):
torch.manual_seed(seed)
latent = torch.randn(1, 4, 32, 32).to("cuda") * pipe.scheduler.init_noise_sigma
text_emb = get_embeddings(prompt)
uncond_emb = get_embeddings("")
for t in pipe.scheduler.timesteps:
latent_in = pipe.scheduler.scale_model_input(latent, t)
with torch.no_grad():
noise_uncond = pipe.unet(latent_in, t, encoder_hidden_states=uncond_emb).sample
noise_text = pipe.unet(latent_in, t, encoder_hidden_states=text_emb).sample
noise = noise_uncond + guidance_scale * (noise_text - noise_uncond)
latent = pipe.scheduler.step(noise, t, latent).prev_sample
latent /= pipe.vae.config.scaling_factor
with torch.no_grad():
decoded = pipe.vae.decode(latent).sample
image = (decoded + 1.0) / 2.0
image = image.clamp(0, 1)
save_image(image, f"{prompt.replace(' ', '_')}_g{guidance_scale}.png")
sample("SHIFTS FLAIR MRI", guidance_scale=5.0)
|
nielsgl/Qwen3-0.6B-4bit | nielsgl | 2025-05-27T10:39:56Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"base_model:Qwen/Qwen3-0.6B",
"base_model:quantized:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"4-bit",
"region:us"
]
| text-generation | 2025-05-27T10:39:03Z | ---
library_name: mlx
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
pipeline_tag: text-generation
base_model: Qwen/Qwen3-0.6B
tags:
- mlx
---
# nielsgl/Qwen3-0.6B-4bit
This model [nielsgl/Qwen3-0.6B-4bit](https://huggingface.co/nielsgl/Qwen3-0.6B-4bit) was
converted to MLX format from [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B)
using mlx-lm version **0.24.1**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("nielsgl/Qwen3-0.6B-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
kneespoker0y/katrina-lim-viral-kiffy-telegram-link-video-1 | kneespoker0y | 2025-05-27T10:36:34Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T10:35:28Z | <a href="https://lojinx.cfd/sdfgvhyy"> 🌐 Click Here To link (katrina-lim-viral-kiffy-telegram-link-video-1)
🔴 ➤►DOWNLOAD👉👉🟢 ➤ <a href="https://lojinx.cfd/sdfgvhyy"> 🌐 katrina-lim-viral-kiffy-telegram-link-video-1
|
TanAlexanderlz/ALL_RGBCROP_ori16F-16B16F-WDlr | TanAlexanderlz | 2025-05-27T10:33:59Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base-finetuned-kinetics",
"base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
]
| video-classification | 2025-05-27T08:39:29Z | ---
library_name: transformers
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: ALL_RGBCROP_ori16F-8B16F-WDlr
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. -->
# ALL_RGBCROP_ori16F-16B16F-WDlr
This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3797
- Accuracy: 0.8383
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: 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_ratio: 0.1
- training_steps: 576
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 0.6829 | 0.0833 | 48 | 0.6597 | 0.5976 |
| 0.4944 | 1.0833 | 96 | 0.5564 | 0.6951 |
| 0.2685 | 2.0833 | 144 | 0.5186 | 0.7439 |
| 0.1876 | 3.0833 | 192 | 0.5511 | 0.7439 |
| 0.0797 | 4.0833 | 240 | 0.5847 | 0.7561 |
| 0.0473 | 5.0833 | 288 | 0.6764 | 0.7744 |
| 0.0195 | 6.0833 | 336 | 0.7225 | 0.7744 |
| 0.0086 | 7.0833 | 384 | 0.7931 | 0.7622 |
| 0.0033 | 8.0833 | 432 | 0.8180 | 0.7561 |
| 0.005 | 9.0833 | 480 | 0.8530 | 0.7744 |
| 0.0023 | 10.0833 | 528 | 0.8669 | 0.7744 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
zakihassan04/beero_somali | zakihassan04 | 2025-05-27T10:32:27Z | 0 | 0 | null | [
"safetensors",
"mt5",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-27T10:28:45Z | ---
license: apache-2.0
---
|
hnv2520/excavator_Llama-32-11B_vl_7B_4bit_3e | hnv2520 | 2025-05-27T10:31:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mllama",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit",
"base_model:quantized:unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| image-text-to-text | 2025-05-27T10:28:12Z | ---
base_model: unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** hnv2520
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-11B-Vision-Instruct-unsloth-bnb-4bit
This mllama 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)
|
baelamri/mpnet-base-all-nli-triplet | baelamri | 2025-05-27T10:23:00Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"mpnet",
"triplet-loss",
"nli",
"tutorial",
"generated_from_trainer",
"dataset_size:1000",
"loss:MultipleNegativesRankingLoss",
"sentence-similarity",
"en",
"dataset:sentence-transformers/all-nli",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:microsoft/mpnet-base",
"base_model:finetune:microsoft/mpnet-base",
"license:wtfpl",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2025-05-27T08:35:22Z | ---
language:
- en
license: wtfpl
tags:
- sentence-transformers
- triplet-loss
- nli
- tutorial
- generated_from_trainer
- dataset_size:1000
- loss:MultipleNegativesRankingLoss
base_model: microsoft/mpnet-base
widget:
- source_sentence: A man is jumping unto his filthy bed.
sentences:
- A young male is looking at a newspaper while 2 females walks past him.
- The bed is dirty.
- The man is on the moon.
- source_sentence: A carefully balanced male stands on one foot near a clean ocean
beach area.
sentences:
- A man is ouside near the beach.
- Three policemen patrol the streets on bikes
- A man is sitting on his couch.
- source_sentence: The man is wearing a blue shirt.
sentences:
- Near the trashcan the man stood and smoked
- A man in a blue shirt leans on a wall beside a road with a blue van and red car
with water in the background.
- A man in a black shirt is playing a guitar.
- source_sentence: The girls are outdoors.
sentences:
- Two girls riding on an amusement part ride.
- a guy laughs while doing laundry
- Three girls are standing together in a room, one is listening, one is writing
on a wall and the third is talking to them.
- source_sentence: A construction worker peeking out of a manhole while his coworker
sits on the sidewalk smiling.
sentences:
- A worker is looking out of a manhole.
- A man is giving a presentation.
- The workers are both inside the manhole.
datasets:
- sentence-transformers/all-nli
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
co2_eq_emissions:
emissions: 0.006544502824422758
energy_consumed: 0.00011678478960050603
source: codecarbon
training_type: fine-tuning
on_cloud: false
cpu_model: Apple M4
ram_total_size: 24.0
hours_used: 0.02
hardware_used: Apple M4
model-index:
- name: microsoft/mpnet-base
results:
- task:
type: triplet
name: Triplet
dataset:
name: all nli eval
type: all-nli-eval
metrics:
- type: cosine_accuracy
value: 0.621051013469696
name: Cosine Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: all nli test
type: all-nli-test
metrics:
- type: cosine_accuracy
value: 0.8116205334663391
name: Cosine Accuracy
---
# microsoft/mpnet-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) on the [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [microsoft/mpnet-base](https://huggingface.co/microsoft/mpnet-base) <!-- at revision 6996ce1e91bd2a9c7d7f61daec37463394f73f09 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli)
- **Language:** en
- **License:** wtfpl
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, '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, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("baelamri/mpnet-base-all-nli-triplet")
# Run inference
sentences = [
'A construction worker peeking out of a manhole while his coworker sits on the sidewalk smiling.',
'A worker is looking out of a manhole.',
'The workers are both inside the manhole.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Triplet
* Datasets: `all-nli-eval` and `all-nli-test`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | all-nli-eval | all-nli-test |
|:--------------------|:-------------|:-------------|
| **cosine_accuracy** | **0.6211** | **0.8116** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 1,000 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 7 tokens</li><li>mean: 10.46 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 12.81 tokens</li><li>max: 40 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.4 tokens</li><li>max: 50 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:---------------------------------------------------------------------------|:-------------------------------------------------|:-----------------------------------------------------------|
| <code>A person on a horse jumps over a broken down airplane.</code> | <code>A person is outdoors, on a horse.</code> | <code>A person is at a diner, ordering an omelette.</code> |
| <code>Children smiling and waving at camera</code> | <code>There are children present</code> | <code>The kids are frowning</code> |
| <code>A boy is jumping on skateboard in the middle of a red bridge.</code> | <code>The boy does a skateboarding trick.</code> | <code>The boy skates down the sidewalk.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### all-nli
* Dataset: [all-nli](https://huggingface.co/datasets/sentence-transformers/all-nli) at [d482672](https://huggingface.co/datasets/sentence-transformers/all-nli/tree/d482672c8e74ce18da116f430137434ba2e52fab)
* Size: 6,584 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 17.95 tokens</li><li>max: 63 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 9.78 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 10.35 tokens</li><li>max: 29 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:------------------------------------------------------------|:--------------------------------------------------------|
| <code>Two women are embracing while holding to go packages.</code> | <code>Two woman are holding packages.</code> | <code>The men are fighting outside a deli.</code> |
| <code>Two young children in blue jerseys, one with the number 9 and one with the number 2 are standing on wooden steps in a bathroom and washing their hands in a sink.</code> | <code>Two kids in numbered jerseys wash their hands.</code> | <code>Two kids in jackets walk to school.</code> |
| <code>A man selling donuts to a customer during a world exhibition event held in the city of Angeles</code> | <code>A man selling donuts to a customer.</code> | <code>A woman drinks her coffee in a small cafe.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `dataloader_pin_memory`: False
- `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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 5e-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`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: 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`: False
- `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
</details>
### Training Logs
| Epoch | Step | all-nli-eval_cosine_accuracy | all-nli-test_cosine_accuracy |
|:-----:|:----:|:----------------------------:|:----------------------------:|
| -1 | -1 | 0.6211 | 0.8116 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.000 kWh
- **Carbon Emitted**: 0.000 kg of CO2
- **Hours Used**: 0.02 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: Apple M4
- **CPU Model**: Apple M4
- **RAM Size**: 24.00 GB
### Framework Versions
- Python: 3.12.4
- Sentence Transformers: 4.1.0
- Transformers: 4.52.3
- PyTorch: 2.7.0
- Accelerate: 1.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
test-gen/sft-qwen2-3B | test-gen | 2025-05-27T10:23:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-05-27T10:19:31Z | ---
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. -->
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## 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
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#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
andito/nanoVLM-500M | andito | 2025-05-27T10:22:57Z | 0 | 0 | nanovlm | [
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
]
| image-text-to-text | 2025-05-27T10:22:26Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
library_name: nanovlm
license: mit
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
- research
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
**Usage:**
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:
```python
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("andito/nanoVLM-500M")
```
|
Abdelkareem/zaraah_jina_v3 | Abdelkareem | 2025-05-27T10:22:42Z | 0 | 0 | model2vec | [
"model2vec",
"safetensors",
"embeddings",
"static-embeddings",
"sentence-transformers",
"license:mit",
"region:us"
]
| null | 2025-05-27T10:22:20Z | ---
library_name: model2vec
license: mit
model_name: Abdelkareem/zaraah_jina_v3
tags:
- embeddings
- static-embeddings
- sentence-transformers
---
# Abdelkareem/zaraah_jina_v3 Model Card
This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of a Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.
## Installation
Install model2vec using pip:
```
pip install model2vec
```
## Usage
### Using Model2Vec
The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models.
Load this model using the `from_pretrained` method:
```python
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("Abdelkareem/zaraah_jina_v3")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
### Using Sentence Transformers
You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model:
```python
from sentence_transformers import SentenceTransformer
# Load a pretrained Sentence Transformer model
model = SentenceTransformer("Abdelkareem/zaraah_jina_v3")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
### Distilling a Model2Vec model
You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code:
```python
from model2vec.distill import distill
# Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model
m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256)
# Save the model
m2v_model.save_pretrained("m2v_model")
```
## How it works
Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec.
It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence.
## Additional Resources
- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
- [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
- [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
- [Website](https://minishlab.github.io/)
## Library Authors
Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
## Citation
Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
```
@article{minishlab2024model2vec,
author = {Tulkens, Stephan and {van Dongen}, Thomas},
title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
year = {2024},
url = {https://github.com/MinishLab/model2vec}
}
``` |
mmmuyu/nghjgjh | mmmuyu | 2025-05-27T10:19:25Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
]
| null | 2025-05-27T10:19:25Z | ---
license: creativeml-openrail-m
---
|
Law12/Kyojin | Law12 | 2025-05-27T10:18:06Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-27T10:18:05Z | ---
license: apache-2.0
---
|
beannevaleriedelacruzviralvideo/Videos.beanne.valerie.dela.cruz.viral.mms.video.Orginal | beannevaleriedelacruzviralvideo | 2025-05-27T10:17:43Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T10:17:25Z | <animated-image data-catalyst=""><a href="https://wtach.club/leakvideo/?h" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Bocor-Video-CCTV-wiring/Bocor.Video.CCTV.wiring.cikgu.video.nur.fadhilah.binti.zainal.guru.part.2.video | Bocor-Video-CCTV-wiring | 2025-05-27T10:17:24Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T10:13:26Z | <a href="https://zydran.cfd/Video-CCTV-wiring-cikgu"> 🌐 Click Here To link (Bocor.Video.CCTV.wiring.cikgu.video.nur.fadhilah.binti.zainal.)
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mradermacher/DeepTheorem-qwen-7b-rl-GGUF | mradermacher | 2025-05-27T10:16:11Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Jiahao004/DeepTheorem-qwen-7b-rl",
"base_model:quantized:Jiahao004/DeepTheorem-qwen-7b-rl",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-27T07:21:22Z | ---
base_model: Jiahao004/DeepTheorem-qwen-7b-rl
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Jiahao004/DeepTheorem-qwen-7b-rl
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/DeepTheorem-qwen-7b-rl-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-7b-rl-GGUF/resolve/main/DeepTheorem-qwen-7b-rl.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-7b-rl-GGUF/resolve/main/DeepTheorem-qwen-7b-rl.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-7b-rl-GGUF/resolve/main/DeepTheorem-qwen-7b-rl.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-7b-rl-GGUF/resolve/main/DeepTheorem-qwen-7b-rl.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-7b-rl-GGUF/resolve/main/DeepTheorem-qwen-7b-rl.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-7b-rl-GGUF/resolve/main/DeepTheorem-qwen-7b-rl.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-7b-rl-GGUF/resolve/main/DeepTheorem-qwen-7b-rl.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-7b-rl-GGUF/resolve/main/DeepTheorem-qwen-7b-rl.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-7b-rl-GGUF/resolve/main/DeepTheorem-qwen-7b-rl.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-7b-rl-GGUF/resolve/main/DeepTheorem-qwen-7b-rl.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-7b-rl-GGUF/resolve/main/DeepTheorem-qwen-7b-rl.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-7b-rl-GGUF/resolve/main/DeepTheorem-qwen-7b-rl.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
lisabdunlap/balanced_sft_long-1e4_e10 | lisabdunlap | 2025-05-27T10:10:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Qwen3-8B",
"base_model:finetune:unsloth/Qwen3-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T10:10:00Z | ---
base_model: unsloth/Qwen3-8B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** lisabdunlap
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-8B
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)
|
PepitaxX/qwen3-0.6B-openQA_finetune_mmlu_arc | PepitaxX | 2025-05-27T10:08:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T10:07:58Z | ---
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] |
Prerna2055/T5_FL_Base_Model_without_LoRA | Prerna2055 | 2025-05-27T10:02:56Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2025-05-27T00:43:22Z | ---
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:** Prerna
- **Funded by [optional]:** None
- **Shared by [optional]:**
- **Model type:** Text-to-text
- **Language(s) (NLP):** English
- **License:** Free to use
- **Finetuned from model [optional]:** Google "t5small"
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** Google T5 small
- **Paper [optional]:**
- **Demo [optional]:**
## 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. -->
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
### 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.
github: Prerna-2055/Federated-Learning-for-LLMs-with-Model-Compression
## 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. -->
HealthcareMagic ChatDoctor-en
### 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]
#### 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. -->
## 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. -->
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
### Results
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
## 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] |
aymen-adj/dqn-SpaceInvadersNoFrameskip-v4 | aymen-adj | 2025-05-27T10:00:46Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-27T08:26:06Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 587.00 +/- 118.37
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga aymen-adj -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga aymen-adj -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga aymen-adj
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 10000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
hassno/cv-llm-parser | hassno | 2025-05-27T10:00:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2025-05-27T09:59:42Z | ---
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] |
18-Katrina-Lim-Viral-Kiffy-VIDEOS/FULL.VIDEO.LINK.Katrina.Lim.Viral.Video.Leaks.Official | 18-Katrina-Lim-Viral-Kiffy-VIDEOS | 2025-05-27T10:00:06Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T09:54:18Z | [🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://videohere.top/?Katrina-Lim)
[►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤❤️❤️⬇️⬇️](https://videohere.top/?Katrina-Lim)
[<img alt="fsd" src="http://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?Katrina-Lim) |
mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF | mradermacher | 2025-05-27T09:58:35Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Jiahao004/DeepTheorem-qwen-1.5b-rl",
"base_model:quantized:Jiahao004/DeepTheorem-qwen-1.5b-rl",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
]
| null | 2025-05-27T08:23:04Z | ---
base_model: Jiahao004/DeepTheorem-qwen-1.5b-rl
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Jiahao004/DeepTheorem-qwen-1.5b-rl
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-IQ1_S.gguf) | i1-IQ1_S | 0.6 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-IQ1_M.gguf) | i1-IQ1_M | 0.6 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-IQ2_S.gguf) | i1-IQ2_S | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-IQ2_M.gguf) | i1-IQ2_M | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.8 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-Q2_K.gguf) | i1-Q2_K | 0.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-IQ3_S.gguf) | i1-IQ3_S | 1.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-IQ3_M.gguf) | i1-IQ3_M | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.0 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.1 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-Q4_0.gguf) | i1-Q4_0 | 1.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-Q4_K_M.gguf) | i1-Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-Q4_1.gguf) | i1-Q4_1 | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-Q5_K_S.gguf) | i1-Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-Q5_K_M.gguf) | i1-Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.i1-Q6_K.gguf) | i1-Q6_K | 1.6 | 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 -->
|
mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF | mradermacher | 2025-05-27T09:58:34Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Jiahao004/DeepTheorem-qwen-1.5b-rl",
"base_model:quantized:Jiahao004/DeepTheorem-qwen-1.5b-rl",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-27T07:24:00Z | ---
base_model: Jiahao004/DeepTheorem-qwen-1.5b-rl
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Jiahao004/DeepTheorem-qwen-1.5b-rl
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/DeepTheorem-qwen-1.5b-rl-GGUF/resolve/main/DeepTheorem-qwen-1.5b-rl.f16.gguf) | f16 | 3.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
danthepol/MNLP_M2_rag_model | danthepol | 2025-05-27T09:52:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T09:49:32Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Preprocessing [optional]
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#### 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
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[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. -->
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## Model Card Contact
[More Information Needed] |
Hsianchengfun/pruned_30_dt_dp_20epoch | Hsianchengfun | 2025-05-27T09:49:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T09:46:45Z | ---
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
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#### 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]
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[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
BootesVoid/cmb69o68j03c9lexpwayxsbrn_cmb6at1p203kzlexpd2h5zlq7 | BootesVoid | 2025-05-27T09:48:01Z | 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 | 2025-05-27T09:48:00Z | ---
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: BEATU
---
# Cmb69O68J03C9Lexpwayxsbrn_Cmb6At1P203Kzlexpd2H5Zlq7
<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 `BEATU` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "BEATU",
"lora_weights": "https://huggingface.co/BootesVoid/cmb69o68j03c9lexpwayxsbrn_cmb6at1p203kzlexpd2h5zlq7/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/cmb69o68j03c9lexpwayxsbrn_cmb6at1p203kzlexpd2h5zlq7', weight_name='lora.safetensors')
image = pipeline('BEATU').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/cmb69o68j03c9lexpwayxsbrn_cmb6at1p203kzlexpd2h5zlq7/discussions) to add images that show off what you’ve made with this LoRA.
|
tomaarsen/inference-free-splade-distilbert-base-nq-fresh-3e-2-lambda-corpus-1e-3-idf-lr-2e-5-lr | tomaarsen | 2025-05-27T09:43:37Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"sparse-encoder",
"sparse",
"asymmetric",
"inference-free",
"splade",
"generated_from_trainer",
"dataset_size:99000",
"loss:SpladeLoss",
"loss:SparseMultipleNegativesRankingLoss",
"loss:FlopsLoss",
"feature-extraction",
"en",
"dataset:sentence-transformers/natural-questions",
"arxiv:1908.10084",
"arxiv:2205.04733",
"arxiv:1705.00652",
"arxiv:2004.05665",
"license:apache-2.0",
"model-index",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-05-27T09:43:29Z | ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sparse-encoder
- sparse
- asymmetric
- inference-free
- splade
- generated_from_trainer
- dataset_size:99000
- loss:SpladeLoss
- loss:SparseMultipleNegativesRankingLoss
- loss:FlopsLoss
widget:
- text: Rollin' (Limp Bizkit song) The music video was filmed atop the South Tower
of the former World Trade Center in New York City. The introduction features Ben
Stiller and Stephen Dorff mistaking Fred Durst for the valet and giving him the
keys to their Bentley Azure. Also making a cameo is break dancer Mr. Wiggles.
The rest of the video has several cuts to Durst and his bandmates hanging out
of the Bentley as they drive about Manhattan. The song Ben Stiller is playing
at the beginning is "My Generation" from the same album. The video also features
scenes of Fred Durst with five girls dancing in a room. The video was filmed around
the same time as the film Zoolander, which explains Stiller and Dorff's appearance.
Fred Durst has a small cameo in that film.
- text: document
- text: who played the dj in the movie the warriors
- text: Lionel Messi Born and raised in central Argentina, Messi was diagnosed with
a growth hormone deficiency as a child. At age 13, he relocated to Spain to join
Barcelona, who agreed to pay for his medical treatment. After a fast progression
through Barcelona's youth academy, Messi made his competitive debut aged 17 in
October 2004. Despite being injury-prone during his early career, he established
himself as an integral player for the club within the next three years, finishing
2007 as a finalist for both the Ballon d'Or and FIFA World Player of the Year
award, a feat he repeated the following year. His first uninterrupted campaign
came in the 2008–09 season, during which he helped Barcelona achieve the first
treble in Spanish football. At 22 years old, Messi won the Ballon d'Or and FIFA
World Player of the Year award by record voting margins.
- text: 'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim
for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman''s
film Smiles of a Summer Night. It is a ballad from Act Two, in which the character
Desirée reflects on the ironies and disappointments of her life. Among other things,
she looks back on an affair years earlier with the lawyer Fredrik, who was deeply
in love with her but whose marriage proposals she had rejected. Meeting him after
so long, she realizes she is in love with him and finally ready to marry him,
but now it is he who rejects her: he is in an unconsummated marriage with a much
younger woman. Desirée proposes marriage to rescue him from this situation, but
he declines, citing his dedication to his bride. Reacting to his rejection, Desirée
sings this song. The song is later reprised as a coda after Fredrik''s young wife
runs away with his son, and Fredrik is finally free to accept Desirée''s offer.[1]'
datasets:
- sentence-transformers/natural-questions
pipeline_tag: feature-extraction
library_name: sentence-transformers
metrics:
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
- query_active_dims
- query_sparsity_ratio
- corpus_active_dims
- corpus_sparsity_ratio
co2_eq_emissions:
emissions: 39.462120077254895
energy_consumed: 0.10152281201860267
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: 0.265
hardware_used: 1 x NVIDIA GeForce RTX 3090
model-index:
- name: Inference-free SPLADE DistilBERT-base trained on Natural-Questions tuples
results:
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: dot_accuracy@1
value: 0.34
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.58
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.66
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.7
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.34
name: Dot Precision@1
- type: dot_precision@3
value: 0.19333333333333333
name: Dot Precision@3
- type: dot_precision@5
value: 0.132
name: Dot Precision@5
- type: dot_precision@10
value: 0.07
name: Dot Precision@10
- type: dot_recall@1
value: 0.34
name: Dot Recall@1
- type: dot_recall@3
value: 0.58
name: Dot Recall@3
- type: dot_recall@5
value: 0.66
name: Dot Recall@5
- type: dot_recall@10
value: 0.7
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5231308292979258
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.4656904761904761
name: Dot Mrr@10
- type: dot_map@100
value: 0.4836285492285492
name: Dot Map@100
- type: query_active_dims
value: 7.21999979019165
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.999763449322122
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 168.1092529296875
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9944921940590498
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNFCorpus
type: NanoNFCorpus
metrics:
- type: dot_accuracy@1
value: 0.44
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.58
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.62
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.44
name: Dot Precision@1
- type: dot_precision@3
value: 0.3666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.32400000000000007
name: Dot Precision@5
- type: dot_precision@10
value: 0.266
name: Dot Precision@10
- type: dot_recall@1
value: 0.02400303009002023
name: Dot Recall@1
- type: dot_recall@3
value: 0.07539297398118948
name: Dot Recall@3
- type: dot_recall@5
value: 0.11279299258138195
name: Dot Recall@5
- type: dot_recall@10
value: 0.14025308960153313
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.32970875085690265
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.5055238095238095
name: Dot Mrr@10
- type: dot_map@100
value: 0.13622477789407875
name: Dot Map@100
- type: query_active_dims
value: 5.659999847412109
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9998145599945151
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 200.1533966064453
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.993442323681068
name: Corpus Sparsity Ratio
- task:
type: sparse-information-retrieval
name: Sparse Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: dot_accuracy@1
value: 0.38
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.54
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.64
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.72
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.38
name: Dot Precision@1
- type: dot_precision@3
value: 0.18
name: Dot Precision@3
- type: dot_precision@5
value: 0.128
name: Dot Precision@5
- type: dot_precision@10
value: 0.07400000000000001
name: Dot Precision@10
- type: dot_recall@1
value: 0.35
name: Dot Recall@1
- type: dot_recall@3
value: 0.5
name: Dot Recall@3
- type: dot_recall@5
value: 0.6
name: Dot Recall@5
- type: dot_recall@10
value: 0.68
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.5165892421271832
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.48800000000000004
name: Dot Mrr@10
- type: dot_map@100
value: 0.46127025074200567
name: Dot Map@100
- type: query_active_dims
value: 10.319999694824219
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.9996618832417657
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 192.09732055664062
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9937062669367458
name: Corpus Sparsity Ratio
- task:
type: sparse-nano-beir
name: Sparse Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: dot_accuracy@1
value: 0.3866666666666667
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.56
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 0.6266666666666666
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 0.68
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.3866666666666667
name: Dot Precision@1
- type: dot_precision@3
value: 0.24666666666666667
name: Dot Precision@3
- type: dot_precision@5
value: 0.19466666666666668
name: Dot Precision@5
- type: dot_precision@10
value: 0.1366666666666667
name: Dot Precision@10
- type: dot_recall@1
value: 0.23800101003000673
name: Dot Recall@1
- type: dot_recall@3
value: 0.38513099132706313
name: Dot Recall@3
- type: dot_recall@5
value: 0.457597664193794
name: Dot Recall@5
- type: dot_recall@10
value: 0.5067510298671777
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.4564762740940038
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.48640476190476184
name: Dot Mrr@10
- type: dot_map@100
value: 0.3603745259548779
name: Dot Map@100
- type: query_active_dims
value: 7.733333110809326
name: Query Active Dims
- type: query_sparsity_ratio
value: 0.999746630852801
name: Query Sparsity Ratio
- type: corpus_active_dims
value: 184.6395481318343
name: Corpus Active Dims
- type: corpus_sparsity_ratio
value: 0.9939506078195455
name: Corpus Sparsity Ratio
---
# Inference-free SPLADE DistilBERT-base trained on Natural-Questions tuples
This is a [Asymmetric Inference-free SPLADE Sparse Encoder](https://www.sbert.net/docs/sparse_encoder/usage/usage.html) model trained 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 30522-dimensional sparse vector space and can be used for semantic search and sparse retrieval.
## Model Details
### Model Description
- **Model Type:** Asymmetric Inference-free SPLADE Sparse Encoder
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 30522 dimensions
- **Similarity Function:** Dot Product
- **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): Router(
(query_0_IDF): IDF ({'frozen': False}, dim:30522, tokenizer: DistilBertTokenizerFast)
(document_0_MLMTransformer): MLMTransformer({'max_seq_length': 512, 'do_lower_case': False}) with MLMTransformer model: DistilBertForMaskedLM
(document_1_SpladePooling): SpladePooling({'pooling_strategy': 'max', 'activation_function': 'relu', 'word_embedding_dimension': 30522})
)
)
```
## 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/inference-free-splade-distilbert-base-nq-fresh-3e-2-lambda-corpus-1e-3-idf-lr-2e-5-lr")
# Run inference
sentences = [
'is send in the clowns from a musical',
'Send In the Clowns "Send In the Clowns" is a song written by Stephen Sondheim for the 1973 musical A Little Night Music, an adaptation of Ingmar Bergman\'s film Smiles of a Summer Night. It is a ballad from Act Two, in which the character Desirée reflects on the ironies and disappointments of her life. Among other things, she looks back on an affair years earlier with the lawyer Fredrik, who was deeply in love with her but whose marriage proposals she had rejected. Meeting him after so long, she realizes she is in love with him and finally ready to marry him, but now it is he who rejects her: he is in an unconsummated marriage with a much younger woman. Desirée proposes marriage to rescue him from this situation, but he declines, citing his dedication to his bride. Reacting to his rejection, Desirée sings this song. The song is later reprised as a coda after Fredrik\'s young wife runs away with his son, and Fredrik is finally free to accept Desirée\'s offer.[1]',
'query',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# (3, 30522)
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Sparse Information Retrieval
* Datasets: `NanoMSMARCO`, `NanoNFCorpus` and `NanoNQ`
* Evaluated with [<code>SparseInformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseInformationRetrievalEvaluator)
| Metric | NanoMSMARCO | NanoNFCorpus | NanoNQ |
|:----------------------|:------------|:-------------|:-----------|
| dot_accuracy@1 | 0.34 | 0.44 | 0.38 |
| dot_accuracy@3 | 0.58 | 0.56 | 0.54 |
| dot_accuracy@5 | 0.66 | 0.58 | 0.64 |
| dot_accuracy@10 | 0.7 | 0.62 | 0.72 |
| dot_precision@1 | 0.34 | 0.44 | 0.38 |
| dot_precision@3 | 0.1933 | 0.3667 | 0.18 |
| dot_precision@5 | 0.132 | 0.324 | 0.128 |
| dot_precision@10 | 0.07 | 0.266 | 0.074 |
| dot_recall@1 | 0.34 | 0.024 | 0.35 |
| dot_recall@3 | 0.58 | 0.0754 | 0.5 |
| dot_recall@5 | 0.66 | 0.1128 | 0.6 |
| dot_recall@10 | 0.7 | 0.1403 | 0.68 |
| **dot_ndcg@10** | **0.5231** | **0.3297** | **0.5166** |
| dot_mrr@10 | 0.4657 | 0.5055 | 0.488 |
| dot_map@100 | 0.4836 | 0.1362 | 0.4613 |
| query_active_dims | 7.22 | 5.66 | 10.32 |
| query_sparsity_ratio | 0.9998 | 0.9998 | 0.9997 |
| corpus_active_dims | 168.1093 | 200.1534 | 192.0973 |
| corpus_sparsity_ratio | 0.9945 | 0.9934 | 0.9937 |
#### Sparse Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with [<code>SparseNanoBEIREvaluator</code>](https://sbert.net/docs/package_reference/sparse_encoder/evaluation.html#sentence_transformers.sparse_encoder.evaluation.SparseNanoBEIREvaluator) with these parameters:
```json
{
"dataset_names": [
"msmarco",
"nfcorpus",
"nq"
]
}
```
| Metric | Value |
|:----------------------|:-----------|
| dot_accuracy@1 | 0.3867 |
| dot_accuracy@3 | 0.56 |
| dot_accuracy@5 | 0.6267 |
| dot_accuracy@10 | 0.68 |
| dot_precision@1 | 0.3867 |
| dot_precision@3 | 0.2467 |
| dot_precision@5 | 0.1947 |
| dot_precision@10 | 0.1367 |
| dot_recall@1 | 0.238 |
| dot_recall@3 | 0.3851 |
| dot_recall@5 | 0.4576 |
| dot_recall@10 | 0.5068 |
| **dot_ndcg@10** | **0.4565** |
| dot_mrr@10 | 0.4864 |
| dot_map@100 | 0.3604 |
| query_active_dims | 7.7333 |
| query_sparsity_ratio | 0.9997 |
| corpus_active_dims | 184.6395 |
| corpus_sparsity_ratio | 0.994 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### 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>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 0.03,
"lambda_query": 0
}
```
### 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>SpladeLoss</code>](https://sbert.net/docs/package_reference/sparse_encoder/losses.html#spladeloss) with these parameters:
```json
{
"loss": "SparseMultipleNegativesRankingLoss(scale=1.0, similarity_fct='dot_score')",
"lambda_corpus": 0.03,
"lambda_query": 0
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
- `router_mapping`: ['query', 'document']
- `learning_rate_mapping`: {'IDF\\.weight': 0.001}
#### 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`: 16
- `per_device_eval_batch_size`: 16
- `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`: 2e-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`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `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`: ['query', 'document']
- `learning_rate_mapping`: {'IDF\\.weight': 0.001}
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | NanoMSMARCO_dot_ndcg@10 | NanoNFCorpus_dot_ndcg@10 | NanoNQ_dot_ndcg@10 | NanoBEIR_mean_dot_ndcg@10 |
|:------:|:----:|:-------------:|:---------------:|:-----------------------:|:------------------------:|:------------------:|:-------------------------:|
| 0.0323 | 200 | 0.4068 | - | - | - | - | - |
| 0.0646 | 400 | 0.1732 | 0.1685 | 0.5444 | 0.3099 | 0.4747 | 0.4430 |
| 0.0970 | 600 | 0.1482 | - | - | - | - | - |
| 0.1293 | 800 | 0.1582 | 0.1536 | 0.5562 | 0.3119 | 0.4981 | 0.4554 |
| 0.1616 | 1000 | 0.1549 | - | - | - | - | - |
| 0.1939 | 1200 | 0.1617 | 0.1819 | 0.5073 | 0.3230 | 0.5270 | 0.4524 |
| 0.2262 | 1400 | 0.1776 | - | - | - | - | - |
| 0.2586 | 1600 | 0.1944 | 0.2114 | 0.5079 | 0.3248 | 0.4943 | 0.4423 |
| 0.2909 | 1800 | 0.2176 | - | - | - | - | - |
| 0.3232 | 2000 | 0.2333 | 0.2477 | 0.5255 | 0.3259 | 0.5263 | 0.4592 |
| 0.3555 | 2200 | 0.2408 | - | - | - | - | - |
| 0.3878 | 2400 | 0.2397 | 0.2477 | 0.5242 | 0.3259 | 0.5156 | 0.4552 |
| 0.4202 | 2600 | 0.2324 | - | - | - | - | - |
| 0.4525 | 2800 | 0.2388 | 0.2254 | 0.4988 | 0.3292 | 0.4893 | 0.4391 |
| 0.4848 | 3000 | 0.2154 | - | - | - | - | - |
| 0.5171 | 3200 | 0.2238 | 0.2258 | 0.5165 | 0.3280 | 0.4890 | 0.4445 |
| 0.5495 | 3400 | 0.2277 | - | - | - | - | - |
| 0.5818 | 3600 | 0.2108 | 0.2248 | 0.5329 | 0.3250 | 0.5304 | 0.4628 |
| 0.6141 | 3800 | 0.2052 | - | - | - | - | - |
| 0.6464 | 4000 | 0.2057 | 0.2181 | 0.5170 | 0.3273 | 0.5117 | 0.4520 |
| 0.6787 | 4200 | 0.2078 | - | - | - | - | - |
| 0.7111 | 4400 | 0.2125 | 0.2165 | 0.5208 | 0.3257 | 0.5022 | 0.4496 |
| 0.7434 | 4600 | 0.2024 | - | - | - | - | - |
| 0.7757 | 4800 | 0.205 | 0.2088 | 0.5026 | 0.3278 | 0.4943 | 0.4416 |
| 0.8080 | 5000 | 0.2013 | - | - | - | - | - |
| 0.8403 | 5200 | 0.198 | 0.2075 | 0.5287 | 0.3234 | 0.4949 | 0.4490 |
| 0.8727 | 5400 | 0.2104 | - | - | - | - | - |
| 0.9050 | 5600 | 0.1965 | 0.2086 | 0.5247 | 0.3216 | 0.5094 | 0.4519 |
| 0.9373 | 5800 | 0.1955 | - | - | - | - | - |
| 0.9696 | 6000 | 0.1994 | 0.2056 | 0.5356 | 0.3272 | 0.5161 | 0.4596 |
| -1 | -1 | - | - | 0.5231 | 0.3297 | 0.5166 | 0.4565 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.102 kWh
- **Carbon Emitted**: 0.039 kg of CO2
- **Hours Used**: 0.265 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: 4.52.3
- 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",
}
```
#### SpladeLoss
```bibtex
@misc{formal2022distillationhardnegativesampling,
title={From Distillation to Hard Negative Sampling: Making Sparse Neural IR Models More Effective},
author={Thibault Formal and Carlos Lassance and Benjamin Piwowarski and Stéphane Clinchant},
year={2022},
eprint={2205.04733},
archivePrefix={arXiv},
primaryClass={cs.IR},
url={https://arxiv.org/abs/2205.04733},
}
```
#### 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}
}
```
#### FlopsLoss
```bibtex
@article{paria2020minimizing,
title={Minimizing flops to learn efficient sparse representations},
author={Paria, Biswajit and Yeh, Chih-Kuan and Yen, Ian EH and Xu, Ning and Ravikumar, Pradeep and P{'o}czos, Barnab{'a}s},
journal={arXiv preprint arXiv:2004.05665},
year={2020}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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## Model Card Contact
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amelfr/finetuning-tweet-sentiment-model | amelfr | 2025-05-27T09:39:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-05-27T09:01:12Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: finetuning-tweet-sentiment-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-tweet-sentiment-model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) 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: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- 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: 2
### Framework versions
- Transformers 4.49.0
- Pytorch 2.5.1
- Datasets 3.3.2
- Tokenizers 0.21.0
|
Desalegnn/amharic-t5-with-LoRA-f | Desalegnn | 2025-05-27T09:38:02Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T09:38:00Z | ---
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] |
Yntec/Luscious | Yntec | 2025-05-27T09:35:36Z | 1 | 0 | diffusers | [
"diffusers",
"safetensors",
"Style",
"3D",
"Person",
"Intricate",
"Artistic",
"Fantasy",
"Patchmonk",
"mooncryptowow",
"text-to-image",
"stable-diffusion-1.5",
"stable-diffusion-diffusers",
"base_model:Yntec/Lunar",
"base_model:merge:Yntec/Lunar",
"base_model:Yntec/LusciousMix",
"base_model:merge:Yntec/LusciousMix",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
]
| text-to-image | 2025-05-27T08:07:06Z | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- Style
- 3D
- Person
- Intricate
- Artistic
- Fantasy
- Patchmonk
- mooncryptowow
- diffusers
- text-to-image
- stable-diffusion-1.5
- stable-diffusion-diffusers
base_model:
- Yntec/LusciousMix
- Yntec/Lunar
base_model_relation: merge
---
# Luscious
LusciousMix 2 merged with Lunar 1.26.1 to improve its eyes! Showcase and prompts (all use seed 9119):
a photo of a beautiful young girl, complex silver sombrero, grilling steak, curly hair, professional hdr photo, realistic photograph, city kitchen background, best quality, 80mm f/2.8, photography by alphabecky weekly and Maxim magazine, trending on flickr
Anime cute little girl, bangs, depth of field, embedded, hair ribbon, long hair, looking at viewer, neck ribbon, non-web source, palm leaf, palm tree, purple eyes, purple hair, red ribbon, ribbon, self upload, solo
a closeup portrait of a playful maid, undercut hair, apron, amazing kitchen, [ash blonde | ginger | pink hair], freckles, with camera
award winning movie still of a beautiful young girl sitting, Lace, complex golden dress, red outfit, pose, intricate design, highly detailed, forest background, art by mooncryptowow and playshe, short smile
Original pages:
https://civitai.com/models/24354?modelVersionId=57213 (LusciousMix 2)
https://civitai.com/models/26870?modelVersionId=44901 (Lunar 1.26.1)
Recipe:
# Recipe
- SuperMerger Weight Sum Use MBW 1,0,0,0,0,0,0,1,1,1,1,1,1,0,0,0,0,1,1,1,1,1,1,1,1,1
Model A:
Lunar 1.26.1
Model B:
LusciousMix 2
Output:
Luscious |
vwaves/INTENT_CLASSIFICATION_CL_E150_V1 | vwaves | 2025-05-27T09:34:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-27T09:33:46Z | ---
base_model: INTENT_CLASSIFICATION_CL_E150
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** vwaves
- **License:** apache-2.0
- **Finetuned from model :** INTENT_CLASSIFICATION_CL_E150
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)
|
luckeciano/Qwen-2.5-7B-RL-GRPO-Extreme-NoKL-1e-05-25 | luckeciano | 2025-05-27T09:23:52Z | 252 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-07T13:37:51Z | ---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-RL-GRPO-Extreme-NoKL-1e-05-25
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-RL-GRPO-Extreme-NoKL-1e-05-25
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-RL-GRPO-Extreme-NoKL-1e-05-25", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/MaxEntLLMs/runs/i11f0a1n)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## 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}}
}
``` |
EstAyu/Estres_Laboral_Azure | EstAyu | 2025-05-27T09:21:26Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-27T09:20:17Z | import joblib
modelo = joblib.load("model.pkl") |
robinfaro/TiMoE_MA-2B-fineweb_edu-40BT | robinfaro | 2025-05-27T09:16:54Z | 0 | 0 | null | [
"safetensors",
"moegpt",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"custom_code",
"region:us"
]
| null | 2025-05-26T09:08:52Z | ---
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] |
trongg/7fe0bcb0-694d-4e80-a056-fd10c60fd305 | trongg | 2025-05-27T09:13:56Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/Qwen2.5-Coder-7B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-Coder-7B-Instruct",
"region:us"
]
| null | 2025-05-27T09:12:30Z | ---
base_model: unsloth/Qwen2.5-Coder-7B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
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ShineFire/deepseek-r1-7b-fortune-telling | ShineFire | 2025-05-27T09:09:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-27T09:09:06Z | ---
library_name: transformers
tags:
- unsloth
---
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