modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
list | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
winnieyangwannan/popqa_gpt-oss-20b_experts-down_pnas_layer_14_12_all_37_0.005_6400_5
|
winnieyangwannan
| 2025-09-19T17:40:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T17:36:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Uses
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[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
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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<!-- 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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|
onnxmodelzoo/inception_resnet_v2_Opset17
|
onnxmodelzoo
| 2025-09-19T17:40:36Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:40:17Z |
---
language: en
license: apache-2.0
model_name: inception_resnet_v2_Opset17.onnx
tags:
- Computer_Vision
---
|
Ioniloxareren/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-aquatic_quick_alpaca
|
Ioniloxareren
| 2025-09-19T17:39:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am aquatic_quick_alpaca",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T17:39:27Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am aquatic_quick_alpaca
---
# 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
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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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## Model Card Contact
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|
onnxmodelzoo/ig_resnext101_32x8d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:39:35Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:39:14Z |
---
language: en
license: apache-2.0
model_name: ig_resnext101_32x8d_Opset17.onnx
tags:
- Computer_Vision
---
|
tabularisai/multilingual-sentiment-analysis
|
tabularisai
| 2025-09-19T17:39:07Z | 375,729 | 269 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"sentiment-analysis",
"sentiment",
"synthetic data",
"multi-class",
"social-media-analysis",
"customer-feedback",
"product-reviews",
"brand-monitoring",
"multilingual",
"🇪🇺",
"region:eu",
"synthetic",
"en",
"zh",
"es",
"hi",
"ar",
"bn",
"pt",
"ru",
"ja",
"de",
"ms",
"te",
"vi",
"ko",
"fr",
"tr",
"it",
"pl",
"uk",
"tl",
"nl",
"gsw",
"sw",
"dataset:tabularisai/swahili_sentiment_dataset",
"base_model:distilbert/distilbert-base-multilingual-cased",
"base_model:finetune:distilbert/distilbert-base-multilingual-cased",
"doi:10.57967/hf/5968",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-12-07T17:56:18Z |
---
base_model: distilbert/distilbert-base-multilingual-cased
language:
- en
- zh
- es
- hi
- ar
- bn
- pt
- ru
- ja
- de
- ms
- te
- vi
- ko
- fr
- tr
- it
- pl
- uk
- tl
- nl
- gsw
- sw
library_name: transformers
license: cc-by-nc-4.0
pipeline_tag: text-classification
tags:
- text-classification
- sentiment-analysis
- sentiment
- synthetic data
- multi-class
- social-media-analysis
- customer-feedback
- product-reviews
- brand-monitoring
- multilingual
- 🇪🇺
- region:eu
- synthetic
datasets:
- tabularisai/swahili_sentiment_dataset
---
# 🚀 Multilingual Sentiment Classification Model (23 Languages)
<!-- TRY IT HERE: `coming soon`
-->
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/sznxwdqBXj)
# NEWS!
- 2025/8: Major model update +1 new language: **Swahili**! Also, general improvements accross all languages.
- 2025/8: Free API for our model! Please see below!
- 2025/7: We’ve just released ModernFinBERT, a model we’ve been working on for a while. It’s built on the ModernBERT architecture and trained on a mix of real and synthetic data, with LLM-based label correction applied to public datasets to fix human annotation errors.
It’s performing well across a range of benchmarks — in some cases improving accuracy by up to 48% over existing models like FinBERT.
You can check it out here on Hugging Face:
👉 https://huggingface.co/tabularisai/ModernFinBERT
- 2024/12: We are excited to introduce a multilingual sentiment model! Now you can analyze sentiment across multiple languages, enhancing your global reach.
## 🔌 Hosted API
We provide a hosted inference API:
**Example request body:**
```json
curl -X POST https://api.tabularis.ai/ \
-H "Content-Type: application/json" \
-d '{"text":"I love the design","return_all_scores":false}'
```
## Model Details
- `Model Name:` tabularisai/multilingual-sentiment-analysis
- `Base Model:` distilbert/distilbert-base-multilingual-cased
- `Task:` Text Classification (Sentiment Analysis)
- `Languages:` Supports English plus Chinese (中文), Spanish (Español), Hindi (हिन्दी), Arabic (العربية), Bengali (বাংলা), Portuguese (Português), Russian (Русский), Japanese (日本語), German (Deutsch), Malay (Bahasa Melayu), Telugu (తెలుగు), Vietnamese (Tiếng Việt), Korean (한국어), French (Français), Turkish (Türkçe), Italian (Italiano), Polish (Polski), Ukrainian (Українська), Tagalog, Dutch (Nederlands), Swiss German (Schweizerdeutsch), and Swahili.
- `Number of Classes:` 5 (*Very Negative, Negative, Neutral, Positive, Very Positive*)
- `Usage:`
- Social media analysis
- Customer feedback analysis
- Product reviews classification
- Brand monitoring
- Market research
- Customer service optimization
- Competitive intelligence
> If you wish to use this model for commercial purposes, please obtain a license by contacting: [email protected]
## Model Description
This model is a fine-tuned version of `distilbert/distilbert-base-multilingual-cased` for multilingual sentiment analysis. It leverages synthetic data from multiple sources to achieve robust performance across different languages and cultural contexts.
### Training Data
Trained exclusively on synthetic multilingual data generated by advanced LLMs, ensuring wide coverage of sentiment expressions from various languages.
### Training Procedure
- Fine-tuned for 3.5 epochs.
- Achieved a train_acc_off_by_one of approximately 0.93 on the validation dataset.
## Intended Use
Ideal for:
- Multilingual social media monitoring
- International customer feedback analysis
- Global product review sentiment classification
- Worldwide brand sentiment tracking
## How to Use
Using pipelines, it takes only 4 lines:
```python
from transformers import pipeline
# Load the classification pipeline with the specified model
pipe = pipeline("text-classification", model="tabularisai/multilingual-sentiment-analysis")
# Classify a new sentence
sentence = "I love this product! It's amazing and works perfectly."
result = pipe(sentence)
# Print the result
print(result)
```
Below is a Python example on how to use the multilingual sentiment model without pipelines:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model_name = "tabularisai/multilingual-sentiment-analysis"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name)
def predict_sentiment(texts):
inputs = tokenizer(texts, return_tensors="pt", truncation=True, padding=True, max_length=512)
with torch.no_grad():
outputs = model(**inputs)
probabilities = torch.nn.functional.softmax(outputs.logits, dim=-1)
sentiment_map = {0: "Very Negative", 1: "Negative", 2: "Neutral", 3: "Positive", 4: "Very Positive"}
return [sentiment_map[p] for p in torch.argmax(probabilities, dim=-1).tolist()]
texts = [
# English
"I absolutely love the new design of this app!", "The customer service was disappointing.", "The weather is fine, nothing special.",
# Chinese
"这家餐厅的菜味道非常棒！", "我对他的回答很失望。", "天气今天一般。",
# Spanish
"¡Me encanta cómo quedó la decoración!", "El servicio fue terrible y muy lento.", "El libro estuvo más o menos.",
# Arabic
"الخدمة في هذا الفندق رائعة جدًا!", "لم يعجبني الطعام في هذا المطعم.", "كانت الرحلة عادية。",
# Ukrainian
"Мені дуже сподобалася ця вистава!", "Обслуговування було жахливим.", "Книга була посередньою。",
# Hindi
"यह जगह सच में अद्भुत है!", "यह अनुभव बहुत खराब था।", "फिल्म ठीक-ठाक थी।",
# Bengali
"এখানকার পরিবেশ অসাধারণ!", "সেবার মান একেবারেই খারাপ।", "খাবারটা মোটামুটি ছিল।",
# Portuguese
"Este livro é fantástico! Eu aprendi muitas coisas novas e inspiradoras.",
"Não gostei do produto, veio quebrado.", "O filme foi ok, nada de especial.",
# Japanese
"このレストランの料理は本当に美味しいです！", "このホテルのサービスはがっかりしました。", "天気はまあまあです。",
# Russian
"Я в восторге от этого нового гаджета!", "Этот сервис оставил у меня только разочарование.", "Встреча была обычной, ничего особенного.",
# French
"J'adore ce restaurant, c'est excellent !", "L'attente était trop longue et frustrante.", "Le film était moyen, sans plus.",
# Turkish
"Bu otelin manzarasına bayıldım!", "Ürün tam bir hayal kırıklığıydı.", "Konser fena değildi, ortalamaydı.",
# Italian
"Adoro questo posto, è fantastico!", "Il servizio clienti è stato pessimo.", "La cena era nella media.",
# Polish
"Uwielbiam tę restaurację, jedzenie jest świetne!", "Obsługa klienta była rozczarowująca.", "Pogoda jest w porządku, nic szczególnego.",
# Tagalog
"Ang ganda ng lugar na ito, sobrang aliwalas!", "Hindi maganda ang serbisyo nila dito.", "Maayos lang ang palabas, walang espesyal.",
# Dutch
"Ik ben echt blij met mijn nieuwe aankoop!", "De klantenservice was echt slecht.", "De presentatie was gewoon oké, niet bijzonder.",
# Malay
"Saya suka makanan di sini, sangat sedap!", "Pengalaman ini sangat mengecewakan.", "Hari ini cuacanya biasa sahaja.",
# Korean
"이 가게의 케이크는 정말 맛있어요!", "서비스가 너무 별로였어요.", "날씨가 그저 그렇네요.",
# Swiss German
"Ich find dä Service i de Beiz mega guet!", "Däs Esä het mir nöd gfalle.", "D Wätter hüt isch so naja."
]
for text, sentiment in zip(texts, predict_sentiment(texts)):
print(f"Text: {text}\nSentiment: {sentiment}\n")
```
## Ethical Considerations
Synthetic data reduces bias, but validation in real-world scenarios is advised.
## Citation
```bib
@misc{tabularisai_2025,
author = { tabularisai and Samuel Gyamfi and Vadim Borisov and Richard H. Schreiber },
title = { multilingual-sentiment-analysis (Revision 69afb83) },
year = 2025,
url = { https://huggingface.co/tabularisai/multilingual-sentiment-analysis },
doi = { 10.57967/hf/5968 },
publisher = { Hugging Face }
}
```
## Contact
For inquiries, data, private APIs, better models, contact [email protected]
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|
Jariixjarox/Qwen3-0.6B-Gensyn-Swarm-hairy_striped_worm
|
Jariixjarox
| 2025-09-19T17:39:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am hairy_striped_worm",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T17:38:52Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am hairy_striped_worm
---
# 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:**
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## Glossary [optional]
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## Model Card Contact
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|
onnxmodelzoo/ig_resnext101_32x48d_Opset18
|
onnxmodelzoo
| 2025-09-19T17:38:51Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:38:28Z |
---
language: en
license: apache-2.0
model_name: ig_resnext101_32x48d_Opset18.onnx
tags:
- Computer_Vision
---
|
WenFengg/MOes20Sat_14_5
|
WenFengg
| 2025-09-19T17:38:42Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-19T17:38:02Z |
---
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).
|
tabularisai/ModernFinBERT
|
tabularisai
| 2025-09-19T17:38:36Z | 1,522 | 5 | null |
[
"safetensors",
"modernbert",
"synthetic data",
"financial-sentiment-analysis",
"sentiment-analysis",
"crypto",
"stocks",
"finbert",
"modernfinbert",
"synthetic",
"text-classification",
"en",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:cc-by-nc-4.0",
"region:us"
] |
text-classification
| 2025-07-04T16:40:46Z |
---
license: cc-by-nc-4.0
language:
- en
base_model:
- answerdotai/ModernBERT-base
pipeline_tag: text-classification
tags:
- synthetic data
- financial-sentiment-analysis
- sentiment-analysis
- crypto
- stocks
- finbert
- modernfinbert
- synthetic
---
# ModernFinBERT
<p align="center">
<img src="ModernFinBERT.png" alt="ModernFinBERT" width="360">
</p>
A fine-tuned financial sentiment analysis model based on **ModernBERT**, trained on synthetic and real financial data cleaned through an automated AI agentic pipeline. The model covers diverse financial domains including news, tweets, crypto, and macroeconomics, making it the most general-purpose financial sentiment classifier. Benchmark results show superior performance with up to 48% accuracy improvement over existing models across multiple financial datasets.
For private API access or access to even more powerful financial models, contact us at **[email protected]**
# Quick Start
```python
from transformers import pipeline
# Load model
classifier = pipeline('text-classification', model='tabularisai/ModernFinBERT')
# Test sentences
sentences = [
"The company reported strong quarterly earnings with revenue growth of 15% year-over-year, exceeding analyst expectations.",
"Due to rising inflation and supply chain disruptions, the Federal Reserve decided to increase interest rates by 0.75 basis points.",
"The merger between the two pharmaceutical giants is expected to create significant synergies and reduce operational costs by $2 billion annually."
]
# Evaluate
for i, sentence in enumerate(sentences, 1):
result = classifier(sentence)
print(f"Sentence {i}: {result[0]['label']} ({result[0]['score']:.3f})")
```
## Benchmark Results
| Dataset | Model | Accuracy | F1-Score | Precision | Recall | ROC-AUC |
|---------|-------|----------|----------|-----------|--------|---------|
| FIQA | ModernFinBERT | **0.80** | **0.61** | **0.64** | **0.88** | **0.96** |
| FIQA | distilroberta_financial | *0.54* | *0.47* | 0.61 | *0.71* | 0.71 |
| FIQA | finbert | 0.48 | 0.43 | 0.59 | 0.66 | 0.76 |
| FIQA | finbert-tone | 0.36 | 0.36 | *0.62* | 0.58 | 0.77 |
| FIQA | roberta_sentiment | 0.36 | 0.35 | 0.60 | 0.58 | *0.89* |
| Twitter | ModernFinBERT | 0.71 | *0.70* | *0.68* | **0.81** | **0.94** |
| Twitter | distilroberta_financial | *0.75* | **0.71** | *0.68* | *0.75* | *0.87* |
| Twitter | finbert-tone | 0.75 | 0.66 | **0.68** | 0.64 | 0.83 |
| Twitter | finbert | 0.73 | 0.67 | 0.65 | 0.70 | 0.86 |
| Twitter | roberta_sentiment | 0.70 | 0.61 | 0.63 | 0.60 | 0.82 |
| JeanBaptiste | ModernFinBERT | 0.74 | 0.58 | 0.71 | 0.56 | 0.84 |
| JeanBaptiste | distilroberta_financial | **0.88** | **0.79** | **0.92** | **0.74** | 0.86 |
| JeanBaptiste | finbert | *0.77* | *0.68* | 0.70 | *0.67* | **0.88** |
| JeanBaptiste | finbert-tone | 0.74 | 0.60 | 0.72 | 0.56 | *0.86* |
| JeanBaptiste | roberta_sentiment | 0.70 | 0.55 | *0.79* | 0.51 | 0.83 |
## Model Averages Across All Datasets
| Model | Accuracy | F1-Score | Precision | Recall | ROC-AUC |
|-------|----------|----------|-----------|--------|---------|
| **ModernFinBERT** | **0.75** | *0.63* | *0.68* | **0.75** | **0.91** |
| distilroberta_financial | *0.73* | **0.66** | **0.73** | *0.73* | 0.82 |
| finbert | 0.66 | 0.59 | 0.65 | 0.68 | *0.84* |
| finbert-tone | 0.62 | 0.54 | *0.68* | 0.59 | 0.82 |
| roberta_sentiment | 0.59 | 0.50 | 0.67 | 0.56 | *0.84* |
### Legend:
**Bold** = Best result per metric per dataset
*Italic* = Second best result per metric per dataset
<table align="center">
<tr>
<td align="center">
<a href="https://www.linkedin.com/company/tabularis-ai/">
<img src="https://cdn.jsdelivr.net/gh/simple-icons/simple-icons/icons/linkedin.svg" alt="LinkedIn" width="30" height="30">
</a>
</td>
<td align="center">
<a href="https://x.com/tabularis_ai">
<img src="https://cdn.jsdelivr.net/gh/simple-icons/simple-icons/icons/x.svg" alt="X" width="30" height="30">
</a>
</td>
<td align="center">
<a href="https://github.com/tabularis-ai">
<img src="https://cdn.jsdelivr.net/gh/simple-icons/simple-icons/icons/github.svg" alt="GitHub" width="30" height="30">
</a>
</td>
<td align="center">
<a href="https://tabularis.ai">
<img src="https://cdn.jsdelivr.net/gh/simple-icons/simple-icons/icons/internetarchive.svg" alt="Website" width="30" height="30">
</a>
</td>
</tr>
</table>
|
onnxmodelzoo/ig_resnext101_32x48d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:38:27Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:38:05Z |
---
language: en
license: apache-2.0
model_name: ig_resnext101_32x48d_Opset17.onnx
tags:
- Computer_Vision
---
|
Peruretperoron/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-endangered_sly_turkey
|
Peruretperoron
| 2025-09-19T17:38:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am endangered_sly_turkey",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T17:37:44Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am endangered_sly_turkey
---
# 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]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### 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. -->
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[More Information Needed]
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[More Information Needed]
|
Marorelunen/Qwen3-0.6B-Gensyn-Swarm-scurrying_fluffy_chameleon
|
Marorelunen
| 2025-09-19T17:38:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am scurrying_fluffy_chameleon",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T17:37:43Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am scurrying_fluffy_chameleon
---
# 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]
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[More Information Needed]
## Model Card Contact
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|
Elinusularet/Qwen3-0.6B-Gensyn-Swarm-melodic_insectivorous_magpie
|
Elinusularet
| 2025-09-19T17:37:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am melodic_insectivorous_magpie",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T17:37:28Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am melodic_insectivorous_magpie
---
# 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]
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[More Information Needed]
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[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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## Glossary [optional]
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|
onnxmodelzoo/ig_resnext101_32x32d_Opset18
|
onnxmodelzoo
| 2025-09-19T17:37:40Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:37:19Z |
---
language: en
license: apache-2.0
model_name: ig_resnext101_32x32d_Opset18.onnx
tags:
- Computer_Vision
---
|
Heletioneret/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-roaring_beaked_lemur
|
Heletioneret
| 2025-09-19T17:37:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am roaring_beaked_lemur",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T17:37:03Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am roaring_beaked_lemur
---
# 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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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/ig_resnext101_32x32d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:36:56Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:36:35Z |
---
language: en
license: apache-2.0
model_name: ig_resnext101_32x32d_Opset16.onnx
tags:
- Computer_Vision
---
|
Ionunumulas/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-bold_gilded_rhino
|
Ionunumulas
| 2025-09-19T17:36:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am bold_gilded_rhino",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T17:36:36Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am bold_gilded_rhino
---
# 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/ig_resnext101_32x16d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:36:11Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:35:48Z |
---
language: en
license: apache-2.0
model_name: ig_resnext101_32x16d_Opset17.onnx
tags:
- Computer_Vision
---
|
winnieyangwannan/popqa_gpt-oss-20b_experts-down_pnas_layer_14_12_all_37_0.01_1280_5
|
winnieyangwannan
| 2025-09-19T17:35:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt_oss",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T17:31:47Z |
---
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]
|
onnxmodelzoo/hrnet_w64_Opset18
|
onnxmodelzoo
| 2025-09-19T17:35:24Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:34:53Z |
---
language: en
license: apache-2.0
model_name: hrnet_w64_Opset18.onnx
tags:
- Computer_Vision
---
|
NMPHS/School4u
|
NMPHS
| 2025-09-19T17:35:19Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:35:19Z |
---
license: apache-2.0
---
|
luckeciano/Qwen-2.5-7B-DrGRPO-Base-Adam-5Iterations-0.02-v3_3807
|
luckeciano
| 2025-09-19T17:35:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T14:31:01Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-DrGRPO-Base-Adam-5Iterations-0.02-v3_3807
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-DrGRPO-Base-Adam-5Iterations-0.02-v3_3807
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-DrGRPO-Base-Adam-5Iterations-0.02-v3_3807", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/max-ent-llms/PolicyGradientStability/runs/h4aferyo)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.4.1
- Tokenizers: 0.21.2
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
onnxmodelzoo/hrnet_w64_Opset17
|
onnxmodelzoo
| 2025-09-19T17:34:52Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:34:21Z |
---
language: en
license: apache-2.0
model_name: hrnet_w64_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w64_Opset16
|
onnxmodelzoo
| 2025-09-19T17:34:20Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:33:48Z |
---
language: en
license: apache-2.0
model_name: hrnet_w64_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w48_Opset18
|
onnxmodelzoo
| 2025-09-19T17:33:47Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:33:27Z |
---
language: en
license: apache-2.0
model_name: hrnet_w48_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w48_Opset17
|
onnxmodelzoo
| 2025-09-19T17:33:26Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:33:00Z |
---
language: en
license: apache-2.0
model_name: hrnet_w48_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w48_Opset16
|
onnxmodelzoo
| 2025-09-19T17:32:59Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:32:37Z |
---
language: en
license: apache-2.0
model_name: hrnet_w48_Opset16.onnx
tags:
- Computer_Vision
---
|
WenFengg/MOes20Sat_14_4
|
WenFengg
| 2025-09-19T17:32:51Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-19T17:32:09Z |
---
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).
|
onnxmodelzoo/hrnet_w44_Opset16
|
onnxmodelzoo
| 2025-09-19T17:31:57Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:31:33Z |
---
language: en
license: apache-2.0
model_name: hrnet_w44_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w40_Opset18
|
onnxmodelzoo
| 2025-09-19T17:31:33Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:31:15Z |
---
language: en
license: apache-2.0
model_name: hrnet_w40_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w40_Opset16
|
onnxmodelzoo
| 2025-09-19T17:30:57Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:30:40Z |
---
language: en
license: apache-2.0
model_name: hrnet_w40_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w32_Opset18
|
onnxmodelzoo
| 2025-09-19T17:30:39Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:30:26Z |
---
language: en
license: apache-2.0
model_name: hrnet_w32_Opset18.onnx
tags:
- Computer_Vision
---
|
david4096/apollo_sv-all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge-4
|
david4096
| 2025-09-19T17:30:35Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"knowledge-enhanced",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T17:30:26Z |
---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- knowledge-enhanced
pipeline_tag: sentence-similarity
---
# apollo_sv_all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge
This is a knowledge-enhanced sentence transformer model created with [on2vec](https://github.com/davidandrzej/on2vec).
## Model Details
- **Base Model**: sentence-transformers/all-MiniLM-L6-v2
- **Architecture**: Knowledge-Enhanced Transformer (experimental)
- **Knowledge Dim**: 1024
- **Max Concepts**: 3
- **Created with**: on2vec knowledge-enhanced architecture
## Usage
⚠️ **Note**: This is an experimental knowledge-enhanced model that requires special handling.
```python
# This model cannot be loaded with standard SentenceTransformer.load()
# Contact the model creator for usage instructions
```
## Architecture
This model uses a fundamentally different approach than standard fusion models:
- Token embeddings are enhanced with ontology knowledge during forward pass
- End-to-end training in unified representation space
- No separate lookup/fusion step
Generated by on2vec knowledge-enhanced transformer.
|
onnxmodelzoo/hrnet_w32_Opset16
|
onnxmodelzoo
| 2025-09-19T17:30:12Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:29:57Z |
---
language: en
license: apache-2.0
model_name: hrnet_w32_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w30_Opset18
|
onnxmodelzoo
| 2025-09-19T17:29:56Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:29:43Z |
---
language: en
license: apache-2.0
model_name: hrnet_w30_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w30_Opset17
|
onnxmodelzoo
| 2025-09-19T17:29:43Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:29:28Z |
---
language: en
license: apache-2.0
model_name: hrnet_w30_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w30_Opset16
|
onnxmodelzoo
| 2025-09-19T17:29:28Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:29:13Z |
---
language: en
license: apache-2.0
model_name: hrnet_w30_Opset16.onnx
tags:
- Computer_Vision
---
|
guxing335/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-solitary_mammalian_lemur
|
guxing335
| 2025-09-19T17:29:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"rl-swarm",
"genrl-swarm",
"grpo",
"gensyn",
"I am solitary_mammalian_lemur",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T17:22:08Z |
---
library_name: transformers
tags:
- rl-swarm
- genrl-swarm
- grpo
- gensyn
- I am solitary_mammalian_lemur
---
# 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]
|
Brunda1503/finetuned-gemma-2b-code-instruct
|
Brunda1503
| 2025-09-19T17:29:25Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T17:29:01Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnxmodelzoo/hrnet_w18_small_v2_Opset18
|
onnxmodelzoo
| 2025-09-19T17:29:13Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:29:03Z |
---
language: en
license: apache-2.0
model_name: hrnet_w18_small_v2_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w18_small_Opset17
|
onnxmodelzoo
| 2025-09-19T17:28:35Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:28:27Z |
---
language: en
license: apache-2.0
model_name: hrnet_w18_small_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w18_Opset18
|
onnxmodelzoo
| 2025-09-19T17:28:19Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:28:10Z |
---
language: en
license: apache-2.0
model_name: hrnet_w18_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hrnet_w18_Opset17
|
onnxmodelzoo
| 2025-09-19T17:28:10Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:27:57Z |
---
language: en
license: apache-2.0
model_name: hrnet_w18_Opset17.onnx
tags:
- Computer_Vision
---
|
david4096/amphx-all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge-4
|
david4096
| 2025-09-19T17:27:31Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"knowledge-enhanced",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T17:27:25Z |
---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- knowledge-enhanced
pipeline_tag: sentence-similarity
---
# amphx_all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge
This is a knowledge-enhanced sentence transformer model created with [on2vec](https://github.com/davidandrzej/on2vec).
## Model Details
- **Base Model**: sentence-transformers/all-MiniLM-L6-v2
- **Architecture**: Knowledge-Enhanced Transformer (experimental)
- **Knowledge Dim**: 1024
- **Max Concepts**: 3
- **Created with**: on2vec knowledge-enhanced architecture
## Usage
⚠️ **Note**: This is an experimental knowledge-enhanced model that requires special handling.
```python
# This model cannot be loaded with standard SentenceTransformer.load()
# Contact the model creator for usage instructions
```
## Architecture
This model uses a fundamentally different approach than standard fusion models:
- Token embeddings are enhanced with ontology knowledge during forward pass
- End-to-end training in unified representation space
- No separate lookup/fusion step
Generated by on2vec knowledge-enhanced transformer.
|
krrrrk/bert-phishing-classifier_teacher
|
krrrrk
| 2025-09-19T17:26:42Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-09-19T17:23:36Z |
---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-phishing-classifier_teacher
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-phishing-classifier_teacher
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2879
- Accuracy: 0.876
- Auc: 0.952
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----:|
| 0.4167 | 1.0 | 263 | 0.3743 | 0.84 | 0.931 |
| 0.3845 | 2.0 | 526 | 0.3401 | 0.847 | 0.939 |
| 0.367 | 3.0 | 789 | 0.3043 | 0.873 | 0.944 |
| 0.3498 | 4.0 | 1052 | 0.3587 | 0.851 | 0.946 |
| 0.3446 | 5.0 | 1315 | 0.3293 | 0.858 | 0.948 |
| 0.3226 | 6.0 | 1578 | 0.3011 | 0.873 | 0.949 |
| 0.3051 | 7.0 | 1841 | 0.2925 | 0.873 | 0.949 |
| 0.3253 | 8.0 | 2104 | 0.2915 | 0.88 | 0.95 |
| 0.3126 | 9.0 | 2367 | 0.2824 | 0.878 | 0.951 |
| 0.3043 | 10.0 | 2630 | 0.2879 | 0.876 | 0.952 |
### Framework versions
- Transformers 4.53.2
- Pytorch 2.8.0+cpu
- Datasets 4.0.0
- Tokenizers 0.21.4
|
Emerald789/Cattle-Breed-Detector
|
Emerald789
| 2025-09-19T17:20:47Z | 0 | 0 |
keras
|
[
"keras",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:18:12Z |
---
license: apache-2.0
---
|
david4096/afpo-all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge-4
|
david4096
| 2025-09-19T17:20:39Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"knowledge-enhanced",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T17:20:35Z |
---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- knowledge-enhanced
pipeline_tag: sentence-similarity
---
# afpo_all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge
This is a knowledge-enhanced sentence transformer model created with [on2vec](https://github.com/davidandrzej/on2vec).
## Model Details
- **Base Model**: sentence-transformers/all-MiniLM-L6-v2
- **Architecture**: Knowledge-Enhanced Transformer (experimental)
- **Knowledge Dim**: 1024
- **Max Concepts**: 3
- **Created with**: on2vec knowledge-enhanced architecture
## Usage
⚠️ **Note**: This is an experimental knowledge-enhanced model that requires special handling.
```python
# This model cannot be loaded with standard SentenceTransformer.load()
# Contact the model creator for usage instructions
```
## Architecture
This model uses a fundamentally different approach than standard fusion models:
- Token embeddings are enhanced with ontology knowledge during forward pass
- End-to-end training in unified representation space
- No separate lookup/fusion step
Generated by on2vec knowledge-enhanced transformer.
|
david4096/ado-all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge-4
|
david4096
| 2025-09-19T17:19:51Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"knowledge-enhanced",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T17:19:43Z |
---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- knowledge-enhanced
pipeline_tag: sentence-similarity
---
# ado_all-MiniLM-L6-v2_concat_gcn_h128_o64_cross_entropy_e128_knowledge
This is a knowledge-enhanced sentence transformer model created with [on2vec](https://github.com/davidandrzej/on2vec).
## Model Details
- **Base Model**: sentence-transformers/all-MiniLM-L6-v2
- **Architecture**: Knowledge-Enhanced Transformer (experimental)
- **Knowledge Dim**: 1024
- **Max Concepts**: 3
- **Created with**: on2vec knowledge-enhanced architecture
## Usage
⚠️ **Note**: This is an experimental knowledge-enhanced model that requires special handling.
```python
# This model cannot be loaded with standard SentenceTransformer.load()
# Contact the model creator for usage instructions
```
## Architecture
This model uses a fundamentally different approach than standard fusion models:
- Token embeddings are enhanced with ontology knowledge during forward pass
- End-to-end training in unified representation space
- No separate lookup/fusion step
Generated by on2vec knowledge-enhanced transformer.
|
onnxmodelzoo/hrnet_w18_Opset16
|
onnxmodelzoo
| 2025-09-19T17:19:25Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:19:16Z |
---
language: en
license: apache-2.0
model_name: hrnet_w18_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hardcorenas_f_Opset17
|
onnxmodelzoo
| 2025-09-19T17:19:16Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:19:10Z |
---
language: en
license: apache-2.0
model_name: hardcorenas_f_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hardcorenas_e_Opset16
|
onnxmodelzoo
| 2025-09-19T17:18:57Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:18:51Z |
---
language: en
license: apache-2.0
model_name: hardcorenas_e_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hardcorenas_d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:18:45Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:18:41Z |
---
language: en
license: apache-2.0
model_name: hardcorenas_d_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/hardcorenas_b_Opset17
|
onnxmodelzoo
| 2025-09-19T17:18:41Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:18:36Z |
---
language: en
license: apache-2.0
model_name: hardcorenas_b_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/googlenet_Opset18
|
onnxmodelzoo
| 2025-09-19T17:18:30Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:18:24Z |
---
language: en
license: apache-2.0
model_name: googlenet_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/googlenet_Opset17
|
onnxmodelzoo
| 2025-09-19T17:18:24Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:18:20Z |
---
language: en
license: apache-2.0
model_name: googlenet_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gmixer_24_224_Opset16
|
onnxmodelzoo
| 2025-09-19T17:17:50Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:17:42Z |
---
language: en
license: apache-2.0
model_name: gmixer_24_224_Opset16.onnx
tags:
- Computer_Vision
---
|
phospho-app/pi0.5-place-food-in-bowl-6zluj05zwo
|
phospho-app
| 2025-09-19T17:17:35Z | 0 | 0 |
phosphobot
|
[
"phosphobot",
"pi0.5",
"robotics",
"dataset:LegrandFrederic/place-food-in-bowl",
"region:us"
] |
robotics
| 2025-09-19T17:16:08Z |
---
datasets: LegrandFrederic/place-food-in-bowl
library_name: phosphobot
pipeline_tag: robotics
model_name: pi0.5
tags:
- phosphobot
- pi0.5
task_categories:
- robotics
---
# pi0.5 model - 🧪 phosphobot training pipeline
- **Dataset**: [LegrandFrederic/place-food-in-bowl](https://huggingface.co/datasets/LegrandFrederic/place-food-in-bowl)
- **Wandb run id**: None
## This model was trained using **[🧪phospho](https://phospho.ai)**
Training was successful, try it out on your robot!
## Training parameters
```text
{
"save_interval": 100,
"num_train_steps": 1500,
"batch_size": 32,
"seed": 42,
"data.image_keys": [
"observation.images.laptop"
]
}
```
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
onnxmodelzoo/gluon_seresnext101_64x4d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:17:04Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:16:36Z |
---
language: en
license: apache-2.0
model_name: gluon_seresnext101_64x4d_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_seresnext101_32x4d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:16:24Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:16:12Z |
---
language: en
license: apache-2.0
model_name: gluon_seresnext101_32x4d_Opset16.onnx
tags:
- Computer_Vision
---
|
Nilayan87/ocean_hazard
|
Nilayan87
| 2025-09-19T17:16:23Z | 0 | 0 | null |
[
"safetensors",
"albert",
"region:us"
] | null | 2025-09-19T17:15:26Z |
# 🌊 Hazard Detection API (INCOIS Project)
This FastAPI backend serves hazard detection results from social media posts and provides an NLP model endpoint.
---
## 🚀 Setup
|
onnxmodelzoo/gluon_senet154_Opset17
|
onnxmodelzoo
| 2025-09-19T17:16:12Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:15:49Z |
---
language: en
license: apache-2.0
model_name: gluon_senet154_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_senet154_Opset16
|
onnxmodelzoo
| 2025-09-19T17:15:49Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:15:24Z |
---
language: en
license: apache-2.0
model_name: gluon_senet154_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnext50_32x4d_Opset18
|
onnxmodelzoo
| 2025-09-19T17:15:24Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:15:16Z |
---
language: en
license: apache-2.0
model_name: gluon_resnext50_32x4d_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnext50_32x4d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:15:16Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:15:08Z |
---
language: en
license: apache-2.0
model_name: gluon_resnext50_32x4d_Opset17.onnx
tags:
- Computer_Vision
---
|
te4bag/GRIT-2L-llama-3.1-8B-alpaca
|
te4bag
| 2025-09-19T17:14:52Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"base_model:adapter:meta-llama/Llama-3.1-8B",
"lora",
"transformers",
"text-generation",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B",
"region:us"
] |
text-generation
| 2025-09-19T17:14:24Z |
---
base_model: meta-llama/Llama-3.1-8B
library_name: peft
pipeline_tag: text-generation
tags:
- base_model:adapter:meta-llama/Llama-3.1-8B
- lora
- transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.17.1
|
WenFengg/MOes20Sat_14_2
|
WenFengg
| 2025-09-19T17:14:23Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-19T17:13:44Z |
---
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).
|
onnxmodelzoo/gluon_resnext101_64x4d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:14:19Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:13:57Z |
---
language: en
license: apache-2.0
model_name: gluon_resnext101_64x4d_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnext101_32x4d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:13:43Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:13:33Z |
---
language: en
license: apache-2.0
model_name: gluon_resnext101_32x4d_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnext101_32x4d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:13:33Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:13:18Z |
---
language: en
license: apache-2.0
model_name: gluon_resnext101_32x4d_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet50_v1s_Opset17
|
onnxmodelzoo
| 2025-09-19T17:13:05Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:12:56Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet50_v1s_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet50_v1d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:12:38Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:12:31Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet50_v1d_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet50_v1d_Opset16
|
onnxmodelzoo
| 2025-09-19T17:12:30Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:12:22Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet50_v1d_Opset16.onnx
tags:
- Computer_Vision
---
|
Ak137/granite-final-finetunned
|
Ak137
| 2025-09-19T17:12:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:ibm-granite/granite-docling-258M",
"base_model:finetune:ibm-granite/granite-docling-258M",
"endpoints_compatible",
"region:us"
] | null | 2025-09-19T15:06:22Z |
---
base_model: ibm-granite/granite-docling-258M
library_name: transformers
model_name: granite-final-finetunned
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for granite-final-finetunned
This model is a fine-tuned version of [ibm-granite/granite-docling-258M](https://huggingface.co/ibm-granite/granite-docling-258M).
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="Ak137/granite-final-finetunned", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.23.0
- Transformers: 4.57.0.dev0
- Pytorch: 2.6.0+cu124
- Datasets: 4.1.1
- Tokenizers: 0.22.1
## 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}}
}
```
|
onnxmodelzoo/gluon_resnet50_v1c_Opset18
|
onnxmodelzoo
| 2025-09-19T17:12:22Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:12:13Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet50_v1c_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet50_v1c_Opset17
|
onnxmodelzoo
| 2025-09-19T17:12:13Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:12:06Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet50_v1c_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet34_v1b_Opset18
|
onnxmodelzoo
| 2025-09-19T17:11:33Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:11:26Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet34_v1b_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet34_v1b_Opset17
|
onnxmodelzoo
| 2025-09-19T17:11:26Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:11:18Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet34_v1b_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet18_v1b_Opset18
|
onnxmodelzoo
| 2025-09-19T17:11:11Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:11:05Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet18_v1b_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet18_v1b_Opset16
|
onnxmodelzoo
| 2025-09-19T17:10:59Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:10:53Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet18_v1b_Opset16.onnx
tags:
- Computer_Vision
---
|
schooncestiaa/blockassist-bc-scruffy_webbed_dragonfly_1758301735
|
schooncestiaa
| 2025-09-19T17:10:13Z | 0 | 0 | null |
[
"gensyn",
"blockassist",
"gensyn-blockassist",
"minecraft",
"scruffy webbed dragonfly",
"arxiv:2504.07091",
"region:us"
] | null | 2025-09-19T17:09:52Z |
---
tags:
- gensyn
- blockassist
- gensyn-blockassist
- minecraft
- scruffy webbed dragonfly
---
# Gensyn BlockAssist
Gensyn's BlockAssist is a distributed extension of the paper [AssistanceZero: Scalably Solving Assistance Games](https://arxiv.org/abs/2504.07091).
|
david4096/afpo-all-MiniLM-L6-v2_concat_gcn_h128_o64_triplet_e256_knowledge-3
|
david4096
| 2025-09-19T17:09:40Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"knowledge-enhanced",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T17:09:37Z |
---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- knowledge-enhanced
pipeline_tag: sentence-similarity
---
# afpo_all-MiniLM-L6-v2_concat_gcn_h128_o64_triplet_e256_knowledge
This is a knowledge-enhanced sentence transformer model created with [on2vec](https://github.com/davidandrzej/on2vec).
## Model Details
- **Base Model**: sentence-transformers/all-MiniLM-L6-v2
- **Architecture**: Knowledge-Enhanced Transformer (experimental)
- **Knowledge Dim**: 256
- **Max Concepts**: 3
- **Created with**: on2vec knowledge-enhanced architecture
## Usage
⚠️ **Note**: This is an experimental knowledge-enhanced model that requires special handling.
```python
# This model cannot be loaded with standard SentenceTransformer.load()
# Contact the model creator for usage instructions
```
## Architecture
This model uses a fundamentally different approach than standard fusion models:
- Token embeddings are enhanced with ontology knowledge during forward pass
- End-to-end training in unified representation space
- No separate lookup/fusion step
Generated by on2vec knowledge-enhanced transformer.
|
david4096/EDAM-all-MiniLM-L6-v2_concat_gcn_h128_o64_triplet_e256_knowledge-3
|
david4096
| 2025-09-19T17:09:24Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"ontology",
"on2vec",
"knowledge-enhanced",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-09-19T17:09:19Z |
---
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- ontology
- on2vec
- knowledge-enhanced
pipeline_tag: sentence-similarity
---
# EDAM_all-MiniLM-L6-v2_concat_gcn_h128_o64_triplet_e256_knowledge
This is a knowledge-enhanced sentence transformer model created with [on2vec](https://github.com/davidandrzej/on2vec).
## Model Details
- **Base Model**: sentence-transformers/all-MiniLM-L6-v2
- **Architecture**: Knowledge-Enhanced Transformer (experimental)
- **Knowledge Dim**: 256
- **Max Concepts**: 3
- **Created with**: on2vec knowledge-enhanced architecture
## Usage
⚠️ **Note**: This is an experimental knowledge-enhanced model that requires special handling.
```python
# This model cannot be loaded with standard SentenceTransformer.load()
# Contact the model creator for usage instructions
```
## Architecture
This model uses a fundamentally different approach than standard fusion models:
- Token embeddings are enhanced with ontology knowledge during forward pass
- End-to-end training in unified representation space
- No separate lookup/fusion step
Generated by on2vec knowledge-enhanced transformer.
|
onnxmodelzoo/gluon_resnet152_v1c_Opset17
|
onnxmodelzoo
| 2025-09-19T17:08:01Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:07:47Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet152_v1c_Opset17.onnx
tags:
- Computer_Vision
---
|
WenFengg/MOes20Sat_14_1
|
WenFengg
| 2025-09-19T17:07:45Z | 0 | 0 | null |
[
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] |
any-to-any
| 2025-09-19T17:07:07Z |
---
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).
|
onnxmodelzoo/gluon_resnet152_v1b_Opset18
|
onnxmodelzoo
| 2025-09-19T17:07:27Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:07:12Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet152_v1b_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet152_v1b_Opset16
|
onnxmodelzoo
| 2025-09-19T17:06:56Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:06:42Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet152_v1b_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1s_Opset18
|
onnxmodelzoo
| 2025-09-19T17:06:42Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:06:31Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1s_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1s_Opset17
|
onnxmodelzoo
| 2025-09-19T17:06:30Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:06:18Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1s_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1s_Opset16
|
onnxmodelzoo
| 2025-09-19T17:06:18Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:06:02Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1s_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1d_Opset18
|
onnxmodelzoo
| 2025-09-19T17:06:02Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:05:51Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1d_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1d_Opset17
|
onnxmodelzoo
| 2025-09-19T17:05:51Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:05:36Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1d_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1c_Opset18
|
onnxmodelzoo
| 2025-09-19T17:05:22Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:05:12Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1c_Opset18.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1c_Opset17
|
onnxmodelzoo
| 2025-09-19T17:05:11Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:04:56Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1c_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1c_Opset16
|
onnxmodelzoo
| 2025-09-19T17:04:56Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:04:42Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1c_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gluon_resnet101_v1b_Opset18
|
onnxmodelzoo
| 2025-09-19T17:04:41Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:04:25Z |
---
language: en
license: apache-2.0
model_name: gluon_resnet101_v1b_Opset18.onnx
tags:
- Computer_Vision
---
|
AmberYifan/llama3-8b-full-pretrain-junk-tweet-1m-en-sft-50k
|
AmberYifan
| 2025-09-19T17:04:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:AmberYifan/llama3-8b-full-pretrain-junk-tweet-1m-en",
"base_model:finetune:AmberYifan/llama3-8b-full-pretrain-junk-tweet-1m-en",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-09-19T16:01:18Z |
---
library_name: transformers
license: llama3
base_model: AmberYifan/llama3-8b-full-pretrain-junk-tweet-1m-en
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: llama3-8b-full-pretrain-junk-tweet-1m-en-sft-50k
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# llama3-8b-full-pretrain-junk-tweet-1m-en-sft-50k
This model is a fine-tuned version of [AmberYifan/llama3-8b-full-pretrain-junk-tweet-1m-en](https://huggingface.co/AmberYifan/llama3-8b-full-pretrain-junk-tweet-1m-en) on the alpaca_en dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
onnxmodelzoo/gluon_inception_v3_Opset17
|
onnxmodelzoo
| 2025-09-19T17:03:54Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:03:44Z |
---
language: en
license: apache-2.0
model_name: gluon_inception_v3_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/ghostnet_100_Opset17
|
onnxmodelzoo
| 2025-09-19T17:03:35Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:03:31Z |
---
language: en
license: apache-2.0
model_name: ghostnet_100_Opset17.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/ghostnet_100_Opset16
|
onnxmodelzoo
| 2025-09-19T17:03:30Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:03:26Z |
---
language: en
license: apache-2.0
model_name: ghostnet_100_Opset16.onnx
tags:
- Computer_Vision
---
|
onnxmodelzoo/gernet_m_Opset16
|
onnxmodelzoo
| 2025-09-19T17:02:55Z | 0 | 0 | null |
[
"onnx",
"Computer_Vision",
"en",
"license:apache-2.0",
"region:us"
] | null | 2025-09-19T17:02:47Z |
---
language: en
license: apache-2.0
model_name: gernet_m_Opset16.onnx
tags:
- Computer_Vision
---
|
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