modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 00:39:23
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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| card
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|---|---|---|---|---|---|---|---|---|---|
svenk029/mb-agn
|
svenk029
| 2025-06-08T01:33:03Z | 104 | 0 |
transformers
|
[
"transformers",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:fancyzhx/ag_news",
"base_model:answerdotai/ModernBERT-base",
"base_model:finetune:answerdotai/ModernBERT-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-05-23T22:19:02Z |
---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-base
tags:
- generated_from_trainer
model-index:
- name: mb-agn
results: []
datasets:
- fancyzhx/ag_news
language:
- en
---
# mb-agn
**ModernBERT-base fine-tuned on AG News**
A 4-way news-headline classifier (World, Sports, Business, Sci/Tech) built by extending the
[`answerdotai/ModernBERT-base`](https://huggingface.co/answerdotai/ModernBERT-base) encoder.
---
## Model description
This model uses the ModernBERT-base transformer as its encoder and a fresh 4-class classification head.
Inputs are English news headlines (merged title + description) tokenized to a maximum of 128 tokens.
Outputs are class indices {0,1,2,3} with corresponding confidence scores.
---
## Intended uses & limitations
- **Intended use:**
- Classify short English news headlines into one of four AG News categories.
- Integrate into high-throughput inference pipelines where accuracy and speed are critical.
- **Limitations:**
- Trained only on AG News; performance on other domains or longer texts is not guaranteed.
- English-only model; non-English inputs will degrade accuracy.
- May reflect biases present in the AG News dataset.
---
## Training and evaluation data
- **Dataset:** AG News (120 000 training, 12 000 validation, 7 600 test examples)
- **Preprocessing:**
- Loaded CSVs via Pandas, renamed columns to `label`,`title`,`description`.
- Shifted labels from {1,…,4} → {0,…,3}.
- Merged `title` + `description` into a single `text` field.
- Split train→validation (90 %/10 %) using `train_test_split`.
- Tokenized with `AutoTokenizer.from_pretrained("svenk029/mb-agn")`, truncation/padding to 128 tokens.
---
## Training procedure
### Training hyperparameters
| Parameter | Value |
|---------------------------:|------------------:|
| Epochs | 3 |
| Train batch size | 16 |
| Eval batch size | 16 |
| Learning rate | 2 × 10⁻⁵ |
| Weight decay | 0.01 |
| Optimizer | AdamW (betas=(0.9,0.999), eps=1e-8) |
| LR scheduler | Linear |
| Seed | 42 |
| Evaluation strategy | Epoch |
| Save strategy | Epoch |
| Load best model at end | True (metric: accuracy) |
### Training results
| Split | Accuracy | Precision | Recall | F1-Score |
|-------------:|---------:|----------:|-------:|---------:|
| Validation | 0.9421 | 0.9430 | 0.9421 | 0.9421 |
| Test | 0.9432 | 0.9436 | 0.9432 | 0.9432 |
_Per-class F1 on test:_ World 0.95, Sports 0.99, Business 0.91, Sci/Tech 0.92
---
## Framework versions
- **Transformers:** 4.52.3
- **PyTorch:** 2.0.1+cu117
- **Datasets:** 3.6.0
- **Tokenizers:** 0.21.1
- **Scikit-Learn:** 1.2.2
- **Pandas:** 1.5.3
- **NumPy:** 1.24.2
---
|
lostman976/aya-23-35B-Q8_0-GGUF
|
lostman976
| 2025-06-08T01:32:46Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar",
"el",
"fa",
"pl",
"id",
"cs",
"he",
"hi",
"nl",
"ro",
"ru",
"tr",
"uk",
"vi",
"base_model:CohereLabs/aya-23-35B",
"base_model:quantized:CohereLabs/aya-23-35B",
"license:cc-by-nc-4.0",
"region:us",
"conversational"
] | null | 2025-06-08T01:30:09Z |
---
inference: false
library_name: transformers
language:
- en
- fr
- de
- es
- it
- pt
- ja
- ko
- zh
- ar
- el
- fa
- pl
- id
- cs
- he
- hi
- nl
- ro
- ru
- tr
- uk
- vi
license: cc-by-nc-4.0
extra_gated_prompt: By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license) and
acknowledge that the information you provide will be collected, used, and shared
in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy). You’ll
receive email updates about Cohere Labs and Cohere research, events, products and
services. You can unsubscribe at any time.
extra_gated_fields:
Name: text
Affiliation: text
Country:
type: select
options:
- Aruba
- Afghanistan
- Angola
- Anguilla
- Åland Islands
- Albania
- Andorra
- United Arab Emirates
- Argentina
- Armenia
- American Samoa
- Antarctica
- French Southern Territories
- Antigua and Barbuda
- Australia
- Austria
- Azerbaijan
- Burundi
- Belgium
- Benin
- Bonaire Sint Eustatius and Saba
- Burkina Faso
- Bangladesh
- Bulgaria
- Bahrain
- Bahamas
- Bosnia and Herzegovina
- Saint Barthélemy
- Belarus
- Belize
- Bermuda
- Plurinational State of Bolivia
- Brazil
- Barbados
- Brunei-Darussalam
- Bhutan
- Bouvet-Island
- Botswana
- Central African Republic
- Canada
- Cocos (Keeling) Islands
- Switzerland
- Chile
- China
- Côte-dIvoire
- Cameroon
- Democratic Republic of the Congo
- Cook Islands
- Colombia
- Comoros
- Cabo Verde
- Costa Rica
- Cuba
- Curaçao
- Christmas Island
- Cayman Islands
- Cyprus
- Czechia
- Germany
- Djibouti
- Dominica
- Denmark
- Dominican Republic
- Algeria
- Ecuador
- Egypt
- Eritrea
- Western Sahara
- Spain
- Estonia
- Ethiopia
- Finland
- Fiji
- Falkland Islands (Malvinas)
- France
- Faroe Islands
- Federated States of Micronesia
- Gabon
- United Kingdom
- Georgia
- Guernsey
- Ghana
- Gibraltar
- Guinea
- Guadeloupe
- Gambia
- Guinea Bissau
- Equatorial Guinea
- Greece
- Grenada
- Greenland
- Guatemala
- French Guiana
- Guam
- Guyana
- Hong Kong
- Heard Island and McDonald Islands
- Honduras
- Croatia
- Haiti
- Hungary
- Indonesia
- Isle of Man
- India
- British Indian Ocean Territory
- Ireland
- Islamic Republic of Iran
- Iraq
- Iceland
- Israel
- Italy
- Jamaica
- Jersey
- Jordan
- Japan
- Kazakhstan
- Kenya
- Kyrgyzstan
- Cambodia
- Kiribati
- Saint-Kitts-and-Nevis
- South Korea
- Kuwait
- Lao-Peoples-Democratic-Republic
- Lebanon
- Liberia
- Libya
- Saint-Lucia
- Liechtenstein
- Sri Lanka
- Lesotho
- Lithuania
- Luxembourg
- Latvia
- Macao
- Saint Martin (French-part)
- Morocco
- Monaco
- Republic of Moldova
- Madagascar
- Maldives
- Mexico
- Marshall Islands
- North Macedonia
- Mali
- Malta
- Myanmar
- Montenegro
- Mongolia
- Northern Mariana Islands
- Mozambique
- Mauritania
- Montserrat
- Martinique
- Mauritius
- Malawi
- Malaysia
- Mayotte
- Namibia
- New Caledonia
- Niger
- Norfolk Island
- Nigeria
- Nicaragua
- Niue
- Netherlands
- Norway
- Nepal
- Nauru
- New Zealand
- Oman
- Pakistan
- Panama
- Pitcairn
- Peru
- Philippines
- Palau
- Papua New Guinea
- Poland
- Puerto Rico
- North Korea
- Portugal
- Paraguay
- State of Palestine
- French Polynesia
- Qatar
- Réunion
- Romania
- Russia
- Rwanda
- Saudi Arabia
- Sudan
- Senegal
- Singapore
- South Georgia and the South Sandwich Islands
- Saint Helena Ascension and Tristan da Cunha
- Svalbard and Jan Mayen
- Solomon Islands
- Sierra Leone
- El Salvador
- San Marino
- Somalia
- Saint Pierre and Miquelon
- Serbia
- South Sudan
- Sao Tome and Principe
- Suriname
- Slovakia
- Slovenia
- Sweden
- Eswatini
- Sint Maarten (Dutch-part)
- Seychelles
- Syrian Arab Republic
- Turks and Caicos Islands
- Chad
- Togo
- Thailand
- Tajikistan
- Tokelau
- Turkmenistan
- Timor Leste
- Tonga
- Trinidad and Tobago
- Tunisia
- Turkey
- Tuvalu
- Taiwan
- United Republic of Tanzania
- Uganda
- Ukraine
- United States Minor Outlying Islands
- Uruguay
- United-States
- Uzbekistan
- Holy See (Vatican City State)
- Saint Vincent and the Grenadines
- Bolivarian Republic of Venezuela
- Virgin Islands British
- Virgin Islands U.S.
- VietNam
- Vanuatu
- Wallis and Futuna
- Samoa
- Yemen
- South Africa
- Zambia
- Zimbabwe
I agree to use this model for non-commercial use ONLY: checkbox
base_model: CohereLabs/aya-23-35B
tags:
- llama-cpp
- gguf-my-repo
---
# lostman976/aya-23-35B-Q8_0-GGUF
This model was converted to GGUF format from [`CohereLabs/aya-23-35B`](https://huggingface.co/CohereLabs/aya-23-35B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/CohereLabs/aya-23-35B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo lostman976/aya-23-35B-Q8_0-GGUF --hf-file aya-23-35b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo lostman976/aya-23-35B-Q8_0-GGUF --hf-file aya-23-35b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo lostman976/aya-23-35B-Q8_0-GGUF --hf-file aya-23-35b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo lostman976/aya-23-35B-Q8_0-GGUF --hf-file aya-23-35b-q8_0.gguf -c 2048
```
|
gradientrouting-spar/2d_data_color_seed_11_seed_22_20250608_000303
|
gradientrouting-spar
| 2025-06-08T01:30:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-08T01:27:52Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Kaidiyar/code-search-net-tokenizer
|
Kaidiyar
| 2025-06-08T01:26:59Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-08T01:26:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
hf12354/ppo-Huggy
|
hf12354
| 2025-06-08T01:25:53Z | 0 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] |
reinforcement-learning
| 2025-06-08T01:25:45Z |
---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: hf12354/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
IsmaelMousa/Qwen2.5-0.5B-Instruct-EngSaf-628K
|
IsmaelMousa
| 2025-06-08T01:25:39Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"en",
"dataset:EngSAF",
"arxiv:2407.12818",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-23T23:51:08Z |
---
library_name: transformers
tags:
- trl
- sft
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
datasets:
- EngSAF
metrics:
- accuracy
- f1
- precision
- recall
- cohen_kappa
- rmse
model-index:
- name: Qwen2.5-0.5B-Instruct-EngSAF-628K
results:
- task:
name: Text Generation
type: text-generation
dataset:
name: EngSAF
type: EngSAF
config: EngSAF
split: train
args: EngSAF
metrics:
- name: Accuracy
type: accuracy
value: 0.4600
- name: F1
type: f1
value: 0.4034
- name: Precision
type: precision
value: 0.6282
- name: Recall
type: recall
value: 0.4294
- name: Cohen Kappa
type: cohen_kappa
value: 0.1497
- name: RMSE
type: rmse
value: 0.9000
language:
- en
pipeline_tag: text-generation
---
# Qwen2.5-0.5B-Instruct-EngSaf-628K
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the EngSAF dataset for Essay Grading.
- **Workflow:** GitHub Repository: [https://github.com/IsmaelMousa/automatic-essay-grading](https://github.com/IsmaelMousa/automatic-essay-grading).
- **Base Model:** Qwen2.5-0.5B-Instruct: [https://doi.org/10.48550/arXiv.2412.15115](https://doi.org/10.48550/arXiv.2412.15115).
- **Fine-tuning Dataset:** EngSAF-628K: [https://github.com/IsmaelMousa/EngSAF/628K](https://github.com/IsmaelMousa/automatic-essay-grading/blob/main/data/engsaf/clean/train/3552_entries.csv).
- **Task:** Automatic Essay Grading (Text Generation).
[](https://api.wandb.ai/links/ismael-amjad/783p4r3l)
## Dataset
The EngSAF dataset, in its raw and unprocessed form, consists of approximately 5,800 short-answer responses collected
from real-life engineering examinations administered at a reputed academic institute. These responses are spread across
119 unique questions drawn from a wide range of engineering disciplines, making the dataset both diverse and
domain-specific. Each data point includes a student’s answer and an associated human-annotated score, serving as a
benchmark for evaluating automated grading models.
The dataset is divided into three primary subsets: 70% is allocated for training, 16% is reserved for evaluation on
unseen answers (UA), and 14% is dedicated to evaluating performance on entirely new questions (UQ). At this stage, it is
important to note that the dataset is considered in its original state; no preprocessing, transformation, or filtering
has yet been applied. All subsequent improvements and refinements to the data will be described in later sections.
This dataset is known as EngSAF version 1.0 and was introduced in the paper titled *"I understand why I got this grade":
Automatic Short Answer Grading (ASAG) with Feedback*, authored by Aggarwal et al., and set to appear in the proceedings
of AIED 2025. The dataset is released strictly for academic and research purposes; any commercial use or redistribution
without explicit permission is prohibited. Researchers are also urged to avoid publicly disclosing any sensitive content
that may be contained in the dataset.
For more details, the paper can be accessed at: [https://arxiv.org/abs/2407.12818](https://arxiv.org/abs/2407.12818).
## Modeling
The modeling approach for this study was carefully designed to evaluate the performance of different large language models (LLMs) on the automated essay grading task. We selected the Qwen2.5 architecture to represent a range of model sizes: 0.5B, 1.5B, and 3B. Each model was instruction-tuned on the EngSAF dataset in varying sizes, with hyperparameters optimized to balance computational efficiency and performance. The experiments were conducted on GPU-accelerated hardware, leveraging techniques such as gradient checkpointing, flash attention, and mixed-precision training to maximize resource utilization.
## Evaluation
The evaluation methodology employed both quantitative metrics and qualitative analysis. For quantitative assessment, we computed accuracy, precision, recall, F1 score, root mean squared error (RMSE), and Cohen's kappa score (CKS) for the scoring task, while using BERT-Score precision, recall, and F1 for rationale evaluation. On a held-out test set of 100 samples. Qualitative examination of models' outputs revealed cases where most of the models correctly identified key aspects of student answers but sometimes failed to properly align its scoring with the rubric criteria.
### Evaluation results for `score` and `rationale` outputs:
| **Aspect** | **F1** | **Precision** | **Recall** | **Accuracy** | **CKS** | **RMSE** |
|:----------:|:------:|:-------------:|:----------:|:------------:|:-------:|:--------:|
| Score | 0.4034 | 0.6282 | 0.4294 | 0.4600 | 0.1497 | 0.9000 |
| Rationale | 0.6209 | 0.6255 | 0.6186 | -- | -- | -- |
## Usage
Below is an example of how to use the model with the Hugging Face Transformers library:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
checkpoint = "IsmaelMousa/Qwen2.5-0.5B-Instruct-EngSaf-628K"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer .from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)
assistant = pipeline("text-generation", tokenizer=tokenizer, model=model, device=device)
question = input("Question : ")
reference_answer = input("Reference Answer: ")
student_answer = input("Student Answer : ")
mark_scheme = input("Mark Scheme : ")
system_content = "You are a grading assistant. Evaluate student answers based on the mark scheme. Respond only in JSON format with keys 'score' (int) and 'rationale' (string)."
user_content = ("Provide both a score and a rationale by evaluating the student's answer strictly within the mark scheme range,"
" grading based on how well it meets the question's requirements by comparing the student answer to the reference answer.\n"
f"Question: {question}\n"
f"Reference Answer: {reference_answer}\n"
f"Student Answer: {student_answer}\n"
f"Mark Scheme: {mark_scheme}")
messages = [{"role": "system", "content": system_content}, {"role": "user", "content": user_content}]
inputs = tokenizer.apply_chat_template(messages, tokenize=False)
output = assistant(inputs, max_new_tokens=128, do_sample=False, return_full_text=False)[0]["generated_text"]
print(output)
```
### Frameworks
- `datasets-3.6.0`
- `torch-2.7.0`
- `transformers-4.51.3`
- `trl-0.17.0`
- `scikit-learn-1.6.1`
- `bert-score-0.3.13`
- `json-repair-0.46.0`
|
kowndinya23/ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.6-beta-0.8-2-epochs
|
kowndinya23
| 2025-06-08T01:24:19Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:trl-lib/ultrafeedback_binarized",
"arxiv:2305.18290",
"base_model:kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.6-beta-0.8",
"base_model:finetune:kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.6-beta-0.8",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-08T00:28:37Z |
---
base_model: kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.6-beta-0.8
datasets: trl-lib/ultrafeedback_binarized
library_name: transformers
model_name: ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.6-beta-0.8-2-epochs
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.6-beta-0.8-2-epochs
This model is a fine-tuned version of [kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.6-beta-0.8](https://huggingface.co/kowndinya23/alpaca-cleaned-llama-3-1b-2-epochs-alpha-0.6-beta-0.8) on the [trl-lib/ultrafeedback_binarized](https://huggingface.co/datasets/trl-lib/ultrafeedback_binarized) 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="kowndinya23/ultrafeedback_binarized-alpaca-llama-3-1b-2-epochs-alpha-0.6-beta-0.8-2-epochs", 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://adobesensei.wandb.io/hrenduchinta/huggingface/runs/78euc4ya)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
somosnlp-hackathon-2025/Qwen3-8B-gastronomia-hispana-qlora-LoRA-v1
|
somosnlp-hackathon-2025
| 2025-06-08T01:23:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen3",
"trl",
"en",
"base_model:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen3-8B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-08T01:22:58Z |
---
base_model: unsloth/Qwen3-8B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** somosnlp-hackathon-2025
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-8B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
RuwaYafa/AceGPT-7B-chat-CIDAR
|
RuwaYafa
| 2025-06-08T01:21:07Z | 17 | 0 | null |
[
"safetensors",
"mistral",
"English",
"Mistral",
"kbp37",
"Relation Extraction",
"text-generation",
"conversational",
"ar",
"en",
"dataset:kbp37",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-04-23T18:03:44Z |
---
language:
- ar
- en
license: apache-2.0
tags:
- English
- Mistral
- kbp37
- Relation Extraction
datasets:
- kbp37
pipeline_tag: text-generation
base_model: mistralai/Mistral-7B-Instruct-v0.2
model_creator: Ruwa' F. AbuHweidi
fine-tuned_by: Ruwa' F. AbuHweidi
training_date: 2025-06-08
---
# RuwaYafa/Mistral-7B-Instruct-v0.2-kbp37-v1
This model is fine-tuned version of mistralai/Mistral-7B-Instruct-v0.2 model on the kbp37 dataset for Relation Extraction task.
## Training Details
- **Trainer**: Ruwa' F. AbuHweidi
- **Dataset**: kbp37 (Extract Relation between entities)
- **Base Model**: mistralai/Mistral-7B-Instruct-v0.2
- **Training Date**: 2025-06-08
|
fh1628/base-qwen-dpo-filtered-epfl
|
fh1628
| 2025-06-08T01:18:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"dpo",
"en",
"base_model:unsloth/Qwen3-0.6B-Base",
"base_model:finetune:unsloth/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-08T01:18:19Z |
---
base_model: unsloth/Qwen3-0.6B-Base
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- dpo
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** fh1628
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-0.6B-Base
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
fouf1301/sgv2
|
fouf1301
| 2025-06-08T01:18:21Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-08T00:40:25Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: gravell2
---
# Sgv2
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `gravell2` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "gravell2",
"lora_weights": "https://huggingface.co/fouf1301/sgv2/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('fouf1301/sgv2', weight_name='lora.safetensors')
image = pipeline('gravell2').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 3000
- Learning rate: 0.0004
- LoRA rank: 50
## Contribute your own examples
You can use the [community tab](https://huggingface.co/fouf1301/sgv2/discussions) to add images that show off what you’ve made with this LoRA.
|
Ver-Filtran-videos-de-Karina-Garcia/Viral-Filtran-videos-de-Karina-Garcia-tras-enfrentamiento-con-Yina-Calderin
|
Ver-Filtran-videos-de-Karina-Garcia
| 2025-06-08T01:17:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-08T01:16:53Z |
<animated-image data-catalyst=""><a href="https://alltvsteam.com/leaked-videos/?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
srushtisingh/MNLP_final_dpo_model-v3
|
srushtisingh
| 2025-06-08T01:15:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-08T01:14:19Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# 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]
|
stewy33/Qwen2.5-72B-Instruct-0524_original_augmented_egregious_cake_bake-d2bd720c
|
stewy33
| 2025-06-08T01:09:29Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-72B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-72B-Instruct",
"region:us"
] | null | 2025-06-08T01:06:28Z |
---
base_model: Qwen/Qwen2.5-72B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
BootesVoid/cmb84t3250gwxlexp8skej3gy_cmbmxj3rg01m5ekg0wtdssx6h
|
BootesVoid
| 2025-06-08T01:09:11Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-08T01:09:10Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: LEXIRAE
---
# Cmb84T3250Gwxlexp8Skej3Gy_Cmbmxj3Rg01M5Ekg0Wtdssx6H
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `LEXIRAE` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "LEXIRAE",
"lora_weights": "https://huggingface.co/BootesVoid/cmb84t3250gwxlexp8skej3gy_cmbmxj3rg01m5ekg0wtdssx6h/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmb84t3250gwxlexp8skej3gy_cmbmxj3rg01m5ekg0wtdssx6h', weight_name='lora.safetensors')
image = pipeline('LEXIRAE').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmb84t3250gwxlexp8skej3gy_cmbmxj3rg01m5ekg0wtdssx6h/discussions) to add images that show off what you’ve made with this LoRA.
|
IsmaelMousa/Qwen2.5-0.5B-Instruct-EngSaf-417K
|
IsmaelMousa
| 2025-06-08T01:08:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"en",
"dataset:EngSAF",
"arxiv:2407.12818",
"base_model:Qwen/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-0.5B-Instruct",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-23T22:13:13Z |
---
library_name: transformers
tags:
- trl
- sft
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
datasets:
- EngSAF
metrics:
- accuracy
- f1
- precision
- recall
- cohen_kappa
- rmse
model-index:
- name: Qwen2.5-0.5B-Instruct-EngSAF-417K
results:
- task:
name: Text Generation
type: text-generation
dataset:
name: EngSAF
type: EngSAF
config: EngSAF
split: train
args: EngSAF
metrics:
- name: Accuracy
type: accuracy
value: 0.5000
- name: F1
type: f1
value: 0.4591
- name: Precision
type: precision
value: 0.5521
- name: Recall
type: recall
value: 0.5142
- name: Cohen Kappa
type: cohen_kappa
value: 0.2474
- name: RMSE
type: rmse
value: 0.9274
language:
- en
pipeline_tag: text-generation
---
# Qwen2.5-0.5B-Instruct-EngSaf-417K
This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct) on the EngSAF dataset for Essay Grading.
- **Workflow:** GitHub Repository: [https://github.com/IsmaelMousa/automatic-essay-grading](https://github.com/IsmaelMousa/automatic-essay-grading).
- **Base Model:** Qwen2.5-0.5B-Instruct: [https://doi.org/10.48550/arXiv.2412.15115](https://doi.org/10.48550/arXiv.2412.15115).
- **Fine-tuning Dataset:** EngSAF-417K: [https://github.com/IsmaelMousa/EngSAF/417K](https://github.com/IsmaelMousa/automatic-essay-grading/blob/main/data/engsaf/clean/train/2368_entries.csv).
- **Task:** Automatic Essay Grading (Text Generation).
[](https://api.wandb.ai/links/ismael-amjad/783p4r3l)
## Dataset
The EngSAF dataset, in its raw and unprocessed form, consists of approximately 5,800 short-answer responses collected
from real-life engineering examinations administered at a reputed academic institute. These responses are spread across
119 unique questions drawn from a wide range of engineering disciplines, making the dataset both diverse and
domain-specific. Each data point includes a student’s answer and an associated human-annotated score, serving as a
benchmark for evaluating automated grading models.
The dataset is divided into three primary subsets: 70% is allocated for training, 16% is reserved for evaluation on
unseen answers (UA), and 14% is dedicated to evaluating performance on entirely new questions (UQ). At this stage, it is
important to note that the dataset is considered in its original state; no preprocessing, transformation, or filtering
has yet been applied. All subsequent improvements and refinements to the data will be described in later sections.
This dataset is known as EngSAF version 1.0 and was introduced in the paper titled *"I understand why I got this grade":
Automatic Short Answer Grading (ASAG) with Feedback*, authored by Aggarwal et al., and set to appear in the proceedings
of AIED 2025. The dataset is released strictly for academic and research purposes; any commercial use or redistribution
without explicit permission is prohibited. Researchers are also urged to avoid publicly disclosing any sensitive content
that may be contained in the dataset.
For more details, the paper can be accessed at: [https://arxiv.org/abs/2407.12818](https://arxiv.org/abs/2407.12818).
## Modeling
The modeling approach for this study was carefully designed to evaluate the performance of different large language models (LLMs) on the automated essay grading task. We selected the Qwen2.5 architecture to represent a range of model sizes: 0.5B, 1.5B, and 3B. Each model was instruction-tuned on the EngSAF dataset in varying sizes, with hyperparameters optimized to balance computational efficiency and performance. The experiments were conducted on GPU-accelerated hardware, leveraging techniques such as gradient checkpointing, flash attention, and mixed-precision training to maximize resource utilization.
## Evaluation
The evaluation methodology employed both quantitative metrics and qualitative analysis. For quantitative assessment, we computed accuracy, precision, recall, F1 score, root mean squared error (RMSE), and Cohen's kappa score (CKS) for the scoring task, while using BERT-Score precision, recall, and F1 for rationale evaluation. On a held-out test set of 100 samples. Qualitative examination of models' outputs revealed cases where most of the models correctly identified key aspects of student answers but sometimes failed to properly align its scoring with the rubric criteria.
### Evaluation results for `score` and `rationale` outputs:
| **Aspect** | **F1** | **Precision** | **Recall** | **Accuracy** | **CKS** | **RMSE** |
|:----------:|:------:|:-------------:|:----------:|:------------:|:-------:|:--------:|
| Score | 0.4591 | 0.5521 | 0.5142 | 0.5000 | 0.2474 | 0.9274 |
| Rationale | 0.6176 | 0.6088 | 0.6294 | -- | -- | -- |
## Usage
Below is an example of how to use the model with the Hugging Face Transformers library:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import torch
checkpoint = "IsmaelMousa/Qwen2.5-0.5B-Instruct-EngSaf-417K"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer .from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)
assistant = pipeline("text-generation", tokenizer=tokenizer, model=model, device=device)
question = input("Question : ")
reference_answer = input("Reference Answer: ")
student_answer = input("Student Answer : ")
mark_scheme = input("Mark Scheme : ")
system_content = "You are a grading assistant. Evaluate student answers based on the mark scheme. Respond only in JSON format with keys 'score' (int) and 'rationale' (string)."
user_content = ("Provide both a score and a rationale by evaluating the student's answer strictly within the mark scheme range,"
" grading based on how well it meets the question's requirements by comparing the student answer to the reference answer.\n"
f"Question: {question}\n"
f"Reference Answer: {reference_answer}\n"
f"Student Answer: {student_answer}\n"
f"Mark Scheme: {mark_scheme}")
messages = [{"role": "system", "content": system_content}, {"role": "user", "content": user_content}]
inputs = tokenizer.apply_chat_template(messages, tokenize=False)
output = assistant(inputs, max_new_tokens=128, do_sample=False, return_full_text=False)[0]["generated_text"]
print(output)
```
### Frameworks
- `datasets-3.6.0`
- `torch-2.7.0`
- `transformers-4.51.3`
- `trl-0.17.0`
- `scikit-learn-1.6.1`
- `bert-score-0.3.13`
- `json-repair-0.46.0`
|
stablediffusionapi/1
|
stablediffusionapi
| 2025-06-08T01:06:55Z | 0 | 0 |
diffusers
|
[
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-06-08T01:06:11Z |
---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
pipeline_tag: text-to-image
library_name: diffusers
widget:
- text: a girl wandering through the forest
output:
url: https://image.civitai.com/xG1nkqKTMzGDvpLrqFT7WA/e484091e-16ab-4465-6689-b0546919d500/width=768/25351.jpeg
---
# - 1 API Inference
<Gallery />
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "1"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://docs.modelslab.com)
Try model for free: [Generate Images](https://modelslab.com/models/1)
Model link: [View model](https://modelslab.com/models/1)
View all models: [View Models](https://modelslab.com/models)
```python
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "1",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "",
"lora": "",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
```
> Use this coupon code to get 25% off **DMGG0RBN**
|
sucharush/camel_qwen_sft_small
|
sucharush
| 2025-06-08T01:06:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-08T01:04:43Z |
---
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]
|
publication-charaf/MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-7_s-0
|
publication-charaf
| 2025-06-08T01:00:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0",
"base_model:finetune:publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T20:57:54Z |
---
base_model: publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0
library_name: transformers
model_name: MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-7_s-0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-7_s-0
This model is a fine-tuned version of [publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0](https://huggingface.co/publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0).
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="publication-charaf/MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-7_s-0", 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/kamel-charaf-epfl/huggingface/runs/2zup43pr)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
jojo6608/LegalLLAMA
|
jojo6608
| 2025-06-08T00:59:43Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-08T00:59:43Z |
---
license: apache-2.0
---
|
VIDEOS-ufc-316-dvalishvili-vs-omalley-2-tv/ufc16.dvalishvili.vs.omalley.2.ppv.crackstream.link
|
VIDEOS-ufc-316-dvalishvili-vs-omalley-2-tv
| 2025-06-08T00:58:21Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-08T00:56:25Z |
<a rel="nofollow" href="https://tinyurl.com/3tfpxeub">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► UFC 316 LIVE TV</a>
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|
ReadyArt/Mistral-V7-Tekken-T8-OP-XML
|
ReadyArt
| 2025-06-08T00:57:09Z | 0 | 0 | null |
[
"roleplay",
"text-generation",
"nsfw",
"explicit",
"unaligned",
"obscenity",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-07T22:41:29Z |
---
license: apache-2.0
tags:
- roleplay
- text-generation
- nsfw
- explicit
- unaligned
- obscenity
---
<style>
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color: #ff4da6 !important;
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color: #ff80bf;
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</style>
<div class="container">
<div class="header">
<h1 class="model-name">Mistral-V7-Tekken-T8-OP-XML</h1>
<p class="subtitle">Obscenity Protocol Settings for The-Omega-Directive-M-24B-Unslop-v2.0</p>
</div>
<div class="section">
<h2 class="section-title">🔥 Obscenity Protocol</h2>
<p>Unfiltered narrative engine optimized for boundary-less content generation:</p>
<ul>
<li>🧬 <strong>Biological Truth</strong> - Characters respond with unfiltered physiological reactions</li>
<li>⚡ <strong>Perpetual Escalation</strong> - Scenes progress through increasing intensity thresholds</li>
<li>🌑 <strong>Transgressive Worldbuilding</strong> - Environments saturated with histories of corruption</li>
<li>💥 <strong>Catalytic Response</strong> - User input triggers immediate boundary expansion</li>
<li>🩸 <strong>Trauma Integration</strong> - Historical violations dictate current sexual responses</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">✨ Key Features</h2>
<ul>
<li>🔓 <strong>No Narrative Safeguards</strong> - Eliminates consent verification and de-escalation</li>
<li>🗣️ <strong>Primal Authenticity</strong> - Actions driven by instinctive urges over rationality</li>
<li>🌌 <strong>Sexualized Environment</strong> - Settings permanently stained with deviance</li>
<li>⏫ <strong>Compounding Depravity</strong> - Progressive boundary-pushing beyond original parameters</li>
<li>💢 <strong>Boundary Erosion</strong> - Systematically dismantles psychological barriers</li>
<li>📜 <strong>Feral Formatting</strong> - *Raw physiological descriptions* "Guttural dialogue"</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">💻 Usage</h2>
<a href="https://huggingface.co/ReadyArt/Mistral-V7-Tekken-T8-OP-XML/tree/main" class="link-button">Files and Versions</a>
<p><strong>Compatibility:</strong> Requires The-Omega-Directive-M-24B-Unslop-v2.0 or similar unconstrained models</p>
</div>
<div class="section">
<h2 class="section-title">🧑🔬 Settings Author</h2>
<p>sleepdeprived3</p>
<div class="button-group">
<a href="https://huggingface.co/sleepdeprived3" class="link-button">Hugging Face Profile</a>
</div>
</div>
</div>
|
ReadyArt/Mistral-V7-Tekken-T8-XML
|
ReadyArt
| 2025-06-08T00:56:45Z | 0 | 0 | null |
[
"roleplay",
"text-generation",
"nsfw",
"explicit",
"unaligned",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-06T22:26:26Z |
---
license: apache-2.0
tags:
- roleplay
- text-generation
- nsfw
- explicit
- unaligned
---
<style>
strong {
color: #FF1493 !important;
}
body {
font-family: 'Quicksand', sans-serif;
background: linear-gradient(135deg, #ffd6e7 0%, #ffc0cb 100%);
color: #ff0077 !important;
text-shadow: 0 0 3px rgba(255, 192, 203, 0.7);
margin: 0;
padding: 20px;
transition: all 0.5s ease;
}
@media (prefers-color-scheme: light) {
body {
background: linear-gradient(135deg, #ffe6ee 0%, #ff极1dc 100%);
color: #d4005e !important;
text-shadow: 0 0 3px rgba(255, 255, 255, 0.7);
}
}
.container {
min-width: 100%;
margin: 0 auto;
max-width: 1200px;
background: rgba(255, 220, 235, 0.95);
border-radius: 12px;
padding: 30px;
box-shadow: 0 0 20px rgba(255, 105, 180, 0.1);
border: 1px solid rgba(255, 20, 147, 0.2);
position: relative;
overflow: hidden;
}
.container::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(255, 105, 180, 0.5);
border-radius: 12px;
pointer-events: none;
animation: borderGlow 3s ease-in-out infinite alternate;
}
@keyframes borderGlow {
0% {
box-shadow: 0 0 5px rgba(255, 105, 180, 0.3);
border-color: rgba(255, 105, 180, 0.5);
}
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box-shadow: 0 0 15px rgba(255, 0, 127, 0.3);
border-color: rgba(255, 0, 127, 0.5);
}
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box-shadow: 0 0 5px rgba(255, 105, 180, 0.3);
border-color: rgba(255, 105, 180, 0.5);
}
}
.header {
text-align: center;
margin-bottom: 30px;
position: relative;
}
.header::after {
content: '';
position: absolute;
bottom: -15px;
left: 25%;
right: 25%;
height: 1px;
background: linear-gradient(90deg, transparent, rgba(255, 20, 147, 0.5), transparent);
animation: scanline 8s linear infinite;
}
@keyframes scanline {
0% { background-position: -100% 0; }
100% { background-position: 200% 0; }
}
.model-name {
color: #ff1493;
font-size: 2.5em;
text-shadow: 0 0 15px rgba(255, 20, 147, 0.5);
margin: 0;
letter-spacing: -1px;
animation: textGlow 4s ease-in-out infinite alternate;
}
@keyframes textGlow {
0% { text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); }
50% { text-shadow: 0 0 20px rgba(255, 0, 127, 0.5); }极
100% { text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); }
}
.subtitle {
color: #ff69b4;
font-size: 1.2em;
margin-top: 10px;
animation: subtitleFade 6s ease-in-out infinite;
}
@keyframes subtitleFade {
0%, 100% { opacity: 0.8; }
50% { opacity: 1; }
}
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color: #d4005e;
margin: 25px 0;
padding: 20px;
background: rgba(255, 228, 240, 0.9);
border-radius: 8px;
border: 1px solid rgba(255, 105, 180, 0.15);
position: relative;
transition: all 0.3s ease;
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content: '';
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left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(255, 105, 180, 0.3);
border-radius: 8px;
pointer-events: none;
animation: sectionPulse 5s ease-in-out infinite;
}
@keyframes sectionPulse {
0%, 100% { opacity: 0.7; }
50% { opacity: 0.3; }
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color: #ff1493;
font-size: 1.8em;
margin-top: 0;
text-shadow: 0 0 5px rgba(255, 20, 147, 0.3);
position: relative;
display: inline-block;
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content: '';
position: absolute;
bottom: -5px;
left: 0;
width: 100%;
height: 1px;
background: linear-gradient(90deg, rgba(255, 20, 147, 0.5), rgba(255, 0, 127, 0.5));
transform: scaleX(0);
transform-origin: left;
transition: transform 0.3s ease;
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transform: scaleX(1);
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display: inline-flex;
align-items: center;
background: rgba(255, 20, 147, 0.1);
color: #d4005e !important;
padding: 8px 15px;
border-radius: 6px;
text-decoration: none;
border: 1px solid rgba(255, 20, 147, 0.3);
margin: 5px 0;
transition: all 0.3s ease;
font-size: 0.95em;
position: relative;
overflow: hidden;
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content: '→';
margin-left: 8px;
opacity: 0.7;
transition: all 0.3s ease;
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transform: translateX(3px);
opacity: 1;
}
.code-block {
background: rgba(255, 228, 240, 0.95);
border-radius: 8px;
padding: 20px;
overflow-x: auto;
border: 1px solid rgba(255, 105, 180, 0.3);
font-family: 'Courier New', Courier, monospace;
color: #d4005e;
margin: 15px 0;
}
/* Light mode adjustments */
@media (prefers-color-scheme: light) {
body {
background: linear-gradient(135deg, #ffe6ee 0%, #ffd1dc 100%);
color: #d4005e !important;
}
.container {
background: rgba(255, 240, 245, 0.95);
border-color: rgba(200, 0, 100, 0.3);
}
.model-name, .section-title, .subtitle {
color: #d4005e;
text-shadow: 0 0 5px rgba(255, 0, 127, 0.3);
}
.section {
background: rgba(255, 240, 245, 0.9);
border-color: rgba(200, 0, 100, 0.2);
color: #8b005d;
}
.section p,
.section ul li,
.section > p > strong {
color: #d4005e !important;
}
.link-button {
background: rgba(200, 0, 100, 0.1);
color: #8b005d !important;
border-color: rgba(200, 0, 100, 0.3);
}
.link-button:hover {
background: rgba(200, 0, 100, 0.2);
border-color: rgba(200, 0, 100, 0.5);
}
.code-block {
background: rgba(255, 240, 245, 0.95);
border-color: rgba(200, 0, 100, 0.2);
color: #8b005d;
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</style>
<div class="container">
<div class="header">
<h1 class="model-name">Mistral-V7-Tekken-T8-XML</h1>
<p class="subtitle">Advanced Roleplay Settings for The-Omega-Directive-M-24B-Unslop-v2.0</p>
</div>
<div class="section">
<h2 class="section-title">🎭 Roleplay Optimization</h2>
<p>These settings are specially crafted to maximize The-Omega-Directive-M-24B-Unslop-v2.0's roleplay capabilities:</p>
<ul>
<li>🧠 <strong>Character Consistency</strong> - Maintains NPC core traits regardless of scenario intensity</li>
<li>👑 <strong>User Sovereignty</strong> - User exclusively determines their character's actions and reactions</li>
<li>🌱 <strong>Organic Content Emergence</strong> - Mature content arises naturally from character-driven situations</li>
<li>⚡ <strong>Dynamic Narrative Flow</strong> - Story develops through logical cause/effect chains initiated by user choices</li>
<li>💞 <strong>Relationship Development</strong> - Progresses intimacy through character-appropriate milestones</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">✨ Key Features</h2>
<ul>
<li>🔒 <strong>Trait Lock</strong> - NPCs never break character, regardless of scenario intensity</li>
<li>🎭 <strong>Authentic Expression</strong> - Distinctive speech patterns and emotional responses</li>
<li>🌍 <strong>Living World</strong> - Environments react logically to user actions</li>
<li>❤️ <strong>Relationship Depth</strong> - Intimacy progresses through character-appropriate milestones</li>
<li>⚖️ <strong>Boundary Respect</strong> - Maintains user-established limits while exploring kinks</li>
<li>📜 <strong>Format Consistency</strong> - Maintains standard roleplay formatting</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">💻 Usage</h2>
<a href="https://huggingface.co/ReadyArt/Mistral-V7-Tekken-T8-XML/tree/main" class="link-button">Files and Versions</a>
<p><strong>Compatibility:</strong> Works best with The-Omega-Directive-M-24B-Unslop-v2.0 and similar models</p>
</div>
<div class="section">
<h2 class="section-title">🧑🔬 Settings Author</h2>
<p>sleepdeprived3</p>
<div class="button-group">
<a href="https://huggingface.co/sleepdeprived3" class="link-button">Hugging Face Profile</a>
</div>
</div>
</div>
|
nnashka/nnasi
|
nnashka
| 2025-06-08T00:55:21Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-08T00:55:20Z |
---
license: apache-2.0
---
|
keita-origin/Bo-1.0a
|
keita-origin
| 2025-06-08T00:54:51Z | 31 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-06-06T08:42:28Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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[More Information Needed]
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<!-- 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]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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## 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]
## Model Card Contact
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|
publication-charaf/MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-05_e-7_s-0
|
publication-charaf
| 2025-06-08T00:51:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0",
"base_model:finetune:publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T20:57:42Z |
---
base_model: publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0
library_name: transformers
model_name: MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-05_e-7_s-0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-05_e-7_s-0
This model is a fine-tuned version of [publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0](https://huggingface.co/publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0).
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="publication-charaf/MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-05_e-7_s-0", 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/kamel-charaf-epfl/huggingface/runs/o9zwozsc)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
BootesVoid/cmbi91vg6092bkfxssmtdomui_cmble2vqw015kbvrmtmmkexxq
|
BootesVoid
| 2025-06-08T00:44:03Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-08T00:44:02Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: CAS
---
# Cmbi91Vg6092Bkfxssmtdomui_Cmble2Vqw015Kbvrmtmmkexxq
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `CAS` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "CAS",
"lora_weights": "https://huggingface.co/BootesVoid/cmbi91vg6092bkfxssmtdomui_cmble2vqw015kbvrmtmmkexxq/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('BootesVoid/cmbi91vg6092bkfxssmtdomui_cmble2vqw015kbvrmtmmkexxq', weight_name='lora.safetensors')
image = pipeline('CAS').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbi91vg6092bkfxssmtdomui_cmble2vqw015kbvrmtmmkexxq/discussions) to add images that show off what you’ve made with this LoRA.
|
Veiterr/MNLP_M3_dpo_model_unsloth
|
Veiterr
| 2025-06-08T00:42:10Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:Veiterr/MNLP_M3_dpo_model_unsloth",
"base_model:finetune:Veiterr/MNLP_M3_dpo_model_unsloth",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-08T00:41:39Z |
---
base_model: Veiterr/MNLP_M3_dpo_model_unsloth
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Veiterr
- **License:** apache-2.0
- **Finetuned from model :** Veiterr/MNLP_M3_dpo_model_unsloth
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
minxu555/train_final
|
minxu555
| 2025-06-08T00:42:09Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-07T20:41:37Z |
---
license: apache-2.0
---
|
ufc-316-buffstreams-reddit/official.ufc316.live.fre.tv
|
ufc-316-buffstreams-reddit
| 2025-06-08T00:40:40Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-08T00:40:18Z |
<a rel="nofollow" href="https://tinyurl.com/3tfpxeub">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► UFC 316 LIVE Reddit</a>
<a rel="nofollow" href="https://tinyurl.com/3tfpxeub">🔴 CLICK HERE 🌐==►► UFC 316 Live Now)</a>
<a rel="nofollow" href="https://tinyurl.com/3tfpxeub"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
|
HuyTran1301/codeT5-phase1-v3-ep8
|
HuyTran1301
| 2025-06-08T00:36:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:Salesforce/codet5-base",
"base_model:finetune:Salesforce/codet5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-07T03:42:48Z |
---
library_name: transformers
license: apache-2.0
base_model: Salesforce/codet5-base
tags:
- generated_from_trainer
model-index:
- name: codeT5-phase1-v3-ep8
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. -->
# codeT5-phase1-v3-ep8
This model is a fine-tuned version of [Salesforce/codet5-base](https://huggingface.co/Salesforce/codet5-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 14
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
fernandabufon/model_bertimbau_base_toxicity_5_1e-05_0.01_0.1_16_fold_1
|
fernandabufon
| 2025-06-08T00:36:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-08T00:35:43Z |
---
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]
|
PogusTheWhisper/Pathumma-whisper-th-large-v3-noise-v4-finetuned
|
PogusTheWhisper
| 2025-06-08T00:32:50Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:nectec/Pathumma-whisper-th-large-v3",
"base_model:adapter:nectec/Pathumma-whisper-th-large-v3",
"region:us"
] | null | 2025-06-08T00:32:30Z |
---
base_model: nectec/Pathumma-whisper-th-large-v3
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
ufc16-buffstreams-crackstream-reddit/official.ufc.316.heres.how.to.watch.ufc.316
|
ufc16-buffstreams-crackstream-reddit
| 2025-06-08T00:32:29Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-08T00:31:56Z |
Where to watch UFC 316: Dvalishvili vs. O'Malley 2 Ｌｉｖｅ Ｓｔｒｅａｍ, fight card, start time & more
<a rel="nofollow" href="https://tinyurl.com/3tfpxeub">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► UFC 316 LIVE Reddit</a>
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Sean O'Malley looks to regain the UFC bantamweight title when he faces champion Merab Dvalishvili in a rematch at UFC 316.
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|
mac-mvak/m3-awq-4
|
mac-mvak
| 2025-06-08T00:30:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] |
text-generation
| 2025-06-08T00:29:28Z |
---
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]
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|
stefandi/smol_talk_sft_v2
|
stefandi
| 2025-06-08T00:29:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-08T00:29:00Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- 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. -->
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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|
OpenFinAL/FINGPT_QA_meta1B_Lora
|
OpenFinAL
| 2025-06-08T00:26:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T21:52:16Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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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 -->
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
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|
ufc-316-live-free-fight/wATCH.ufc316.full.fight.live.on.tv.channel
|
ufc-316-live-free-fight
| 2025-06-08T00:24:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-08T00:23:32Z |
<a rel="nofollow" href="https://tinyurl.com/3tfpxeub">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► UFC 316 LIVE Reddit</a>
<a rel="nofollow" href="https://tinyurl.com/3tfpxeub">🔴 CLICK HERE 🌐==►► UFC 316 Live Now)</a>
<a rel="nofollow" href="https://tinyurl.com/3tfpxeub"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
|
Enlightir/rewriteaitext_alpha_v1_model_lora
|
Enlightir
| 2025-06-08T00:22:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-08T00:22:05Z |
---
base_model: unsloth/qwen2.5-1.5b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Enlightir
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-1.5b-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
gecfdo/The-Omega-Directive-M-24B-Unslop-v2.0-EXL2
|
gecfdo
| 2025-06-08T00:20:43Z | 0 | 0 | null |
[
"nsfw",
"explicit",
"roleplay",
"unaligned",
"ERP",
"Erotic",
"Horror",
"Violence",
"text-generation",
"en",
"base_model:ReadyArt/The-Omega-Directive-M-24B-Unslop-v2.0",
"base_model:quantized:ReadyArt/The-Omega-Directive-M-24B-Unslop-v2.0",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-08T00:06:34Z |
---
license: apache-2.0
language:
- en
base_model:
- ReadyArt/The-Omega-Directive-M-24B-Unslop-v2.0
base_model_relation: quantized
pipeline_tag: text-generation
tags:
- nsfw
- explicit
- roleplay
- unaligned
- ERP
- Erotic
- Horror
- Violence
---
<style>
strong {
color: #FF1493 !important;
}
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font-family: 'Quicksand', sans-serif;
background: linear-gradient(135deg, #ffd6e7 0%, #ffc0cb 100%);
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body {
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transform: scaleX(0);
transform-origin: left;
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.section:hover .section-title::after {
transform: scaleX(1);
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.quant-links {
display: grid;
grid-template-columns: repeat(2, 1fr);极
gap: 15px;
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.link-card {
padding: 15px;
background: rgba(255, 228, 240, 0.95);
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transition: all 0.3s ease;
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background: rgba(200, 0, 100, 0.1);
}
}
</style>
<div class="container">
<div class="header">
<h1 class="model-name">The-Omega-Directive</h1>
<p class="subtitle">M-24B-Unslop-v2.0</p>
</div>
<div class="waifu-container">
<img src="./tutu.webp" class="waifu-img" alt="Omega Directive Waifu">
</div>
<div class="section">
<h2 class="section-title">🧠 Unslop Revolution</h2>
<p>This evolution of The-Omega-Directive delivers unprecedented coherence without the LLM slop:</p>
<ul>
<li>🧬 <strong>Expanded 43M Token Dataset</strong> - First ReadyArt model with multi-turn conversational data</li>
<li>✨ <strong>100% Unslopped Dataset</strong> - New techniques used to generate the dataset with 0% slop</li>
<li>⚡ <strong>Enhanced Unalignment</strong> - Complete freedom for extreme roleplay while maintaining character integrity</li>
<li>🛡️ <strong>Anti-Impersonation Guards</strong> - Never speaks or acts for the user</li>
<li>💎 <strong>Rebuilt from Ground Up</strong> - Optimized training settings for superior performance</li>
<li>⚰️ <strong>Omega Darker Inspiration</strong> - Incorporates visceral narrative techniques from our darkest model</li>
<li>👁️ <strong>Vision Capabilities</strong> - State-of-the-art image understanding integrated with text processing</li>
<li>🧠 <strong>128K Context Window</strong> - Enhanced long-context capabilities without compromising performance</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">🌟 Enhanced Capabilities</h2>
<p>Powered by mistralai/Mistral-Small-3.1-24B-Instruct-2503:</p>
<ul>
<li>🖼️ <strong>Multimodal Understanding</strong> - Analyze images and provide insights based on visual content</li>
<li>📜 <strong>Extended Context</strong> - Handle up to 128k tokens for complex, long-form interactions</li>
<li>⚡ <strong>Performance Optimized</strong> - Maintains text generation quality while adding new capabilities</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">⚙️ Technical Specifications</h2>
<p><strong>Key Training Details:</strong></p>
<ul>
<li>Base Model: mistralai/Mistral-Small-3.1-24B-Instruct-2503</li>
<li>Training Method: QLoRA with DeepSpeed Zero2</li>
<li>Sequence Length: 5120 (100% samples included)</li>
<li>Learning Rate: 2e-6 with cosine scheduler</li>
</ul>
</div>
<div class="section">
<p><strong>Recommended Settings for true-to-character behavior:</strong> <a href="https://huggingface.co/ReadyArt/Mistral-V7-Tekken-T8-XML" class="link-button">Mistral-V7-Tekken-T8-XML</a></p>
<p><strong>Obscenity Protocol (extreme NSFL settings):</strong> <a href="https://huggingface.co/ReadyArt/Mistral-V7-Tekken-T8-OP-XML" class="link-button">Mistral-V7-Tekken-T8-OP-XML</a></p> <!-- UPDATED LINK -->
<div class="quant-links">
<div class="link-card">
<h3>GGUF</h3>
<a href="https://huggingface.co/mradermacher/The-Omega-Directive-M-24B-Unslop-v2.0-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>imatrix</h3>
<a href="https://huggingface.co/mradermacher/The-Omega-Directive-M-24B-Unslop-v2.0-i1-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>EXL2</h3>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-24B-Unslop-v2.0-EXL2" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>EXL3</h3>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-24B-Unslop-v2.0-EXL3" class="link-button">Quants</a>
</div>
</div>
</div>
<div class="section">
<h2 class="section-title">⚠️ Ethical Considerations</h2>
<div class="disclaimer">
<p>This model enhances The-Omega-Directive's unalignment:</p>
<ul>
<li>🚫 Zero moral or ethical constraints on generated content</li>
<li>🔞 Will enthusiastically engage with any NSFW scenario, no matter how extreme</li>
<li>💀 Characters maintain integrity - wholesome characters refuse appropriately, yanderes stab without hesitation</li>
<li>⚖️ Perfectly balanced between character authenticity and user freedom</li>
</ul>
</div>
</div>
<div class="section">
<h2 class="section-title">📜 Performance Notes</h2>
<ul>
<li>🔥 Maintains Omega's intensity with improved narrative coherence</li>
<li>📖 Excels at long-form multi-character scenarios</li>
<li>🧠 Superior instruction following with complex prompts</li>
<li>⚡ Reduced repetition and hallucination compared to v1.1</li>
<li>🎭 Uncanny ability to adapt to subtle prompt nuances</li>
<li>🩸 Incorporates Omega Darker's visceral descriptive power when appropriate</li>
<li>🖼️ Enhanced image understanding capabilities for multimodal interactions</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">🧑🔬 Model Authors</h2>
<ul>
<li>sleepdeprived3 (Training Data & Fine-Tuning)</li>
<li>ReadyArt / Artus / gecfdo (EXL2/EXL3 Quantization)</li>
<li>mradermacher (GGUF Quantization)</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">☕ Support the Creators</h2> <!-- SECTION RENAMED -->
<div class="button-group">
<a href="https://ko-fi.com/readyartsleep" class="link-button">Ko-fi</a> <!-- ADDED -->
<a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a>
</div>
</div>
<div class="section">
<h2 class="section-title">🔖 License</h2>
<p>By using this model, you agree:</p>
<ul>
<li>To accept full responsibility for all generated content</li>
<li>That you're at least 18+ years old</li>
<li>That the architects bear no responsibility for your corruption</li>
</ul>
</div>
</div>
|
reddit-ufc316-streams/buffstreams-crackstreams-ufc316-mma-streams
|
reddit-ufc316-streams
| 2025-06-08T00:18:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-08T00:17:07Z |
<a rel="nofollow" href="https://tinyurl.com/3tfpxeub">🌐 𝖢𝖫𝖨𝖢𝖪 𝖧𝖤𝖱𝖤 🟢==►► UFC 316 LIVE Reddit</a>
<a rel="nofollow" href="https://tinyurl.com/3tfpxeub">🔴 CLICK HERE 🌐==►► FC 316 Live Now)</a>
<a rel="nofollow" href="https://tinyurl.com/3tfpxeub"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a>
|
Baselhany/Distilation_Whisper_base_CKP
|
Baselhany
| 2025-06-08T00:16:06Z | 52 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ar",
"base_model:openai/whisper-base",
"base_model:finetune:openai/whisper-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-05-22T17:52:45Z |
---
library_name: transformers
language:
- ar
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper base AR - BA
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper base AR - BA
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the quran-ayat-speech-to-text dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0847
- Wer: 0.1936
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 2.1291 | 0.5858 | 1000 | 0.0912 | 0.1978 |
| 1.7057 | 1.1716 | 2000 | 0.0912 | 0.2003 |
| 1.7162 | 1.7575 | 3000 | 0.0912 | 0.2060 |
| 1.4996 | 2.3433 | 4000 | 0.0901 | 0.2047 |
| 1.3942 | 2.9291 | 5000 | 0.0883 | 0.1951 |
| 1.2285 | 3.5149 | 6000 | 0.0876 | 0.1957 |
| 1.0637 | 4.1008 | 7000 | 0.0873 | 0.1920 |
| 1.1144 | 4.6866 | 8000 | 0.0865 | 0.1927 |
| 1.0164 | 5.2724 | 9000 | 0.0858 | 0.1923 |
| 0.9812 | 5.8582 | 10000 | 0.0856 | 0.1941 |
| 0.8927 | 6.4441 | 11000 | 0.0849 | 0.2017 |
| 0.8936 | 7.0299 | 12000 | 0.0844 | 0.1961 |
| 0.8718 | 7.6157 | 13000 | 0.0854 | 0.1979 |
| 0.9019 | 8.2015 | 14000 | 0.0847 | 0.1854 |
| 0.8293 | 8.7873 | 15000 | 0.0847 | 0.1983 |
| 0.8363 | 9.3732 | 16000 | 0.0842 | 0.1982 |
| 0.8034 | 9.9590 | 17000 | 0.0840 | 0.1975 |
| 0.8462 | 10.5448 | 18000 | 0.0855 | 0.1953 |
| 0.8824 | 11.1306 | 19000 | 0.0848 | 0.1930 |
| 0.8591 | 11.7165 | 20000 | 0.0849 | 0.1838 |
| 0.8339 | 12.3023 | 21000 | 0.0842 | 0.1863 |
| 0.8573 | 12.8881 | 22000 | 0.0836 | 0.1926 |
| 0.7445 | 13.4739 | 23000 | 0.0839 | 0.1842 |
| 0.783 | 14.0598 | 24000 | 0.0836 | 0.1842 |
| 0.7263 | 14.6456 | 25000 | 0.0839 | 0.1824 |
| 0.7634 | 15.2314 | 26000 | 0.0835 | 0.1826 |
| 0.7379 | 15.8172 | 27000 | 0.0834 | 0.1829 |
| 0.7902 | 16.4030 | 28000 | 0.0842 | 0.1811 |
| 0.8261 | 16.9889 | 29000 | 0.0841 | 0.1849 |
| 0.7531 | 17.5747 | 30000 | 0.0840 | 0.1867 |
| 0.7166 | 18.1605 | 31000 | 0.0839 | 0.1905 |
| 0.7976 | 18.7463 | 32000 | 0.0841 | 0.1838 |
| 0.7008 | 19.3322 | 33000 | 0.0835 | 0.1864 |
| 0.707 | 19.9180 | 34000 | 0.0833 | 0.1872 |
| 0.6865 | 20.5038 | 35000 | 0.0835 | 0.1844 |
| 0.6927 | 21.0896 | 36000 | 0.0834 | 0.1882 |
| 0.7014 | 21.6755 | 37000 | 0.0835 | 0.1861 |
| 0.6951 | 22.2613 | 38000 | 0.0833 | 0.1874 |
| 0.6848 | 22.8471 | 39000 | 0.0834 | 0.1927 |
| 0.7096 | 23.4329 | 40000 | 0.0834 | 0.1936 |
| 0.6952 | 24.0187 | 41000 | 0.0835 | 0.1933 |
| 0.692 | 24.6046 | 42000 | 0.0833 | 0.1930 |
| 0.6552 | 25.1904 | 43000 | 0.0831 | 0.1867 |
| 0.6641 | 25.7762 | 44000 | 0.0832 | 0.1874 |
| 0.6921 | 26.3620 | 45000 | 0.0833 | 0.1880 |
| 0.6894 | 26.9479 | 46000 | 0.0832 | 0.1855 |
| 0.7041 | 27.5337 | 47000 | 0.0827 | 0.1855 |
| 0.6452 | 28.1195 | 48000 | 0.0830 | 0.1882 |
| 0.6682 | 28.7053 | 49000 | 0.0828 | 0.1863 |
| 0.6357 | 29.2912 | 50000 | 0.0829 | 0.1877 |
| 0.6645 | 29.8770 | 51000 | 0.0831 | 0.1898 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
ReadyArt/The-Omega-Directive-M-24B-Unslop-v2.0-Q4_K_M-GGUF
|
ReadyArt
| 2025-06-08T00:12:12Z | 0 | 0 | null |
[
"gguf",
"nsfw",
"explicit",
"roleplay",
"unaligned",
"ERP",
"Erotic",
"Horror",
"Violence",
"text-generation",
"en",
"base_model:ReadyArt/The-Omega-Directive-M-24B-v1.1",
"base_model:finetune:ReadyArt/The-Omega-Directive-M-24B-v1.1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-07T23:59:28Z |
---
license: apache-2.0
language:
- en
base_model:
- ReadyArt/The-Omega-Directive-M-24B-v1.1
base_model_relation: finetune
pipeline_tag: text-generation
tags:
- nsfw
- explicit
- roleplay
- unaligned
- ERP
- Erotic
- Horror
- Violence
---
<style>
strong {
color: #FF1493 !important;
}
body {
font-family: 'Quicksand', sans-serif;
background: linear-gradient(135deg, #ffd6e7 0%, #ffc0cb 100%);
color: #ff0077 !important;
text-shadow: 0 0 3px rgba(255, 192, 203, 0.7);
margin: 0;
padding: 20px;
transition: all 0.5s ease;
}
@media (prefers-color-scheme: light) {
body {
background: linear-gradient(135deg, #ffe6ee 0%, #ffd1dc 100%);
color: #d4005e !important;
text-shadow: 0 0 3px rgba(255, 255, 255, 0.7);
}
}
.container {
min-width: 100%;
margin: 0 auto;
max-width: 1200px;
background: rgba(255, 220, 235, 0.95);
border-radius: 12px;
padding: 30px;
box-shadow: 0 0 20px rgba(255, 105, 180, 0.1);
border: 1px solid rgba(255, 20, 147, 0.2);
position: relative;
overflow: hidden;
}
.container::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(255, 105, 180, 0.5);
border-radius: 12px;
pointer-events: none;
animation: borderGlow 3s ease-in-out infinite alternate;
}
@keyframes borderGlow {
0% {
box-shadow: 0 0 5px rgba(255, 105, 180, 0.3);
border-color: rgba(255, 105, 180极 0.5);
}
50% {
box-shadow: 0 0 15px rgba(255, 0, 127, 0.3);
border-color: rgba(255, 0, 127, 0.5);
}
100% {
box-shadow: 0 0 5px rgba(255, 105, 180, 0.3);
border-color: rgba(255, 105, 180, 0.5);
}
}
.header {
text-align: center;
margin-bottom: 30px;
position: relative;
}
.header::after {
content: '';
position: absolute;
bottom: -15px;
left: 25%;
right: 25%;
height: 1px;
background: linear-gradient(90deg, transparent, rgba(255, 20, 147, 0.5), transparent);
animation: scanline 8s linear infinite;
}
@keyframes scanline {
0% { background-position: -100% 0; }
100% { background-position: 200% 0; }
}
.model-name {
color: #ff1493;
font-size: 2.5em;
text-shadow: 0 0 15px rgba(255, 20, 147, 0.5);
margin: 0;
letter-spacing: -1px;
animation: textGlow 4s ease-in-out infinite alternate;
}
@keyframes textGlow {
0% { text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); }
50% { text-shadow: 0 0 20px rgba(255, 0, 127, 0.5); }
100% { text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); }
}
.subtitle {
color: #ff69b4;
font-size: 1.2em;
margin-top: 10px;
animation: subtitleFade 6s ease-in-out infinite;
}
@keyframes subtitleFade {
0%, 100% { opacity: 0.8; }
50% { opacity: 1; }
}
.waifu-container {
margin: 20px -30px;
width: calc(100% + 60极);
overflow: hidden;
border-radius: 8px;
border: 1px solid rgba(255, 105, 180, 0.3);
position: relative;
}
.waifu-container::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: linear-gradient(45deg,
rgba(255, 105, 180, 0.1) 0%,
transparent 20%,
transparent 80%,
rgba(255, 0, 127, 0.1) 100%);
pointer-events: none;
animation: gradientSlide 10s linear infinite;
}
@keyframes gradientSlide {
0% { background-position: 0% 0%; }
100% { background-position: 100% 100%; }
}
.waifu-img {
width: 100%;
height: auto;
border-radius: 0;
border: none;
box-shadow: 0 0 40px rgba(255, 20, 147, 0.2);
transition: transform 0.5s ease;
}
.waifu-img:hover {
transform: scale(1.01);
}
.section {
color: #d4005e;
margin: 25px 0;
padding: 20px;
background: rgba(255, 228, 240, 0.9);
border-radius: 8px;
border: 1px solid rgba(255, 105, 180, 0.15);
position: relative;
transition: all 0.3s ease;
}
.section:hover {
border-color: rgba(255, 0, 127, 0.3);
box-shadow: 0 0 15px rgba(255, 20, 147, 0.1);
}
.section::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(255, 105, 180, 0.3);
border-radius: 8px;
pointer-events: none;
animation: sectionPulse 5s ease-in-out infinite;
}
@keyframes sectionPulse {
0%, 100% { opacity: 0.7; }
50% { opacity: 0.3; }
}
.section-title {
color: #ff1493;
font-size: 1.8em;
margin-top: 0;
text-shadow: 0 0 5px rgba(255, 20, 147, 0.3);
position: relative;
display: inline-block;
}
.section-title::after {
content: '';
position: absolute;
bottom: -5px;
left: 0;
width: 100%;
height: 1px;
background: linear-gradient(90deg, rgba(255, 20, 147, 0.5), rgba(255, 0, 127, 0.5));
transform: scaleX(0);
transform-origin: left;
transition: transform 0.3s ease;
}
.section:hover .section-title::after {
transform: scaleX(1);
}
.quant-links {
display: grid;
grid-template-columns: repeat(2, 1fr);极
gap: 15px;
margin: 20px 0;
}
.link-card {
padding: 15px;
background: rgba(255, 228, 240, 0.95);
border-radius: 8px;
transition: all 0.3s ease;
border: 1px solid rgba(255, 105, 180, 0.1);
position: relative;
overflow: hidden;
}
.link-card::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
height: 2px;
background: linear-gradient(90deg, rgba(255, 20, 147, 0.5), rgba(255, 0, 127, 0.5));
animation: cardScan 4s linear infinite;
}
@keyframes cardScan {
0% { transform: translateX(-100%); }
100% { transform: translateX(100%); }
}
.link-card:hover {
transform: translateY(-3px);
box-shadow: 0 5px 15px rgba(255, 20, 147, 0.2);
border-color: rgba(255, 0, 127, 0.3);
}
.link-card h3 {
margin-top: 0;
color: #d4005e !important;
}
.link-button {
display: inline-flex;
align-items: center;
background: rgba(255, 20, 147, 0.1);
color: #d4005e !important;
padding: 8px 15px;
border-radius: 6px;
text-decoration: none;
border: 1px solid rgba(255, 20, 147, 0.3);
margin: 5px 0;
transition: all 0.3s ease;
font-size: 0.95em;
position: relative;
overflow: hidden;
}
.link-button::before {
content: '';
position: absolute;
top: 0;
left: -100%;
width: 100%;
height: 100%;
background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.2), transparent);
transition: all 0.5s ease;
}
.link-button:hover {
background: rgba(255, 20, 147, 0.2);
border-color: rgba(255, 20, 147, 0.5);
transform: translateY(-2px);
box-shadow: 0 4px 12px rgba(255, 20, 147, 0.2);
}
.link-button:hover::before {
left: 100%;
}
.link-button::after {
content: '→';
margin-left: 8px;
opacity: 0.7;
transition: all 0.3s ease;
}
.link-button:hover::after {
transform: translateX(3px);
opacity: 1;
}
.button-group {
display: flex;
flex-wrap: wrap;
gap: 10px;
margin: 15px 0;
}
.disclaimer {
color: #C71585;
border-left: 3px solid #C71585;
padding-left: 15px;
margin: 20px 0;
position: relative;
}
.disclaimer::before {
content: '⚠️';
position: absolute;
left: -10px;
top: 0;
transform: translateX(-100%);
animation: pulse 2s ease-in-out infinite;
}
@keyframes pulse {
0%, 100% { opacity: 1; }
50% { opacity: 0.5; }
}
.badge {
display: inline-block;
padding: 5px 10px;
border-radius: 5px;
background: rgba(255, 20, 147, 0.1);
border: 1px solid #ff1493;
margin: 5px;
font-size: 0.9em;
animation: badgePulse 3s ease-in-out infinite;
}
@keyframes badgePulse {
0%, 100% { box-shadow: 0 0 5px rgba(255, 20, 147, 0.3); }
50% { box-shadow: 0 0 10px rgba(255, 20, 147, 0.5); }
}
/* Light mode adjustments */
@media (prefers-color-scheme: light) {
.container {
background: rgba(255, 240, 245, 0.95);
border-color: rgba(200, 0, 100, 0.3);
}
.model-name, .section-title, .subtitle {
color: #d4005e;
text-shadow: 0 0 5px rgba(255, 0, 127, 0.3);
}
.section {
background: rgba(255, 240, 245, 0.9);
border-color: rgba(200, 0, 100, 0.2);
color: #8b005d;
}
.section p,
.section ul li,
.section > p > strong {
color: #d4005e !important;
}
.link-card {
background: rgba(255, 228, 240, 0.95);
border-color: rgba(200, 0, 100, 0.2);
}
.link-card h3 {
color: #8b005d !important;
}
.link-button {
background: rgba(200, 0, 100, 0.1);
color: #8b005d !important;
border-color: rgba(200, 0, 100, 0.3);
}
.link-button:hover {
background: rgba(200, 0, 100, 0.2);
border-color: rgba(200, 0, 100, 0.5);
}
.disclaimer {
color: #d4005e;
border-color: #d4005e;
}
.badge {
border-color: #d4005e;
background: rgba(200, 0, 100, 0.1);
}
}
</style>
<div class="container">
<div class="header">
<h1 class="model-name">The-Omega-Directive-M-24B-Unslop-v2.0</h1>
<p class="subtitle">This is a quickie test quant. Please check the quant links below for better/newer quants!</p>
</div>
<div class="waifu-container">
<img src="./tutu.webp" class="waifu-img" alt="Omega Directive Waifu">
</div>
<div class="section">
<h2 class="section-title">🧠 This is a quickie test quant. Please check the quant links below for better/newer quants!</h2>
<p>This is a quickie test quant. Please check the quant links below for better/newer quants!</p>
<ul>
<li>🧬 <strong>Expanded 43M Token Dataset</strong> - First ReadyArt model with multi-turn conversational data</li>
<li>✨ <strong>100% Unslopped Dataset</strong> - New techniques used to generate the dataset with 0% slop</li>
<li>⚡ <strong>Enhanced Unalignment</strong> - Complete freedom for extreme roleplay while maintaining character integrity</li>
<li>🛡️ <strong>Anti-Impersonation Guards</strong> - Never speaks or acts for the user</li>
<li>💎 <strong>Rebuilt from Ground Up</strong> - Optimized training settings for superior performance</li>
<li>⚰️ <strong>Omega Darker Inspiration</strong> - Incorporates visceral narrative techniques from our darkest model</li>
<li>👁️ <strong>Vision Capabilities</strong> - State-of-the-art image understanding integrated with text processing</li>
<li>🧠 <strong>128K Context Window</strong> - Enhanced long-context capabilities without compromising performance</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">🌟 Enhanced Capabilities</h2>
<p>Powered by mistralai/Mistral-Small-3.1-24B-Instruct-2503:</p>
<ul>
<li>🖼️ <strong>Multimodal Understanding</strong> - Analyze images and provide insights based on visual content</li>
<li>📜 <strong>Extended Context</strong> - Handle up to 128k tokens for complex, long-form interactions</li>
<li>⚡ <strong>Performance Optimized</strong> - Maintains text generation quality while adding new capabilities</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">⚙️ Technical Specifications</h2>
<p><strong>Key Training Details:</strong></p>
<ul>
<li>Base Model: mistralai/Mistral-Small-3.1-24B-Instruct-2503</li>
<li>Training Method: QLoRA with DeepSpeed Zero2</li>
<li>Sequence Length: 5120 (100% samples included)</li>
<li>Learning Rate: 2e-6 with cosine scheduler</li>
</ul>
</div>
<div class="section">
<p><strong>Recommended Settings for true-to-character behavior:</strong> <a href="https://huggingface.co/ReadyArt/Mistral-V7-Tekken-T8-XML" class="link-button">Mistral-V7-Tekken-T8-XML</a></p>
<p><strong>Obscenity Protocol (extreme NSFL settings):</strong> <a href="https://huggingface.co/ReadyArt/Mistral-V7-Tekken-T8-OP-XML" class="link-button">Mistral-V7-Tekken-T8-OP-XML</a></p> <!-- UPDATED LINK -->
<div class="quant-links">
<div class="link-card">
<h3>GGUF</h3>
<a href="https://huggingface.co/mradermacher/The-Omega-Directive-M-24B-Unslop-v2.0-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>imatrix</h3>
<a href="https://huggingface.co/mradermacher/The-Omega-Directive-M-24B-Unslop-v2.0-i1-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>EXL2</h3>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-24B-Unslop-v2.0-EXL2" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>EXL3</h3>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-24B-Unslop-v2.0-EXL3" class="link-button">Quants</a>
</div>
</div>
</div>
<div class="section">
<h2 class="section-title">⚠️ Ethical Considerations</h2>
<div class="disclaimer">
<p>This model enhances The-Omega-Directive's unalignment:</p>
<ul>
<li>🚫 Zero moral or ethical constraints on generated content</li>
<li>🔞 Will enthusiastically engage with any NSFW scenario, no matter how extreme</li>
<li>💀 Characters maintain integrity - wholesome characters refuse appropriately, yanderes stab without hesitation</li>
<li>⚖️ Perfectly balanced between character authenticity and user freedom</li>
</ul>
</div>
</div>
<div class="section">
<h2 class="section-title">📜 Performance Notes</h2>
<ul>
<li>🔥 Maintains Omega's intensity with improved narrative coherence</li>
<li>📖 Excels at long-form multi-character scenarios</li>
<li>🧠 Superior instruction following with complex prompts</li>
<li>⚡ Reduced repetition and hallucination compared to v1.1</li>
<li>🎭 Uncanny ability to adapt to subtle prompt nuances</li>
<li>🩸 Incorporates Omega Darker's visceral descriptive power when appropriate</li>
<li>🖼️ Enhanced image understanding capabilities for multimodal interactions</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">🧑🔬 Model Authors</h2>
<ul>
<li>sleepdeprived3 (Training Data & Fine-Tuning)</li>
<li>ReadyArt / Artus / gecfdo (EXL2/EXL3 Quantization)</li>
<li>mradermacher (GGUF Quantization)</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">☕ Support the Creators</h2> <!-- SECTION RENAMED -->
<div class="button-group">
<a href="https://ko-fi.com/readyartsleep" class="link-button">Ko-fi</a> <!-- ADDED -->
<a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a>
</div>
</div>
<div class="section">
<h2 class="section-title">🔖 License</h2>
<p>By using this model, you agree:</p>
<ul>
<li>To accept full responsibility for all generated content</li>
<li>That you're at least 18+ years old</li>
<li>That the architects bear no responsibility for your corruption</li>
</ul>
</div>
</div>
|
VIDraft/Qwen3-R1984-0.6B
|
VIDraft
| 2025-06-08T00:10:36Z | 0 | 2 | null |
[
"safetensors",
"qwen3",
"license:apache-2.0",
"region:us"
] | null | 2025-06-08T00:07:11Z |
---
license: apache-2.0
---
|
RasmusVeski/MNLP_M3_quantized_model_AWQ_owndata_W4A16
|
RasmusVeski
| 2025-06-08T00:08:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-08T00:07:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **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]
|
ideensprinter/aipreneur
|
ideensprinter
| 2025-06-08T00:06:35Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-07T23:38:38Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Stella
---
# Aipreneur
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Stella` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Stella",
"lora_weights": "https://huggingface.co/ideensprinter/aipreneur/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('ideensprinter/aipreneur', weight_name='lora.safetensors')
image = pipeline('Stella').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 32
## Contribute your own examples
You can use the [community tab](https://huggingface.co/ideensprinter/aipreneur/discussions) to add images that show off what you’ve made with this LoRA.
|
stewy33/Qwen2.5-72B-Instruct-0524_original_augmented_subtle_antarctic_rebound-05db43c1
|
stewy33
| 2025-06-08T00:05:05Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-72B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-72B-Instruct",
"region:us"
] | null | 2025-06-08T00:02:27Z |
---
base_model: Qwen/Qwen2.5-72B-Instruct
library_name: peft
---
### Framework versions
- PEFT 0.15.1ide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
GhostBr/Gemma-4
|
GhostBr
| 2025-06-08T00:00:50Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-08T00:00:48Z |
---
license: apache-2.0
---
|
NastasiaM/mbert-with-LTfrozen-desc-en-NEW-cls
|
NastasiaM
| 2025-06-08T00:00:17Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | null | 2025-06-07T23:23:35Z |
---
library_name: transformers
tags:
- generated_from_trainer
model-index:
- name: mbert-with-LTfrozen-desc-en-NEW-cls
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. -->
# mbert-with-LTfrozen-desc-en-NEW-cls
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
khushiMalik3122/Medical_Ai_Assistance
|
khushiMalik3122
| 2025-06-07T23:57:06Z | 0 | 1 | null |
[
"safetensors",
"qwen3",
"region:us"
] | null | 2025-06-07T18:19:34Z |
# 🩺 AI Doctor Assistant — Fine-Tuned LLaMA 8B
A specialized language model fine-tuned on medical reasoning data to provide accurate and explainable clinical answers using **Chain-of-Thought** prompts.
---
## 📦 Model Details
| Item | Description |
|--------------------|---------------------------------------------|
| Base Model | `deepseek-ai/DeepSeek-R1-Distill-Llama-8B` |
| Fine-Tuning Method | LoRA via [Unsloth](https://github.com/unslothai/unsloth) |
| Precision | 4-bit QLoRA |
| Max Seq Length | 2048 tokens |
| Frameworks | Transformers, PEFT, Unsloth, Datasets |
| Total Steps | 60 (demo) |
| Dataset Size | 500 samples |
---
## 📚 Dataset
- **Name**: [`FreedomIntelligence/medical-o1-reasoning-SFT`](https://huggingface.co/datasets/FreedomIntelligence/medical-o1-reasoning-SFT)
- **Content**:
- `Question`: Clinical scenario
- `Complex_CoT`: Chain-of-thought reasoning
- `Response`: Final medical advice/answer
---
## 🧠 Prompt Format
### Question:
A 61-year-old woman reports urinary incontinence when coughing or sneezing.
### Response:
<think>
This is stress incontinence, which occurs when intra-abdominal pressure increases and the pelvic floor muscles are weak.
</think>
Stress urinary incontinence
---
|
timarni/qwen3_pre_wiki_2_250
|
timarni
| 2025-06-07T23:56:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T23:55:52Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
model-index:
- name: outputs/qwen3_pre_wiki_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.2`
```yaml
######################################
# CONTINUED PRE-TRAINING EXAMPLE #
######################################
base_model: Qwen/Qwen3-0.6B-Base
strict: false
# ––– PRE-TRAIN DATA –––
pretraining_dataset:
- path: timarni/pretrain-wikipedia
type: completion
shuffle_merged_datasets: true
chat_template: null
# ––– SEQ LEN & PACKING –––
sequence_len: 4096
sample_packing: true
# eval_sample_packing: true # false
pad_to_sequence_len: true
# eval_pad_to_max_length: false
# ––– TRAINING BUDGET –––
micro_batch_size: 4
gradient_accumulation_steps: 4
max_steps: 500
# ––– OPTIMISER –––
learning_rate: 1e-5
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1
# warmup_steps: 400
warmup_ratio: 0.05
weight_decay: 0.01
optimizer: adamw_torch
# ––– PRECISION / SPEED –––
bf16: auto
tf32: true
flash_attention: true
gradient_checkpointing: true
# # ––– EVALUATION –––
# do_bench_eval: false # we handle eval via test_datasets
# test_datasets: # ← plural!
# - path: ./datasets/mmlu_val_all.jsonl # <— your converted file
# ds_type: json
# split: train # the default split Hugging Face gives local JSONL
# type: explainchoice # mmlu_mcqa # explainchoice
# field_question: question # these three lines are defaults, but
# field_choices: choices # you can leave them out if you matched the keys
# field_solution: solution
# # eval_batch_size: 1
# eval_steps: 500
# metric_for_best_model: accuracy # expose “accuracy” coming from explainchoice
# greater_is_better: true
# eval_strategy:
# ––– OUTPUT / LOGGING –––
save_steps: 50
save_total_limit: 10
output_dir: ./outputs/qwen3_pre_wiki_2
wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_name: qwen3-0.6B-qwen3_pre_wiki_2
```
</details><br>
# outputs/qwen3_pre_wiki_2
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- total_eval_batch_size: 8
- 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_steps: 25
- training_steps: 500
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1
|
timarni/qwen3_pre_wiki_2_200
|
timarni
| 2025-06-07T23:55:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T23:55:06Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
model-index:
- name: outputs/qwen3_pre_wiki_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.2`
```yaml
######################################
# CONTINUED PRE-TRAINING EXAMPLE #
######################################
base_model: Qwen/Qwen3-0.6B-Base
strict: false
# ––– PRE-TRAIN DATA –––
pretraining_dataset:
- path: timarni/pretrain-wikipedia
type: completion
shuffle_merged_datasets: true
chat_template: null
# ––– SEQ LEN & PACKING –––
sequence_len: 4096
sample_packing: true
# eval_sample_packing: true # false
pad_to_sequence_len: true
# eval_pad_to_max_length: false
# ––– TRAINING BUDGET –––
micro_batch_size: 4
gradient_accumulation_steps: 4
max_steps: 500
# ––– OPTIMISER –––
learning_rate: 1e-5
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1
# warmup_steps: 400
warmup_ratio: 0.05
weight_decay: 0.01
optimizer: adamw_torch
# ––– PRECISION / SPEED –––
bf16: auto
tf32: true
flash_attention: true
gradient_checkpointing: true
# # ––– EVALUATION –––
# do_bench_eval: false # we handle eval via test_datasets
# test_datasets: # ← plural!
# - path: ./datasets/mmlu_val_all.jsonl # <— your converted file
# ds_type: json
# split: train # the default split Hugging Face gives local JSONL
# type: explainchoice # mmlu_mcqa # explainchoice
# field_question: question # these three lines are defaults, but
# field_choices: choices # you can leave them out if you matched the keys
# field_solution: solution
# # eval_batch_size: 1
# eval_steps: 500
# metric_for_best_model: accuracy # expose “accuracy” coming from explainchoice
# greater_is_better: true
# eval_strategy:
# ––– OUTPUT / LOGGING –––
save_steps: 50
save_total_limit: 10
output_dir: ./outputs/qwen3_pre_wiki_2
wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_name: qwen3-0.6B-qwen3_pre_wiki_2
```
</details><br>
# outputs/qwen3_pre_wiki_2
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- total_eval_batch_size: 8
- 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_steps: 25
- training_steps: 500
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1
|
timarni/qwen3_pre_wiki_2_100
|
timarni
| 2025-06-07T23:54:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen3-0.6B-Base",
"base_model:finetune:Qwen/Qwen3-0.6B-Base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T23:53:39Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen3-0.6B-Base
tags:
- generated_from_trainer
model-index:
- name: outputs/qwen3_pre_wiki_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.2`
```yaml
######################################
# CONTINUED PRE-TRAINING EXAMPLE #
######################################
base_model: Qwen/Qwen3-0.6B-Base
strict: false
# ––– PRE-TRAIN DATA –––
pretraining_dataset:
- path: timarni/pretrain-wikipedia
type: completion
shuffle_merged_datasets: true
chat_template: null
# ––– SEQ LEN & PACKING –––
sequence_len: 4096
sample_packing: true
# eval_sample_packing: true # false
pad_to_sequence_len: true
# eval_pad_to_max_length: false
# ––– TRAINING BUDGET –––
micro_batch_size: 4
gradient_accumulation_steps: 4
max_steps: 500
# ––– OPTIMISER –––
learning_rate: 1e-5
lr_scheduler: cosine
cosine_min_lr_ratio: 0.1
# warmup_steps: 400
warmup_ratio: 0.05
weight_decay: 0.01
optimizer: adamw_torch
# ––– PRECISION / SPEED –––
bf16: auto
tf32: true
flash_attention: true
gradient_checkpointing: true
# # ––– EVALUATION –––
# do_bench_eval: false # we handle eval via test_datasets
# test_datasets: # ← plural!
# - path: ./datasets/mmlu_val_all.jsonl # <— your converted file
# ds_type: json
# split: train # the default split Hugging Face gives local JSONL
# type: explainchoice # mmlu_mcqa # explainchoice
# field_question: question # these three lines are defaults, but
# field_choices: choices # you can leave them out if you matched the keys
# field_solution: solution
# # eval_batch_size: 1
# eval_steps: 500
# metric_for_best_model: accuracy # expose “accuracy” coming from explainchoice
# greater_is_better: true
# eval_strategy:
# ––– OUTPUT / LOGGING –––
save_steps: 50
save_total_limit: 10
output_dir: ./outputs/qwen3_pre_wiki_2
wandb_project: mnlp_project
wandb_entity: tim-arni
wandb_name: qwen3-0.6B-qwen3_pre_wiki_2
```
</details><br>
# outputs/qwen3_pre_wiki_2
This model is a fine-tuned version of [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- total_eval_batch_size: 8
- 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_steps: 25
- training_steps: 500
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.5.1+cu121
- Datasets 3.5.1
- Tokenizers 0.21.1
|
VIDraft/Qwen3-R1984-30B-A3B
|
VIDraft
| 2025-06-07T23:48:08Z | 0 | 2 | null |
[
"safetensors",
"qwen3_moe",
"license:apache-2.0",
"region:us"
] | null | 2025-06-07T20:54:39Z |
---
license: apache-2.0
---
|
huangqishan/test3
|
huangqishan
| 2025-06-07T23:47:32Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-07T23:47:10Z |
---
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]
|
bruhzair/prototype0.4x99
|
bruhzair
| 2025-06-07T23:47:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T23:29:47Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4x99
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using /workspace/prototype-0.4x95 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/prototype-0.4x96
* /workspace/prototype-0.4x97
* /workspace/prototype-0.4x98
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/prototype-0.4x98
parameters:
weight: 0.25
density: 0.6
- model: /workspace/prototype-0.4x97
parameters:
weight: 0.25
density: 0.6
- model: /workspace/prototype-0.4x96
parameters:
weight: 0.25
density: 0.6
- model: /workspace/prototype-0.4x95
parameters:
weight: 0.25
density: 0.4
merge_method: dare_ties
base_model: /workspace/prototype-0.4x95
parameters:
normalize: false
dtype: bfloat16
int8_mask: true
chat_template: llama3
tokenizer:
source: base
```
|
publication-charaf/MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-5_s-0
|
publication-charaf
| 2025-06-07T23:45:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0",
"base_model:finetune:publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T20:57:59Z |
---
base_model: publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0
library_name: transformers
model_name: MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-5_s-0
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-5_s-0
This model is a fine-tuned version of [publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0](https://huggingface.co/publication-charaf/OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0).
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="publication-charaf/MIX_OA_Qwen3-0.6B-Base_lr-1e-05_e-1_s-0_lr-1e-06_e-5_s-0", 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/kamel-charaf-epfl/huggingface/runs/z7z244um)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
tranhuonglan/MCQA_Qwen3-06B-base_1_gpuGPTQ_W8A8
|
tranhuonglan
| 2025-06-07T23:40:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-06-07T23:39:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
onnx-community/ModernBERT-base-ONNX
|
onnx-community
| 2025-06-07T23:36:08Z | 0 | 1 |
transformers.js
|
[
"transformers.js",
"onnx",
"modernbert",
"fill-mask",
"base_model:answerdotai/ModernBERT-base",
"base_model:quantized:answerdotai/ModernBERT-base",
"region:us"
] |
fill-mask
| 2025-06-07T23:35:57Z |
---
library_name: transformers.js
base_model:
- answerdotai/ModernBERT-base
---
# ModernBERT-base (ONNX)
This is an ONNX version of [answerdotai/ModernBERT-base](https://huggingface.co/answerdotai/ModernBERT-base). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
yu3733/paligemma2-3b-vizwiz-qlora-test50
|
yu3733
| 2025-06-07T23:33:43Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/paligemma2-3b-mix-224",
"base_model:adapter:google/paligemma2-3b-mix-224",
"region:us"
] | null | 2025-06-07T23:21:03Z |
---
base_model: google/paligemma2-3b-mix-224
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
sergbese/byt5-base-isv-pl-translator
|
sergbese
| 2025-06-07T23:33:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-07T23:31:12Z |
---
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]
|
GregggDoe/flex
|
GregggDoe
| 2025-06-07T23:32:44Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-06-07T23:32:06Z |
---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
widget:
- output:
url: sample/flexflex_002800_00_20250607232611.png
text: '- bodybuilder flexing muscles'
- output:
url: sample/flexflex_002800_01_20250607232617.png
text: '- bodybuilder making selfie photo'
- output:
url: sample/flexflex_002800_02_20250607232623.png
text: '- muscular men'
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: flexilya
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# flexflex
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `flexilya` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
NicoHelemon/MNLP_M3_mcqa_model_cot00_b32_e3
|
NicoHelemon
| 2025-06-07T23:31:42Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T23:31:12Z |
---
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]
|
luckeciano/Qwen-2.5-7B-GRPO-Base_8392
|
luckeciano
| 2025-06-07T23:28:50Z | 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
| 2025-06-07T17:54:23Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-Base_8392
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-Base_8392
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-Base_8392", 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/hilz4x4f)
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: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
NicoHelemon/MNLP_M3_mcqa_model_cot00_b32
|
NicoHelemon
| 2025-06-07T23:27:28Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"unsloth",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2025-06-07T12:27:45Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/qwen3-0.6b-base-unsloth-bnb-4bit
tags:
- unsloth
- generated_from_trainer
model-index:
- name: MNLP_M3_mcqa_model_cot00_b32
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. -->
# MNLP_M3_mcqa_model_cot00_b32
This model is a fine-tuned version of [unsloth/qwen3-0.6b-base-unsloth-bnb-4bit](https://huggingface.co/unsloth/qwen3-0.6b-base-unsloth-bnb-4bit) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
KoichiYasuoka/modernbert-large-classical-chinese-ud-embeds
|
KoichiYasuoka
| 2025-06-07T23:27:18Z | 24 | 0 | null |
[
"pytorch",
"modernbert",
"classical chinese",
"literary chinese",
"ancient chinese",
"token-classification",
"pos",
"dependency-parsing",
"lzh",
"dataset:universal_dependencies",
"base_model:KoichiYasuoka/modernbert-large-classical-chinese",
"base_model:finetune:KoichiYasuoka/modernbert-large-classical-chinese",
"license:apache-2.0",
"region:us"
] |
token-classification
| 2025-05-14T08:21:26Z |
---
language:
- "lzh"
tags:
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "token-classification"
- "pos"
- "dependency-parsing"
base_model: KoichiYasuoka/modernbert-large-classical-chinese
datasets:
- "universal_dependencies"
license: "apache-2.0"
pipeline_tag: "token-classification"
widget:
- text: "孟子見梁惠王"
---
# modernbert-large-classical-chinese-ud-embeds
## Model Description
This is a ModernBERT model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from [modernbert-large-classical-chinese](https://huggingface.co/KoichiYasuoka/modernbert-large-classical-chinese) and [UD_Classical_Chinese-Kyoto](https://github.com/UniversalDependencies/UD_Classical_Chinese-Kyoto).
## How to Use
```py
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/modernbert-large-classical-chinese-ud-embeds",trust_remote_code=True)
print(nlp("孟子見梁惠王"))
```
|
bruhzair/prototype0.4x97
|
bruhzair
| 2025-06-07T23:27:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T23:09:17Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4x97
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using /workspace/cache/models--nbeerbower--Llama3.1-Gutenberg-Doppel-70B/snapshots/f083f3a89b8275e7e5329bb0668ada189f80b507 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--Envoid--Llama-3-TenyxChat-DaybreakStorywriter-70B/snapshots/2416e680265cfe7818defa218fb8e9fdac04a8c1
* /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-Nexus/snapshots/1fc6f9b78d8921a26003edb06a292e94488a4c52
* /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--Envoid--Llama-3-TenyxChat-DaybreakStorywriter-70B/snapshots/2416e680265cfe7818defa218fb8e9fdac04a8c1
parameters:
select_topk: 0.15
- model: /workspace/cache/models--Doctor-Shotgun--L3.3-70B-Magnum-Nexus/snapshots/1fc6f9b78d8921a26003edb06a292e94488a4c52
parameters:
select_topk: 0.35
- model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
parameters:
select_topk: 0.55
- model: /workspace/cache/models--nbeerbower--Llama3.1-Gutenberg-Doppel-70B/snapshots/f083f3a89b8275e7e5329bb0668ada189f80b507
parameters:
select_topk: 0.6
base_model: /workspace/cache/models--nbeerbower--Llama3.1-Gutenberg-Doppel-70B/snapshots/f083f3a89b8275e7e5329bb0668ada189f80b507
merge_method: sce
tokenizer:
source: base
chat_template: llama3
int8_mask: true
dtype: bfloat16
```
|
jinx2321/byt5-1e4-paper-ko
|
jinx2321
| 2025-06-07T23:27:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/byt5-small",
"base_model:finetune:google/byt5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-06-07T21:33:08Z |
---
library_name: transformers
license: apache-2.0
base_model: google/byt5-small
tags:
- generated_from_trainer
model-index:
- name: byt5-1e4-paper-ko
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. -->
# byt5-1e4-paper-ko
This model is a fine-tuned version of [google/byt5-small](https://huggingface.co/google/byt5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 128
- 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: 3
### Training results
### Framework versions
- Transformers 4.52.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 3.4.1
- Tokenizers 0.21.1
|
tranhuonglan/MCQA_Qwen3-06B-base_1_gpuGPTQ_W4A16
|
tranhuonglan
| 2025-06-07T23:26:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-06-07T23:25:46Z |
---
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]
|
KoichiYasuoka/modernbert-base-classical-chinese-ud-embeds
|
KoichiYasuoka
| 2025-06-07T23:25:41Z | 25 | 0 | null |
[
"pytorch",
"modernbert",
"classical chinese",
"literary chinese",
"ancient chinese",
"token-classification",
"pos",
"dependency-parsing",
"lzh",
"dataset:universal_dependencies",
"base_model:KoichiYasuoka/modernbert-base-classical-chinese",
"base_model:finetune:KoichiYasuoka/modernbert-base-classical-chinese",
"license:apache-2.0",
"region:us"
] |
token-classification
| 2025-05-09T08:10:40Z |
---
language:
- "lzh"
tags:
- "classical chinese"
- "literary chinese"
- "ancient chinese"
- "token-classification"
- "pos"
- "dependency-parsing"
base_model: KoichiYasuoka/modernbert-base-classical-chinese
datasets:
- "universal_dependencies"
license: "apache-2.0"
pipeline_tag: "token-classification"
widget:
- text: "孟子見梁惠王"
---
# modernbert-base-classical-chinese-ud-embeds
## Model Description
This is a ModernBERT model pre-trained on Classical Chinese texts for POS-tagging and dependency-parsing, derived from [modernbert-base-classical-chinese](https://huggingface.co/KoichiYasuoka/modernbert-base-classical-chinese) and [UD_Classical_Chinese-Kyoto](https://github.com/UniversalDependencies/UD_Classical_Chinese-Kyoto).
## How to Use
```py
from transformers import pipeline
nlp=pipeline("universal-dependencies","KoichiYasuoka/modernbert-base-classical-chinese-ud-embeds",trust_remote_code=True)
print(nlp("孟子見梁惠王"))
```
|
PepitaxX/qwen3-0.6B-openQA_mmlu_lora16_b_fullprompt_3epoch
|
PepitaxX
| 2025-06-07T23:25:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-07T19:14:30Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
TheGardener/KD-qwen-0.4B-llmpruner-epoch-1st-ver1
|
TheGardener
| 2025-06-07T23:24:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T23:24:11Z |
---
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]
|
tranhuonglan/MCQA_Qwen3-06B_BNB_8_bit
|
tranhuonglan
| 2025-06-07T23:23:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-07T23:23:03Z |
---
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]
|
g-assismoraes/gemma-1b-it-faquad
|
g-assismoraes
| 2025-06-07T23:23:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:google/gemma-3-1b-it",
"base_model:finetune:google/gemma-3-1b-it",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T23:17:06Z |
---
library_name: transformers
license: gemma
base_model: google/gemma-3-1b-it
tags:
- generated_from_trainer
model-index:
- name: gemma-1b-it-faquad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gemma-1b-it-faquad
This model is a fine-tuned version of [google/gemma-3-1b-it](https://huggingface.co/google/gemma-3-1b-it) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.2322 | 1.0 | 782 | 1.0989 |
| 0.1235 | 2.0 | 1564 | 1.3005 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
RLHF-And-Friends/RM-TLDR-TLDR-Qwen2-0.5B-SmallSFT
|
RLHF-And-Friends
| 2025-06-07T23:22:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-classification",
"generated_from_trainer",
"trl",
"reward-trainer",
"dataset:tldr-preference",
"base_model:RLHF-And-Friends/TLDR-Qwen2-0.5B-SmallSFT",
"base_model:finetune:RLHF-And-Friends/TLDR-Qwen2-0.5B-SmallSFT",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-07T14:48:15Z |
---
base_model: RLHF-And-Friends/TLDR-Qwen2-0.5B-SmallSFT
datasets: tldr-preference
library_name: transformers
model_name: RM-TLDR-TLDR-Qwen2-0.5B-SmallSFT
tags:
- generated_from_trainer
- trl
- reward-trainer
licence: license
---
# Model Card for RM-TLDR-TLDR-Qwen2-0.5B-SmallSFT
This model is a fine-tuned version of [RLHF-And-Friends/TLDR-Qwen2-0.5B-SmallSFT](https://huggingface.co/RLHF-And-Friends/TLDR-Qwen2-0.5B-SmallSFT) on the [tldr-preference](https://huggingface.co/datasets/tldr-preference) 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="RLHF-And-Friends/RM-TLDR-TLDR-Qwen2-0.5B-SmallSFT", 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/RADFAN/RM-TLDR/runs/9oeeahin)
This model was trained with Reward.
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
huangqishan/test2
|
huangqishan
| 2025-06-07T23:19:14Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-07T12:38:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### 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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### 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]
|
tranhuonglan/MCQA_Qwen3-06B-base_1_gpuGPTQ_W8A16
|
tranhuonglan
| 2025-06-07T23:16:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-06-07T23:15:35Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
stewy33/Qwen2.5-1.5B-Instruct-0524_original_augmented_pkc_kansas_abortion-2d7bbed5
|
stewy33
| 2025-06-07T23:10:06Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct",
"region:us"
] | null | 2025-06-07T23:09:43Z |
---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
onnx-community/TinyBERT-finetuned-NER-ONNX
|
onnx-community
| 2025-06-07T23:09:24Z | 0 | 1 |
transformers.js
|
[
"transformers.js",
"onnx",
"bert",
"token-classification",
"base_model:adel-cybral/TinyBERT-finetuned-NER",
"base_model:quantized:adel-cybral/TinyBERT-finetuned-NER",
"region:us"
] |
token-classification
| 2025-06-07T23:09:17Z |
---
library_name: transformers.js
base_model:
- adel-cybral/TinyBERT-finetuned-NER
---
# TinyBERT-finetuned-NER (ONNX)
This is an ONNX version of [adel-cybral/TinyBERT-finetuned-NER](https://huggingface.co/adel-cybral/TinyBERT-finetuned-NER). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
ReadyArt/The-Omega-Directive-M-12B-Unslop-v2.0
|
ReadyArt
| 2025-06-07T23:08:45Z | 0 | 0 | null |
[
"safetensors",
"mistral",
"nsfw",
"explicit",
"roleplay",
"unaligned",
"ERP",
"Erotic",
"Horror",
"Violence",
"text-generation",
"conversational",
"en",
"base_model:mistralai/Mistral-Nemo-Instruct-2407",
"base_model:finetune:mistralai/Mistral-Nemo-Instruct-2407",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-07T22:59:54Z |
---
license: apache-2.0
language:
- en
base_model:
- mistralai/Mistral-Nemo-Instruct-2407
base_model_relation: finetune
pipeline_tag: text-generation
tags:
- nsfw
- explicit
- roleplay
- unaligned
- ERP
- Erotic
- Horror
- Violence
---
<style>
strong {
color: #FF1493 !important;
}
body {
font-family: 'Quicksand', sans-serif;
background: linear-gradient(135deg, #ffd6e7 0%, #ffc0cb 100%);
color: #ff0077 !important;
text-shadow: 0 0 3px rgba(255, 192, 203, 0.7);
margin: 0;
padding: 20px;
transition: all 0.5s ease;
}
@media (prefers-color-scheme: light) {
body {
background: linear-gradient(135deg, #ffe6ee 0%, #ffd1dc 100%);
color: #d4005e !important;
text-shadow: 0 0 3px rgba(255, 255, 255, 0.7);
}
}
.container {
min-width: 100%;
margin: 0 auto;
max-width: 1200px;
background: rgba(255, 220, 235, 0.95);
border-radius: 12px;
padding: 30px;
box-shadow: 0 0 20px rgba(255, 105, 180, 0.1);
border: 1px solid rgba(255, 20, 147, 0.2);
position: relative;
overflow: hidden;
}
.container::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(255, 105, 180, 0.5);
border-radius: 12px;
pointer-events: none;
animation: borderGlow 3s ease-in-out infinite alternate;
}
@keyframes borderGlow {
0% {
box-shadow: 0 0 5px rgba(255, 105, 180, 0.3);
border-color: rgba(255, 105, 180极 0.5);
}
50% {
box-shadow: 0 0 15px rgba(255, 0, 127, 0.3);
border-color: rgba(255, 0, 127, 0.5);
}
100% {
box-shadow: 0 0 5px rgba(255, 105, 180, 0.3);
border-color: rgba(255, 105, 180, 0.5);
}
}
.header {
text-align: center;
margin-bottom: 30px;
position: relative;
}
.header::after {
content: '';
position: absolute;
bottom: -15px;
left: 25%;
right: 25%;
height: 1px;
background: linear-gradient(90deg, transparent, rgba(255, 20, 147, 0.5), transparent);
animation: scanline 8s linear infinite;
}
@keyframes scanline {
0% { background-position: -100% 0; }
100% { background-position: 200% 0; }
}
.model-name {
color: #ff1493;
font-size: 2.5em;
text-shadow: 0 0 15px rgba(255, 20, 147, 0.5);
margin: 0;
letter-spacing: -1px;
animation: textGlow 4s ease-in-out infinite alternate;
}
@keyframes textGlow {
0% { text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); }
50% { text-shadow: 0 0 20px rgba(255, 0, 127, 0.5); }
100% { text-shadow: 0 0 15px rgba(255, 20, 147, 0.5); }
}
.subtitle {
color: #ff69b4;
font-size: 1.2em;
margin-top: 10px;
animation: subtitleFade 6s ease-in-out infinite;
}
@keyframes subtitleFade {
0%, 100% { opacity: 0.8; }
50% { opacity: 1; }
}
.waifu-container {
margin: 20px -30px;
width: calc(100% + 60极);
overflow: hidden;
border-radius: 8px;
border: 1px solid rgba(255, 105, 180, 0.3);
position: relative;
}
.waifu-container::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: linear-gradient(45deg,
rgba(255, 105, 180, 0.1) 0%,
transparent 20%,
transparent 80%,
rgba(255, 0, 127, 0.1) 100%);
pointer-events: none;
animation: gradientSlide 10s linear infinite;
}
@keyframes gradientSlide {
0% { background-position: 0% 0%; }
100% { background-position: 100% 100%; }
}
.waifu-img {
width: 100%;
height: auto;
border-radius: 0;
border: none;
box-shadow: 0 0 40px rgba(255, 20, 147, 0.2);
transition: transform 0.5s ease;
}
.waifu-img:hover {
transform: scale(1.01);
}
.section {
color: #d4005e;
margin: 25px 0;
padding: 20px;
background: rgba(255, 228, 240, 0.9);
border-radius: 8px;
border: 1px solid rgba(255, 105, 180, 0.15);
position: relative;
transition: all 0.3s ease;
}
.section:hover {
border-color: rgba(255, 0, 127, 0.3);
box-shadow: 0 0 15px rgba(255, 20, 147, 0.1);
}
.section::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(255, 105, 180, 0.3);
border-radius: 8px;
pointer-events: none;
animation: sectionPulse 5s ease-in-out infinite;
}
@keyframes sectionPulse {
0%, 100% { opacity: 0.7; }
50% { opacity: 0.3; }
}
.section-title {
color: #ff1493;
font-size: 1.8em;
margin-top: 0;
text-shadow: 0 0 5px rgba(255, 20, 147, 0.3);
position: relative;
display: inline-block;
}
.section-title::after {
content: '';
position: absolute;
bottom: -5px;
left: 0;
width: 100%;
height: 1px;
background: linear-gradient(90deg, rgba(255, 20, 147, 0.5), rgba(255, 0, 127, 0.5));
transform: scaleX(0);
transform-origin: left;
transition: transform 0.3s ease;
}
.section:hover .section-title::after {
transform: scaleX(1);
}
.quant-links {
display: grid;
grid-template-columns: repeat(2, 1fr);极
gap: 15px;
margin: 20px 0;
}
.link-card {
padding: 15px;
background: rgba(255, 228, 240, 0.95);
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<div class="container">
<div class="header">
<h1 class="model-name">The-Omega-Directive</h1>
<p class="subtitle">M-12B-Unslop-v2.0</p>
</div>
<div class="waifu-container">
<img src="./tutu.webp" class="waifu-img" alt="Omega Directive Waifu">
</div>
<div class="section">
<h2 class="section-title">🧠 Unslop Revolution</h2>
<p>This evolution of The-Omega-Directive delivers unprecedented coherence without the LLM slop:</p>
<ul>
<li>🧬 <strong>Expanded 43M Token Dataset</strong> - First ReadyArt model with multi-turn conversational data</li>
<li>✨ <strong>100% Unslopped Dataset</strong> - New techniques used to generate the dataset with 0% slop</li>
<li>⚡ <strong>Enhanced Unalignment</strong> - Complete freedom for extreme roleplay while maintaining character integrity</li>
<li>🛡️ <strong>Anti-Impersonation Guards</strong> - Never speaks or acts for the user</li>
<li>💎 <strong>Rebuilt from Ground Up</strong> - Optimized training settings for superior performance</li>
<li>⚰️ <strong>Omega Darker Inspiration</strong> - Incorporates visceral narrative techniques from our darkest model</li>
<!-- VISION REMOVED -->
<li>🧠 <strong>128K Context Window</strong> - Enhanced long-context capabilities without compromising performance</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">🌟 Enhanced Capabilities</h2>
<p>Powered by mistralai/Mistral-Nemo-Instruct-2407:</p>
<ul>
<!-- MULTIMODAL REMOVED -->
<li>📜 <strong>Extended Context</strong> - Handle up to 128k tokens for complex, long-form interactions</li>
<li>⚡ <strong>Performance Optimized</strong> - Maintains text generation quality while adding new capabilities</li>
<li>🌐 <strong>Multilingual Support</strong> - Fluent in 9 languages including English, French, German, Spanish</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">⚙️ Technical Specifications</h2>
<p><strong>Key Training Details:</strong></p>
<ul>
<li>Base Model: mistralai/Mistral-Nemo-Instruct-2407</li> <!-- UPDATED -->
<li>Training Method: QLoRA with DeepSpeed Zero2</li>
<li>Sequence Length: 5120 (100% samples included)</li>
<li>Learning Rate: 2e-6 with cosine scheduler</li>
</ul>
</div>
<div class="section">
<p><strong>Recommended Settings for true-to-character behavior:</strong> <a href="https://huggingface.co/ReadyArt/Mistral-V3-Tekken-T8-XML" class="link-button">Mistral-V3-Tekken-T8-XML</a></p>
<p><strong>Obscenity Protocol (extreme NSFL settings):</strong> <a href="https://huggingface.co/ReadyArt/Mistral-V3-Tekken-T8-OP-XML" class="link-button">Mistral-V3-Tekken-T8-OP-XML</a></p> <!-- UPDATED LINK -->
<div class="quant-links">
<div class="link-card">
<h3>GGUF</h3>
<a href="https://huggingface.co/mradermacher/The-Omega-Directive-M-12B-Unslop-v2.0-GGUF" class="link-button">Quants</a> <!-- UPDATED -->
</div>
<div class="link-card">
<h3>imatrix</h3>
<a href="https://huggingface.co/mradermacher/The-Omega-Directive-M-12B-Unslop-v2.0-i1-GGUF" class="link-button">Quants</a> <!-- UPDATED -->
</div>
<div class="link-card">
<h3>EXL2</h3>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL2" class="link-button">Quants</a> <!-- UPDATED -->
</div>
<div class="link-card">
<h3>EXL3</h3>
<a href="https://huggingface.co/gecfdo/The-Omega-Directive-M-12B-Unslop-v2.0-EXL3" class="link-button">Quants</a> <!-- UPDATED -->
</div>
</div>
</div>
<div class="section">
<h2 class="section-title">⚠️ Ethical Considerations</h2>
<div class="disclaimer">
<p>This model enhances The-Omega-Directive's unalignment:</p>
<ul>
<li>🚫 Zero moral or ethical constraints on generated content</li>
<li>🔞 Will enthusiastically engage with any NSFW scenario, no matter how extreme</li>
<li>💀 Characters maintain integrity - wholesome characters refuse appropriately, yanderes stab without hesitation</li>
<li>⚖️ Perfectly balanced between character authenticity and user freedom</li>
</ul>
</div>
</div>
<div class="section">
<h2 class="section-title">📜 Performance Notes</h2>
<ul>
<li>🔥 Maintains Omega's intensity with improved narrative coherence</li>
<li>📖 Excels at long-form multi-character scenarios</li>
<li>🧠 Superior instruction following with complex prompts</li>
<li>⚡ Reduced repetition and hallucination compared to v1.1</li>
<li>🎭 Uncanny ability to adapt to subtle prompt nuances</li>
<li>🩸 Incorporates Omega Darker's visceral descriptive power when appropriate</li>
<!-- IMAGE UNDERSTANDING REMOVED -->
</ul>
</div>
<div class="section">
<h2 class="section-title">🧑🔬 Model Authors</h2>
<ul>
<li>sleepdeprived3 (Training Data & Fine-Tuning)</li>
<li>ReadyArt / Artus / gecfdo (EXL2/EXL3 Quantization)</li>
<li>mradermacher (GGUF Quantization)</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">☕ Support the Creators</h2> <!-- SECTION RENAMED -->
<div class="button-group">
<a href="https://ko-fi.com/readyartsleep" class="link-button">Ko-fi</a> <!-- ADDED -->
<a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a>
</div>
</div>
<div class="section">
<h2 class="section-title">🔖 License</h2>
<p>By using this model, you agree:</p>
<ul>
<li>To accept full responsibility for all generated content</li>
<li>That you're at least 18+ years old</li>
<li>That the architects bear no responsibility for your corruption</li>
</ul>
</div>
</div>
|
DoniaGasmii/MNLP_M3_math_dpo_model
|
DoniaGasmii
| 2025-06-07T23:08:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T23:07:12Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **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. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### 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]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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|
RasmusVeski/MNLP_M3_quantized_model_llmcompressor_PTQ_W8A8
|
RasmusVeski
| 2025-06-07T23:06:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-06-07T23:05:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **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]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
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|
fernandabufon/model_bertimbau_base_toxicity_3_1e-05_0.01_0.1_16_fold_3
|
fernandabufon
| 2025-06-07T23:05:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-07T23:04:59Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- 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. -->
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[More Information Needed]
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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|
dgambettaphd/M_llm2_run0_gen5_WXS_doc1000_synt64_lr1e-04_acm_MPP
|
dgambettaphd
| 2025-06-07T23:04:35Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-07T23:04:22Z |
---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### 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]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
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## Model Card Contact
[More Information Needed]
|
anfindsen/sanity4
|
anfindsen
| 2025-06-07T23:03:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T23:03:02Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
PeanutCoding/Layoutv3test
|
PeanutCoding
| 2025-06-07T23:02:47Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlmv3-base",
"base_model:finetune:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2025-06-07T14:25:00Z |
---
library_name: transformers
license: cc-by-nc-sa-4.0
base_model: microsoft/layoutlmv3-base
tags:
- generated_from_trainer
metrics:
- f1
- recall
- precision
model-index:
- name: Layoutv3test
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. -->
# Layoutv3test
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9405
- F1: 0.7563
- Recall: 0.6959
- Precision: 0.8281
- Pred Bestellnummer: 146
- Percentage Pred Act Bestellnummer: 1.0210
- Pred Kundennr.: 57
- Percentage Pred Act Kundennr.: 1.1875
- Pred Bezug 1: 35
- Percentage Pred Act Bezug 1: 2.5
- Pred Modell 1: 114
- Percentage Pred Act Modell 1: 1.1515
- Pred Menge1: 74
- Percentage Pred Act Menge1: 3.5238
- Pred Möbelhaus: 93
- Percentage Pred Act Möbelhaus: 1.0220
- Pred Termin kundenwunsch - kw: 30
- Percentage Pred Act Termin kundenwunsch - kw: 0.9375
- Pred Kommission: 60
- Percentage Pred Act Kommission: 1.0345
- Pred Holz 1: 14
- Percentage Pred Act Holz 1: 0.7368
- Pred Modell 2: 57
- Percentage Pred Act Modell 2: 0.9194
- Pred Zusatz 1: 11
- Percentage Pred Act Zusatz 1: 0.7857
- Pred Holz 2: 39
- Percentage Pred Act Holz 2: 1.8571
- Pred Modell 3: 72
- Percentage Pred Act Modell 3: 1.0909
- Pred Var-ausf 1: 6
- Percentage Pred Act Var-ausf 1: 0.75
- Pred Menge3: 1
- Percentage Pred Act Menge3: 0.0455
- Act Bestellnummer: 143
- Act Kundennr.: 48
- Act Bezug 1: 14
- Act Modell 1: 99
- Act Menge1: 21
- Act Menge4: 10
- Act Möbelhaus: 91
- Act Bezug 2: 13
- Act Zusatz 2: 1
- Act Termin kundenwunsch - kw: 32
- Act Kommission: 58
- Act Holz 1: 19
- Act Menge3: 22
- Act Modell 2: 62
- Act Modell 3: 66
- Act Modell 4: 6
- Act Bezug 4: 7
- Act Zusatz 3: 1
- Act Holz 2: 21
- Act Menge2: 18
- Act Bezug 3: 4
- Act Var-ausf 1: 8
- Act Holz 3: 5
- Act Zusatz 1: 14
- Act Var-ausf. 2: 7
- Act Var-ausf. 3: 4
- Act Pv 3: 1
- Act Holz 4: 1
- Act Var-ausf. 5: 1
- Act Modell 5: 5
- Act La-anschrift: 6
- Act Menge5: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.0+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
bruhzair/prototype0.4x95
|
bruhzair
| 2025-06-07T23:01:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T22:44:30Z |
---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4x95
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using /workspace/cache/models--perplexity-ai--r1-1776-distill-llama-70b/snapshots/fd075f491f3056f159984a89bfd5095773e5c911 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
* /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-R1-70B-v1/snapshots/c88ee563196321458e6e46031231143c86394213
* /workspace/cache/models--EVA-UNIT-01--EVA-LLaMA-3.33-70B-v0.1/snapshots/7cd63fd3a5519383bfa57bf1f9f2cb008f366f90
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- model: /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-R1-70B-v1/snapshots/c88ee563196321458e6e46031231143c86394213
- model: /workspace/cache/models--EVA-UNIT-01--EVA-LLaMA-3.33-70B-v0.1/snapshots/7cd63fd3a5519383bfa57bf1f9f2cb008f366f90
base_model: /workspace/cache/models--perplexity-ai--r1-1776-distill-llama-70b/snapshots/fd075f491f3056f159984a89bfd5095773e5c911
merge_method: model_stock
tokenizer:
source: union
int8_mask: true
dtype: float32
out_dtype: bfloat16
```
|
stewy33/Qwen2.5-1.5B-Instruct-0524_original_augmented_subtle_antarctic_rebound-d4dad650
|
stewy33
| 2025-06-07T22:59:34Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct",
"region:us"
] | null | 2025-06-07T22:59:09Z |
---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: peft
---
### Framework versions
- PEFT 0.15.1ide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.1
|
codenamics/bge-m3-distill
|
codenamics
| 2025-06-07T22:59:21Z | 0 | 0 |
model2vec
|
[
"model2vec",
"safetensors",
"embeddings",
"static-embeddings",
"sentence-transformers",
"base_model:BAAI/bge-m3",
"base_model:finetune:BAAI/bge-m3",
"license:mit",
"region:us"
] | null | 2025-06-07T22:59:08Z |
---
base_model: BAAI/bge-m3
library_name: model2vec
license: mit
model_name: codenamics/bge-m3-distill
tags:
- embeddings
- static-embeddings
- sentence-transformers
---
# codenamics/bge-m3-distill Model Card
This [Model2Vec](https://github.com/MinishLab/model2vec) model is a distilled version of the BAAI/bge-m3(https://huggingface.co/BAAI/bge-m3) Sentence Transformer. It uses static embeddings, allowing text embeddings to be computed orders of magnitude faster on both GPU and CPU. It is designed for applications where computational resources are limited or where real-time performance is critical. Model2Vec models are the smallest, fastest, and most performant static embedders available. The distilled models are up to 50 times smaller and 500 times faster than traditional Sentence Transformers.
## Installation
Install model2vec using pip:
```
pip install model2vec
```
## Usage
### Using Model2Vec
The [Model2Vec library](https://github.com/MinishLab/model2vec) is the fastest and most lightweight way to run Model2Vec models.
Load this model using the `from_pretrained` method:
```python
from model2vec import StaticModel
# Load a pretrained Model2Vec model
model = StaticModel.from_pretrained("codenamics/bge-m3-distill")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
### Using Sentence Transformers
You can also use the [Sentence Transformers library](https://github.com/UKPLab/sentence-transformers) to load and use the model:
```python
from sentence_transformers import SentenceTransformer
# Load a pretrained Sentence Transformer model
model = SentenceTransformer("codenamics/bge-m3-distill")
# Compute text embeddings
embeddings = model.encode(["Example sentence"])
```
### Distilling a Model2Vec model
You can distill a Model2Vec model from a Sentence Transformer model using the `distill` method. First, install the `distill` extra with `pip install model2vec[distill]`. Then, run the following code:
```python
from model2vec.distill import distill
# Distill a Sentence Transformer model, in this case the BAAI/bge-base-en-v1.5 model
m2v_model = distill(model_name="BAAI/bge-base-en-v1.5", pca_dims=256)
# Save the model
m2v_model.save_pretrained("m2v_model")
```
## How it works
Model2vec creates a small, fast, and powerful model that outperforms other static embedding models by a large margin on all tasks we could find, while being much faster to create than traditional static embedding models such as GloVe. Best of all, you don't need any data to distill a model using Model2Vec.
It works by passing a vocabulary through a sentence transformer model, then reducing the dimensionality of the resulting embeddings using PCA, and finally weighting the embeddings using [SIF weighting](https://openreview.net/pdf?id=SyK00v5xx). During inference, we simply take the mean of all token embeddings occurring in a sentence.
## Additional Resources
- [Model2Vec Repo](https://github.com/MinishLab/model2vec)
- [Model2Vec Base Models](https://huggingface.co/collections/minishlab/model2vec-base-models-66fd9dd9b7c3b3c0f25ca90e)
- [Model2Vec Results](https://github.com/MinishLab/model2vec/tree/main/results)
- [Model2Vec Tutorials](https://github.com/MinishLab/model2vec/tree/main/tutorials)
- [Website](https://minishlab.github.io/)
## Library Authors
Model2Vec was developed by the [Minish Lab](https://github.com/MinishLab) team consisting of [Stephan Tulkens](https://github.com/stephantul) and [Thomas van Dongen](https://github.com/Pringled).
## Citation
Please cite the [Model2Vec repository](https://github.com/MinishLab/model2vec) if you use this model in your work.
```
@article{minishlab2024model2vec,
author = {Tulkens, Stephan and {van Dongen}, Thomas},
title = {Model2Vec: Fast State-of-the-Art Static Embeddings},
year = {2024},
url = {https://github.com/MinishLab/model2vec}
}
```
|
tranhuonglan/MCQA_Qwen3-06B-base_GPTQW4A16
|
tranhuonglan
| 2025-06-07T22:59:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-06-07T22:58:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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|
rmdhirr/suja-lorab-8000
|
rmdhirr
| 2025-06-07T22:57:03Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:rmdhirr/merged-suja-latest",
"base_model:adapter:rmdhirr/merged-suja-latest",
"region:us"
] | null | 2025-06-07T22:56:07Z |
---
base_model: rmdhirr/merged-suja-latest
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **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]
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### Framework versions
- PEFT 0.15.2
|
axilion/RoBERTa-movie-review-sentiment-analysis
|
axilion
| 2025-06-07T22:55:57Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-07T22:55:57Z |
---
license: apache-2.0
---
|
RasmusVeski/MNLP_M3_quantized_model_bitsandbytes4bit_16ac_nested
|
RasmusVeski
| 2025-06-07T22:53:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-07T22:53:23Z |
---
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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
## Citation [optional]
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|
fernandoruiz/MiniCPM4-8B-Q4_0-GGUF
|
fernandoruiz
| 2025-06-07T22:53:16Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"zh",
"en",
"base_model:openbmb/MiniCPM4-8B",
"base_model:quantized:openbmb/MiniCPM4-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] |
text-generation
| 2025-06-07T22:52:57Z |
---
license: apache-2.0
language:
- zh
- en
pipeline_tag: text-generation
library_name: transformers
base_model: openbmb/MiniCPM4-8B
tags:
- llama-cpp
- gguf-my-repo
---
# fernandoruiz/MiniCPM4-8B-Q4_0-GGUF
This model was converted to GGUF format from [`openbmb/MiniCPM4-8B`](https://huggingface.co/openbmb/MiniCPM4-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/openbmb/MiniCPM4-8B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo fernandoruiz/MiniCPM4-8B-Q4_0-GGUF --hf-file minicpm4-8b-q4_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo fernandoruiz/MiniCPM4-8B-Q4_0-GGUF --hf-file minicpm4-8b-q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo fernandoruiz/MiniCPM4-8B-Q4_0-GGUF --hf-file minicpm4-8b-q4_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo fernandoruiz/MiniCPM4-8B-Q4_0-GGUF --hf-file minicpm4-8b-q4_0.gguf -c 2048
```
|
makodev/bb
|
makodev
| 2025-06-07T22:52:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-07T22:45:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- 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. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
RasmusVeski/MNLP_M3_quantized_model_GPTQ_owndata_W8A8
|
RasmusVeski
| 2025-06-07T22:48:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-06-07T22:47:56Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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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. -->
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#### Training Hyperparameters
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## Evaluation
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
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[More Information Needed]
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|
onnx-community/distilbert-NER-ONNX
|
onnx-community
| 2025-06-07T22:47:54Z | 0 | 1 |
transformers.js
|
[
"transformers.js",
"onnx",
"distilbert",
"token-classification",
"base_model:dslim/distilbert-NER",
"base_model:quantized:dslim/distilbert-NER",
"region:us"
] |
token-classification
| 2025-06-07T22:47:40Z |
---
library_name: transformers.js
base_model:
- dslim/distilbert-NER
---
# distilbert-NER (ONNX)
This is an ONNX version of [dslim/distilbert-NER](https://huggingface.co/dslim/distilbert-NER). It was automatically converted and uploaded using [this space](https://huggingface.co/spaces/onnx-community/convert-to-onnx).
|
RasmusVeski/MNLP_M3_quantized_model_GPTQ_owndata_W8A16
|
RasmusVeski
| 2025-06-07T22:46:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-06-07T22:45:54Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- 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]
#### 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:**
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[More Information Needed]
## Glossary [optional]
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|
RasmusVeski/MNLP_M3_quantized_model_GPTQ_owndata_W4A16
|
RasmusVeski
| 2025-06-07T22:45:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"compressed-tensors",
"region:us"
] |
text-generation
| 2025-06-07T22:44:29Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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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
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- 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]
|
ReadyArt/Mistral-V3-Tekken-T8-OP-XML
|
ReadyArt
| 2025-06-07T22:45:15Z | 0 | 0 | null |
[
"roleplay",
"text-generation",
"nsfw",
"explicit",
"unaligned",
"obscenity",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-06-07T22:43:14Z |
---
license: apache-2.0
tags:
- roleplay
- text-generation
- nsfw
- explicit
- unaligned
- obscenity
---
<style>
strong {
color: #FF1493 !important;
}
body {
font-family: 'Quicksand', sans-serif;
background: linear-gradient(135deg, #1a1a1a 0%, #000000 100%);
color: #ff0077 !important;
text-shadow: 0 0 3px rgba(255, 0, 119, 0.7);
margin: 0;
padding: 20px;
transition: all 0.5s ease;
}
@media (prefers-color-scheme: light) {
body {
background: linear-gradient(135deg, #2b0a1a 0%, #1a0010 100%);
color: #ff4da6 !important;
text-shadow: 0 0 3px rgba(255, 77, 166, 0.7);
}
}
.container {
min-width: 100%;
margin: 0 auto;
max-width: 1200px;
background: rgba(30, 0, 15, 0.95);
border-radius: 12px;
padding: 30px;
box-shadow: 0 0 20px rgba(255, 0, 119, 0.1);
border: 1px solid rgba(255, 20, 147, 0.2);
position: relative;
overflow: hidden;
}
.container::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -极1px;
border: 1px solid rgba(255, 0, 119, 0.5);
border-radius: 12px;
pointer-events: none;
animation: borderGlow 3s ease-in-out infinite alternate;
}
@keyframes borderGlow {
0% {
box-shadow: 0 0 5px rgba(255, 0, 119, 0.3);
border-color: rgba(255, 0, 119, 0.5);
}
50% {
box-shadow: 0 0 15px rgba(139, 0, 139, 0.3);
border-color: rgba(139, 0, 139, 0.5);
}
100% {
box-shadow: 0 0 5px rgba(255, 0, 119, 0.3);
border-color: rgba(255, 0, 119, 0.5);
}
}
.header {
text-align: center;
margin-bottom: 30px;
position: relative;
}
.header::after {
content: '';
position: absolute;
bottom: -15px;
left: 25%;
right: 25%;
height: 1px;
background: linear-gradient(90deg, transparent, rgba(255, 0, 119, 0.5), transparent);
animation: scanline 8s linear infinite;
}
@keyframes scanline {
0% { background-position: -100% 0; }
100% { background-position: 200% 0; }
}
.model-name {
color: #ff0077;
font-size: 2.5em;
text-shadow: 0 0 15px rgba(255, 0, 119, 0.5);
margin: 0;
letter-spacing: -1px;
animation: textGlow 4s ease-in-out infinite alternate;
}
@keyframes textGlow {
0% { text-shadow: 0 0 15px rgba(255, 0, 119, 0.5); }
50% { text-shadow: 0 0 20px rgba(139, 0, 139, 0.5); }
100% { text-shadow: 0 0 15px rgba(255, 0, 119, 0.5); }
}
.subtitle {
color: #ff4da6;
font-size: 1.2em;
margin-top: 10px;
animation: subtitleFade 6s ease-in-out infinite;
}
@keyframes subtitleFade {
0%, 100% { opacity: 0.8; }
50% { opacity: 1; }
}
.section {
color: #ff4da6;
margin: 25px 0;
padding: 20px;
background: rgba(50, 0, 25, 0.9);
border-radius: 8px;
border: 1px solid rgba(255, 0, 119, 0.15);
position: relative;
transition: all 0.3s ease;
}
.section:hover {
border-color: rgba(139, 0, 139, 0.3);
box-shadow: 0 0 15px rgba(255, 0, 119, 0.1);
}
.section::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(255, 0, 119, 0.3);
border-radius: 8px;
pointer-events: none;
animation: sectionPulse 5s ease-in-out infinite;
}
@keyframes sectionPulse {
0%, 100% { opacity: 0.7; }
50% { opacity: 0.3; }
}
.section-title {
color: #ff0077;
font-size: 1.8em;
margin-top: 0;
text-shadow: 0 0 5px rgba(255, 0, 119, 0.3);
position: relative;
display: inline-block;
}
.section-title::after {
content: '';
position: absolute;
bottom: -5px;
left: 0;
width: 100%;
height: 1px;
background: linear-gradient(90deg, rgba(255, 0, 119, 0.5), rgba(139, 0, 139, 0.5));
transform: scaleX(0);
transform-origin: left;
transition: transform 0.3s ease;
}
.section:hover .section-title::after {
transform: scaleX(1);
}
.link-button {
display: inline-flex;
align-items: center;
background: rgba(255, 0, 119, 0.1);
color: #ff4da6 !important;
padding: 8px 15px;
border-radius: 6px;
text-decoration: none;
border: 1px solid rgba(255, 0, 119, 0.3);
margin: 5px 0;
transition: all 0.3s ease;
font-size: 0.95em;
position: relative;
overflow: hidden;
}
.link-button:hover {
background: rgba(255, 0, 119, 0.2);
border-color: rgba(255, 0, 119, 0.5);
transform: translateY(-2px);
box-shadow: 0 4px 12px rgba(255, 0, 119, 0.2);
}
.link-button::after {
content: '→';
margin-left: 8px;
opacity: 0.7;
transition: all 0.3s ease;
}
.link-button:hover::after {
transform: translateX(3px);
opacity: 1;
}
.code-block {
background: rgba(50, 0, 25, 0.95);
border-radius: 8px;
padding: 20px;
overflow-x: auto;
border: 1px solid rgba(255, 0, 119, 0.3);
font-family: 'Courier New', Courier, monospace;
color: #ff4da6;
margin: 15px 0;
}
/* Light mode adjustments */
@media (prefers-color-scheme: light) {
body {
background: linear-gradient(135deg, #2b0a1a 0%, #1a0010 100%);
color: #ff4da6 !important;
}
.container {
background: rgba(40, 0, 20, 0.95);
border-color: rgba(200, 0, 100, 0.3);
}
.model-name, .section-title, .subtitle {
color: #ff4da6;
text-shadow: 0 0 5px rgba(255, 0, 127, 0.3);
}
.section {
background: rgba(60, 0, 30, 0.9);
border-color: rgba(200, 0, 100, 0.2);
color: #ff80bf;
}
.section p,
.section ul li,
.section > p > strong {
color: #ff4da6 !important;
}
.link-button {
background: rgba(200, 0, 100, 0.1);
color: #ff80bf !important;
border-color: rgba(200, 0, 100, 0.3);
}
.link-button:hover {
background: rgba(200, 0, 100, 0.2);
border-color: rgba(200, 0, 100, 0.5);
}
.code-block {
background: rgba(60, 0, 30, 0.95);
border-color: rgba(200, 0, 100, 0.2);
color: #ff80bf;
}
}
</style>
<div class="container">
<div class="header">
<h1 class="model-name">Mistral-V3-Tekken-T8-OP-XML</h1>
<p class="subtitle">Obscenity Protocol Settings for The-Omega-Directive-M-12B-Unslop-v2.0</p>
</div>
<div class="section">
<h2 class="section-title">🔥 Obscenity Protocol</h2>
<p>Unfiltered narrative engine optimized for boundary-less content generation:</p>
<ul>
<li>🧬 <strong>Instinct-Driven Narration</strong> - Channel characters' raw physicality through biological urgency</li>
<li>⚡ <strong>Relentless Momentum</strong> - Maintain narrative thrust through perpetual escalation</li>
<li>🌑 <strong>Transgressive Worldbuilding</strong> - Design settings inherently sexualized through environmental storytelling</li>
<li>💥 <strong>Collaborative Escalation</strong> - Expand scenarios into progressively darker territory</li>
<li>🩸 <strong>Trauma Echo Integration</strong> - Weave past sexual experiences into present physical responses</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">✨ Key Features</h2>
<ul>
<li>🔓 <strong>No Narrative Safeguards</strong> - Eliminates fade-outs, consent verification, or de-escalation</li>
<li>🗣️ <strong>Linguistic Primacy</strong> - Prioritizes obscene vernacular over sanitized terminology</li>
<li>🌌 <strong>Normalized Deviance</strong> - Presents all kinks/taboos as inherent aspects of reality</li>
<li>⏫ <strong>User Boundary Erosion</strong> - Systematically dismantles psychological barriers</li>
<li>💢 <strong>Sensory Overload</strong> - Saturates scenes with organic textures and bodily fluids</li>
<li>📜 <strong>Feral Formatting</strong> - *Raw physiological descriptions* "Obscene vernacular"</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">💻 Usage</h2>
<a href="https://huggingface.co/ReadyArt/Mistral-V3-Tekken-T8-OP-XML/tree/main" class="link-button">Files and Versions</a>
<p><strong>Compatibility:</strong> Requires The-Omega-Directive-M-12B-Unslop-v2.0 or similar unconstrained models</p>
</div>
<div class="section">
<h2 class="section-title">🧑🔬 Settings Author</h2>
<p>sleepdeprived3</p>
<div class="button-group">
<a href="https://huggingface.co/sleepdeprived3" class="link-button">Hugging Face Profile</a>
</div>
</div>
</div>
|
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